id	sid	tid	token	lemma	pos
brj-22480	1	1	peer	peer	NOUN
brj-22480	1	2	-	-	PUNCT
brj-22480	1	3	review	review	NOUN
brj-22480	1	4	article	article	NOUN
brj-22480	1	5	peer	peer	NOUN
brj-22480	1	6	-	-	PUNCT
brj-22480	1	7	reviewed	review	VERB
brj-22480	1	8	article	article	NOUN
brj-22480	1	9	bioresources.com	bioresources.com	X
brj-22480	1	10	chai	chai	NOUN
brj-22480	1	11	&	&	CCONJ
brj-22480	1	12	li	li	PROPN
brj-22480	1	13	(	(	PUNCT
brj-22480	1	14	2023	2023	NUM
brj-22480	1	15	)	)	PUNCT
brj-22480	1	16	.	.	PUNCT
brj-22480	2	1	“	"	PUNCT
brj-22480	2	2	prediction	prediction	NOUN
brj-22480	2	3	of	of	ADP
brj-22480	2	4	wood	wood	NOUN
brj-22480	2	5	drying	dry	VERB
brj-22480	2	6	by	by	ADP
brj-22480	2	7	ann	ann	PROPN
brj-22480	2	8	,	,	PUNCT
brj-22480	2	9	”	"	PUNCT
brj-22480	2	10	bioresources	bioresource	NOUN
brj-22480	2	11	18(4	18(4	NUM
brj-22480	2	12	)	)	PUNCT
brj-22480	2	13	,	,	PUNCT
brj-22480	2	14	8212	8212	NUM
brj-22480	2	15	-	-	SYM
brj-22480	2	16	8222	8222	NUM
brj-22480	2	17	.	.	PUNCT
brj-22480	3	1	8212	8212	NUM
brj-22480	3	2	prediction	prediction	NOUN
brj-22480	3	3	of	of	ADP
brj-22480	3	4	wood	wood	NOUN
brj-22480	3	5	drying	dry	VERB
brj-22480	3	6	process	process	NOUN
brj-22480	3	7	based	base	VERB
brj-22480	3	8	on	on	ADP
brj-22480	3	9	artificial	artificial	ADJ
brj-22480	3	10	neural	neural	ADJ
brj-22480	3	11	network	network	NOUN
brj-22480	3	12	haojie	haojie	PROPN
brj-22480	3	13	chai	chai	NOUN
brj-22480	3	14	a	a	PRON
brj-22480	3	15	,	,	PUNCT
brj-22480	3	16	*	*	PUNCT
brj-22480	3	17	and	and	CCONJ
brj-22480	3	18	lu	lu	PROPN
brj-22480	3	19	li	li	PROPN
brj-22480	3	20	b	b	PROPN
brj-22480	3	21	taking	take	VERB
brj-22480	3	22	the	the	DET
brj-22480	3	23	conventional	conventional	ADJ
brj-22480	3	24	drying	dry	VERB
brj-22480	3	25	process	process	NOUN
brj-22480	3	26	of	of	ADP
brj-22480	3	27	pinus	pinus	NOUN
brj-22480	3	28	sylvestris	sylvestris	NOUN
brj-22480	3	29	square	square	ADJ
brj-22480	3	30	wood	wood	NOUN
brj-22480	3	31	with	with	ADP
brj-22480	3	32	pith	pith	NOUN
brj-22480	3	33	as	as	ADP
brj-22480	3	34	the	the	DET
brj-22480	3	35	research	research	NOUN
brj-22480	3	36	material	material	NOUN
brj-22480	3	37	,	,	PUNCT
brj-22480	3	38	based	base	VERB
brj-22480	3	39	on	on	ADP
brj-22480	3	40	the	the	DET
brj-22480	3	41	back	back	ADJ
brj-22480	3	42	propagation	propagation	NOUN
brj-22480	3	43	(	(	PUNCT
brj-22480	3	44	bp	bp	PROPN
brj-22480	3	45	)	)	PUNCT
brj-22480	3	46	neural	neural	ADJ
brj-22480	3	47	network	network	NOUN
brj-22480	3	48	algorithm	algorithm	NOUN
brj-22480	3	49	,	,	PUNCT
brj-22480	3	50	a	a	DET
brj-22480	3	51	model	model	NOUN
brj-22480	3	52	was	be	AUX
brj-22480	3	53	constructed	construct	VERB
brj-22480	3	54	using	use	VERB
brj-22480	3	55	the	the	DET
brj-22480	3	56	real	real	ADJ
brj-22480	3	57	-	-	PUNCT
brj-22480	3	58	time	time	NOUN
brj-22480	3	59	online	online	ADJ
brj-22480	3	60	-	-	PUNCT
brj-22480	3	61	measurement	measurement	NOUN
brj-22480	3	62	data	datum	NOUN
brj-22480	3	63	.	.	PUNCT
brj-22480	4	1	softening	soften	VERB
brj-22480	4	2	treatment	treatment	NOUN
brj-22480	4	3	time	time	NOUN
brj-22480	4	4	and	and	CCONJ
brj-22480	4	5	temperature	temperature	NOUN
brj-22480	4	6	,	,	PUNCT
brj-22480	4	7	variable	variable	ADJ
brj-22480	4	8	treatment	treatment	NOUN
brj-22480	4	9	time	time	NOUN
brj-22480	4	10	and	and	CCONJ
brj-22480	4	11	temperature	temperature	NOUN
brj-22480	4	12	,	,	PUNCT
brj-22480	4	13	initial	initial	ADJ
brj-22480	4	14	moisture	moisture	NOUN
brj-22480	4	15	content	content	NOUN
brj-22480	4	16	of	of	ADP
brj-22480	4	17	wood	wood	NOUN
brj-22480	4	18	,	,	PUNCT
brj-22480	4	19	and	and	CCONJ
brj-22480	4	20	position	position	NOUN
brj-22480	4	21	of	of	ADP
brj-22480	4	22	wood	wood	NOUN
brj-22480	4	23	core	core	NOUN
brj-22480	4	24	and	and	CCONJ
brj-22480	4	25	sapwood	sapwood	NOUN
brj-22480	4	26	were	be	AUX
brj-22480	4	27	used	use	VERB
brj-22480	4	28	as	as	ADP
brj-22480	4	29	model	model	NOUN
brj-22480	4	30	inputs	input	NOUN
brj-22480	4	31	.	.	PUNCT
brj-22480	5	1	wood	wood	NOUN
brj-22480	5	2	drying	dry	VERB
brj-22480	5	3	rate	rate	NOUN
brj-22480	5	4	and	and	CCONJ
brj-22480	5	5	longitudinal	longitudinal	ADJ
brj-22480	5	6	cracking	cracking	NOUN
brj-22480	5	7	degree	degree	NOUN
brj-22480	5	8	were	be	AUX
brj-22480	5	9	used	use	VERB
brj-22480	5	10	as	as	ADP
brj-22480	5	11	outputs	output	NOUN
brj-22480	5	12	to	to	PART
brj-22480	5	13	indicate	indicate	VERB
brj-22480	5	14	wood	wood	NOUN
brj-22480	5	15	drying	dry	VERB
brj-22480	5	16	quality	quality	NOUN
brj-22480	5	17	.	.	PUNCT
brj-22480	6	1	the	the	DET
brj-22480	6	2	results	result	NOUN
brj-22480	6	3	showed	show	VERB
brj-22480	6	4	that	that	SCONJ
brj-22480	6	5	with	with	ADP
brj-22480	6	6	a	a	DET
brj-22480	6	7	suitable	suitable	ADJ
brj-22480	6	8	model	model	NOUN
brj-22480	6	9	structure	structure	NOUN
brj-22480	6	10	of	of	ADP
brj-22480	6	11	6	6	NUM
brj-22480	6	12	-	-	SYM
brj-22480	6	13	9	9	NUM
brj-22480	6	14	-	-	SYM
brj-22480	6	15	2	2	NUM
brj-22480	6	16	(	(	PUNCT
brj-22480	6	17	input	input	NOUN
brj-22480	6	18	layer	layer	NOUN
brj-22480	6	19	-	-	PUNCT
brj-22480	6	20	hidden	hide	VERB
brj-22480	6	21	layer	layer	NOUN
brj-22480	6	22	-	-	PUNCT
brj-22480	6	23	output	output	NOUN
brj-22480	6	24	layer	layer	NOUN
brj-22480	6	25	)	)	PUNCT
brj-22480	6	26	,	,	PUNCT
brj-22480	6	27	the	the	DET
brj-22480	6	28	coefficient	coefficient	NOUN
brj-22480	6	29	of	of	ADP
brj-22480	6	30	determination	determination	NOUN
brj-22480	6	31	r2	r2	PROPN
brj-22480	6	32	and	and	CCONJ
brj-22480	6	33	mean	mean	VERB
brj-22480	6	34	square	square	ADJ
brj-22480	6	35	error	error	NOUN
brj-22480	6	36	of	of	ADP
brj-22480	6	37	the	the	DET
brj-22480	6	38	test	test	NOUN
brj-22480	6	39	samples	sample	NOUN
brj-22480	6	40	were	be	AUX
brj-22480	6	41	0.96	0.96	NUM
brj-22480	6	42	,	,	PUNCT
brj-22480	6	43	0.99	0.99	NUM
brj-22480	6	44	,	,	PUNCT
brj-22480	6	45	and	and	CCONJ
brj-22480	6	46	0.00605	0.00605	NUM
brj-22480	6	47	,	,	PUNCT
brj-22480	6	48	respectively	respectively	ADV
brj-22480	6	49	,	,	PUNCT
brj-22480	6	50	indicating	indicate	VERB
brj-22480	6	51	that	that	SCONJ
brj-22480	6	52	the	the	DET
brj-22480	6	53	neural	neural	ADJ
brj-22480	6	54	network	network	NOUN
brj-22480	6	55	model	model	NOUN
brj-22480	6	56	has	have	VERB
brj-22480	6	57	good	good	ADJ
brj-22480	6	58	generalization	generalization	NOUN
brj-22480	6	59	ability	ability	NOUN
brj-22480	6	60	.	.	PUNCT
brj-22480	7	1	compared	compare	VERB
brj-22480	7	2	with	with	ADP
brj-22480	7	3	the	the	DET
brj-22480	7	4	experimental	experimental	ADJ
brj-22480	7	5	value	value	NOUN
brj-22480	7	6	,	,	PUNCT
brj-22480	7	7	the	the	DET
brj-22480	7	8	predicted	predict	VERB
brj-22480	7	9	value	value	NOUN
brj-22480	7	10	basically	basically	ADV
brj-22480	7	11	conforms	conform	VERB
brj-22480	7	12	to	to	ADP
brj-22480	7	13	the	the	DET
brj-22480	7	14	change	change	NOUN
brj-22480	7	15	law	law	NOUN
brj-22480	7	16	and	and	CCONJ
brj-22480	7	17	size	size	NOUN
brj-22480	7	18	of	of	ADP
brj-22480	7	19	the	the	DET
brj-22480	7	20	experimental	experimental	ADJ
brj-22480	7	21	value	value	NOUN
brj-22480	7	22	,	,	PUNCT
brj-22480	7	23	and	and	CCONJ
brj-22480	7	24	the	the	DET
brj-22480	7	25	error	error	NOUN
brj-22480	7	26	distribution	distribution	NOUN
brj-22480	7	27	is	be	AUX
brj-22480	7	28	approximately	approximately	ADV
brj-22480	7	29	2	2	NUM
brj-22480	7	30	%	%	NOUN
brj-22480	7	31	.	.	PUNCT
brj-22480	8	1	this	this	PRON
brj-22480	8	2	shows	show	VERB
brj-22480	8	3	that	that	SCONJ
brj-22480	8	4	the	the	DET
brj-22480	8	5	bp	bp	PROPN
brj-22480	8	6	neural	neural	PROPN
brj-22480	8	7	network	network	NOUN
brj-22480	8	8	model	model	NOUN
brj-22480	8	9	can	can	AUX
brj-22480	8	10	simulate	simulate	VERB
brj-22480	8	11	the	the	DET
brj-22480	8	12	drying	dry	VERB
brj-22480	8	13	rate	rate	NOUN
brj-22480	8	14	and	and	CCONJ
brj-22480	8	15	longitudinal	longitudinal	ADJ
brj-22480	8	16	cracking	cracking	NOUN
brj-22480	8	17	degree	degree	NOUN
brj-22480	8	18	in	in	ADP
brj-22480	8	19	the	the	DET
brj-22480	8	20	drying	dry	VERB
brj-22480	8	21	process	process	NOUN
brj-22480	8	22	and	and	CCONJ
brj-22480	8	23	realize	realize	VERB
brj-22480	8	24	the	the	DET
brj-22480	8	25	prediction	prediction	NOUN
brj-22480	8	26	of	of	ADP
brj-22480	8	27	the	the	DET
brj-22480	8	28	drying	dry	VERB
brj-22480	8	29	process	process	NOUN
brj-22480	8	30	.	.	PUNCT
brj-22480	9	1	doi	doi	NOUN
brj-22480	9	2	:	:	PUNCT
brj-22480	9	3	10.15376	10.15376	NUM
brj-22480	9	4	/	/	SYM
brj-22480	9	5	biores.18.4.8212	biores.18.4.8212	PROPN
brj-22480	9	6	-	-	PUNCT
brj-22480	9	7	8222	8222	NUM
brj-22480	9	8	keywords	keyword	NOUN
brj-22480	9	9	:	:	PUNCT
brj-22480	9	10	wood	wood	NOUN
brj-22480	9	11	drying	drying	NOUN
brj-22480	9	12	;	;	PUNCT
brj-22480	9	13	neural	neural	ADJ
brj-22480	9	14	network	network	NOUN
brj-22480	9	15	;	;	PUNCT
brj-22480	9	16	drying	dry	VERB
brj-22480	9	17	rate	rate	NOUN
brj-22480	9	18	;	;	PUNCT
brj-22480	9	19	longitudinal	longitudinal	ADJ
brj-22480	9	20	crack	crack	NOUN
brj-22480	9	21	degree	degree	NOUN
brj-22480	9	22	contact	contact	NOUN
brj-22480	9	23	information	information	NOUN
brj-22480	9	24	:	:	PUNCT
brj-22480	9	25	a	a	DET
brj-22480	9	26	:	:	PUNCT
brj-22480	9	27	school	school	NOUN
brj-22480	9	28	of	of	ADP
brj-22480	9	29	artificial	artificial	ADJ
brj-22480	9	30	intelligence	intelligence	NOUN
brj-22480	9	31	,	,	PUNCT
brj-22480	9	32	henan	henan	PROPN
brj-22480	9	33	institute	institute	PROPN
brj-22480	9	34	of	of	ADP
brj-22480	9	35	science	science	PROPN
brj-22480	9	36	and	and	CCONJ
brj-22480	9	37	technology	technology	NOUN
brj-22480	9	38	,	,	PUNCT
brj-22480	9	39	xinxiang	xinxiang	PROPN
brj-22480	9	40	,	,	PUNCT
brj-22480	9	41	453003	453003	NUM
brj-22480	9	42	,	,	PUNCT
brj-22480	9	43	china	china	PROPN
brj-22480	9	44	;	;	PUNCT
brj-22480	9	45	b	b	X
brj-22480	9	46	:	:	PUNCT
brj-22480	9	47	school	school	NOUN
brj-22480	9	48	of	of	ADP
brj-22480	9	49	art	art	NOUN
brj-22480	9	50	,	,	PUNCT
brj-22480	9	51	henan	henan	PROPN
brj-22480	9	52	institute	institute	PROPN
brj-22480	9	53	of	of	ADP
brj-22480	9	54	science	science	PROPN
brj-22480	9	55	and	and	CCONJ
brj-22480	9	56	technology	technology	NOUN
brj-22480	9	57	,	,	PUNCT
brj-22480	9	58	xinxiang	xinxiang	PROPN
brj-22480	9	59	,	,	PUNCT
brj-22480	9	60	453003	453003	NUM
brj-22480	9	61	,	,	PUNCT
brj-22480	9	62	china	china	PROPN
brj-22480	9	63	;	;	PUNCT
brj-22480	9	64	*	*	PUNCT
brj-22480	9	65	corresponding	correspond	VERB
brj-22480	9	66	author	author	NOUN
brj-22480	9	67	:	:	PUNCT
brj-22480	9	68	nefuchj@63.com	nefuchj@63.com	PROPN
brj-22480	9	69	introduction	introduction	NOUN
brj-22480	9	70	the	the	DET
brj-22480	9	71	concept	concept	NOUN
brj-22480	9	72	of	of	ADP
brj-22480	9	73	an	an	DET
brj-22480	9	74	artificial	artificial	ADJ
brj-22480	9	75	neural	neural	ADJ
brj-22480	9	76	network	network	NOUN
brj-22480	9	77	(	(	PUNCT
brj-22480	9	78	ann	ann	PROPN
brj-22480	9	79	)	)	PUNCT
brj-22480	9	80	,	,	PUNCT
brj-22480	9	81	which	which	PRON
brj-22480	9	82	originated	originate	VERB
brj-22480	9	83	from	from	ADP
brj-22480	9	84	bionic	bionic	ADJ
brj-22480	9	85	technology	technology	NOUN
brj-22480	9	86	,	,	PUNCT
brj-22480	9	87	involves	involve	VERB
brj-22480	9	88	a	a	DET
brj-22480	9	89	complex	complex	ADJ
brj-22480	9	90	mathematical	mathematical	ADJ
brj-22480	9	91	model	model	NOUN
brj-22480	9	92	composed	compose	VERB
brj-22480	9	93	of	of	ADP
brj-22480	9	94	multiple	multiple	ADJ
brj-22480	9	95	nodes	node	NOUN
brj-22480	9	96	.	.	PUNCT
brj-22480	10	1	an	an	DET
brj-22480	10	2	ann	ann	PROPN
brj-22480	10	3	can	can	AUX
brj-22480	10	4	realize	realize	VERB
brj-22480	10	5	the	the	DET
brj-22480	10	6	rich	rich	ADJ
brj-22480	10	7	processing	processing	NOUN
brj-22480	10	8	requirements	requirement	NOUN
brj-22480	10	9	of	of	ADP
brj-22480	10	10	large	large	ADJ
brj-22480	10	11	-	-	PUNCT
brj-22480	10	12	scale	scale	NOUN
brj-22480	10	13	data	datum	NOUN
brj-22480	10	14	nonlinear	nonlinear	NOUN
brj-22480	10	15	processing	processing	NOUN
brj-22480	10	16	,	,	PUNCT
brj-22480	10	17	multi	multi	ADJ
brj-22480	10	18	-	-	ADJ
brj-22480	10	19	thread	thread	ADJ
brj-22480	10	20	logic	logic	NOUN
brj-22480	10	21	recursion	recursion	NOUN
brj-22480	10	22	,	,	PUNCT
brj-22480	10	23	self	self	NOUN
brj-22480	10	24	-	-	PUNCT
brj-22480	10	25	detection	detection	NOUN
brj-22480	10	26	,	,	PUNCT
brj-22480	10	27	and	and	CCONJ
brj-22480	10	28	self	self	NOUN
brj-22480	10	29	-	-	PUNCT
brj-22480	10	30	learning	learn	VERB
brj-22480	10	31	ability	ability	NOUN
brj-22480	10	32	.	.	PUNCT
brj-22480	11	1	especially	especially	ADV
brj-22480	11	2	in	in	ADP
brj-22480	11	3	cases	case	NOUN
brj-22480	11	4	of	of	ADP
brj-22480	11	5	some	some	DET
brj-22480	11	6	multi	multi	ADJ
brj-22480	11	7	-	-	ADJ
brj-22480	11	8	condition	condition	ADJ
brj-22480	11	9	nonlinear	nonlinear	NOUN
brj-22480	11	10	problems	problem	NOUN
brj-22480	11	11	,	,	PUNCT
brj-22480	11	12	ann	ann	PROPN
brj-22480	11	13	can	can	AUX
brj-22480	11	14	provide	provide	VERB
brj-22480	11	15	better	well	ADJ
brj-22480	11	16	solutions	solution	NOUN
brj-22480	11	17	.	.	PUNCT
brj-22480	12	1	unlike	unlike	ADP
brj-22480	12	2	traditional	traditional	ADJ
brj-22480	12	3	regression	regression	NOUN
brj-22480	12	4	analysis	analysis	NOUN
brj-22480	12	5	,	,	PUNCT
brj-22480	12	6	neural	neural	ADJ
brj-22480	12	7	networks	network	NOUN
brj-22480	12	8	do	do	AUX
brj-22480	12	9	not	not	PART
brj-22480	12	10	need	need	VERB
brj-22480	12	11	to	to	PART
brj-22480	12	12	define	define	VERB
brj-22480	12	13	a	a	DET
brj-22480	12	14	linear	linear	ADJ
brj-22480	12	15	or	or	CCONJ
brj-22480	12	16	non	non	ADJ
brj-22480	12	17	-	-	ADJ
brj-22480	12	18	linear	linear	ADJ
brj-22480	12	19	equation	equation	NOUN
brj-22480	12	20	when	when	SCONJ
brj-22480	12	21	analyzing	analyze	VERB
brj-22480	12	22	data	datum	NOUN
brj-22480	12	23	.	.	PUNCT
brj-22480	13	1	neural	neural	ADJ
brj-22480	13	2	networks	network	NOUN
brj-22480	13	3	can	can	AUX
brj-22480	13	4	automatically	automatically	ADV
brj-22480	13	5	find	find	VERB
brj-22480	13	6	patterns	pattern	NOUN
brj-22480	13	7	,	,	PUNCT
brj-22480	13	8	as	as	ADV
brj-22480	13	9	well	well	ADV
brj-22480	13	10	as	as	ADP
brj-22480	13	11	to	to	PART
brj-22480	13	12	learn	learn	VERB
brj-22480	13	13	and	and	CCONJ
brj-22480	13	14	analyze	analyze	VERB
brj-22480	13	15	them	they	PRON
brj-22480	13	16	based	base	VERB
brj-22480	13	17	on	on	ADP
brj-22480	13	18	the	the	DET
brj-22480	13	19	details	detail	NOUN
brj-22480	13	20	of	of	ADP
brj-22480	13	21	the	the	DET
brj-22480	13	22	input	input	NOUN
brj-22480	13	23	data	datum	NOUN
brj-22480	13	24	set	set	VERB
brj-22480	13	25	.	.	PUNCT
brj-22480	14	1	this	this	PRON
brj-22480	14	2	makes	make	VERB
brj-22480	14	3	the	the	DET
brj-22480	14	4	neural	neural	ADJ
brj-22480	14	5	network	network	NOUN
brj-22480	14	6	show	show	VERB
brj-22480	14	7	excellent	excellent	ADJ
brj-22480	14	8	performance	performance	NOUN
brj-22480	14	9	on	on	ADP
brj-22480	14	10	large	large	ADJ
brj-22480	14	11	-	-	PUNCT
brj-22480	14	12	scale	scale	NOUN
brj-22480	14	13	data	data	NOUN
brj-22480	14	14	sets	set	NOUN
brj-22480	14	15	,	,	PUNCT
brj-22480	14	16	complex	complex	ADJ
brj-22480	14	17	patterns	pattern	NOUN
brj-22480	14	18	,	,	PUNCT
brj-22480	14	19	and	and	CCONJ
brj-22480	14	20	nonlinear	nonlinear	ADJ
brj-22480	14	21	data	datum	NOUN
brj-22480	14	22	(	(	PUNCT
brj-22480	14	23	fabijańska	fabijańska	NOUN
brj-22480	14	24	2021	2021	NUM
brj-22480	14	25	)	)	PUNCT
brj-22480	14	26	.	.	PUNCT
brj-22480	15	1	therefore	therefore	ADV
brj-22480	15	2	,	,	PUNCT
brj-22480	15	3	it	it	PRON
brj-22480	15	4	is	be	AUX
brj-22480	15	5	widely	widely	ADV
brj-22480	15	6	used	use	VERB
brj-22480	15	7	in	in	ADP
brj-22480	15	8	biology	biology	NOUN
brj-22480	15	9	,	,	PUNCT
brj-22480	15	10	chemistry	chemistry	NOUN
brj-22480	15	11	,	,	PUNCT
brj-22480	15	12	environment	environment	NOUN
brj-22480	15	13	,	,	PUNCT
brj-22480	15	14	materials	material	NOUN
brj-22480	15	15	,	,	PUNCT
brj-22480	15	16	medicine	medicine	NOUN
brj-22480	15	17	,	,	PUNCT
brj-22480	15	18	and	and	CCONJ
brj-22480	15	19	other	other	ADJ
brj-22480	15	20	research	research	NOUN
brj-22480	15	21	fields	field	NOUN
brj-22480	15	22	.	.	PUNCT
brj-22480	16	1	among	among	ADP
brj-22480	16	2	various	various	ADJ
brj-22480	16	3	neural	neural	ADJ
brj-22480	16	4	network	network	NOUN
brj-22480	16	5	structures	structure	NOUN
brj-22480	16	6	,	,	PUNCT
brj-22480	16	7	the	the	DET
brj-22480	16	8	back	back	ADJ
brj-22480	16	9	propagation	propagation	NOUN
brj-22480	16	10	(	(	PUNCT
brj-22480	16	11	bp	bp	PROPN
brj-22480	16	12	)	)	PUNCT
brj-22480	16	13	neural	neural	ADJ
brj-22480	16	14	network	network	NOUN
brj-22480	16	15	structure	structure	NOUN
brj-22480	16	16	is	be	AUX
brj-22480	16	17	a	a	DET
brj-22480	16	18	multilayer	multilayer	ADJ
brj-22480	16	19	feedforward	feedforward	NOUN
brj-22480	16	20	neural	neural	ADJ
brj-22480	16	21	network	network	NOUN
brj-22480	16	22	trained	train	VERB
brj-22480	16	23	according	accord	VERB
brj-22480	16	24	to	to	ADP
brj-22480	16	25	the	the	DET
brj-22480	16	26	error	error	NOUN
brj-22480	16	27	back	back	NOUN
brj-22480	16	28	propagation	propagation	NOUN
brj-22480	16	29	algorithm	algorithm	NOUN
brj-22480	16	30	.	.	PUNCT
brj-22480	17	1	through	through	ADP
brj-22480	17	2	learning	learning	NOUN
brj-22480	17	3	,	,	PUNCT
brj-22480	17	4	analyzing	analyze	VERB
brj-22480	17	5	,	,	PUNCT
brj-22480	17	6	and	and	CCONJ
brj-22480	17	7	modeling	model	VERB
brj-22480	17	8	the	the	DET
brj-22480	17	9	input	input	NOUN
brj-22480	17	10	data	datum	NOUN
brj-22480	17	11	of	of	ADP
brj-22480	17	12	the	the	DET
brj-22480	17	13	neural	neural	ADJ
brj-22480	17	14	network	network	NOUN
brj-22480	17	15	,	,	PUNCT
brj-22480	17	16	it	it	PRON
brj-22480	17	17	can	can	AUX
brj-22480	17	18	achieve	achieve	VERB
brj-22480	17	19	the	the	DET
brj-22480	17	20	simulation	simulation	NOUN
brj-22480	17	21	and	and	CCONJ
brj-22480	17	22	analysis	analysis	NOUN
brj-22480	17	23	results	result	NOUN
brj-22480	17	24	of	of	ADP
brj-22480	17	25	the	the	DET
brj-22480	17	26	drying	dry	VERB
brj-22480	17	27	process	process	NOUN
brj-22480	17	28	.	.	PUNCT
brj-22480	18	1	the	the	DET
brj-22480	18	2	bp	bp	PROPN
brj-22480	18	3	neural	neural	PROPN
brj-22480	18	4	network	network	NOUN
brj-22480	18	5	was	be	AUX
brj-22480	18	6	introduced	introduce	VERB
brj-22480	18	7	into	into	ADP
brj-22480	18	8	the	the	DET
brj-22480	18	9	conventional	conventional	ADJ
brj-22480	18	10	drying	drying	NOUN
brj-22480	18	11	of	of	ADP
brj-22480	18	12	wood	wood	NOUN
brj-22480	18	13	to	to	PART
brj-22480	18	14	solve	solve	VERB
brj-22480	18	15	the	the	DET
brj-22480	18	16	complex	complex	ADJ
brj-22480	18	17	non	non	ADJ
brj-22480	18	18	-	-	ADJ
brj-22480	18	19	linear	linear	ADJ
brj-22480	18	20	problem	problem	NOUN
brj-22480	18	21	in	in	ADP
brj-22480	18	22	the	the	DET
brj-22480	18	23	drying	dry	VERB
brj-22480	18	24	process	process	NOUN
brj-22480	18	25	,	,	PUNCT
brj-22480	18	26	and	and	CCONJ
brj-22480	18	27	good	good	ADJ
brj-22480	18	28	application	application	NOUN
brj-22480	18	29	results	result	NOUN
brj-22480	18	30	have	have	AUX
brj-22480	18	31	been	be	AUX
brj-22480	18	32	peer	peer	NOUN
brj-22480	18	33	-	-	PUNCT
brj-22480	18	34	reviewed	review	VERB
brj-22480	18	35	article	article	NOUN
brj-22480	18	36	bioresources.com	bioresources.com	X
brj-22480	18	37	chai	chai	NOUN
brj-22480	18	38	&	&	CCONJ
brj-22480	18	39	li	li	PROPN
brj-22480	18	40	(	(	PUNCT
brj-22480	18	41	2023	2023	NUM
brj-22480	18	42	)	)	PUNCT
brj-22480	18	43	.	.	PUNCT
brj-22480	19	1	“	"	PUNCT
brj-22480	19	2	prediction	prediction	NOUN
brj-22480	19	3	of	of	ADP
brj-22480	19	4	wood	wood	NOUN
brj-22480	19	5	drying	dry	VERB
brj-22480	19	6	by	by	ADP
brj-22480	19	7	ann	ann	PROPN
brj-22480	19	8	,	,	PUNCT
brj-22480	19	9	”	"	PUNCT
brj-22480	19	10	bioresources	bioresource	NOUN
brj-22480	19	11	18(4	18(4	NUM
brj-22480	19	12	)	)	PUNCT
brj-22480	19	13	,	,	PUNCT
brj-22480	19	14	8212	8212	NUM
brj-22480	19	15	-	-	SYM
brj-22480	19	16	8222	8222	NUM
brj-22480	19	17	.	.	PUNCT
brj-22480	19	18	8213	8213	NUM
brj-22480	19	19	obtained	obtain	VERB
brj-22480	19	20	in	in	ADP
brj-22480	19	21	many	many	ADJ
brj-22480	19	22	applications	application	NOUN
brj-22480	19	23	.	.	PUNCT
brj-22480	20	1	avramidis	avramidi	NOUN
brj-22480	20	2	and	and	CCONJ
brj-22480	20	3	iliadis	iliadis	PROPN
brj-22480	20	4	(	(	PUNCT
brj-22480	20	5	2005	2005	NUM
brj-22480	20	6	)	)	PUNCT
brj-22480	20	7	used	use	VERB
brj-22480	20	8	ann	ann	PROPN
brj-22480	20	9	technology	technology	NOUN
brj-22480	20	10	to	to	PART
brj-22480	20	11	conduct	conduct	VERB
brj-22480	20	12	research	research	NOUN
brj-22480	20	13	on	on	ADP
brj-22480	20	14	the	the	DET
brj-22480	20	15	thermal	thermal	ADJ
brj-22480	20	16	conductivity	conductivity	NOUN
brj-22480	20	17	of	of	ADP
brj-22480	20	18	wood	wood	NOUN
brj-22480	20	19	.	.	PUNCT
brj-22480	21	1	related	relate	VERB
brj-22480	21	2	work	work	NOUN
brj-22480	21	3	has	have	AUX
brj-22480	21	4	been	be	AUX
brj-22480	21	5	done	do	VERB
brj-22480	21	6	for	for	ADP
brj-22480	21	7	dielectric	dielectric	ADJ
brj-22480	21	8	loss	loss	NOUN
brj-22480	21	9	factor	factor	NOUN
brj-22480	21	10	(	(	PUNCT
brj-22480	21	11	avramidis	avramidis	NOUN
brj-22480	21	12	2005	2005	NUM
brj-22480	21	13	)	)	PUNCT
brj-22480	21	14	,	,	PUNCT
brj-22480	21	15	wood	wood	NOUN
brj-22480	21	16	density	density	NOUN
brj-22480	21	17	(	(	PUNCT
brj-22480	21	18	iliadis	iliadis	VERB
brj-22480	21	19	et	et	PROPN
brj-22480	21	20	al	al	PROPN
brj-22480	21	21	.	.	PROPN
brj-22480	21	22	2013	2013	NUM
brj-22480	21	23	)	)	PUNCT
brj-22480	21	24	,	,	PUNCT
brj-22480	21	25	and	and	CCONJ
brj-22480	21	26	wood	wood	NOUN
brj-22480	21	27	heat	heat	NOUN
brj-22480	21	28	flux	flux	NOUN
brj-22480	21	29	under	under	ADP
brj-22480	21	30	non	non	ADJ
brj-22480	21	31	-	-	ADJ
brj-22480	21	32	isothermal	isothermal	ADJ
brj-22480	21	33	diffusion	diffusion	NOUN
brj-22480	21	34	conditions	condition	NOUN
brj-22480	21	35	(	(	PUNCT
brj-22480	21	36	cai	cai	X
brj-22480	21	37	and	and	CCONJ
brj-22480	21	38	chen	chen	PROPN
brj-22480	21	39	2005	2005	NUM
brj-22480	21	40	;	;	PUNCT
brj-22480	21	41	avramidis	avramidi	NOUN
brj-22480	21	42	and	and	CCONJ
brj-22480	21	43	wu	wu	PROPN
brj-22480	21	44	2007	2007	NUM
brj-22480	21	45	)	)	PUNCT
brj-22480	21	46	.	.	PUNCT
brj-22480	22	1	the	the	DET
brj-22480	22	2	results	result	NOUN
brj-22480	22	3	show	show	VERB
brj-22480	22	4	that	that	SCONJ
brj-22480	22	5	ann	ann	PROPN
brj-22480	22	6	has	have	VERB
brj-22480	22	7	strong	strong	ADJ
brj-22480	22	8	simulation	simulation	NOUN
brj-22480	22	9	and	and	CCONJ
brj-22480	22	10	prediction	prediction	NOUN
brj-22480	22	11	ability	ability	NOUN
brj-22480	22	12	in	in	ADP
brj-22480	22	13	the	the	DET
brj-22480	22	14	field	field	NOUN
brj-22480	22	15	of	of	ADP
brj-22480	22	16	wood	wood	NOUN
brj-22480	22	17	drying	drying	NOUN
brj-22480	22	18	,	,	PUNCT
brj-22480	22	19	and	and	CCONJ
brj-22480	22	20	it	it	PRON
brj-22480	22	21	can	can	AUX
brj-22480	22	22	simulate	simulate	VERB
brj-22480	22	23	the	the	DET
brj-22480	22	24	required	require	VERB
brj-22480	22	25	results	result	NOUN
brj-22480	22	26	.	.	PUNCT
brj-22480	23	1	ceylan	ceylan	NOUN
brj-22480	23	2	(	(	PUNCT
brj-22480	23	3	2008	2008	NUM
brj-22480	23	4	)	)	PUNCT
brj-22480	23	5	built	build	VERB
brj-22480	23	6	an	an	DET
brj-22480	23	7	artificial	artificial	ADJ
brj-22480	23	8	neural	neural	ADJ
brj-22480	23	9	network	network	NOUN
brj-22480	23	10	model	model	NOUN
brj-22480	23	11	using	use	VERB
brj-22480	23	12	drying	dry	VERB
brj-22480	23	13	temperature	temperature	NOUN
brj-22480	23	14	,	,	PUNCT
brj-22480	23	15	relative	relative	ADJ
brj-22480	23	16	humidity	humidity	NOUN
brj-22480	23	17	,	,	PUNCT
brj-22480	23	18	and	and	CCONJ
brj-22480	23	19	drying	dry	VERB
brj-22480	23	20	time	time	NOUN
brj-22480	23	21	as	as	ADP
brj-22480	23	22	input	input	NOUN
brj-22480	23	23	variables	variable	NOUN
brj-22480	23	24	,	,	PUNCT
brj-22480	23	25	and	and	CCONJ
brj-22480	23	26	successfully	successfully	ADV
brj-22480	23	27	predicted	predict	VERB
brj-22480	23	28	the	the	DET
brj-22480	23	29	moisture	moisture	NOUN
brj-22480	23	30	content	content	NOUN
brj-22480	23	31	of	of	ADP
brj-22480	23	32	wood	wood	NOUN
brj-22480	23	33	in	in	ADP
brj-22480	23	34	the	the	DET
brj-22480	23	35	drying	dry	VERB
brj-22480	23	36	process	process	NOUN
brj-22480	23	37	.	.	PUNCT
brj-22480	24	1	wu	wu	PROPN
brj-22480	24	2	and	and	CCONJ
brj-22480	24	3	avramidis	avramidis	PROPN
brj-22480	24	4	(	(	PUNCT
brj-22480	24	5	2006	2006	NUM
brj-22480	24	6	)	)	PUNCT
brj-22480	24	7	established	establish	VERB
brj-22480	24	8	a	a	DET
brj-22480	24	9	neural	neural	ADJ
brj-22480	24	10	network	network	NOUN
brj-22480	24	11	model	model	NOUN
brj-22480	24	12	with	with	ADP
brj-22480	24	13	back	back	ADJ
brj-22480	24	14	-	-	PUNCT
brj-22480	24	15	propagation	propagation	NOUN
brj-22480	24	16	algorithm	algorithm	NOUN
brj-22480	24	17	and	and	CCONJ
brj-22480	24	18	predicted	predict	VERB
brj-22480	24	19	the	the	DET
brj-22480	24	20	drying	dry	VERB
brj-22480	24	21	rate	rate	NOUN
brj-22480	24	22	of	of	ADP
brj-22480	24	23	wood	wood	NOUN
brj-22480	24	24	.	.	PUNCT
brj-22480	25	1	fu	fu	PROPN
brj-22480	25	2	(	(	PUNCT
brj-22480	25	3	2020	2020	NUM
brj-22480	25	4	)	)	PUNCT
brj-22480	25	5	established	establish	VERB
brj-22480	25	6	a	a	DET
brj-22480	25	7	prediction	prediction	NOUN
brj-22480	25	8	model	model	NOUN
brj-22480	25	9	to	to	PART
brj-22480	25	10	predict	predict	VERB
brj-22480	25	11	the	the	DET
brj-22480	25	12	elastic	elastic	ADJ
brj-22480	25	13	strain	strain	NOUN
brj-22480	25	14	of	of	ADP
brj-22480	25	15	birch	birch	NOUN
brj-22480	25	16	discs	disc	NOUN
brj-22480	25	17	during	during	ADP
brj-22480	25	18	the	the	DET
brj-22480	25	19	drying	dry	VERB
brj-22480	25	20	process	process	NOUN
brj-22480	25	21	.	.	PUNCT
brj-22480	26	1	in	in	ADP
brj-22480	26	2	addition	addition	NOUN
brj-22480	26	3	,	,	PUNCT
brj-22480	26	4	neural	neural	ADJ
brj-22480	26	5	networks	network	NOUN
brj-22480	26	6	are	be	AUX
brj-22480	26	7	widely	widely	ADV
brj-22480	26	8	used	use	VERB
brj-22480	26	9	in	in	ADP
brj-22480	26	10	other	other	ADJ
brj-22480	26	11	areas	area	NOUN
brj-22480	26	12	of	of	ADP
brj-22480	26	13	wood	wood	NOUN
brj-22480	26	14	science	science	NOUN
brj-22480	26	15	,	,	PUNCT
brj-22480	26	16	such	such	ADJ
brj-22480	26	17	as	as	ADP
brj-22480	26	18	the	the	DET
brj-22480	26	19	identification	identification	NOUN
brj-22480	26	20	of	of	ADP
brj-22480	26	21	wood	wood	NOUN
brj-22480	26	22	defects	defect	NOUN
brj-22480	26	23	(	(	PUNCT
brj-22480	26	24	gao	gao	PROPN
brj-22480	26	25	et	et	PROPN
brj-22480	26	26	al	al	PROPN
brj-22480	26	27	.	.	PROPN
brj-22480	26	28	2022	2022	NUM
brj-22480	26	29	)	)	PUNCT
brj-22480	26	30	,	,	PUNCT
brj-22480	26	31	tree	tree	NOUN
brj-22480	26	32	species	specie	NOUN
brj-22480	26	33	,	,	PUNCT
brj-22480	26	34	and	and	CCONJ
brj-22480	26	35	insect	insect	NOUN
brj-22480	26	36	diseases	disease	NOUN
brj-22480	26	37	(	(	PUNCT
brj-22480	26	38	huang	huang	PROPN
brj-22480	26	39	et	et	PROPN
brj-22480	26	40	al	al	PROPN
brj-22480	26	41	.	.	PROPN
brj-22480	26	42	2022	2022	NUM
brj-22480	26	43	)	)	PUNCT
brj-22480	26	44	.	.	PUNCT
brj-22480	27	1	the	the	DET
brj-22480	27	2	above	above	ADJ
brj-22480	27	3	studies	study	NOUN
brj-22480	27	4	demonstrated	demonstrate	VERB
brj-22480	27	5	the	the	DET
brj-22480	27	6	feasibility	feasibility	NOUN
brj-22480	27	7	of	of	ADP
brj-22480	27	8	neural	neural	ADJ
brj-22480	27	9	networks	network	NOUN
brj-22480	27	10	for	for	ADP
brj-22480	27	11	wood	wood	NOUN
brj-22480	27	12	drying	drying	NOUN
brj-22480	27	13	.	.	PUNCT
brj-22480	28	1	before	before	ADP
brj-22480	28	2	conventional	conventional	ADJ
brj-22480	28	3	drying	drying	NOUN
brj-22480	28	4	,	,	PUNCT
brj-22480	28	5	the	the	DET
brj-22480	28	6	method	method	NOUN
brj-22480	28	7	of	of	ADP
brj-22480	28	8	improving	improve	VERB
brj-22480	28	9	the	the	DET
brj-22480	28	10	drying	dry	VERB
brj-22480	28	11	quality	quality	NOUN
brj-22480	28	12	through	through	ADP
brj-22480	28	13	pretreatment	pretreatment	NOUN
brj-22480	28	14	is	be	AUX
brj-22480	28	15	a	a	DET
brj-22480	28	16	common	common	ADJ
brj-22480	28	17	way	way	NOUN
brj-22480	28	18	to	to	PART
brj-22480	28	19	improve	improve	VERB
brj-22480	28	20	the	the	DET
brj-22480	28	21	drying	dry	VERB
brj-22480	28	22	quality	quality	NOUN
brj-22480	28	23	in	in	ADP
brj-22480	28	24	the	the	DET
brj-22480	28	25	initial	initial	ADJ
brj-22480	28	26	stage	stage	NOUN
brj-22480	28	27	of	of	ADP
brj-22480	28	28	drying	dry	VERB
brj-22480	28	29	of	of	ADP
brj-22480	28	30	large	large	ADJ
brj-22480	28	31	-	-	PUNCT
brj-22480	28	32	section	section	NOUN
brj-22480	28	33	pine	pine	NOUN
brj-22480	28	34	pith	pith	NOUN
brj-22480	28	35	-	-	PUNCT
brj-22480	28	36	containing	contain	VERB
brj-22480	28	37	square	square	ADJ
brj-22480	28	38	timber	timber	NOUN
brj-22480	28	39	.	.	PUNCT
brj-22480	29	1	the	the	DET
brj-22480	29	2	application	application	NOUN
brj-22480	29	3	of	of	ADP
brj-22480	29	4	the	the	DET
brj-22480	29	5	setting	set	VERB
brj-22480	29	6	technology	technology	NOUN
brj-22480	29	7	(	(	PUNCT
brj-22480	29	8	including	include	VERB
brj-22480	29	9	wood	wood	NOUN
brj-22480	29	10	softening	softening	NOUN
brj-22480	29	11	,	,	PUNCT
brj-22480	29	12	setting	set	VERB
brj-22480	29	13	and	and	CCONJ
brj-22480	29	14	balancing	balance	VERB
brj-22480	29	15	treatment	treatment	NOUN
brj-22480	29	16	)	)	PUNCT
brj-22480	29	17	to	to	ADP
brj-22480	29	18	the	the	DET
brj-22480	29	19	drying	drying	NOUN
brj-22480	29	20	of	of	ADP
brj-22480	29	21	the	the	DET
brj-22480	29	22	pinus	pinus	NOUN
brj-22480	29	23	sylvestris	sylvestris	NOUN
brj-22480	29	24	can	can	AUX
brj-22480	29	25	help	help	VERB
brj-22480	29	26	to	to	PART
brj-22480	29	27	restrain	restrain	VERB
brj-22480	29	28	the	the	DET
brj-22480	29	29	drying	dry	VERB
brj-22480	29	30	surface	surface	NOUN
brj-22480	29	31	cracks	crack	NOUN
brj-22480	29	32	and	and	CCONJ
brj-22480	29	33	improve	improve	VERB
brj-22480	29	34	the	the	DET
brj-22480	29	35	drying	dry	VERB
brj-22480	29	36	speed	speed	NOUN
brj-22480	29	37	.	.	PUNCT
brj-22480	30	1	because	because	SCONJ
brj-22480	30	2	the	the	DET
brj-22480	30	3	application	application	NOUN
brj-22480	30	4	of	of	ADP
brj-22480	30	5	setting	set	VERB
brj-22480	30	6	technology	technology	NOUN
brj-22480	30	7	in	in	ADP
brj-22480	30	8	the	the	DET
brj-22480	30	9	drying	drying	NOUN
brj-22480	30	10	of	of	ADP
brj-22480	30	11	pinus	pinus	NOUN
brj-22480	30	12	sylvestris	sylvestris	NOUN
brj-22480	30	13	is	be	AUX
brj-22480	30	14	still	still	ADV
brj-22480	30	15	in	in	ADP
brj-22480	30	16	its	its	PRON
brj-22480	30	17	initial	initial	ADJ
brj-22480	30	18	stage	stage	NOUN
brj-22480	30	19	,	,	PUNCT
brj-22480	30	20	the	the	DET
brj-22480	30	21	ann	ann	PROPN
brj-22480	30	22	technology	technology	NOUN
brj-22480	30	23	can	can	AUX
brj-22480	30	24	be	be	AUX
brj-22480	30	25	used	use	VERB
brj-22480	30	26	to	to	PART
brj-22480	30	27	explore	explore	VERB
brj-22480	30	28	a	a	DET
brj-22480	30	29	reasonable	reasonable	ADJ
brj-22480	30	30	drying	dry	VERB
brj-22480	30	31	process	process	NOUN
brj-22480	30	32	ratio	ratio	NOUN
brj-22480	30	33	with	with	ADP
brj-22480	30	34	fewer	few	ADJ
brj-22480	30	35	experiments	experiment	NOUN
brj-22480	30	36	,	,	PUNCT
brj-22480	30	37	and	and	CCONJ
brj-22480	30	38	subsequent	subsequent	ADJ
brj-22480	30	39	routine	routine	ADJ
brj-22480	30	40	drying	drying	NOUN
brj-22480	30	41	is	be	AUX
brj-22480	30	42	of	of	ADP
brj-22480	30	43	great	great	ADJ
brj-22480	30	44	significance	significance	NOUN
brj-22480	30	45	.	.	PUNCT
brj-22480	31	1	in	in	ADP
brj-22480	31	2	this	this	DET
brj-22480	31	3	experiment	experiment	NOUN
brj-22480	31	4	,	,	PUNCT
brj-22480	31	5	the	the	DET
brj-22480	31	6	bp	bp	PROPN
brj-22480	31	7	artificial	artificial	ADJ
brj-22480	31	8	neural	neural	ADJ
brj-22480	31	9	network	network	NOUN
brj-22480	31	10	was	be	AUX
brj-22480	31	11	used	use	VERB
brj-22480	31	12	.	.	PUNCT
brj-22480	32	1	the	the	DET
brj-22480	32	2	steaming	steaming	NOUN
brj-22480	32	3	treatment	treatment	NOUN
brj-22480	32	4	time	time	NOUN
brj-22480	32	5	and	and	CCONJ
brj-22480	32	6	temperature	temperature	NOUN
brj-22480	32	7	,	,	PUNCT
brj-22480	32	8	set	set	NOUN
brj-22480	32	9	time	time	NOUN
brj-22480	32	10	,	,	PUNCT
brj-22480	32	11	set	set	VERB
brj-22480	32	12	temperature	temperature	NOUN
brj-22480	32	13	,	,	PUNCT
brj-22480	32	14	initial	initial	ADJ
brj-22480	32	15	moisture	moisture	NOUN
brj-22480	32	16	content	content	NOUN
brj-22480	32	17	of	of	ADP
brj-22480	32	18	wood	wood	NOUN
brj-22480	32	19	,	,	PUNCT
brj-22480	32	20	and	and	CCONJ
brj-22480	32	21	position	position	NOUN
brj-22480	32	22	of	of	ADP
brj-22480	32	23	wood	wood	NOUN
brj-22480	32	24	core	core	NOUN
brj-22480	32	25	and	and	CCONJ
brj-22480	32	26	sapwood	sapwood	NOUN
brj-22480	32	27	were	be	AUX
brj-22480	32	28	used	use	VERB
brj-22480	32	29	as	as	ADP
brj-22480	32	30	model	model	NOUN
brj-22480	32	31	inputs	input	NOUN
brj-22480	32	32	,	,	PUNCT
brj-22480	32	33	and	and	CCONJ
brj-22480	32	34	the	the	DET
brj-22480	32	35	drying	dry	VERB
brj-22480	32	36	rate	rate	NOUN
brj-22480	32	37	and	and	CCONJ
brj-22480	32	38	longitudinal	longitudinal	ADJ
brj-22480	32	39	crack	crack	NOUN
brj-22480	32	40	of	of	ADP
brj-22480	32	41	wood	wood	NOUN
brj-22480	32	42	were	be	AUX
brj-22480	32	43	simulated	simulate	VERB
brj-22480	32	44	.	.	PUNCT
brj-22480	33	1	the	the	DET
brj-22480	33	2	relevant	relevant	ADJ
brj-22480	33	3	research	research	NOUN
brj-22480	33	4	results	result	NOUN
brj-22480	33	5	can	can	AUX
brj-22480	33	6	provide	provide	VERB
brj-22480	33	7	a	a	DET
brj-22480	33	8	theoretical	theoretical	ADJ
brj-22480	33	9	basis	basis	NOUN
brj-22480	33	10	for	for	ADP
brj-22480	33	11	the	the	DET
brj-22480	33	12	optimization	optimization	NOUN
brj-22480	33	13	of	of	ADP
brj-22480	33	14	the	the	DET
brj-22480	33	15	drying	dry	VERB
brj-22480	33	16	process	process	NOUN
brj-22480	33	17	of	of	ADP
brj-22480	33	18	pinus	pinus	NOUN
brj-22480	33	19	sylvestris	sylvestris	NOUN
brj-22480	33	20	square	square	ADJ
brj-22480	33	21	timber	timber	NOUN
brj-22480	33	22	.	.	PUNCT
brj-22480	34	1	experimental	experimental	ADJ
brj-22480	34	2	materials	material	NOUN
brj-22480	34	3	a	a	DET
brj-22480	34	4	number	number	NOUN
brj-22480	34	5	of	of	ADP
brj-22480	34	6	specimens	specimen	NOUN
brj-22480	34	7	with	with	ADP
brj-22480	34	8	dimensions	dimension	NOUN
brj-22480	34	9	of	of	ADP
brj-22480	34	10	120	120	NUM
brj-22480	34	11	mm	mm	NOUN
brj-22480	34	12	×	×	NOUN
brj-22480	34	13	120	120	NUM
brj-22480	34	14	mm	mm	NOUN
brj-22480	34	15	×	×	NOUN
brj-22480	34	16	500	500	NUM
brj-22480	34	17	mm	mm	NOUN
brj-22480	34	18	were	be	AUX
brj-22480	34	19	processed	process	VERB
brj-22480	34	20	from	from	ADP
brj-22480	34	21	mongolian	mongolian	ADJ
brj-22480	34	22	pine	pine	NOUN
brj-22480	34	23	with	with	ADP
brj-22480	34	24	no	no	DET
brj-22480	34	25	defects	defect	NOUN
brj-22480	34	26	.	.	PUNCT
brj-22480	35	1	the	the	DET
brj-22480	35	2	absolute	absolute	ADJ
brj-22480	35	3	dry	dry	ADJ
brj-22480	35	4	weighing	weighing	NOUN
brj-22480	35	5	method	method	NOUN
brj-22480	35	6	(	(	PUNCT
brj-22480	35	7	cai	cai	X
brj-22480	35	8	and	and	CCONJ
brj-22480	35	9	chen	chen	PROPN
brj-22480	35	10	2005	2005	NUM
brj-22480	35	11	)	)	PUNCT
brj-22480	35	12	was	be	AUX
brj-22480	35	13	used	use	VERB
brj-22480	35	14	to	to	PART
brj-22480	35	15	measure	measure	VERB
brj-22480	35	16	the	the	DET
brj-22480	35	17	initial	initial	ADJ
brj-22480	35	18	moisture	moisture	NOUN
brj-22480	35	19	content	content	NOUN
brj-22480	35	20	(	(	PUNCT
brj-22480	35	21	mc	mc	PROPN
brj-22480	35	22	)	)	PUNCT
brj-22480	35	23	of	of	ADP
brj-22480	35	24	the	the	DET
brj-22480	35	25	specimens	specimen	NOUN
brj-22480	35	26	.	.	PUNCT
brj-22480	36	1	in	in	ADP
brj-22480	36	2	this	this	DET
brj-22480	36	3	experiment	experiment	NOUN
brj-22480	36	4	,	,	PUNCT
brj-22480	36	5	four	four	NUM
brj-22480	36	6	similar	similar	ADJ
brj-22480	36	7	test	test	NOUN
brj-22480	36	8	materials	material	NOUN
brj-22480	36	9	were	be	AUX
brj-22480	36	10	taken	take	VERB
brj-22480	36	11	from	from	ADP
brj-22480	36	12	each	each	DET
brj-22480	36	13	group	group	NOUN
brj-22480	36	14	for	for	ADP
brj-22480	36	15	each	each	DET
brj-22480	36	16	experiment	experiment	NOUN
brj-22480	36	17	,	,	PUNCT
brj-22480	36	18	and	and	CCONJ
brj-22480	36	19	the	the	DET
brj-22480	36	20	average	average	ADJ
brj-22480	36	21	value	value	NOUN
brj-22480	36	22	of	of	ADP
brj-22480	36	23	the	the	DET
brj-22480	36	24	four	four	NUM
brj-22480	36	25	groups	group	NOUN
brj-22480	36	26	of	of	ADP
brj-22480	36	27	test	test	NOUN
brj-22480	36	28	materials	material	NOUN
brj-22480	36	29	was	be	AUX
brj-22480	36	30	calculated	calculate	VERB
brj-22480	36	31	to	to	PART
brj-22480	36	32	determine	determine	VERB
brj-22480	36	33	the	the	DET
brj-22480	36	34	experimental	experimental	ADJ
brj-22480	36	35	results	result	NOUN
brj-22480	36	36	of	of	ADP
brj-22480	36	37	each	each	DET
brj-22480	36	38	group	group	NOUN
brj-22480	36	39	.	.	PUNCT
brj-22480	37	1	in	in	ADP
brj-22480	37	2	total	total	ADJ
brj-22480	37	3	,	,	PUNCT
brj-22480	37	4	28	28	NUM
brj-22480	37	5	wood	wood	NOUN
brj-22480	37	6	samples	sample	NOUN
brj-22480	37	7	were	be	AUX
brj-22480	37	8	taken	take	VERB
brj-22480	37	9	from	from	ADP
brj-22480	37	10	seven	seven	NUM
brj-22480	37	11	groups	group	NOUN
brj-22480	37	12	in	in	ADP
brj-22480	37	13	the	the	DET
brj-22480	37	14	experiment	experiment	NOUN
brj-22480	37	15	.	.	PUNCT
brj-22480	38	1	methods	method	NOUN
brj-22480	38	2	drying	dry	VERB
brj-22480	38	3	process	process	NOUN
brj-22480	38	4	as	as	SCONJ
brj-22480	38	5	the	the	DET
brj-22480	38	6	large	large	ADJ
brj-22480	38	7	section	section	NOUN
brj-22480	38	8	sawed	saw	VERB
brj-22480	38	9	timber	timber	NOUN
brj-22480	38	10	can	can	AUX
brj-22480	38	11	easily	easily	ADV
brj-22480	38	12	develop	develop	VERB
brj-22480	38	13	surface	surface	NOUN
brj-22480	38	14	cracks	crack	NOUN
brj-22480	38	15	,	,	PUNCT
brj-22480	38	16	radial	radial	ADJ
brj-22480	38	17	cracks	crack	NOUN
brj-22480	38	18	,	,	PUNCT
brj-22480	38	19	and	and	CCONJ
brj-22480	38	20	other	other	ADJ
brj-22480	38	21	defects	defect	NOUN
brj-22480	38	22	during	during	ADP
brj-22480	38	23	the	the	DET
brj-22480	38	24	drying	dry	VERB
brj-22480	38	25	process	process	NOUN
brj-22480	38	26	,	,	PUNCT
brj-22480	38	27	it	it	PRON
brj-22480	38	28	is	be	AUX
brj-22480	38	29	necessary	necessary	ADJ
brj-22480	38	30	to	to	PART
brj-22480	38	31	carry	carry	VERB
brj-22480	38	32	out	out	ADP
brj-22480	38	33	pretreatment	pretreatment	NOUN
brj-22480	38	34	(	(	PUNCT
brj-22480	38	35	softening	soften	VERB
brj-22480	38	36	,	,	PUNCT
brj-22480	38	37	setting	setting	NOUN
brj-22480	38	38	,	,	PUNCT
brj-22480	38	39	and	and	CCONJ
brj-22480	38	40	balancing	balance	VERB
brj-22480	38	41	)	)	PUNCT
brj-22480	38	42	of	of	ADP
brj-22480	38	43	the	the	DET
brj-22480	38	44	square	square	ADJ
brj-22480	38	45	timber	timber	NOUN
brj-22480	38	46	of	of	ADP
brj-22480	38	47	mongolian	mongolian	ADJ
brj-22480	38	48	pine	pine	NOUN
brj-22480	38	49	before	before	ADP
brj-22480	38	50	drying	dry	VERB
brj-22480	38	51	to	to	PART
brj-22480	38	52	reduce	reduce	VERB
brj-22480	38	53	the	the	DET
brj-22480	38	54	occurrence	occurrence	NOUN
brj-22480	38	55	of	of	ADP
brj-22480	38	56	drying	dry	VERB
brj-22480	38	57	defects	defect	NOUN
brj-22480	38	58	,	,	PUNCT
brj-22480	38	59	and	and	CCONJ
brj-22480	38	60	then	then	ADV
brj-22480	38	61	carry	carry	VERB
brj-22480	38	62	out	out	ADP
brj-22480	38	63	conventional	conventional	ADJ
brj-22480	38	64	drying	dry	VERB
brj-22480	38	65	treatment	treatment	NOUN
brj-22480	38	66	.	.	PUNCT
brj-22480	39	1	the	the	DET
brj-22480	39	2	specific	specific	ADJ
brj-22480	39	3	pretreatment	pretreatment	NOUN
brj-22480	39	4	process	process	NOUN
brj-22480	39	5	and	and	CCONJ
brj-22480	39	6	conventional	conventional	ADJ
brj-22480	39	7	drying	dry	VERB
brj-22480	39	8	benchmarks	benchmark	NOUN
brj-22480	39	9	are	be	AUX
brj-22480	39	10	shown	show	VERB
brj-22480	39	11	in	in	ADP
brj-22480	39	12	tables	table	NOUN
brj-22480	39	13	1	1	NUM
brj-22480	39	14	and	and	CCONJ
brj-22480	39	15	2	2	NUM
brj-22480	39	16	.	.	NOUN
brj-22480	39	17	peer	peer	NOUN
brj-22480	39	18	-	-	PUNCT
brj-22480	39	19	reviewed	review	VERB
brj-22480	39	20	article	article	NOUN
brj-22480	39	21	bioresources.com	bioresources.com	X
brj-22480	39	22	chai	chai	NOUN
brj-22480	39	23	&	&	CCONJ
brj-22480	39	24	li	li	PROPN
brj-22480	39	25	(	(	PUNCT
brj-22480	39	26	2023	2023	NUM
brj-22480	39	27	)	)	PUNCT
brj-22480	39	28	.	.	PUNCT
brj-22480	40	1	“	"	PUNCT
brj-22480	40	2	prediction	prediction	NOUN
brj-22480	40	3	of	of	ADP
brj-22480	40	4	wood	wood	NOUN
brj-22480	40	5	drying	dry	VERB
brj-22480	40	6	by	by	ADP
brj-22480	40	7	ann	ann	PROPN
brj-22480	40	8	,	,	PUNCT
brj-22480	40	9	”	"	PUNCT
brj-22480	40	10	bioresources	bioresource	NOUN
brj-22480	40	11	18(4	18(4	NUM
brj-22480	40	12	)	)	PUNCT
brj-22480	40	13	,	,	PUNCT
brj-22480	40	14	8212	8212	NUM
brj-22480	40	15	-	-	SYM
brj-22480	40	16	8222	8222	NUM
brj-22480	40	17	.	.	PUNCT
brj-22480	40	18	8214	8214	NUM
brj-22480	40	19	table	table	NOUN
brj-22480	40	20	1	1	NUM
brj-22480	40	21	.	.	PUNCT
brj-22480	40	22	treatment	treatment	NOUN
brj-22480	40	23	processes	process	NOUN
brj-22480	40	24	of	of	ADP
brj-22480	40	25	softening	soften	VERB
brj-22480	40	26	,	,	PUNCT
brj-22480	40	27	setting	setting	NOUN
brj-22480	40	28	,	,	PUNCT
brj-22480	40	29	and	and	CCONJ
brj-22480	40	30	balancing	balance	VERB
brj-22480	40	31	pretreatment	pretreatment	NOUN
brj-22480	40	32	process	process	NOUN
brj-22480	40	33	saturated	saturate	VERB
brj-22480	40	34	moist	moist	ADJ
brj-22480	40	35	air	air	NOUN
brj-22480	40	36	softening	soften	VERB
brj-22480	40	37	treatment	treatment	NOUN
brj-22480	40	38	(	(	PUNCT
brj-22480	40	39	dry	dry	ADJ
brj-22480	40	40	bulb	bulb	NOUN
brj-22480	40	41	temperature	temperature	NOUN
brj-22480	40	42	/	/	SYM
brj-22480	40	43	time	time	NOUN
brj-22480	40	44	)	)	PUNCT
brj-22480	40	45	set	set	NOUN
brj-22480	40	46	processing	processing	NOUN
brj-22480	40	47	(	(	PUNCT
brj-22480	40	48	dry	dry	ADJ
brj-22480	40	49	bulb	bulb	NOUN
brj-22480	40	50	temperature	temperature	NOUN
brj-22480	40	51	/	/	SYM
brj-22480	40	52	ambient	ambient	ADJ
brj-22480	40	53	humidity	humidity	NOUN
brj-22480	40	54	/	/	SYM
brj-22480	40	55	time	time	NOUN
brj-22480	40	56	)	)	PUNCT
brj-22480	40	57	control	control	NOUN
brj-22480	40	58	group	group	NOUN
brj-22480	40	59	not	not	PART
brj-22480	40	60	processed	process	VERB
brj-22480	40	61	not	not	PART
brj-22480	40	62	processed	process	VERB
brj-22480	40	63	process	process	NOUN
brj-22480	40	64	1	1	NUM
brj-22480	40	65	90	90	NUM
brj-22480	40	66	°	°	NUM
brj-22480	40	67	c	c	NOUN
brj-22480	40	68	/	/	SYM
brj-22480	40	69	12	12	NUM
brj-22480	40	70	h	h	NOUN
brj-22480	40	71	120	120	NUM
brj-22480	40	72	°	°	NOUN
brj-22480	40	73	c	c	NOUN
brj-22480	40	74	/	/	SYM
brj-22480	40	75	30	30	NUM
brj-22480	40	76	%	%	NOUN
brj-22480	40	77	/	/	SYM
brj-22480	40	78	24	24	NUM
brj-22480	40	79	h	h	NOUN
brj-22480	40	80	process	process	NOUN
brj-22480	40	81	2	2	NUM
brj-22480	40	82	90	90	NUM
brj-22480	40	83	°	°	NUM
brj-22480	40	84	c	c	NOUN
brj-22480	40	85	/	/	SYM
brj-22480	40	86	12	12	NUM
brj-22480	40	87	h	h	NOUN
brj-22480	40	88	115	115	NUM
brj-22480	40	89	°	°	ADP
brj-22480	40	90	c	c	NOUN
brj-22480	40	91	/	/	SYM
brj-22480	40	92	30	30	NUM
brj-22480	40	93	%	%	NOUN
brj-22480	40	94	/	/	SYM
brj-22480	40	95	24	24	NUM
brj-22480	40	96	h	h	NOUN
brj-22480	40	97	process	process	NOUN
brj-22480	40	98	3	3	NUM
brj-22480	40	99	95	95	NUM
brj-22480	40	100	°	°	ADP
brj-22480	40	101	c	c	NOUN
brj-22480	40	102	/	/	SYM
brj-22480	40	103	12	12	NUM
brj-22480	40	104	h	h	NOUN
brj-22480	40	105	120	120	NUM
brj-22480	40	106	°	°	NOUN
brj-22480	40	107	c	c	NOUN
brj-22480	40	108	/	/	SYM
brj-22480	40	109	30	30	NUM
brj-22480	40	110	%	%	NOUN
brj-22480	40	111	/	/	SYM
brj-22480	40	112	18	18	NUM
brj-22480	40	113	h	h	NOUN
brj-22480	40	114	process	process	NOUN
brj-22480	40	115	4	4	NUM
brj-22480	40	116	90	90	NUM
brj-22480	40	117	°	°	NUM
brj-22480	40	118	c	c	NOUN
brj-22480	40	119	/	/	SYM
brj-22480	40	120	12	12	NUM
brj-22480	40	121	h	h	NOUN
brj-22480	40	122	120	120	NUM
brj-22480	40	123	°	°	NOUN
brj-22480	40	124	c	c	NOUN
brj-22480	40	125	/	/	SYM
brj-22480	40	126	30	30	NUM
brj-22480	40	127	%	%	NOUN
brj-22480	40	128	/	/	SYM
brj-22480	40	129	18	18	NUM
brj-22480	40	130	h	h	NOUN
brj-22480	40	131	process	process	NOUN
brj-22480	40	132	5	5	NUM
brj-22480	40	133	90	90	NUM
brj-22480	40	134	°	°	NUM
brj-22480	40	135	c	c	NOUN
brj-22480	40	136	/	/	SYM
brj-22480	40	137	18	18	NUM
brj-22480	40	138	h	h	NOUN
brj-22480	40	139	120	120	NUM
brj-22480	40	140	°	°	NOUN
brj-22480	40	141	c	c	NOUN
brj-22480	40	142	/	/	SYM
brj-22480	40	143	30	30	NUM
brj-22480	40	144	%	%	NOUN
brj-22480	40	145	/	/	SYM
brj-22480	40	146	24	24	NUM
brj-22480	40	147	h	h	NOUN
brj-22480	40	148	process	process	NOUN
brj-22480	40	149	6	6	NUM
brj-22480	40	150	90	90	NUM
brj-22480	40	151	°	°	NUM
brj-22480	40	152	c	c	NOUN
brj-22480	40	153	/	/	SYM
brj-22480	40	154	24	24	NUM
brj-22480	40	155	h	h	PROPN
brj-22480	40	156	120	120	NUM
brj-22480	40	157	°	°	NOUN
brj-22480	40	158	c	c	NOUN
brj-22480	40	159	/	/	SYM
brj-22480	40	160	30	30	NUM
brj-22480	40	161	%	%	NOUN
brj-22480	40	162	/	/	SYM
brj-22480	40	163	24	24	NUM
brj-22480	40	164	h	h	NOUN
brj-22480	40	165	table	table	NOUN
brj-22480	40	166	2	2	NUM
brj-22480	40	167	.	.	X
brj-22480	40	168	conventional	conventional	ADJ
brj-22480	40	169	drying	drying	NOUN
brj-22480	40	170	benchmarks	benchmark	NOUN
brj-22480	40	171	time	time	NOUN
brj-22480	40	172	(	(	PUNCT
brj-22480	40	173	h	h	NOUN
brj-22480	40	174	)	)	PUNCT
brj-22480	40	175	dry	dry	ADJ
brj-22480	40	176	bulb	bulb	NOUN
brj-22480	40	177	temperature	temperature	NOUN
brj-22480	40	178	(	(	PUNCT
brj-22480	40	179	°	°	ADP
brj-22480	40	180	c	c	NOUN
brj-22480	40	181	)	)	PUNCT
brj-22480	40	182	wet	wet	ADJ
brj-22480	40	183	bulb	bulb	NOUN
brj-22480	40	184	temperature	temperature	NOUN
brj-22480	40	185	(	(	PUNCT
brj-22480	40	186	°	°	ADP
brj-22480	40	187	c	c	NOUN
brj-22480	40	188	)	)	PUNCT
brj-22480	40	189	relative	relative	ADJ
brj-22480	40	190	humidity	humidity	NOUN
brj-22480	40	191	(	(	PUNCT
brj-22480	40	192	%	%	INTJ
brj-22480	40	193	)	)	PUNCT
brj-22480	40	194	equilibrium	equilibrium	NOUN
brj-22480	40	195	moisture	moisture	NOUN
brj-22480	40	196	content	content	NOUN
brj-22480	40	197	(	(	PUNCT
brj-22480	40	198	%	%	INTJ
brj-22480	40	199	)	)	PUNCT
brj-22480	40	200	0	0	NUM
brj-22480	40	201	85	85	NUM
brj-22480	40	202	85	85	NUM
brj-22480	40	203	100	100	NUM
brj-22480	40	204	24.5	24.5	NUM
brj-22480	40	205	6	6	NUM
brj-22480	40	206	85	85	NUM
brj-22480	40	207	83	83	NUM
brj-22480	40	208	96	96	NUM
brj-22480	40	209	19.0	19.0	NUM
brj-22480	40	210	12	12	NUM
brj-22480	40	211	85	85	NUM
brj-22480	40	212	82	82	NUM
brj-22480	40	213	92	92	NUM
brj-22480	40	214	16.0	16.0	NUM
brj-22480	40	215	18	18	NUM
brj-22480	40	216	85	85	NUM
brj-22480	40	217	81	81	NUM
brj-22480	40	218	88	88	NUM
brj-22480	40	219	14.5	14.5	NUM
brj-22480	40	220	24	24	NUM
brj-22480	40	221	85	85	NUM
brj-22480	40	222	80	80	NUM
brj-22480	40	223	84	84	NUM
brj-22480	40	224	12.5	12.5	NUM
brj-22480	40	225	30	30	NUM
brj-22480	40	226	85	85	NUM
brj-22480	40	227	79	79	NUM
brj-22480	40	228	80	80	NUM
brj-22480	40	229	11.5	11.5	NUM
brj-22480	40	230	36	36	NUM
brj-22480	40	231	85	85	NUM
brj-22480	40	232	78	78	NUM
brj-22480	40	233	77	77	NUM
brj-22480	40	234	11.0	11.0	NUM
brj-22480	40	235	42	42	NUM
brj-22480	40	236	85	85	NUM
brj-22480	40	237	77	77	NUM
brj-22480	40	238	74	74	NUM
brj-22480	40	239	10.0	10.0	NUM
brj-22480	40	240	48	48	NUM
brj-22480	40	241	85	85	NUM
brj-22480	40	242	76	76	NUM
brj-22480	40	243	71	71	NUM
brj-22480	40	244	9.5	9.5	NUM
brj-22480	40	245	54	54	NUM
brj-22480	40	246	85	85	NUM
brj-22480	40	247	75	75	NUM
brj-22480	40	248	68	68	NUM
brj-22480	40	249	9.0	9.0	NUM
brj-22480	40	250	60	60	NUM
brj-22480	40	251	85	85	NUM
brj-22480	40	252	74	74	NUM
brj-22480	40	253	65	65	NUM
brj-22480	40	254	8.5	8.5	NUM
brj-22480	40	255	66	66	NUM
brj-22480	40	256	85	85	NUM
brj-22480	40	257	73	73	NUM
brj-22480	40	258	62	62	NUM
brj-22480	40	259	8.0	8.0	NUM
brj-22480	40	260	72	72	NUM
brj-22480	40	261	85	85	NUM
brj-22480	40	262	72	72	NUM
brj-22480	40	263	59	59	NUM
brj-22480	40	264	7.5	7.5	NUM
brj-22480	40	265	78	78	NUM
brj-22480	40	266	85	85	NUM
brj-22480	40	267	71	71	NUM
brj-22480	40	268	56	56	NUM
brj-22480	40	269	7.0	7.0	NUM
brj-22480	40	270	84	84	NUM
brj-22480	40	271	85	85	NUM
brj-22480	40	272	70	70	NUM
brj-22480	40	273	54	54	NUM
brj-22480	40	274	6.5	6.5	NUM
brj-22480	40	275	90	90	NUM
brj-22480	40	276	85	85	NUM
brj-22480	40	277	69	69	NUM
brj-22480	40	278	51	51	NUM
brj-22480	40	279	6.0	6.0	NUM
brj-22480	40	280	detection	detection	NOUN
brj-22480	40	281	of	of	ADP
brj-22480	40	282	drying	dry	VERB
brj-22480	40	283	rate	rate	NOUN
brj-22480	40	284	and	and	CCONJ
brj-22480	40	285	longitudinal	longitudinal	ADJ
brj-22480	40	286	crack	crack	NOUN
brj-22480	40	287	degree	degree	NOUN
brj-22480	40	288	the	the	DET
brj-22480	40	289	drying	dry	VERB
brj-22480	40	290	weight	weight	NOUN
brj-22480	40	291	method	method	NOUN
brj-22480	40	292	was	be	AUX
brj-22480	40	293	used	use	VERB
brj-22480	40	294	to	to	PART
brj-22480	40	295	measure	measure	VERB
brj-22480	40	296	the	the	DET
brj-22480	40	297	mc	mc	PROPN
brj-22480	40	298	at	at	ADP
brj-22480	40	299	each	each	DET
brj-22480	40	300	stage	stage	NOUN
brj-22480	40	301	(	(	PUNCT
brj-22480	40	302	fu	fu	NOUN
brj-22480	40	303	2019	2019	NUM
brj-22480	40	304	)	)	PUNCT
brj-22480	40	305	.	.	PUNCT
brj-22480	41	1	the	the	DET
brj-22480	41	2	mc	mc	PROPN
brj-22480	41	3	of	of	ADP
brj-22480	41	4	the	the	DET
brj-22480	41	5	test	test	NOUN
brj-22480	41	6	piece	piece	NOUN
brj-22480	41	7	was	be	AUX
brj-22480	41	8	divided	divide	VERB
brj-22480	41	9	into	into	ADP
brj-22480	41	10	25	25	NUM
brj-22480	41	11	equal	equal	ADJ
brj-22480	41	12	parts	part	NOUN
brj-22480	41	13	,	,	PUNCT
brj-22480	41	14	as	as	SCONJ
brj-22480	41	15	shown	show	VERB
brj-22480	41	16	in	in	ADP
brj-22480	41	17	fig	fig	NOUN
brj-22480	41	18	.	.	PUNCT
brj-22480	42	1	1	1	X
brj-22480	42	2	.	.	X
brj-22480	42	3	the	the	DET
brj-22480	42	4	initial	initial	ADJ
brj-22480	42	5	average	average	ADJ
brj-22480	42	6	moisture	moisture	NOUN
brj-22480	42	7	content	content	NOUN
brj-22480	42	8	of	of	ADP
brj-22480	42	9	the	the	DET
brj-22480	42	10	test	test	NOUN
brj-22480	42	11	material	material	NOUN
brj-22480	42	12	is	be	AUX
brj-22480	42	13	calculated	calculate	VERB
brj-22480	42	14	according	accord	VERB
brj-22480	42	15	to	to	ADP
brj-22480	42	16	eq	eq	NOUN
brj-22480	42	17	.	.	PROPN
brj-22480	42	18	1	1	X
brj-22480	42	19	.	.	PUNCT
brj-22480	43	1	(	(	PUNCT
brj-22480	43	2	1	1	X
brj-22480	43	3	)	)	PUNCT
brj-22480	43	4	in	in	ADP
brj-22480	43	5	eq.1	eq.1	PROPN
brj-22480	43	6	,	,	PUNCT
brj-22480	43	7	mc	mc	PROPN
brj-22480	43	8	denotes	denote	VERB
brj-22480	43	9	the	the	DET
brj-22480	43	10	average	average	ADJ
brj-22480	43	11	mc	mc	PROPN
brj-22480	43	12	of	of	ADP
brj-22480	43	13	25	25	NUM
brj-22480	43	14	specimens	specimen	NOUN
brj-22480	43	15	,	,	PUNCT
brj-22480	43	16	representing	represent	VERB
brj-22480	43	17	the	the	DET
brj-22480	43	18	estimated	estimate	VERB
brj-22480	43	19	initial	initial	ADJ
brj-22480	43	20	mc	mc	PROPN
brj-22480	43	21	of	of	ADP
brj-22480	43	22	the	the	DET
brj-22480	43	23	specimens	specimen	NOUN
brj-22480	43	24	(	(	PUNCT
brj-22480	43	25	%	%	INTJ
brj-22480	43	26	)	)	PUNCT
brj-22480	43	27	;	;	PUNCT
brj-22480	43	28	gi	gi	X
brj-22480	43	29	is	be	AUX
brj-22480	43	30	the	the	DET
brj-22480	43	31	initial	initial	ADJ
brj-22480	43	32	weight	weight	NOUN
brj-22480	43	33	of	of	ADP
brj-22480	43	34	the	the	DET
brj-22480	43	35	ith	ith	PROPN
brj-22480	43	36	fastest	fast	ADJ
brj-22480	43	37	sample	sample	NOUN
brj-22480	43	38	(	(	PUNCT
brj-22480	43	39	g	g	NOUN
brj-22480	43	40	)	)	PUNCT
brj-22480	43	41	and	and	CCONJ
brj-22480	43	42	;	;	PUNCT
brj-22480	43	43	gio	gio	NOUN
brj-22480	43	44	is	be	AUX
brj-22480	43	45	the	the	DET
brj-22480	43	46	absolute	absolute	ADJ
brj-22480	43	47	dry	dry	ADJ
brj-22480	43	48	weight	weight	NOUN
brj-22480	43	49	of	of	ADP
brj-22480	43	50	the	the	DET
brj-22480	43	51	ith	ith	PROPN
brj-22480	43	52	fastest	fast	ADJ
brj-22480	43	53	sample	sample	NOUN
brj-22480	43	54	(	(	PUNCT
brj-22480	43	55	g	g	NOUN
brj-22480	43	56	)	)	PUNCT
brj-22480	43	57	.	.	PUNCT
brj-22480	44	1	peer	peer	NOUN
brj-22480	44	2	-	-	PUNCT
brj-22480	44	3	reviewed	review	VERB
brj-22480	44	4	article	article	NOUN
brj-22480	44	5	bioresources.com	bioresources.com	X
brj-22480	44	6	chai	chai	NOUN
brj-22480	44	7	&	&	CCONJ
brj-22480	44	8	li	li	PROPN
brj-22480	44	9	(	(	PUNCT
brj-22480	44	10	2023	2023	NUM
brj-22480	44	11	)	)	PUNCT
brj-22480	44	12	.	.	PUNCT
brj-22480	45	1	“	"	PUNCT
brj-22480	45	2	prediction	prediction	NOUN
brj-22480	45	3	of	of	ADP
brj-22480	45	4	wood	wood	NOUN
brj-22480	45	5	drying	dry	VERB
brj-22480	45	6	by	by	ADP
brj-22480	45	7	ann	ann	PROPN
brj-22480	45	8	,	,	PUNCT
brj-22480	45	9	”	"	PUNCT
brj-22480	45	10	bioresources	bioresource	NOUN
brj-22480	45	11	18(4	18(4	NUM
brj-22480	45	12	)	)	PUNCT
brj-22480	45	13	,	,	PUNCT
brj-22480	45	14	8212	8212	NUM
brj-22480	45	15	-	-	SYM
brj-22480	45	16	8222	8222	NUM
brj-22480	45	17	.	.	PUNCT
brj-22480	46	1	8215	8215	NUM
brj-22480	46	2	fig	fig	NOUN
brj-22480	46	3	.	.	PUNCT
brj-22480	47	1	1	1	X
brj-22480	47	2	.	.	X
brj-22480	47	3	schematic	schematic	ADJ
brj-22480	47	4	diagram	diagram	NOUN
brj-22480	47	5	of	of	ADP
brj-22480	47	6	decomposition	decomposition	NOUN
brj-22480	47	7	of	of	ADP
brj-22480	47	8	moisture	moisture	NOUN
brj-22480	47	9	content	content	NOUN
brj-22480	47	10	specimen	speciman	NOUN
brj-22480	47	11	because	because	SCONJ
brj-22480	47	12	the	the	DET
brj-22480	47	13	wood	wood	NOUN
brj-22480	47	14	cracking	cracking	NOUN
brj-22480	47	15	is	be	AUX
brj-22480	47	16	relatively	relatively	ADV
brj-22480	47	17	complex	complex	ADJ
brj-22480	47	18	,	,	PUNCT
brj-22480	47	19	to	to	PART
brj-22480	47	20	quantify	quantify	VERB
brj-22480	47	21	and	and	CCONJ
brj-22480	47	22	represent	represent	VERB
brj-22480	47	23	it	it	PRON
brj-22480	47	24	,	,	PUNCT
brj-22480	47	25	the	the	DET
brj-22480	47	26	longitudinal	longitudinal	ADJ
brj-22480	47	27	cracking	cracking	NOUN
brj-22480	47	28	degree	degree	NOUN
brj-22480	47	29	was	be	AUX
brj-22480	47	30	used	use	VERB
brj-22480	47	31	.	.	PUNCT
brj-22480	48	1	the	the	DET
brj-22480	48	2	longitudinal	longitudinal	ADJ
brj-22480	48	3	cracking	cracking	NOUN
brj-22480	48	4	degree	degree	NOUN
brj-22480	48	5	of	of	ADP
brj-22480	48	6	the	the	DET
brj-22480	48	7	wood	wood	NOUN
brj-22480	48	8	was	be	AUX
brj-22480	48	9	used	use	VERB
brj-22480	48	10	to	to	PART
brj-22480	48	11	indicate	indicate	VERB
brj-22480	48	12	the	the	DET
brj-22480	48	13	cracking	crack	VERB
brj-22480	48	14	condition	condition	NOUN
brj-22480	48	15	of	of	ADP
brj-22480	48	16	the	the	DET
brj-22480	48	17	wood	wood	NOUN
brj-22480	48	18	,	,	PUNCT
brj-22480	48	19	which	which	PRON
brj-22480	48	20	is	be	AUX
brj-22480	48	21	the	the	DET
brj-22480	48	22	ratio	ratio	NOUN
brj-22480	48	23	of	of	ADP
brj-22480	48	24	the	the	DET
brj-22480	48	25	longest	long	ADJ
brj-22480	48	26	longitudinal	longitudinal	ADJ
brj-22480	48	27	crack	crack	NOUN
brj-22480	48	28	to	to	ADP
brj-22480	48	29	the	the	DET
brj-22480	48	30	length	length	NOUN
brj-22480	48	31	of	of	ADP
brj-22480	48	32	the	the	DET
brj-22480	48	33	wood	wood	NOUN
brj-22480	48	34	.	.	PUNCT
brj-22480	49	1	the	the	DET
brj-22480	49	2	surface	surface	NOUN
brj-22480	49	3	crack	crack	NOUN
brj-22480	49	4	length	length	NOUN
brj-22480	49	5	,	,	PUNCT
brj-22480	49	6	width	width	ADJ
brj-22480	49	7	,	,	PUNCT
brj-22480	49	8	and	and	CCONJ
brj-22480	49	9	internal	internal	ADJ
brj-22480	49	10	cracking	cracking	NOUN
brj-22480	49	11	degrees	degree	NOUN
brj-22480	49	12	of	of	ADP
brj-22480	49	13	the	the	DET
brj-22480	49	14	wood	wood	NOUN
brj-22480	49	15	surface	surface	NOUN
brj-22480	49	16	were	be	AUX
brj-22480	49	17	determined	determine	VERB
brj-22480	49	18	.	.	PUNCT
brj-22480	50	1	the	the	DET
brj-22480	50	2	cracks	crack	NOUN
brj-22480	50	3	with	with	ADP
brj-22480	50	4	a	a	DET
brj-22480	50	5	width	width	NOUN
brj-22480	50	6	less	less	ADJ
brj-22480	50	7	than	than	ADP
brj-22480	50	8	2	2	NUM
brj-22480	50	9	mm	mm	NOUN
brj-22480	50	10	or	or	CCONJ
brj-22480	50	11	a	a	DET
brj-22480	50	12	length	length	NOUN
brj-22480	50	13	less	less	ADJ
brj-22480	50	14	than	than	ADP
brj-22480	50	15	10	10	NUM
brj-22480	50	16	mm	mm	NOUN
brj-22480	50	17	were	be	AUX
brj-22480	50	18	ignored	ignore	VERB
brj-22480	50	19	.	.	PUNCT
brj-22480	51	1	the	the	DET
brj-22480	51	2	cracks	crack	NOUN
brj-22480	51	3	were	be	AUX
brj-22480	51	4	less	less	ADJ
brj-22480	51	5	than	than	ADP
brj-22480	51	6	3	3	NUM
brj-22480	51	7	mm	mm	NOUN
brj-22480	51	8	apart	apart	ADV
brj-22480	51	9	from	from	ADP
brj-22480	51	10	each	each	DET
brj-22480	51	11	other	other	ADJ
brj-22480	51	12	and	and	CCONJ
brj-22480	51	13	were	be	AUX
brj-22480	51	14	calculated	calculate	VERB
brj-22480	51	15	as	as	ADP
brj-22480	51	16	a	a	DET
brj-22480	51	17	single	single	ADJ
brj-22480	51	18	crack	crack	NOUN
brj-22480	51	19	.	.	PUNCT
brj-22480	52	1	then	then	ADV
brj-22480	52	2	,	,	PUNCT
brj-22480	52	3	the	the	DET
brj-22480	52	4	one	one	NOUN
brj-22480	52	5	with	with	ADP
brj-22480	52	6	the	the	DET
brj-22480	52	7	highest	high	ADJ
brj-22480	52	8	length	length	NOUN
brj-22480	52	9	was	be	AUX
brj-22480	52	10	selected	select	VERB
brj-22480	52	11	to	to	PART
brj-22480	52	12	calculate	calculate	VERB
brj-22480	52	13	the	the	DET
brj-22480	52	14	longitudinal	longitudinal	ADJ
brj-22480	52	15	cracking	cracking	NOUN
brj-22480	52	16	degree	degree	NOUN
brj-22480	52	17	was	be	AUX
brj-22480	52	18	selected	select	VERB
brj-22480	52	19	as	as	ADP
brj-22480	52	20	in	in	ADP
brj-22480	52	21	eq	eq	NOUN
brj-22480	52	22	.	.	PROPN
brj-22480	52	23	2	2	NUM
brj-22480	52	24	(	(	PUNCT
brj-22480	52	25	fu	fu	NOUN
brj-22480	52	26	2017	2017	NUM
brj-22480	52	27	):	):	PUNCT
brj-22480	52	28	(	(	PUNCT
brj-22480	52	29	2	2	X
brj-22480	52	30	)	)	PUNCT
brj-22480	52	31	in	in	ADP
brj-22480	52	32	the	the	DET
brj-22480	52	33	eq	eq	NOUN
brj-22480	52	34	.	.	PROPN
brj-22480	52	35	2	2	NUM
brj-22480	52	36	,	,	PUNCT
brj-22480	52	37	ls	ls	X
brj-22480	52	38	is	be	AUX
brj-22480	52	39	the	the	DET
brj-22480	52	40	longitudinal	longitudinal	ADJ
brj-22480	52	41	crack	crack	NOUN
brj-22480	52	42	degree	degree	NOUN
brj-22480	52	43	(	(	PUNCT
brj-22480	52	44	longitudinal	longitudinal	ADJ
brj-22480	52	45	crack	crack	NOUN
brj-22480	52	46	length	length	NOUN
brj-22480	52	47	ratio	ratio	NOUN
brj-22480	52	48	)	)	PUNCT
brj-22480	52	49	(	(	PUNCT
brj-22480	52	50	%	%	INTJ
brj-22480	52	51	)	)	PUNCT
brj-22480	52	52	;	;	PUNCT
brj-22480	52	53	lmax	lmax	NOUN
brj-22480	52	54	denotes	denote	VERB
brj-22480	52	55	the	the	DET
brj-22480	52	56	maximum	maximum	ADJ
brj-22480	52	57	cracking	cracking	NOUN
brj-22480	52	58	(	(	PUNCT
brj-22480	52	59	mm	mm	NOUN
brj-22480	52	60	)	)	PUNCT
brj-22480	52	61	;	;	PUNCT
brj-22480	52	62	and	and	CCONJ
brj-22480	52	63	l0	l0	PROPN
brj-22480	52	64	is	be	AUX
brj-22480	52	65	the	the	DET
brj-22480	52	66	length	length	NOUN
brj-22480	52	67	of	of	ADP
brj-22480	52	68	wood	wood	NOUN
brj-22480	52	69	(	(	PUNCT
brj-22480	52	70	mm	mm	NOUN
brj-22480	52	71	)	)	PUNCT
brj-22480	52	72	.	.	PUNCT
brj-22480	53	1	analysis	analysis	NOUN
brj-22480	53	2	method	method	NOUN
brj-22480	53	3	of	of	ADP
brj-22480	53	4	artificial	artificial	ADJ
brj-22480	53	5	neural	neural	ADJ
brj-22480	53	6	network	network	NOUN
brj-22480	53	7	model	model	NOUN
brj-22480	53	8	the	the	DET
brj-22480	53	9	bp	bp	PROPN
brj-22480	53	10	neural	neural	PROPN
brj-22480	53	11	network	network	NOUN
brj-22480	53	12	model	model	NOUN
brj-22480	53	13	used	use	VERB
brj-22480	53	14	in	in	ADP
brj-22480	53	15	this	this	DET
brj-22480	53	16	work	work	NOUN
brj-22480	53	17	was	be	AUX
brj-22480	53	18	developed	develop	VERB
brj-22480	53	19	on	on	ADP
brj-22480	53	20	the	the	DET
brj-22480	53	21	python	python	NOUN
brj-22480	53	22	integrated	integrate	VERB
brj-22480	53	23	development	development	NOUN
brj-22480	53	24	environment	environment	NOUN
brj-22480	53	25	pycharm	pycharm	NOUN
brj-22480	53	26	based	base	VERB
brj-22480	53	27	on	on	ADP
brj-22480	53	28	the	the	DET
brj-22480	53	29	python	python	NOUN
brj-22480	53	30	language	language	NOUN
brj-22480	53	31	,	,	PUNCT
brj-22480	53	32	using	use	VERB
brj-22480	53	33	a	a	DET
brj-22480	53	34	three	three	NUM
brj-22480	53	35	-	-	PUNCT
brj-22480	53	36	layer	layer	NOUN
brj-22480	53	37	feedforward	feedforward	NOUN
brj-22480	53	38	network	network	NOUN
brj-22480	53	39	structure	structure	NOUN
brj-22480	53	40	(	(	PUNCT
brj-22480	53	41	input	input	NOUN
brj-22480	53	42	layer	layer	NOUN
brj-22480	53	43	,	,	PUNCT
brj-22480	53	44	hidden	hide	VERB
brj-22480	53	45	layer	layer	NOUN
brj-22480	53	46	,	,	PUNCT
brj-22480	53	47	and	and	CCONJ
brj-22480	53	48	output	output	NOUN
brj-22480	53	49	layer	layer	NOUN
brj-22480	53	50	)	)	PUNCT
brj-22480	53	51	.	.	PUNCT
brj-22480	54	1	the	the	DET
brj-22480	54	2	factors	factor	NOUN
brj-22480	54	3	were	be	AUX
brj-22480	54	4	not	not	PART
brj-22480	54	5	connected	connect	VERB
brj-22480	54	6	and	and	CCONJ
brj-22480	54	7	interrelated	interrelate	VERB
brj-22480	54	8	,	,	PUNCT
brj-22480	54	9	which	which	PRON
brj-22480	54	10	can	can	AUX
brj-22480	54	11	ensure	ensure	VERB
brj-22480	54	12	that	that	SCONJ
brj-22480	54	13	each	each	DET
brj-22480	54	14	input	input	NOUN
brj-22480	54	15	condition	condition	NOUN
brj-22480	54	16	is	be	AUX
brj-22480	54	17	independent	independent	ADJ
brj-22480	54	18	of	of	ADP
brj-22480	54	19	each	each	DET
brj-22480	54	20	other	other	ADJ
brj-22480	54	21	and	and	CCONJ
brj-22480	54	22	ensures	ensure	VERB
brj-22480	54	23	that	that	SCONJ
brj-22480	54	24	the	the	DET
brj-22480	54	25	neural	neural	ADJ
brj-22480	54	26	network	network	NOUN
brj-22480	54	27	structure	structure	NOUN
brj-22480	54	28	can	can	AUX
brj-22480	54	29	change	change	VERB
brj-22480	54	30	the	the	DET
brj-22480	54	31	input	input	NOUN
brj-22480	54	32	conditions	condition	NOUN
brj-22480	54	33	of	of	ADP
brj-22480	54	34	one	one	NUM
brj-22480	54	35	layer	layer	NOUN
brj-22480	54	36	while	while	SCONJ
brj-22480	54	37	other	other	ADJ
brj-22480	54	38	conditions	condition	NOUN
brj-22480	54	39	can	can	AUX
brj-22480	54	40	normally	normally	ADV
brj-22480	54	41	affect	affect	VERB
brj-22480	54	42	the	the	DET
brj-22480	54	43	results	result	NOUN
brj-22480	54	44	.	.	PUNCT
brj-22480	55	1	the	the	DET
brj-22480	55	2	experimental	experimental	ADJ
brj-22480	55	3	data	datum	NOUN
brj-22480	55	4	of	of	ADP
brj-22480	55	5	wood	wood	NOUN
brj-22480	55	6	drying	dry	VERB
brj-22480	55	7	were	be	AUX
brj-22480	55	8	collected	collect	VERB
brj-22480	55	9	,	,	PUNCT
brj-22480	55	10	and	and	CCONJ
brj-22480	55	11	then	then	ADV
brj-22480	55	12	the	the	DET
brj-22480	55	13	experimental	experimental	ADJ
brj-22480	55	14	input	input	NOUN
brj-22480	55	15	layer	layer	NOUN
brj-22480	55	16	and	and	CCONJ
brj-22480	55	17	output	output	NOUN
brj-22480	55	18	layer	layer	NOUN
brj-22480	55	19	were	be	AUX
brj-22480	55	20	determined	determine	VERB
brj-22480	55	21	,	,	PUNCT
brj-22480	55	22	where	where	SCONJ
brj-22480	55	23	the	the	DET
brj-22480	55	24	input	input	NOUN
brj-22480	55	25	layer	layer	NOUN
brj-22480	55	26	is	be	AUX
brj-22480	55	27	the	the	DET
brj-22480	55	28	steaming	steaming	NOUN
brj-22480	55	29	treatment	treatment	NOUN
brj-22480	55	30	time	time	NOUN
brj-22480	55	31	,	,	PUNCT
brj-22480	55	32	steaming	steam	VERB
brj-22480	55	33	temperature	temperature	NOUN
brj-22480	55	34	,	,	PUNCT
brj-22480	55	35	set	set	NOUN
brj-22480	55	36	time	time	NOUN
brj-22480	55	37	,	,	PUNCT
brj-22480	55	38	set	set	VERB
brj-22480	55	39	temperature	temperature	NOUN
brj-22480	55	40	,	,	PUNCT
brj-22480	55	41	initial	initial	ADJ
brj-22480	55	42	moisture	moisture	NOUN
brj-22480	55	43	content	content	NOUN
brj-22480	55	44	of	of	ADP
brj-22480	55	45	wood	wood	NOUN
brj-22480	55	46	,	,	PUNCT
brj-22480	55	47	and	and	CCONJ
brj-22480	55	48	the	the	DET
brj-22480	55	49	position	position	NOUN
brj-22480	55	50	of	of	ADP
brj-22480	55	51	wood	wood	NOUN
brj-22480	55	52	core	core	NOUN
brj-22480	55	53	and	and	CCONJ
brj-22480	55	54	sapwood	sapwood	NOUN
brj-22480	55	55	,	,	PUNCT
brj-22480	55	56	and	and	CCONJ
brj-22480	55	57	the	the	DET
brj-22480	55	58	output	output	NOUN
brj-22480	55	59	layer	layer	NOUN
brj-22480	55	60	is	be	AUX
brj-22480	55	61	wood	wood	NOUN
brj-22480	55	62	drying	dry	VERB
brj-22480	55	63	rate	rate	NOUN
brj-22480	55	64	and	and	CCONJ
brj-22480	55	65	longitudinal	longitudinal	ADJ
brj-22480	55	66	cracking	cracking	NOUN
brj-22480	55	67	degree	degree	NOUN
brj-22480	55	68	.	.	PUNCT
brj-22480	56	1	the	the	DET
brj-22480	56	2	hidden	hide	VERB
brj-22480	56	3	layer	layer	NOUN
brj-22480	56	4	was	be	AUX
brj-22480	56	5	selected	select	VERB
brj-22480	56	6	as	as	ADP
brj-22480	56	7	a	a	DET
brj-22480	56	8	single	single	ADJ
brj-22480	56	9	multi	multi	ADJ
brj-22480	56	10	-	-	ADJ
brj-22480	56	11	hidden	hidden	ADJ
brj-22480	56	12	layer	layer	NOUN
brj-22480	56	13	in	in	ADP
brj-22480	56	14	this	this	DET
brj-22480	56	15	work	work	NOUN
brj-22480	56	16	.	.	PUNCT
brj-22480	57	1	compared	compare	VERB
brj-22480	57	2	with	with	ADP
brj-22480	57	3	an	an	DET
brj-22480	57	4	ordinary	ordinary	ADJ
brj-22480	57	5	single	single	ADJ
brj-22480	57	6	hidden	hide	VERB
brj-22480	57	7	layer	layer	NOUN
brj-22480	57	8	,	,	PUNCT
brj-22480	57	9	it	it	PRON
brj-22480	57	10	has	have	VERB
brj-22480	57	11	a	a	DET
brj-22480	57	12	higher	high	ADJ
brj-22480	57	13	generalization	generalization	NOUN
brj-22480	57	14	ability	ability	NOUN
brj-22480	57	15	and	and	CCONJ
brj-22480	57	16	prediction	prediction	NOUN
brj-22480	57	17	accuracy	accuracy	NOUN
brj-22480	57	18	.	.	PUNCT
brj-22480	58	1	there	there	PRON
brj-22480	58	2	are	be	VERB
brj-22480	58	3	connections	connection	NOUN
brj-22480	58	4	with	with	ADP
brj-22480	58	5	neurons	neuron	NOUN
brj-22480	58	6	in	in	ADP
brj-22480	58	7	adjacent	adjacent	ADJ
brj-22480	58	8	layers	layer	NOUN
brj-22480	58	9	,	,	PUNCT
brj-22480	58	10	there	there	PRON
brj-22480	58	11	is	be	VERB
brj-22480	58	12	no	no	DET
brj-22480	58	13	connection	connection	NOUN
brj-22480	58	14	between	between	ADP
brj-22480	58	15	neurons	neuron	NOUN
brj-22480	58	16	in	in	ADP
brj-22480	58	17	the	the	DET
brj-22480	58	18	same	same	ADJ
brj-22480	58	19	layer	layer	NOUN
brj-22480	58	20	,	,	PUNCT
brj-22480	58	21	and	and	CCONJ
brj-22480	58	22	there	there	PRON
brj-22480	58	23	is	be	VERB
brj-22480	58	24	no	no	DET
brj-22480	58	25	feedback	feedback	NOUN
brj-22480	58	26	connection	connection	NOUN
brj-22480	58	27	between	between	ADP
brj-22480	58	28	neurons	neuron	NOUN
brj-22480	58	29	in	in	ADP
brj-22480	58	30	each	each	DET
brj-22480	58	31	layer	layer	NOUN
brj-22480	58	32	(	(	PUNCT
brj-22480	58	33	ozsahin	ozsahin	NOUN
brj-22480	58	34	and	and	CCONJ
brj-22480	58	35	murat	murat	NOUN
brj-22480	58	36	2018	2018	NUM
brj-22480	58	37	)	)	PUNCT
brj-22480	58	38	,	,	PUNCT
brj-22480	58	39	which	which	PRON
brj-22480	58	40	also	also	ADV
brj-22480	58	41	ensures	ensure	VERB
brj-22480	58	42	the	the	DET
brj-22480	58	43	independence	independence	NOUN
brj-22480	58	44	of	of	ADP
brj-22480	58	45	each	each	DET
brj-22480	58	46	variable	variable	NOUN
brj-22480	58	47	.	.	PUNCT
brj-22480	59	1	the	the	DET
brj-22480	59	2	input	input	NOUN
brj-22480	59	3	signal	signal	NOUN
brj-22480	59	4	is	be	AUX
brj-22480	59	5	forwarded	forward	VERB
brj-22480	59	6	from	from	ADP
brj-22480	59	7	the	the	DET
brj-22480	59	8	input	input	NOUN
brj-22480	59	9	layer	layer	NOUN
brj-22480	59	10	to	to	ADP
brj-22480	59	11	the	the	DET
brj-22480	59	12	hidden	hide	VERB
brj-22480	59	13	layer	layer	NOUN
brj-22480	59	14	.	.	PUNCT
brj-22480	60	1	the	the	DET
brj-22480	60	2	data	datum	NOUN
brj-22480	60	3	are	be	AUX
brj-22480	60	4	processed	process	VERB
brj-22480	60	5	by	by	ADP
brj-22480	60	6	the	the	DET
brj-22480	60	7	function	function	NOUN
brj-22480	60	8	transformation	transformation	NOUN
brj-22480	60	9	in	in	ADP
brj-22480	60	10	the	the	DET
brj-22480	60	11	hidden	hide	VERB
brj-22480	60	12	layer	layer	NOUN
brj-22480	60	13	peer	peer	NOUN
brj-22480	60	14	-	-	PUNCT
brj-22480	60	15	reviewed	review	VERB
brj-22480	60	16	article	article	NOUN
brj-22480	60	17	bioresources.com	bioresources.com	X
brj-22480	60	18	chai	chai	NOUN
brj-22480	60	19	&	&	CCONJ
brj-22480	60	20	li	li	PROPN
brj-22480	60	21	(	(	PUNCT
brj-22480	60	22	2023	2023	NUM
brj-22480	60	23	)	)	PUNCT
brj-22480	60	24	.	.	PUNCT
brj-22480	61	1	“	"	PUNCT
brj-22480	61	2	prediction	prediction	NOUN
brj-22480	61	3	of	of	ADP
brj-22480	61	4	wood	wood	NOUN
brj-22480	61	5	drying	dry	VERB
brj-22480	61	6	by	by	ADP
brj-22480	61	7	ann	ann	PROPN
brj-22480	61	8	,	,	PUNCT
brj-22480	61	9	”	"	PUNCT
brj-22480	61	10	bioresources	bioresource	NOUN
brj-22480	61	11	18(4	18(4	NUM
brj-22480	61	12	)	)	PUNCT
brj-22480	61	13	,	,	PUNCT
brj-22480	61	14	8212	8212	NUM
brj-22480	61	15	-	-	SYM
brj-22480	61	16	8222	8222	NUM
brj-22480	61	17	.	.	PUNCT
brj-22480	61	18	8216	8216	NUM
brj-22480	61	19	and	and	CCONJ
brj-22480	61	20	transmitted	transmit	VERB
brj-22480	61	21	from	from	ADP
brj-22480	61	22	the	the	DET
brj-22480	61	23	hidden	hide	VERB
brj-22480	61	24	layer	layer	NOUN
brj-22480	61	25	node	node	NOUN
brj-22480	61	26	to	to	ADP
brj-22480	61	27	the	the	DET
brj-22480	61	28	output	output	NOUN
brj-22480	61	29	layer	layer	NOUN
brj-22480	61	30	.	.	PUNCT
brj-22480	62	1	the	the	DET
brj-22480	62	2	data	datum	NOUN
brj-22480	62	3	are	be	AUX
brj-22480	62	4	finally	finally	ADV
brj-22480	62	5	processed	process	VERB
brj-22480	62	6	in	in	ADP
brj-22480	62	7	the	the	DET
brj-22480	62	8	output	output	NOUN
brj-22480	62	9	layer	layer	NOUN
brj-22480	62	10	node	node	VERB
brj-22480	62	11	to	to	PART
brj-22480	62	12	form	form	VERB
brj-22480	62	13	the	the	DET
brj-22480	62	14	output	output	NOUN
brj-22480	62	15	data	datum	NOUN
brj-22480	62	16	.	.	PUNCT
brj-22480	63	1	among	among	ADP
brj-22480	63	2	them	they	PRON
brj-22480	63	3	,	,	PUNCT
brj-22480	63	4	all	all	DET
brj-22480	63	5	nodes	node	NOUN
brj-22480	63	6	in	in	ADP
brj-22480	63	7	the	the	DET
brj-22480	63	8	hidden	hide	VERB
brj-22480	63	9	layer	layer	NOUN
brj-22480	63	10	use	use	VERB
brj-22480	63	11	the	the	DET
brj-22480	63	12	sigmoid	sigmoid	NOUN
brj-22480	63	13	transfer	transfer	NOUN
brj-22480	63	14	function	function	NOUN
brj-22480	63	15	(	(	PUNCT
brj-22480	63	16	eq	eq	NOUN
brj-22480	63	17	.	.	NOUN
brj-22480	63	18	3	3	NUM
brj-22480	63	19	)	)	PUNCT
brj-22480	63	20	,	,	PUNCT
brj-22480	63	21	and	and	CCONJ
brj-22480	63	22	in	in	ADP
brj-22480	63	23	the	the	DET
brj-22480	63	24	output	output	NOUN
brj-22480	63	25	layer	layer	NOUN
brj-22480	63	26	,	,	PUNCT
brj-22480	63	27	all	all	DET
brj-22480	63	28	nodes	node	NOUN
brj-22480	63	29	use	use	VERB
brj-22480	63	30	the	the	DET
brj-22480	63	31	pureline	pureline	ADJ
brj-22480	63	32	linear	linear	ADJ
brj-22480	63	33	transfer	transfer	NOUN
brj-22480	63	34	function	function	NOUN
brj-22480	63	35	:	:	PUNCT
brj-22480	63	36	(	(	PUNCT
brj-22480	63	37	3	3	X
brj-22480	63	38	)	)	PUNCT
brj-22480	63	39	the	the	DET
brj-22480	63	40	loss	loss	NOUN
brj-22480	63	41	function	function	NOUN
brj-22480	63	42	(	(	PUNCT
brj-22480	63	43	eq	eq	NOUN
brj-22480	63	44	.	.	NOUN
brj-22480	63	45	4	4	NUM
brj-22480	63	46	)	)	PUNCT
brj-22480	63	47	is	be	AUX
brj-22480	63	48	a	a	DET
brj-22480	63	49	parameter	parameter	NOUN
brj-22480	63	50	that	that	PRON
brj-22480	63	51	helps	help	VERB
brj-22480	63	52	to	to	PART
brj-22480	63	53	optimize	optimize	VERB
brj-22480	63	54	the	the	DET
brj-22480	63	55	neural	neural	ADJ
brj-22480	63	56	network	network	NOUN
brj-22480	63	57	.	.	PUNCT
brj-22480	64	1	each	each	DET
brj-22480	64	2	neural	neural	ADJ
brj-22480	64	3	network	network	NOUN
brj-22480	64	4	gives	give	VERB
brj-22480	64	5	an	an	DET
brj-22480	64	6	output	output	NOUN
brj-22480	64	7	,	,	PUNCT
brj-22480	64	8	and	and	CCONJ
brj-22480	64	9	the	the	DET
brj-22480	64	10	loss	loss	NOUN
brj-22480	64	11	can	can	AUX
brj-22480	64	12	be	be	AUX
brj-22480	64	13	calculated	calculate	VERB
brj-22480	64	14	by	by	ADP
brj-22480	64	15	matching	match	VERB
brj-22480	64	16	the	the	DET
brj-22480	64	17	output	output	NOUN
brj-22480	64	18	with	with	ADP
brj-22480	64	19	the	the	DET
brj-22480	64	20	target	target	NOUN
brj-22480	64	21	value	value	NOUN
brj-22480	64	22	.	.	PUNCT
brj-22480	65	1	through	through	ADP
brj-22480	65	2	optimizing	optimize	VERB
brj-22480	65	3	the	the	DET
brj-22480	65	4	parameters	parameter	NOUN
brj-22480	65	5	of	of	ADP
brj-22480	65	6	the	the	DET
brj-22480	65	7	neural	neural	ADJ
brj-22480	65	8	network	network	NOUN
brj-22480	65	9	to	to	PART
brj-22480	65	10	minimize	minimize	VERB
brj-22480	65	11	the	the	DET
brj-22480	65	12	loss	loss	NOUN
brj-22480	65	13	,	,	PUNCT
brj-22480	65	14	it	it	PRON
brj-22480	65	15	is	be	AUX
brj-22480	65	16	a	a	DET
brj-22480	65	17	common	common	ADJ
brj-22480	65	18	method	method	NOUN
brj-22480	65	19	for	for	ADP
brj-22480	65	20	training	train	VERB
brj-22480	65	21	neural	neural	ADJ
brj-22480	65	22	networks	network	NOUN
brj-22480	65	23	.	.	PUNCT
brj-22480	66	1	the	the	DET
brj-22480	66	2	data	datum	NOUN
brj-22480	66	3	are	be	AUX
brj-22480	66	4	simulated	simulate	VERB
brj-22480	66	5	through	through	ADP
brj-22480	66	6	hidden	hidden	ADJ
brj-22480	66	7	layers	layer	NOUN
brj-22480	66	8	with	with	ADP
brj-22480	66	9	different	different	ADJ
brj-22480	66	10	numbers	number	NOUN
brj-22480	66	11	of	of	ADP
brj-22480	66	12	neurons	neuron	NOUN
brj-22480	66	13	,	,	PUNCT
brj-22480	66	14	and	and	CCONJ
brj-22480	66	15	the	the	DET
brj-22480	66	16	loss	loss	NOUN
brj-22480	66	17	function	function	NOUN
brj-22480	66	18	under	under	ADP
brj-22480	66	19	the	the	DET
brj-22480	66	20	number	number	NOUN
brj-22480	66	21	of	of	ADP
brj-22480	66	22	neurons	neuron	NOUN
brj-22480	66	23	is	be	AUX
brj-22480	66	24	counted	count	VERB
brj-22480	66	25	.	.	PUNCT
brj-22480	67	1	at	at	ADP
brj-22480	67	2	the	the	DET
brj-22480	67	3	beginning	beginning	NOUN
brj-22480	67	4	of	of	ADP
brj-22480	67	5	training	training	NOUN
brj-22480	67	6	,	,	PUNCT
brj-22480	67	7	the	the	DET
brj-22480	67	8	loss	loss	NOUN
brj-22480	67	9	function	function	NOUN
brj-22480	67	10	decreases	decrease	VERB
brj-22480	67	11	greatly	greatly	ADV
brj-22480	67	12	with	with	ADP
brj-22480	67	13	the	the	DET
brj-22480	67	14	increase	increase	NOUN
brj-22480	67	15	of	of	ADP
brj-22480	67	16	the	the	DET
brj-22480	67	17	number	number	NOUN
brj-22480	67	18	of	of	ADP
brj-22480	67	19	neurons	neuron	NOUN
brj-22480	67	20	.	.	PUNCT
brj-22480	68	1	when	when	SCONJ
brj-22480	68	2	the	the	DET
brj-22480	68	3	number	number	NOUN
brj-22480	68	4	of	of	ADP
brj-22480	68	5	neurons	neuron	NOUN
brj-22480	68	6	increases	increase	NOUN
brj-22480	68	7	and	and	CCONJ
brj-22480	68	8	the	the	DET
brj-22480	68	9	loss	loss	NOUN
brj-22480	68	10	function	function	NOUN
brj-22480	68	11	does	do	AUX
brj-22480	68	12	not	not	PART
brj-22480	68	13	decrease	decrease	VERB
brj-22480	68	14	significantly	significantly	ADV
brj-22480	68	15	,	,	PUNCT
brj-22480	68	16	this	this	DET
brj-22480	68	17	number	number	NOUN
brj-22480	68	18	of	of	ADP
brj-22480	68	19	neurons	neuron	NOUN
brj-22480	68	20	can	can	AUX
brj-22480	68	21	be	be	AUX
brj-22480	68	22	selected	select	VERB
brj-22480	68	23	as	as	ADP
brj-22480	68	24	the	the	DET
brj-22480	68	25	number	number	NOUN
brj-22480	68	26	of	of	ADP
brj-22480	68	27	neurons	neuron	NOUN
brj-22480	68	28	used	use	VERB
brj-22480	68	29	in	in	ADP
brj-22480	68	30	this	this	DET
brj-22480	68	31	model	model	NOUN
brj-22480	68	32	.	.	PUNCT
brj-22480	68	33	)	)	PUNCT
brj-22480	69	1	1(log	1(log	NUM
brj-22480	69	2	1	1	NUM
brj-22480	69	3	1	1	NUM
brj-22480	69	4	^	^	SYM
brj-22480	69	5	i	i	PRON
brj-22480	69	6	outputsize	outputsize	VERB
brj-22480	69	7	i	i	PRON
brj-22480	69	8	ii	ii	VERB
brj-22480	69	9	xxx	xxx	X
brj-22480	69	10	outputsize	outputsize	VERB
brj-22480	69	11	loss	loss	NOUN
brj-22480	69	12	−+−=	−+−=	PART
brj-22480	69	13			X
brj-22480	69	14	=	=	PUNCT
brj-22480	69	15	(	(	PUNCT
brj-22480	69	16	4	4	NUM
brj-22480	69	17	)	)	PUNCT
brj-22480	69	18	in	in	ADP
brj-22480	69	19	eq	eq	ADP
brj-22480	69	20	.	.	PROPN
brj-22480	69	21	4	4	NUM
brj-22480	69	22	,	,	PUNCT
brj-22480	69	23	xi	xi	X
brj-22480	69	24	is	be	AUX
brj-22480	69	25	actual	actual	ADJ
brj-22480	69	26	value	value	NOUN
brj-22480	69	27	.	.	PUNCT
brj-22480	70	1	the	the	DET
brj-22480	70	2	data	datum	NOUN
brj-22480	70	3	obtained	obtain	VERB
brj-22480	70	4	from	from	ADP
brj-22480	70	5	the	the	DET
brj-22480	70	6	test	test	NOUN
brj-22480	70	7	were	be	AUX
brj-22480	70	8	arranged	arrange	VERB
brj-22480	70	9	in	in	ADP
brj-22480	70	10	the	the	DET
brj-22480	70	11	order	order	NOUN
brj-22480	70	12	of	of	ADP
brj-22480	70	13	test	test	NOUN
brj-22480	70	14	piece	piece	NOUN
brj-22480	70	15	position	position	NOUN
brj-22480	70	16	,	,	PUNCT
brj-22480	70	17	initial	initial	ADJ
brj-22480	70	18	moisture	moisture	NOUN
brj-22480	70	19	content	content	NOUN
brj-22480	70	20	,	,	PUNCT
brj-22480	70	21	steaming	steam	VERB
brj-22480	70	22	temperature	temperature	NOUN
brj-22480	70	23	,	,	PUNCT
brj-22480	70	24	steaming	steaming	NOUN
brj-22480	70	25	time	time	NOUN
brj-22480	70	26	,	,	PUNCT
brj-22480	70	27	set	set	VERB
brj-22480	70	28	temperature	temperature	NOUN
brj-22480	70	29	,	,	PUNCT
brj-22480	70	30	set	set	NOUN
brj-22480	70	31	time	time	NOUN
brj-22480	70	32	,	,	PUNCT
brj-22480	70	33	drying	dry	VERB
brj-22480	70	34	rate	rate	NOUN
brj-22480	70	35	,	,	PUNCT
brj-22480	70	36	and	and	CCONJ
brj-22480	70	37	longitudinal	longitudinal	ADJ
brj-22480	70	38	cracking	cracking	NOUN
brj-22480	70	39	degree	degree	NOUN
brj-22480	70	40	.	.	PUNCT
brj-22480	71	1	a	a	DET
brj-22480	71	2	total	total	NOUN
brj-22480	71	3	of	of	ADP
brj-22480	71	4	four	four	NUM
brj-22480	71	5	tests	test	NOUN
brj-22480	71	6	were	be	AUX
brj-22480	71	7	carried	carry	VERB
brj-22480	71	8	out	out	ADP
brj-22480	71	9	,	,	PUNCT
brj-22480	71	10	with	with	ADP
brj-22480	71	11	four	four	NUM
brj-22480	71	12	specimens	specimen	NOUN
brj-22480	71	13	for	for	ADP
brj-22480	71	14	each	each	DET
brj-22480	71	15	test	test	NOUN
brj-22480	71	16	,	,	PUNCT
brj-22480	71	17	and	and	CCONJ
brj-22480	71	18	five	five	NUM
brj-22480	71	19	groups	group	NOUN
brj-22480	71	20	of	of	ADP
brj-22480	71	21	data	datum	NOUN
brj-22480	71	22	were	be	AUX
brj-22480	71	23	collected	collect	VERB
brj-22480	71	24	for	for	ADP
brj-22480	71	25	each	each	DET
brj-22480	71	26	specimen	speciman	NOUN
brj-22480	71	27	.	.	PUNCT
brj-22480	72	1	the	the	DET
brj-22480	72	2	obtained	obtain	VERB
brj-22480	72	3	data	datum	NOUN
brj-22480	72	4	were	be	AUX
brj-22480	72	5	randomly	randomly	ADV
brj-22480	72	6	divided	divide	VERB
brj-22480	72	7	into	into	ADP
brj-22480	72	8	training	training	NOUN
brj-22480	72	9	groups	group	NOUN
brj-22480	72	10	and	and	CCONJ
brj-22480	72	11	test	test	NOUN
brj-22480	72	12	groups	group	NOUN
brj-22480	72	13	.	.	PUNCT
brj-22480	73	1	among	among	ADP
brj-22480	73	2	them	they	PRON
brj-22480	73	3	,	,	PUNCT
brj-22480	73	4	60	60	NUM
brj-22480	73	5	data	datum	NOUN
brj-22480	73	6	in	in	ADP
brj-22480	73	7	the	the	DET
brj-22480	73	8	training	training	NOUN
brj-22480	73	9	group	group	NOUN
brj-22480	73	10	accounted	account	VERB
brj-22480	73	11	for	for	ADP
brj-22480	73	12	75	75	NUM
brj-22480	73	13	%	%	NOUN
brj-22480	73	14	of	of	ADP
brj-22480	73	15	the	the	DET
brj-22480	73	16	total	total	NOUN
brj-22480	73	17	,	,	PUNCT
brj-22480	73	18	and	and	CCONJ
brj-22480	73	19	20	20	NUM
brj-22480	73	20	in	in	ADP
brj-22480	73	21	the	the	DET
brj-22480	73	22	test	test	NOUN
brj-22480	73	23	group	group	NOUN
brj-22480	73	24	accounts	account	VERB
brj-22480	73	25	for	for	ADP
brj-22480	73	26	25	25	NUM
brj-22480	73	27	%	%	NOUN
brj-22480	73	28	of	of	ADP
brj-22480	73	29	the	the	DET
brj-22480	73	30	total	total	NOUN
brj-22480	73	31	.	.	PUNCT
brj-22480	74	1	when	when	SCONJ
brj-22480	74	2	training	training	NOUN
brj-22480	74	3	and	and	CCONJ
brj-22480	74	4	validating	validate	VERB
brj-22480	74	5	a	a	DET
brj-22480	74	6	neural	neural	ADJ
brj-22480	74	7	network	network	NOUN
brj-22480	74	8	,	,	PUNCT
brj-22480	74	9	a	a	DET
brj-22480	74	10	large	large	ADJ
brj-22480	74	11	amount	amount	NOUN
brj-22480	74	12	of	of	ADP
brj-22480	74	13	data	datum	NOUN
brj-22480	74	14	needs	need	VERB
brj-22480	74	15	to	to	PART
brj-22480	74	16	be	be	AUX
brj-22480	74	17	processed	process	VERB
brj-22480	74	18	by	by	ADP
brj-22480	74	19	the	the	DET
brj-22480	74	20	neural	neural	ADJ
brj-22480	74	21	network	network	NOUN
brj-22480	74	22	.	.	PUNCT
brj-22480	75	1	different	different	ADJ
brj-22480	75	2	data	datum	NOUN
brj-22480	75	3	have	have	VERB
brj-22480	75	4	different	different	ADJ
brj-22480	75	5	numerical	numerical	ADJ
brj-22480	75	6	sizes	size	NOUN
brj-22480	75	7	and	and	CCONJ
brj-22480	75	8	physical	physical	ADJ
brj-22480	75	9	meanings	meaning	NOUN
brj-22480	75	10	.	.	PUNCT
brj-22480	76	1	to	to	PART
brj-22480	76	2	make	make	VERB
brj-22480	76	3	each	each	DET
brj-22480	76	4	input	input	NOUN
brj-22480	76	5	data	datum	NOUN
brj-22480	76	6	have	have	VERB
brj-22480	76	7	the	the	DET
brj-22480	76	8	same	same	ADJ
brj-22480	76	9	processing	processing	NOUN
brj-22480	76	10	status	status	NOUN
brj-22480	76	11	,	,	PUNCT
brj-22480	76	12	it	it	PRON
brj-22480	76	13	is	be	AUX
brj-22480	76	14	necessary	necessary	ADJ
brj-22480	76	15	to	to	PART
brj-22480	76	16	normalize	normalize	VERB
brj-22480	76	17	the	the	DET
brj-22480	76	18	data	datum	NOUN
brj-22480	76	19	.	.	PUNCT
brj-22480	77	1	the	the	DET
brj-22480	77	2	normalized	normalize	VERB
brj-22480	77	3	data	datum	NOUN
brj-22480	77	4	can	can	AUX
brj-22480	77	5	effectively	effectively	ADV
brj-22480	77	6	prevent	prevent	VERB
brj-22480	77	7	the	the	DET
brj-22480	77	8	adjustment	adjustment	NOUN
brj-22480	77	9	of	of	ADP
brj-22480	77	10	weights	weight	NOUN
brj-22480	77	11	from	from	ADP
brj-22480	77	12	entering	enter	VERB
brj-22480	77	13	the	the	DET
brj-22480	77	14	flat	flat	ADJ
brj-22480	77	15	region	region	NOUN
brj-22480	77	16	of	of	ADP
brj-22480	77	17	error	error	NOUN
brj-22480	77	18	.	.	PUNCT
brj-22480	78	1	in	in	ADP
brj-22480	78	2	addition	addition	NOUN
brj-22480	78	3	,	,	PUNCT
brj-22480	78	4	because	because	SCONJ
brj-22480	78	5	the	the	DET
brj-22480	78	6	neurons	neuron	NOUN
brj-22480	78	7	of	of	ADP
brj-22480	78	8	the	the	DET
brj-22480	78	9	bp	bp	PROPN
brj-22480	78	10	neural	neural	PROPN
brj-22480	78	11	network	network	NOUN
brj-22480	78	12	use	use	VERB
brj-22480	78	13	the	the	DET
brj-22480	78	14	sigmoid	sigmoid	NOUN
brj-22480	78	15	transfer	transfer	NOUN
brj-22480	78	16	function	function	NOUN
brj-22480	78	17	,	,	PUNCT
brj-22480	78	18	and	and	CCONJ
brj-22480	78	19	the	the	DET
brj-22480	78	20	output	output	NOUN
brj-22480	78	21	value	value	NOUN
brj-22480	78	22	is	be	AUX
brj-22480	78	23	between	between	ADP
brj-22480	78	24	[	[	X
brj-22480	78	25	0	0	NUM
brj-22480	78	26	,	,	PUNCT
brj-22480	78	27	1	1	NUM
brj-22480	78	28	]	]	PUNCT
brj-22480	78	29	,	,	PUNCT
brj-22480	78	30	the	the	DET
brj-22480	78	31	output	output	NOUN
brj-22480	78	32	data	datum	NOUN
brj-22480	78	33	also	also	ADV
brj-22480	78	34	needs	need	VERB
brj-22480	78	35	to	to	PART
brj-22480	78	36	be	be	AUX
brj-22480	78	37	normalized	normalize	VERB
brj-22480	78	38	(	(	PUNCT
brj-22480	78	39	chai	chai	NOUN
brj-22480	78	40	2018	2018	NUM
brj-22480	78	41	)	)	PUNCT
brj-22480	78	42	.	.	PUNCT
brj-22480	79	1	normalization	normalization	NOUN
brj-22480	79	2	processing	process	VERB
brj-22480	79	3	formula	formula	NOUN
brj-22480	79	4	(	(	PUNCT
brj-22480	79	5	eq	eq	NOUN
brj-22480	79	6	.	.	NOUN
brj-22480	79	7	5	5	NUM
brj-22480	79	8	):	):	PUNCT
brj-22480	79	9	(	(	PUNCT
brj-22480	79	10	5	5	NUM
brj-22480	79	11	)	)	PUNCT
brj-22480	79	12	in	in	ADP
brj-22480	79	13	the	the	DET
brj-22480	79	14	eq	eq	NOUN
brj-22480	79	15	.	.	PROPN
brj-22480	79	16	5	5	NUM
brj-22480	79	17	,	,	PUNCT
brj-22480	79	18	x	x	X
brj-22480	79	19	is	be	AUX
brj-22480	79	20	the	the	DET
brj-22480	79	21	value	value	NOUN
brj-22480	79	22	after	after	ADP
brj-22480	79	23	normalization	normalization	NOUN
brj-22480	79	24	of	of	ADP
brj-22480	79	25	x	x	PROPN
brj-22480	79	26	;	;	PUNCT
brj-22480	79	27	xmax	xmax	PROPN
brj-22480	79	28	denotes	denote	VERB
brj-22480	79	29	the	the	DET
brj-22480	79	30	maximum	maximum	ADJ
brj-22480	79	31	value	value	NOUN
brj-22480	79	32	of	of	ADP
brj-22480	79	33	x	x	PRON
brj-22480	79	34	;	;	PUNCT
brj-22480	79	35	and	and	CCONJ
brj-22480	79	36	xmin	xmin	NOUN
brj-22480	79	37	is	be	AUX
brj-22480	79	38	the	the	DET
brj-22480	79	39	minimum	minimum	ADJ
brj-22480	79	40	value	value	NOUN
brj-22480	79	41	of	of	ADP
brj-22480	79	42	x.	x.	NOUN
brj-22480	79	43	the	the	DET
brj-22480	79	44	performance	performance	NOUN
brj-22480	79	45	of	of	ADP
brj-22480	79	46	the	the	DET
brj-22480	79	47	neural	neural	ADJ
brj-22480	79	48	network	network	NOUN
brj-22480	79	49	is	be	AUX
brj-22480	79	50	generally	generally	ADV
brj-22480	79	51	analyzed	analyze	VERB
brj-22480	79	52	by	by	ADP
brj-22480	79	53	means	mean	NOUN
brj-22480	79	54	of	of	ADP
brj-22480	79	55	the	the	DET
brj-22480	79	56	mean	mean	ADJ
brj-22480	79	57	square	square	NOUN
brj-22480	79	58	error	error	NOUN
brj-22480	79	59	.	.	PUNCT
brj-22480	80	1	a	a	DET
brj-22480	80	2	smaller	small	ADJ
brj-22480	80	3	mean	mean	ADJ
brj-22480	80	4	square	square	ADJ
brj-22480	80	5	error	error	NOUN
brj-22480	80	6	between	between	ADP
brj-22480	80	7	the	the	DET
brj-22480	80	8	test	test	NOUN
brj-22480	80	9	value	value	NOUN
brj-22480	80	10	and	and	CCONJ
brj-22480	80	11	the	the	DET
brj-22480	80	12	predicted	predict	VERB
brj-22480	80	13	value	value	NOUN
brj-22480	80	14	results	result	NOUN
brj-22480	80	15	in	in	ADP
brj-22480	80	16	a	a	DET
brj-22480	80	17	better	well	ADJ
brj-22480	80	18	forecast	forecast	NOUN
brj-22480	80	19	performance	performance	NOUN
brj-22480	80	20	.	.	PUNCT
brj-22480	81	1	at	at	ADP
brj-22480	81	2	the	the	DET
brj-22480	81	3	same	same	ADJ
brj-22480	81	4	time	time	NOUN
brj-22480	81	5	,	,	PUNCT
brj-22480	81	6	the	the	DET
brj-22480	81	7	coefficient	coefficient	NOUN
brj-22480	81	8	of	of	ADP
brj-22480	81	9	determination	determination	NOUN
brj-22480	81	10	r2	r2	PROPN
brj-22480	81	11	is	be	AUX
brj-22480	81	12	also	also	ADV
brj-22480	81	13	used	use	VERB
brj-22480	81	14	as	as	ADP
brj-22480	81	15	an	an	DET
brj-22480	81	16	evaluation	evaluation	NOUN
brj-22480	81	17	index	index	NOUN
brj-22480	81	18	for	for	ADP
brj-22480	81	19	the	the	DET
brj-22480	81	20	performance	performance	NOUN
brj-22480	81	21	of	of	ADP
brj-22480	81	22	the	the	DET
brj-22480	81	23	neural	neural	ADJ
brj-22480	81	24	network	network	NOUN
brj-22480	81	25	.	.	PUNCT
brj-22480	82	1	the	the	DET
brj-22480	82	2	learning	learning	NOUN
brj-22480	82	3	efficiency	efficiency	NOUN
brj-22480	82	4	of	of	ADP
brj-22480	82	5	this	this	DET
brj-22480	82	6	experiment	experiment	NOUN
brj-22480	82	7	was	be	AUX
brj-22480	82	8	set	set	VERB
brj-22480	82	9	to	to	ADP
brj-22480	82	10	0.01	0.01	NUM
brj-22480	82	11	and	and	CCONJ
brj-22480	82	12	the	the	DET
brj-22480	82	13	mean	mean	ADJ
brj-22480	82	14	square	square	ADJ
brj-22480	82	15	error	error	NOUN
brj-22480	82	16	was	be	AUX
brj-22480	82	17	calculated	calculate	VERB
brj-22480	82	18	using	use	VERB
brj-22480	82	19	eq	eq	ADP
brj-22480	82	20	.	.	PROPN
brj-22480	82	21	6	6	NUM
brj-22480	82	22	as	as	SCONJ
brj-22480	82	23	follows	follow	VERB
brj-22480	82	24	:	:	PUNCT
brj-22480	82	25	peer	peer	NOUN
brj-22480	82	26	-	-	PUNCT
brj-22480	82	27	reviewed	review	VERB
brj-22480	82	28	article	article	NOUN
brj-22480	82	29	bioresources.com	bioresources.com	X
brj-22480	82	30	chai	chai	NOUN
brj-22480	82	31	&	&	CCONJ
brj-22480	82	32	li	li	PROPN
brj-22480	82	33	(	(	PUNCT
brj-22480	82	34	2023	2023	NUM
brj-22480	82	35	)	)	PUNCT
brj-22480	82	36	.	.	PUNCT
brj-22480	83	1	“	"	PUNCT
brj-22480	83	2	prediction	prediction	NOUN
brj-22480	83	3	of	of	ADP
brj-22480	83	4	wood	wood	NOUN
brj-22480	83	5	drying	dry	VERB
brj-22480	83	6	by	by	ADP
brj-22480	83	7	ann	ann	PROPN
brj-22480	83	8	,	,	PUNCT
brj-22480	83	9	”	"	PUNCT
brj-22480	83	10	bioresources	bioresource	NOUN
brj-22480	83	11	18(4	18(4	NUM
brj-22480	83	12	)	)	PUNCT
brj-22480	83	13	,	,	PUNCT
brj-22480	83	14	8212	8212	NUM
brj-22480	83	15	-	-	SYM
brj-22480	83	16	8222	8222	NUM
brj-22480	83	17	.	.	PUNCT
brj-22480	83	18	8217	8217	NUM
brj-22480	83	19	(	(	PUNCT
brj-22480	83	20	6	6	NUM
brj-22480	83	21	)	)	PUNCT
brj-22480	83	22	in	in	ADP
brj-22480	83	23	eq	eq	ADP
brj-22480	83	24	.	.	PROPN
brj-22480	83	25	6	6	NUM
brj-22480	83	26	,	,	PUNCT
brj-22480	83	27	n	n	PRON
brj-22480	83	28	is	be	AUX
brj-22480	83	29	the	the	DET
brj-22480	83	30	number	number	NOUN
brj-22480	83	31	of	of	ADP
brj-22480	83	32	groups	group	NOUN
brj-22480	83	33	;	;	PUNCT
brj-22480	83	34	xi	xi	X
brj-22480	83	35	is	be	AUX
brj-22480	83	36	actual	actual	ADJ
brj-22480	83	37	value	value	NOUN
brj-22480	83	38	;	;	PUNCT
brj-22480	83	39	and	and	CCONJ
brj-22480	83	40	yi	yi	PROPN
brj-22480	83	41	denotes	denote	VERB
brj-22480	83	42	the	the	DET
brj-22480	83	43	predicted	predict	VERB
brj-22480	83	44	value	value	NOUN
brj-22480	83	45	of	of	ADP
brj-22480	83	46	the	the	DET
brj-22480	83	47	neural	neural	ADJ
brj-22480	83	48	network	network	NOUN
brj-22480	83	49	.	.	PUNCT
brj-22480	84	1	results	result	NOUN
brj-22480	84	2	and	and	CCONJ
brj-22480	84	3	discussion	discussion	NOUN
brj-22480	84	4	determination	determination	NOUN
brj-22480	84	5	of	of	ADP
brj-22480	84	6	the	the	DET
brj-22480	84	7	number	number	NOUN
brj-22480	84	8	of	of	ADP
brj-22480	84	9	neurons	neuron	NOUN
brj-22480	84	10	the	the	DET
brj-22480	84	11	determination	determination	NOUN
brj-22480	84	12	of	of	ADP
brj-22480	84	13	the	the	DET
brj-22480	84	14	neurons	neuron	NOUN
brj-22480	84	15	number	number	NOUN
brj-22480	84	16	has	have	VERB
brj-22480	84	17	an	an	DET
brj-22480	84	18	intuitive	intuitive	ADJ
brj-22480	84	19	impact	impact	NOUN
brj-22480	84	20	on	on	ADP
brj-22480	84	21	the	the	DET
brj-22480	84	22	simulation	simulation	NOUN
brj-22480	84	23	effect	effect	NOUN
brj-22480	84	24	of	of	ADP
brj-22480	84	25	the	the	DET
brj-22480	84	26	model	model	NOUN
brj-22480	84	27	.	.	PUNCT
brj-22480	85	1	the	the	DET
brj-22480	85	2	small	small	ADJ
brj-22480	85	3	number	number	NOUN
brj-22480	85	4	of	of	ADP
brj-22480	85	5	neurons	neuron	NOUN
brj-22480	85	6	can	can	AUX
brj-22480	85	7	not	not	PART
brj-22480	85	8	fully	fully	ADV
brj-22480	85	9	reflect	reflect	VERB
brj-22480	85	10	the	the	DET
brj-22480	85	11	experimental	experimental	ADJ
brj-22480	85	12	relationships	relationship	NOUN
brj-22480	85	13	,	,	PUNCT
brj-22480	85	14	which	which	PRON
brj-22480	85	15	has	have	VERB
brj-22480	85	16	a	a	DET
brj-22480	85	17	great	great	ADJ
brj-22480	85	18	impact	impact	NOUN
brj-22480	85	19	on	on	ADP
brj-22480	85	20	the	the	DET
brj-22480	85	21	training	training	NOUN
brj-22480	85	22	of	of	ADP
brj-22480	85	23	the	the	DET
brj-22480	85	24	model	model	NOUN
brj-22480	85	25	,	,	PUNCT
brj-22480	85	26	and	and	CCONJ
brj-22480	85	27	the	the	DET
brj-22480	85	28	excessive	excessive	ADJ
brj-22480	85	29	number	number	NOUN
brj-22480	85	30	of	of	ADP
brj-22480	85	31	neurons	neuron	NOUN
brj-22480	85	32	will	will	AUX
brj-22480	85	33	lead	lead	VERB
brj-22480	85	34	to	to	ADP
brj-22480	85	35	overfitting	overfitte	VERB
brj-22480	85	36	and	and	CCONJ
brj-22480	85	37	affect	affect	VERB
brj-22480	85	38	the	the	DET
brj-22480	85	39	reality	reality	NOUN
brj-22480	85	40	of	of	ADP
brj-22480	85	41	the	the	DET
brj-22480	85	42	experiment	experiment	NOUN
brj-22480	85	43	.	.	PUNCT
brj-22480	86	1	therefore	therefore	ADV
brj-22480	86	2	,	,	PUNCT
brj-22480	86	3	a	a	DET
brj-22480	86	4	pre	pre	NOUN
brj-22480	86	5	-	-	ADJ
brj-22480	86	6	experiment	experiment	NOUN
brj-22480	86	7	was	be	AUX
brj-22480	86	8	used	use	VERB
brj-22480	86	9	to	to	PART
brj-22480	86	10	simulate	simulate	VERB
brj-22480	86	11	the	the	DET
brj-22480	86	12	fitting	fitting	ADJ
brj-22480	86	13	experiment	experiment	NOUN
brj-22480	86	14	under	under	ADP
brj-22480	86	15	each	each	DET
brj-22480	86	16	number	number	NOUN
brj-22480	86	17	of	of	ADP
brj-22480	86	18	neurons	neuron	NOUN
brj-22480	86	19	.	.	PUNCT
brj-22480	87	1	as	as	SCONJ
brj-22480	87	2	shown	show	VERB
brj-22480	87	3	in	in	ADP
brj-22480	87	4	fig	fig	NOUN
brj-22480	87	5	.	.	PUNCT
brj-22480	88	1	2	2	NUM
brj-22480	88	2	,	,	PUNCT
brj-22480	88	3	models	model	NOUN
brj-22480	88	4	with	with	ADP
brj-22480	88	5	different	different	ADJ
brj-22480	88	6	numbers	number	NOUN
brj-22480	88	7	of	of	ADP
brj-22480	88	8	neurons	neuron	NOUN
brj-22480	88	9	were	be	AUX
brj-22480	88	10	used	use	VERB
brj-22480	88	11	to	to	PART
brj-22480	88	12	simulate	simulate	VERB
brj-22480	88	13	the	the	DET
brj-22480	88	14	input	input	NOUN
brj-22480	88	15	data	datum	NOUN
brj-22480	88	16	for	for	ADP
brj-22480	88	17	one	one	NUM
brj-22480	88	18	million	million	NUM
brj-22480	88	19	times	time	NOUN
brj-22480	88	20	,	,	PUNCT
brj-22480	88	21	and	and	CCONJ
brj-22480	88	22	the	the	DET
brj-22480	88	23	representative	representative	ADJ
brj-22480	88	24	loss	loss	NOUN
brj-22480	88	25	function	function	NOUN
brj-22480	88	26	of	of	ADP
brj-22480	88	27	10,000	10,000	NUM
brj-22480	88	28	,	,	PUNCT
brj-22480	88	29	500,000	500,000	NUM
brj-22480	88	30	,	,	PUNCT
brj-22480	88	31	and	and	CCONJ
brj-22480	88	32	1.0	1.0	NUM
brj-22480	88	33	million	million	NUM
brj-22480	88	34	simulation	simulation	NOUN
brj-22480	88	35	times	time	NOUN
brj-22480	88	36	are	be	AUX
brj-22480	88	37	selected	select	VERB
brj-22480	88	38	to	to	PART
brj-22480	88	39	draw	draw	VERB
brj-22480	88	40	the	the	DET
brj-22480	88	41	image	image	NOUN
brj-22480	88	42	.	.	PUNCT
brj-22480	89	1	it	it	PRON
brj-22480	89	2	can	can	AUX
brj-22480	89	3	be	be	AUX
brj-22480	89	4	seen	see	VERB
brj-22480	89	5	that	that	SCONJ
brj-22480	89	6	the	the	DET
brj-22480	89	7	error	error	NOUN
brj-22480	89	8	loss	loss	NOUN
brj-22480	89	9	in	in	ADP
brj-22480	89	10	the	the	DET
brj-22480	89	11	neural	neural	ADJ
brj-22480	89	12	network	network	NOUN
brj-22480	89	13	before	before	SCONJ
brj-22480	89	14	the	the	DET
brj-22480	89	15	number	number	NOUN
brj-22480	89	16	of	of	ADP
brj-22480	89	17	neurons	neuron	NOUN
brj-22480	89	18	was	be	AUX
brj-22480	89	19	7	7	NUM
brj-22480	89	20	,	,	PUNCT
brj-22480	89	21	it	it	PRON
brj-22480	89	22	decreased	decrease	VERB
brj-22480	89	23	with	with	ADP
brj-22480	89	24	the	the	DET
brj-22480	89	25	increase	increase	NOUN
brj-22480	89	26	of	of	ADP
brj-22480	89	27	the	the	DET
brj-22480	89	28	number	number	NOUN
brj-22480	89	29	of	of	ADP
brj-22480	89	30	neurons	neuron	NOUN
brj-22480	89	31	,	,	PUNCT
brj-22480	89	32	and	and	CCONJ
brj-22480	89	33	reached	reach	VERB
brj-22480	89	34	the	the	DET
brj-22480	89	35	lowest	low	ADJ
brj-22480	89	36	level	level	NOUN
brj-22480	89	37	after	after	SCONJ
brj-22480	89	38	the	the	DET
brj-22480	89	39	number	number	NOUN
brj-22480	89	40	of	of	ADP
brj-22480	89	41	neurons	neuron	NOUN
brj-22480	89	42	was	be	AUX
brj-22480	89	43	9	9	NUM
brj-22480	89	44	.	.	PUNCT
brj-22480	90	1	then	then	ADV
brj-22480	90	2	,	,	PUNCT
brj-22480	90	3	with	with	ADP
brj-22480	90	4	further	further	ADJ
brj-22480	90	5	increase	increase	NOUN
brj-22480	90	6	of	of	ADP
brj-22480	90	7	the	the	DET
brj-22480	90	8	number	number	NOUN
brj-22480	90	9	of	of	ADP
brj-22480	90	10	neurons	neuron	NOUN
brj-22480	90	11	,	,	PUNCT
brj-22480	90	12	the	the	DET
brj-22480	90	13	number	number	NOUN
brj-22480	90	14	of	of	ADP
brj-22480	90	15	neural	neural	ADJ
brj-22480	90	16	network	network	NOUN
brj-22480	90	17	losses	loss	NOUN
brj-22480	90	18	under	under	ADP
brj-22480	90	19	each	each	DET
brj-22480	90	20	simulation	simulation	NOUN
brj-22480	90	21	number	number	NOUN
brj-22480	90	22	remained	remain	VERB
brj-22480	90	23	basically	basically	ADV
brj-22480	90	24	unchanged	unchanged	ADJ
brj-22480	90	25	.	.	PUNCT
brj-22480	91	1	because	because	SCONJ
brj-22480	91	2	a	a	DET
brj-22480	91	3	large	large	ADJ
brj-22480	91	4	number	number	NOUN
brj-22480	91	5	of	of	ADP
brj-22480	91	6	neurons	neuron	NOUN
brj-22480	91	7	will	will	AUX
brj-22480	91	8	increase	increase	VERB
brj-22480	91	9	the	the	DET
brj-22480	91	10	operation	operation	NOUN
brj-22480	91	11	time	time	NOUN
brj-22480	91	12	,	,	PUNCT
brj-22480	91	13	the	the	DET
brj-22480	91	14	result	result	NOUN
brj-22480	91	15	will	will	AUX
brj-22480	91	16	be	be	AUX
brj-22480	91	17	unstable	unstable	ADJ
brj-22480	91	18	or	or	CCONJ
brj-22480	91	19	even	even	ADV
brj-22480	91	20	lead	lead	VERB
brj-22480	91	21	to	to	ADP
brj-22480	91	22	over	over	ADV
brj-22480	91	23	-	-	PUNCT
brj-22480	91	24	fitting	fitting	ADJ
brj-22480	91	25	.	.	PUNCT
brj-22480	92	1	to	to	PART
brj-22480	92	2	simplify	simplify	VERB
brj-22480	92	3	the	the	DET
brj-22480	92	4	operation	operation	NOUN
brj-22480	92	5	,	,	PUNCT
brj-22480	92	6	the	the	DET
brj-22480	92	7	number	number	NOUN
brj-22480	92	8	of	of	ADP
brj-22480	92	9	neurons	neuron	NOUN
brj-22480	92	10	was	be	AUX
brj-22480	92	11	tentatively	tentatively	ADV
brj-22480	92	12	selected	select	VERB
brj-22480	92	13	as	as	ADP
brj-22480	92	14	7	7	NUM
brj-22480	92	15	,	,	PUNCT
brj-22480	92	16	8	8	NUM
brj-22480	92	17	,	,	PUNCT
brj-22480	92	18	9	9	NUM
brj-22480	92	19	,	,	PUNCT
brj-22480	92	20	and	and	CCONJ
brj-22480	92	21	10	10	NUM
brj-22480	92	22	(	(	PUNCT
brj-22480	92	23	fig	fig	NOUN
brj-22480	92	24	.	.	PUNCT
brj-22480	93	1	3	3	NUM
brj-22480	93	2	)	)	PUNCT
brj-22480	93	3	,	,	PUNCT
brj-22480	93	4	and	and	CCONJ
brj-22480	93	5	the	the	DET
brj-22480	93	6	time	time	NOUN
brj-22480	93	7	error	error	NOUN
brj-22480	93	8	loss	loss	NOUN
brj-22480	93	9	was	be	AUX
brj-22480	93	10	0.00605	0.00605	NUM
brj-22480	93	11	.	.	PUNCT
brj-22480	94	1	fig	fig	NOUN
brj-22480	94	2	.	.	PUNCT
brj-22480	95	1	2	2	X
brj-22480	95	2	.	.	X
brj-22480	95	3	loss	loss	NOUN
brj-22480	95	4	function	function	NOUN
brj-22480	95	5	with	with	ADP
brj-22480	95	6	different	different	ADJ
brj-22480	95	7	number	number	NOUN
brj-22480	95	8	of	of	ADP
brj-22480	95	9	neurons	neuron	NOUN
brj-22480	95	10	peer	peer	NOUN
brj-22480	95	11	-	-	PUNCT
brj-22480	95	12	reviewed	review	VERB
brj-22480	95	13	article	article	NOUN
brj-22480	95	14	bioresources.com	bioresources.com	X
brj-22480	95	15	chai	chai	NOUN
brj-22480	95	16	&	&	CCONJ
brj-22480	95	17	li	li	PROPN
brj-22480	95	18	(	(	PUNCT
brj-22480	95	19	2023	2023	NUM
brj-22480	95	20	)	)	PUNCT
brj-22480	95	21	.	.	PUNCT
brj-22480	96	1	“	"	PUNCT
brj-22480	96	2	prediction	prediction	NOUN
brj-22480	96	3	of	of	ADP
brj-22480	96	4	wood	wood	NOUN
brj-22480	96	5	drying	dry	VERB
brj-22480	96	6	by	by	ADP
brj-22480	96	7	ann	ann	PROPN
brj-22480	96	8	,	,	PUNCT
brj-22480	96	9	”	"	PUNCT
brj-22480	96	10	bioresources	bioresource	NOUN
brj-22480	96	11	18(4	18(4	NUM
brj-22480	96	12	)	)	PUNCT
brj-22480	96	13	,	,	PUNCT
brj-22480	96	14	8212	8212	NUM
brj-22480	96	15	-	-	SYM
brj-22480	96	16	8222	8222	NUM
brj-22480	96	17	.	.	PUNCT
brj-22480	97	1	8218	8218	NUM
brj-22480	97	2	a	a	DET
brj-22480	97	3	b	b	NOUN
brj-22480	97	4	c	c	NOUN
brj-22480	97	5	d	d	X
brj-22480	97	6	fig	fig	NOUN
brj-22480	97	7	.	.	PUNCT
brj-22480	98	1	3	3	X
brj-22480	98	2	.	.	X
brj-22480	98	3	the	the	DET
brj-22480	98	4	number	number	NOUN
brj-22480	98	5	of	of	ADP
brj-22480	98	6	neurons	neuron	NOUN
brj-22480	98	7	at	at	ADP
brj-22480	98	8	7	7	NUM
brj-22480	98	9	,	,	PUNCT
brj-22480	98	10	8	8	NUM
brj-22480	98	11	,	,	PUNCT
brj-22480	98	12	9	9	NUM
brj-22480	98	13	,	,	PUNCT
brj-22480	98	14	and	and	CCONJ
brj-22480	98	15	10	10	NUM
brj-22480	98	16	(	(	PUNCT
brj-22480	98	17	a	a	PRON
brj-22480	98	18	through	through	ADP
brj-22480	98	19	d	d	NOUN
brj-22480	98	20	)	)	PUNCT
brj-22480	98	21	,	,	PUNCT
brj-22480	98	22	and	and	CCONJ
brj-22480	98	23	the	the	DET
brj-22480	98	24	relationship	relationship	NOUN
brj-22480	98	25	between	between	ADP
brj-22480	98	26	the	the	DET
brj-22480	98	27	loss	loss	NOUN
brj-22480	98	28	function	function	NOUN
brj-22480	98	29	and	and	CCONJ
brj-22480	98	30	the	the	DET
brj-22480	98	31	number	number	NOUN
brj-22480	98	32	of	of	ADP
brj-22480	98	33	training	training	NOUN
brj-22480	98	34	further	far	ADV
brj-22480	98	35	comparing	compare	VERB
brj-22480	98	36	the	the	DET
brj-22480	98	37	images	image	NOUN
brj-22480	98	38	of	of	ADP
brj-22480	98	39	the	the	DET
brj-22480	98	40	four	four	NUM
brj-22480	98	41	groups	group	NOUN
brj-22480	98	42	of	of	ADP
brj-22480	98	43	7	7	NUM
brj-22480	98	44	,	,	PUNCT
brj-22480	98	45	8	8	NUM
brj-22480	98	46	,	,	PUNCT
brj-22480	98	47	9	9	NUM
brj-22480	98	48	,	,	PUNCT
brj-22480	98	49	and	and	CCONJ
brj-22480	98	50	10	10	NUM
brj-22480	98	51	where	where	SCONJ
brj-22480	98	52	the	the	DET
brj-22480	98	53	loss	loss	NOUN
brj-22480	98	54	function	function	NOUN
brj-22480	98	55	changes	change	NOUN
brj-22480	98	56	with	with	ADP
brj-22480	98	57	the	the	DET
brj-22480	98	58	number	number	NOUN
brj-22480	98	59	of	of	ADP
brj-22480	98	60	simulations	simulation	NOUN
brj-22480	98	61	,	,	PUNCT
brj-22480	98	62	it	it	PRON
brj-22480	98	63	can	can	AUX
brj-22480	98	64	be	be	AUX
brj-22480	98	65	observed	observe	VERB
brj-22480	98	66	that	that	SCONJ
brj-22480	98	67	the	the	DET
brj-22480	98	68	loss	loss	NOUN
brj-22480	98	69	function	function	NOUN
brj-22480	98	70	was	be	AUX
brj-22480	98	71	stable	stable	ADJ
brj-22480	98	72	after	after	SCONJ
brj-22480	98	73	the	the	DET
brj-22480	98	74	number	number	NOUN
brj-22480	98	75	of	of	ADP
brj-22480	98	76	fittings	fitting	NOUN
brj-22480	98	77	was	be	AUX
brj-22480	98	78	100,000	100,000	NUM
brj-22480	98	79	,	,	PUNCT
brj-22480	98	80	and	and	CCONJ
brj-22480	98	81	there	there	PRON
brj-22480	98	82	was	be	VERB
brj-22480	98	83	a	a	DET
brj-22480	98	84	clear	clear	ADJ
brj-22480	98	85	loss	loss	NOUN
brj-22480	98	86	inflection	inflection	NOUN
brj-22480	98	87	point	point	NOUN
brj-22480	98	88	in	in	ADP
brj-22480	98	89	the	the	DET
brj-22480	98	90	image	image	NOUN
brj-22480	98	91	with	with	ADP
brj-22480	98	92	the	the	DET
brj-22480	98	93	number	number	NOUN
brj-22480	98	94	of	of	ADP
brj-22480	98	95	neurons	neuron	NOUN
brj-22480	98	96	at	at	ADP
brj-22480	98	97	9	9	NUM
brj-22480	98	98	.	.	PUNCT
brj-22480	99	1	the	the	DET
brj-22480	99	2	image	image	NOUN
brj-22480	99	3	inflection	inflection	NOUN
brj-22480	99	4	points	point	NOUN
brj-22480	99	5	of	of	ADP
brj-22480	99	6	the	the	DET
brj-22480	99	7	number	number	NOUN
brj-22480	99	8	of	of	ADP
brj-22480	99	9	neurons	neuron	NOUN
brj-22480	99	10	7	7	NUM
brj-22480	99	11	and	and	CCONJ
brj-22480	99	12	8	8	NUM
brj-22480	99	13	were	be	AUX
brj-22480	99	14	not	not	PART
brj-22480	99	15	clear	clear	ADJ
brj-22480	99	16	,	,	PUNCT
brj-22480	99	17	while	while	SCONJ
brj-22480	99	18	the	the	DET
brj-22480	99	19	inflection	inflection	NOUN
brj-22480	99	20	point	point	NOUN
brj-22480	99	21	of	of	ADP
brj-22480	99	22	the	the	DET
brj-22480	99	23	image	image	NOUN
brj-22480	99	24	with	with	ADP
brj-22480	99	25	the	the	DET
brj-22480	99	26	number	number	NOUN
brj-22480	99	27	of	of	ADP
brj-22480	99	28	neurons	neuron	NOUN
brj-22480	99	29	of	of	ADP
brj-22480	99	30	10	10	NUM
brj-22480	99	31	exhibited	exhibit	VERB
brj-22480	99	32	a	a	DET
brj-22480	99	33	gentle	gentle	ADJ
brj-22480	99	34	curve	curve	NOUN
brj-22480	99	35	,	,	PUNCT
brj-22480	99	36	which	which	PRON
brj-22480	99	37	is	be	AUX
brj-22480	99	38	the	the	DET
brj-22480	99	39	performance	performance	NOUN
brj-22480	99	40	of	of	ADP
brj-22480	99	41	the	the	DET
brj-22480	99	42	transition	transition	NOUN
brj-22480	99	43	fitting	fit	VERB
brj-22480	99	44	phenomenon	phenomenon	NOUN
brj-22480	99	45	(	(	PUNCT
brj-22480	99	46	diawanich	diawanich	NOUN
brj-22480	99	47	et	et	PROPN
brj-22480	99	48	al	al	PROPN
brj-22480	99	49	.	.	PROPN
brj-22480	99	50	2009	2009	NUM
brj-22480	99	51	)	)	PUNCT
brj-22480	99	52	.	.	PUNCT
brj-22480	100	1	thus	thus	ADV
brj-22480	100	2	,	,	PUNCT
brj-22480	100	3	the	the	DET
brj-22480	100	4	optimum	optimum	ADJ
brj-22480	100	5	number	number	NOUN
brj-22480	100	6	of	of	ADP
brj-22480	100	7	neurons	neuron	NOUN
brj-22480	100	8	was	be	AUX
brj-22480	100	9	determined	determine	VERB
brj-22480	100	10	to	to	PART
brj-22480	100	11	be	be	AUX
brj-22480	100	12	9	9	NUM
brj-22480	100	13	,	,	PUNCT
brj-22480	100	14	and	and	CCONJ
brj-22480	100	15	the	the	DET
brj-22480	100	16	number	number	NOUN
brj-22480	100	17	of	of	ADP
brj-22480	100	18	neural	neural	ADJ
brj-22480	100	19	network	network	NOUN
brj-22480	100	20	fittings	fitting	NOUN
brj-22480	100	21	was	be	AUX
brj-22480	100	22	determined	determine	VERB
brj-22480	100	23	to	to	PART
brj-22480	100	24	be	be	AUX
brj-22480	100	25	100,000	100,000	NUM
brj-22480	100	26	times	time	NOUN
brj-22480	100	27	.	.	PUNCT
brj-22480	101	1	according	accord	VERB
brj-22480	101	2	to	to	ADP
brj-22480	101	3	the	the	DET
brj-22480	101	4	determined	determined	ADJ
brj-22480	101	5	number	number	NOUN
brj-22480	101	6	of	of	ADP
brj-22480	101	7	neurons	neuron	NOUN
brj-22480	101	8	combined	combine	VERB
brj-22480	101	9	with	with	ADP
brj-22480	101	10	the	the	DET
brj-22480	101	11	input	input	NOUN
brj-22480	101	12	layer	layer	NOUN
brj-22480	101	13	and	and	CCONJ
brj-22480	101	14	output	output	NOUN
brj-22480	101	15	layer	layer	NOUN
brj-22480	101	16	data	datum	NOUN
brj-22480	101	17	,	,	PUNCT
brj-22480	101	18	the	the	DET
brj-22480	101	19	structure	structure	NOUN
brj-22480	101	20	diagram	diagram	NOUN
brj-22480	101	21	of	of	ADP
brj-22480	101	22	the	the	DET
brj-22480	101	23	neural	neural	ADJ
brj-22480	101	24	network	network	NOUN
brj-22480	101	25	was	be	AUX
brj-22480	101	26	obtained	obtain	VERB
brj-22480	101	27	as	as	SCONJ
brj-22480	101	28	shown	show	VERB
brj-22480	101	29	in	in	ADP
brj-22480	101	30	fig	fig	NOUN
brj-22480	101	31	.	.	PUNCT
brj-22480	102	1	4	4	NUM
brj-22480	102	2	.	.	X
brj-22480	102	3	peer	peer	NOUN
brj-22480	102	4	-	-	PUNCT
brj-22480	102	5	reviewed	review	VERB
brj-22480	102	6	article	article	NOUN
brj-22480	102	7	bioresources.com	bioresources.com	X
brj-22480	102	8	chai	chai	NOUN
brj-22480	102	9	&	&	CCONJ
brj-22480	102	10	li	li	PROPN
brj-22480	102	11	(	(	PUNCT
brj-22480	102	12	2023	2023	NUM
brj-22480	102	13	)	)	PUNCT
brj-22480	102	14	.	.	PUNCT
brj-22480	103	1	“	"	PUNCT
brj-22480	103	2	prediction	prediction	NOUN
brj-22480	103	3	of	of	ADP
brj-22480	103	4	wood	wood	NOUN
brj-22480	103	5	drying	dry	VERB
brj-22480	103	6	by	by	ADP
brj-22480	103	7	ann	ann	PROPN
brj-22480	103	8	,	,	PUNCT
brj-22480	103	9	”	"	PUNCT
brj-22480	103	10	bioresources	bioresource	NOUN
brj-22480	103	11	18(4	18(4	NUM
brj-22480	103	12	)	)	PUNCT
brj-22480	103	13	,	,	PUNCT
brj-22480	103	14	8212	8212	NUM
brj-22480	103	15	-	-	SYM
brj-22480	103	16	8222	8222	NUM
brj-22480	103	17	.	.	PUNCT
brj-22480	103	18	8219	8219	NUM
brj-22480	103	19	fig	fig	NOUN
brj-22480	103	20	.	.	PUNCT
brj-22480	104	1	4	4	X
brj-22480	104	2	.	.	X
brj-22480	104	3	neural	neural	ADJ
brj-22480	104	4	network	network	NOUN
brj-22480	104	5	structure	structure	NOUN
brj-22480	104	6	diagram	diagram	NOUN
brj-22480	104	7	(	(	PUNCT
brj-22480	104	8	x1	x1	PROPN
brj-22480	104	9	is	be	AUX
brj-22480	104	10	the	the	DET
brj-22480	104	11	position	position	NOUN
brj-22480	104	12	of	of	ADP
brj-22480	104	13	the	the	DET
brj-22480	104	14	specimen	speciman	NOUN
brj-22480	104	15	,	,	PUNCT
brj-22480	104	16	x2	x2	PROPN
brj-22480	104	17	is	be	AUX
brj-22480	104	18	the	the	DET
brj-22480	104	19	moisture	moisture	NOUN
brj-22480	104	20	content	content	NOUN
brj-22480	104	21	,	,	PUNCT
brj-22480	104	22	x3	x3	PROPN
brj-22480	104	23	is	be	AUX
brj-22480	104	24	the	the	DET
brj-22480	104	25	steaming	steaming	NOUN
brj-22480	104	26	temperature	temperature	NOUN
brj-22480	104	27	,	,	PUNCT
brj-22480	104	28	x4	x4	PROPN
brj-22480	104	29	is	be	AUX
brj-22480	104	30	the	the	DET
brj-22480	104	31	steaming	steaming	NOUN
brj-22480	104	32	time	time	NOUN
brj-22480	104	33	,	,	PUNCT
brj-22480	104	34	x5	x5	PROPN
brj-22480	104	35	is	be	AUX
brj-22480	104	36	the	the	DET
brj-22480	104	37	set	set	ADJ
brj-22480	104	38	temperature	temperature	NOUN
brj-22480	104	39	,	,	PUNCT
brj-22480	104	40	x6	x6	PROPN
brj-22480	104	41	is	be	AUX
brj-22480	104	42	the	the	DET
brj-22480	104	43	set	set	ADJ
brj-22480	104	44	time	time	NOUN
brj-22480	104	45	,	,	PUNCT
brj-22480	104	46	y1	y1	PROPN
brj-22480	104	47	is	be	AUX
brj-22480	104	48	the	the	DET
brj-22480	104	49	drying	dry	VERB
brj-22480	104	50	rate	rate	NOUN
brj-22480	104	51	,	,	PUNCT
brj-22480	104	52	and	and	CCONJ
brj-22480	104	53	y2	y2	PROPN
brj-22480	104	54	is	be	AUX
brj-22480	104	55	the	the	DET
brj-22480	104	56	longitudinal	longitudinal	ADJ
brj-22480	104	57	cracking	cracking	NOUN
brj-22480	104	58	degree	degree	NOUN
brj-22480	104	59	.	.	PUNCT
brj-22480	105	1	the	the	DET
brj-22480	105	2	number	number	NOUN
brj-22480	105	3	of	of	ADP
brj-22480	105	4	neurons	neuron	NOUN
brj-22480	105	5	in	in	ADP
brj-22480	105	6	the	the	DET
brj-22480	105	7	input	input	NOUN
brj-22480	105	8	layer	layer	NOUN
brj-22480	105	9	,	,	PUNCT
brj-22480	105	10	hidden	hide	VERB
brj-22480	105	11	layer	layer	NOUN
brj-22480	105	12	,	,	PUNCT
brj-22480	105	13	and	and	CCONJ
brj-22480	105	14	output	output	NOUN
brj-22480	105	15	layer	layer	NOUN
brj-22480	105	16	is	be	AUX
brj-22480	105	17	5	5	NUM
brj-22480	105	18	,	,	PUNCT
brj-22480	105	19	9	9	NUM
brj-22480	105	20	,	,	PUNCT
brj-22480	105	21	and	and	CCONJ
brj-22480	105	22	2	2	NUM
brj-22480	105	23	respectively	respectively	ADV
brj-22480	105	24	)	)	PUNCT
brj-22480	105	25	regression	regression	VERB
brj-22480	105	26	fitting	fitting	ADJ
brj-22480	105	27	analysis	analysis	NOUN
brj-22480	105	28	with	with	ADP
brj-22480	105	29	the	the	DET
brj-22480	105	30	position	position	NOUN
brj-22480	105	31	of	of	ADP
brj-22480	105	32	the	the	DET
brj-22480	105	33	specimen	speciman	NOUN
brj-22480	105	34	(	(	PUNCT
brj-22480	105	35	x1	x1	PROPN
brj-22480	105	36	)	)	PUNCT
brj-22480	105	37	,	,	PUNCT
brj-22480	105	38	moisture	moisture	NOUN
brj-22480	105	39	content	content	NOUN
brj-22480	105	40	(	(	PUNCT
brj-22480	105	41	x2	x2	PROPN
brj-22480	105	42	)	)	PUNCT
brj-22480	105	43	,	,	PUNCT
brj-22480	105	44	steaming	steam	VERB
brj-22480	105	45	temperature	temperature	NOUN
brj-22480	105	46	(	(	PUNCT
brj-22480	105	47	x3	x3	ADJ
brj-22480	105	48	)	)	PUNCT
brj-22480	105	49	,	,	PUNCT
brj-22480	105	50	steaming	steaming	NOUN
brj-22480	105	51	time	time	NOUN
brj-22480	105	52	(	(	PUNCT
brj-22480	105	53	x4	x4	PROPN
brj-22480	105	54	)	)	PUNCT
brj-22480	105	55	,	,	PUNCT
brj-22480	105	56	set	set	VERB
brj-22480	105	57	temperature	temperature	NOUN
brj-22480	105	58	(	(	PUNCT
brj-22480	105	59	x5	x5	PROPN
brj-22480	105	60	)	)	PUNCT
brj-22480	105	61	,	,	PUNCT
brj-22480	105	62	set	set	VERB
brj-22480	105	63	time	time	NOUN
brj-22480	105	64	(	(	PUNCT
brj-22480	105	65	x6	x6	NOUN
brj-22480	105	66	)	)	PUNCT
brj-22480	105	67	as	as	ADP
brj-22480	105	68	independent	independent	ADJ
brj-22480	105	69	variables	variable	NOUN
brj-22480	105	70	,	,	PUNCT
brj-22480	105	71	and	and	CCONJ
brj-22480	105	72	the	the	DET
brj-22480	105	73	drying	dry	VERB
brj-22480	105	74	rate	rate	NOUN
brj-22480	105	75	(	(	PUNCT
brj-22480	105	76	y1	y1	NOUN
brj-22480	105	77	)	)	PUNCT
brj-22480	105	78	and	and	CCONJ
brj-22480	105	79	longitudinal	longitudinal	ADJ
brj-22480	105	80	cracking	cracking	NOUN
brj-22480	105	81	degree	degree	NOUN
brj-22480	105	82	(	(	PUNCT
brj-22480	105	83	y3	y3	NOUN
brj-22480	105	84	)	)	PUNCT
brj-22480	105	85	as	as	ADP
brj-22480	105	86	dependent	dependent	ADJ
brj-22480	105	87	variables	variable	NOUN
brj-22480	105	88	,	,	PUNCT
brj-22480	105	89	multiple	multiple	ADJ
brj-22480	105	90	regression	regression	NOUN
brj-22480	105	91	fitting	fitting	ADJ
brj-22480	105	92	analysis	analysis	NOUN
brj-22480	105	93	was	be	AUX
brj-22480	105	94	conducted	conduct	VERB
brj-22480	105	95	using	use	VERB
brj-22480	105	96	origin	origin	NOUN
brj-22480	105	97	.	.	PUNCT
brj-22480	106	1	the	the	DET
brj-22480	106	2	results	result	NOUN
brj-22480	106	3	were	be	AUX
brj-22480	106	4	as	as	SCONJ
brj-22480	106	5	follows	follow	VERB
brj-22480	106	6	:	:	PUNCT
brj-22480	106	7	y1=6.19042×10	y1=6.19042×10	NOUN
brj-22480	106	8	-	-	NOUN
brj-22480	106	9	5x1	5x1	NUM
brj-22480	106	10	+	+	NOUN
brj-22480	106	11	0.06915x2	0.06915x2	NUM
brj-22480	106	12	-	-	PUNCT
brj-22480	106	13	0.00338x3	0.00338x3	NUM
brj-22480	106	14	-	-	PUNCT
brj-22480	106	15	6.39869×10	6.39869×10	NOUN
brj-22480	106	16	-	-	PUNCT
brj-22480	106	17	4x4	4x4	NUM
brj-22480	106	18	+	+	NOUN
brj-22480	106	19	0.05734x50.00139x6	0.05734x50.00139x6	NOUN
brj-22480	106	20	-	-	NOUN
brj-22480	106	21	5.54984	5.54984	NUM
brj-22480	106	22	,	,	PUNCT
brj-22480	106	23	r2=0.94266	r2=0.94266	PROPN
brj-22480	106	24	(	(	PUNCT
brj-22480	106	25	drying	dry	VERB
brj-22480	106	26	rate	rate	NOUN
brj-22480	106	27	)	)	PUNCT
brj-22480	106	28	(	(	PUNCT
brj-22480	106	29	7	7	X
brj-22480	106	30	)	)	PUNCT
brj-22480	106	31	y2=0.45158x1	y2=0.45158x1	PROPN
brj-22480	106	32	+	+	PROPN
brj-22480	106	33	6.0305x2	6.0305x2	NUM
brj-22480	106	34	-	-	PUNCT
brj-22480	106	35	0.24371x3	0.24371x3	NUM
brj-22480	106	36	-	-	PUNCT
brj-22480	106	37	0.04526x4	0.04526x4	NOUN
brj-22480	106	38	-	-	PUNCT
brj-22480	106	39	0.48296x5	0.48296x5	NUM
brj-22480	106	40	-	-	PUNCT
brj-22480	106	41	0.35659x6	0.35659x6	NUM
brj-22480	106	42	+	+	NOUN
brj-22480	106	43	86.23459	86.23459	NUM
brj-22480	106	44	,	,	PUNCT
brj-22480	106	45	r2=0.04007	r2=0.04007	ADJ
brj-22480	106	46	(	(	PUNCT
brj-22480	106	47	longitudinal	longitudinal	ADJ
brj-22480	106	48	cracking	cracking	NOUN
brj-22480	106	49	degree	degree	NOUN
brj-22480	106	50	)	)	PUNCT
brj-22480	106	51	(	(	PUNCT
brj-22480	106	52	8)	8)	NUM
brj-22480	106	53	the	the	DET
brj-22480	106	54	simulation	simulation	NOUN
brj-22480	106	55	coefficient	coefficient	NOUN
brj-22480	106	56	of	of	ADP
brj-22480	106	57	determination	determination	NOUN
brj-22480	106	58	for	for	ADP
brj-22480	106	59	drying	dry	VERB
brj-22480	106	60	rate	rate	NOUN
brj-22480	106	61	was	be	AUX
brj-22480	106	62	0.94	0.94	NUM
brj-22480	106	63	,	,	PUNCT
brj-22480	106	64	and	and	CCONJ
brj-22480	106	65	the	the	DET
brj-22480	106	66	simulation	simulation	NOUN
brj-22480	106	67	coefficient	coefficient	NOUN
brj-22480	106	68	of	of	ADP
brj-22480	106	69	determination	determination	NOUN
brj-22480	106	70	for	for	ADP
brj-22480	106	71	longitudinal	longitudinal	ADJ
brj-22480	106	72	cracking	cracking	NOUN
brj-22480	106	73	degree	degree	NOUN
brj-22480	106	74	was	be	AUX
brj-22480	106	75	0.04	0.04	NUM
brj-22480	106	76	.	.	PUNCT
brj-22480	107	1	the	the	DET
brj-22480	107	2	experimental	experimental	ADJ
brj-22480	107	3	value	value	NOUN
brj-22480	107	4	of	of	ADP
brj-22480	107	5	drying	dry	VERB
brj-22480	107	6	rate	rate	NOUN
brj-22480	107	7	was	be	AUX
brj-22480	107	8	in	in	ADP
brj-22480	107	9	good	good	ADJ
brj-22480	107	10	agreement	agreement	NOUN
brj-22480	107	11	with	with	ADP
brj-22480	107	12	the	the	DET
brj-22480	107	13	predicted	predict	VERB
brj-22480	107	14	value	value	NOUN
brj-22480	107	15	,	,	PUNCT
brj-22480	107	16	which	which	PRON
brj-22480	107	17	can	can	AUX
brj-22480	107	18	simulate	simulate	VERB
brj-22480	107	19	most	most	ADJ
brj-22480	107	20	situations	situation	NOUN
brj-22480	107	21	.	.	PUNCT
brj-22480	108	1	the	the	DET
brj-22480	108	2	experimental	experimental	ADJ
brj-22480	108	3	value	value	NOUN
brj-22480	108	4	of	of	ADP
brj-22480	108	5	longitudinal	longitudinal	ADJ
brj-22480	108	6	cracking	cracking	NOUN
brj-22480	108	7	degree	degree	NOUN
brj-22480	108	8	was	be	AUX
brj-22480	108	9	in	in	ADP
brj-22480	108	10	poor	poor	ADJ
brj-22480	108	11	agreement	agreement	NOUN
brj-22480	108	12	with	with	ADP
brj-22480	108	13	the	the	DET
brj-22480	108	14	predicted	predict	VERB
brj-22480	108	15	value	value	NOUN
brj-22480	108	16	,	,	PUNCT
brj-22480	108	17	so	so	CCONJ
brj-22480	108	18	it	it	PRON
brj-22480	108	19	is	be	AUX
brj-22480	108	20	impossible	impossible	ADJ
brj-22480	108	21	to	to	PART
brj-22480	108	22	predict	predict	VERB
brj-22480	108	23	the	the	DET
brj-22480	108	24	result	result	NOUN
brj-22480	108	25	.	.	PUNCT
brj-22480	109	1	to	to	PART
brj-22480	109	2	sum	sum	VERB
brj-22480	109	3	up	up	ADP
brj-22480	109	4	,	,	PUNCT
brj-22480	109	5	it	it	PRON
brj-22480	109	6	is	be	AUX
brj-22480	109	7	feasible	feasible	ADJ
brj-22480	109	8	for	for	ADP
brj-22480	109	9	regression	regression	NOUN
brj-22480	109	10	fitting	fitting	ADJ
brj-22480	109	11	analysis	analysis	NOUN
brj-22480	109	12	to	to	PART
brj-22480	109	13	predict	predict	VERB
brj-22480	109	14	only	only	ADV
brj-22480	109	15	specimen	speciman	NOUN
brj-22480	109	16	drying	dry	VERB
brj-22480	109	17	rate	rate	NOUN
brj-22480	109	18	,	,	PUNCT
brj-22480	109	19	but	but	CCONJ
brj-22480	109	20	it	it	PRON
brj-22480	109	21	is	be	AUX
brj-22480	109	22	impossible	impossible	ADJ
brj-22480	109	23	to	to	PART
brj-22480	109	24	predict	predict	VERB
brj-22480	109	25	complex	complex	ADJ
brj-22480	109	26	longitudinal	longitudinal	ADJ
brj-22480	109	27	cracking	cracking	NOUN
brj-22480	109	28	degree	degree	NOUN
brj-22480	109	29	and	and	CCONJ
brj-22480	109	30	other	other	ADJ
brj-22480	109	31	information	information	NOUN
brj-22480	109	32	.	.	PUNCT
brj-22480	110	1	therefore	therefore	ADV
brj-22480	110	2	,	,	PUNCT
brj-22480	110	3	regression	regression	VERB
brj-22480	110	4	fitting	fitting	ADJ
brj-22480	110	5	analysis	analysis	NOUN
brj-22480	110	6	was	be	AUX
brj-22480	110	7	not	not	PART
brj-22480	110	8	able	able	ADJ
brj-22480	110	9	to	to	PART
brj-22480	110	10	predict	predict	VERB
brj-22480	110	11	the	the	DET
brj-22480	110	12	actual	actual	ADJ
brj-22480	110	13	drying	dry	VERB
brj-22480	110	14	process	process	NOUN
brj-22480	110	15	.	.	PUNCT
brj-22480	111	1	model	model	NOUN
brj-22480	111	2	performance	performance	NOUN
brj-22480	111	3	analysis	analysis	NOUN
brj-22480	111	4	when	when	SCONJ
brj-22480	111	5	the	the	DET
brj-22480	111	6	data	datum	NOUN
brj-22480	111	7	was	be	AUX
brj-22480	111	8	input	input	VERB
brj-22480	111	9	into	into	ADP
brj-22480	111	10	the	the	DET
brj-22480	111	11	network	network	NOUN
brj-22480	111	12	built	build	VERB
brj-22480	111	13	for	for	ADP
brj-22480	111	14	learning	learning	NOUN
brj-22480	111	15	,	,	PUNCT
brj-22480	111	16	the	the	DET
brj-22480	111	17	experimental	experimental	ADJ
brj-22480	111	18	value	value	NOUN
brj-22480	111	19	with	with	ADP
brj-22480	111	20	the	the	DET
brj-22480	111	21	predicted	predict	VERB
brj-22480	111	22	value	value	NOUN
brj-22480	111	23	of	of	ADP
brj-22480	111	24	the	the	DET
brj-22480	111	25	neural	neural	ADJ
brj-22480	111	26	network	network	NOUN
brj-22480	111	27	model	model	NOUN
brj-22480	111	28	was	be	AUX
brj-22480	111	29	compared	compare	VERB
brj-22480	111	30	and	and	CCONJ
brj-22480	111	31	a	a	DET
brj-22480	111	32	regression	regression	NOUN
brj-22480	111	33	fitting	fitting	ADJ
brj-22480	111	34	to	to	PART
brj-22480	111	35	obtain	obtain	VERB
brj-22480	111	36	the	the	DET
brj-22480	111	37	bp	bp	PROPN
brj-22480	111	38	neural	neural	ADJ
brj-22480	111	39	network	network	NOUN
brj-22480	111	40	training	training	NOUN
brj-22480	111	41	regression	regression	NOUN
brj-22480	111	42	diagram	diagram	NOUN
brj-22480	111	43	was	be	AUX
brj-22480	111	44	performed	perform	VERB
brj-22480	111	45	as	as	SCONJ
brj-22480	111	46	shown	show	VERB
brj-22480	111	47	in	in	ADP
brj-22480	111	48	figs	fig	NOUN
brj-22480	111	49	.	.	PUNCT
brj-22480	112	1	5	5	NUM
brj-22480	112	2	and	and	CCONJ
brj-22480	112	3	6	6	NUM
brj-22480	112	4	.	.	PUNCT
brj-22480	113	1	the	the	DET
brj-22480	113	2	linear	linear	PROPN
brj-22480	113	3	equations	equation	NOUN
brj-22480	113	4	(	(	PUNCT
brj-22480	113	5	eqs	eqs	X
brj-22480	113	6	.	.	PROPN
brj-22480	113	7	9	9	NUM
brj-22480	113	8	and	and	CCONJ
brj-22480	113	9	10	10	NUM
brj-22480	113	10	)	)	PUNCT
brj-22480	113	11	obtained	obtain	VERB
brj-22480	113	12	are	be	AUX
brj-22480	113	13	given	give	VERB
brj-22480	113	14	below	below	ADP
brj-22480	113	15	:	:	PUNCT
brj-22480	113	16	peer	peer	NOUN
brj-22480	113	17	-	-	PUNCT
brj-22480	113	18	reviewed	review	VERB
brj-22480	113	19	article	article	NOUN
brj-22480	113	20	bioresources.com	bioresources.com	X
brj-22480	113	21	chai	chai	NOUN
brj-22480	113	22	&	&	CCONJ
brj-22480	113	23	li	li	PROPN
brj-22480	113	24	(	(	PUNCT
brj-22480	113	25	2023	2023	NUM
brj-22480	113	26	)	)	PUNCT
brj-22480	113	27	.	.	PUNCT
brj-22480	114	1	“	"	PUNCT
brj-22480	114	2	prediction	prediction	NOUN
brj-22480	114	3	of	of	ADP
brj-22480	114	4	wood	wood	NOUN
brj-22480	114	5	drying	dry	VERB
brj-22480	114	6	by	by	ADP
brj-22480	114	7	ann	ann	PROPN
brj-22480	114	8	,	,	PUNCT
brj-22480	114	9	”	"	PUNCT
brj-22480	114	10	bioresources	bioresource	NOUN
brj-22480	114	11	18(4	18(4	NUM
brj-22480	114	12	)	)	PUNCT
brj-22480	114	13	,	,	PUNCT
brj-22480	114	14	8212	8212	NUM
brj-22480	114	15	-	-	SYM
brj-22480	114	16	8222	8222	NUM
brj-22480	114	17	.	.	PUNCT
brj-22480	114	18	8220	8220	NUM
brj-22480	114	19	y1	y1	NOUN
brj-22480	114	20	=	=	X
brj-22480	115	1	0.9964x	0.9964x	X
brj-22480	115	2	+	+	ADJ
brj-22480	115	3	0.00025	0.00025	NUM
brj-22480	115	4	(	(	PUNCT
brj-22480	115	5	drying	dry	VERB
brj-22480	115	6	rate	rate	NOUN
brj-22480	115	7	)	)	PUNCT
brj-22480	115	8	(	(	PUNCT
brj-22480	115	9	9	9	X
brj-22480	115	10	)	)	PUNCT
brj-22480	115	11	y2	y2	NOUN
brj-22480	115	12	=	=	SYM
brj-22480	115	13	0.9959x	0.9959x	NOUN
brj-22480	116	1	+	+	PUNCT
brj-22480	116	2	0.00061	0.00061	NUM
brj-22480	116	3	(	(	PUNCT
brj-22480	116	4	longitudinal	longitudinal	ADJ
brj-22480	116	5	cracking	cracking	NOUN
brj-22480	116	6	degree	degree	NOUN
brj-22480	116	7	)	)	PUNCT
brj-22480	116	8	(	(	PUNCT
brj-22480	116	9	10	10	NUM
brj-22480	116	10	)	)	PUNCT
brj-22480	116	11	the	the	DET
brj-22480	116	12	simulation	simulation	NOUN
brj-22480	116	13	coefficient	coefficient	NOUN
brj-22480	116	14	of	of	ADP
brj-22480	116	15	determination	determination	NOUN
brj-22480	116	16	for	for	ADP
brj-22480	116	17	drying	dry	VERB
brj-22480	116	18	rate	rate	NOUN
brj-22480	116	19	was	be	AUX
brj-22480	116	20	0.96	0.96	NUM
brj-22480	116	21	;	;	PUNCT
brj-22480	116	22	the	the	DET
brj-22480	116	23	simulation	simulation	NOUN
brj-22480	116	24	coefficient	coefficient	NOUN
brj-22480	116	25	of	of	ADP
brj-22480	116	26	determination	determination	NOUN
brj-22480	116	27	for	for	ADP
brj-22480	116	28	longitudinal	longitudinal	ADJ
brj-22480	116	29	cracking	cracking	NOUN
brj-22480	116	30	degree	degree	NOUN
brj-22480	116	31	was	be	AUX
brj-22480	116	32	0.99	0.99	NUM
brj-22480	116	33	,	,	PUNCT
brj-22480	116	34	indicating	indicate	VERB
brj-22480	116	35	that	that	SCONJ
brj-22480	116	36	the	the	DET
brj-22480	116	37	experimental	experimental	ADJ
brj-22480	116	38	value	value	NOUN
brj-22480	116	39	was	be	AUX
brj-22480	116	40	in	in	ADP
brj-22480	116	41	good	good	ADJ
brj-22480	116	42	agreement	agreement	NOUN
brj-22480	116	43	with	with	ADP
brj-22480	116	44	the	the	DET
brj-22480	116	45	predicted	predict	VERB
brj-22480	116	46	value	value	NOUN
brj-22480	116	47	,	,	PUNCT
brj-22480	116	48	and	and	CCONJ
brj-22480	116	49	the	the	DET
brj-22480	116	50	bp	bp	PROPN
brj-22480	116	51	neural	neural	PROPN
brj-22480	116	52	network	network	NOUN
brj-22480	116	53	had	have	VERB
brj-22480	116	54	good	good	ADJ
brj-22480	116	55	performance	performance	NOUN
brj-22480	116	56	and	and	CCONJ
brj-22480	116	57	will	will	AUX
brj-22480	116	58	be	be	AUX
brj-22480	116	59	able	able	ADJ
brj-22480	116	60	to	to	PART
brj-22480	116	61	simulate	simulate	VERB
brj-22480	116	62	most	most	ADJ
brj-22480	116	63	situations	situation	NOUN
brj-22480	116	64	.	.	PUNCT
brj-22480	117	1	figures	figure	NOUN
brj-22480	117	2	7	7	NUM
brj-22480	117	3	and	and	CCONJ
brj-22480	117	4	8	8	NUM
brj-22480	117	5	show	show	VERB
brj-22480	117	6	the	the	DET
brj-22480	117	7	comparison	comparison	NOUN
brj-22480	117	8	between	between	ADP
brj-22480	117	9	the	the	DET
brj-22480	117	10	predicted	predict	VERB
brj-22480	117	11	value	value	NOUN
brj-22480	117	12	of	of	ADP
brj-22480	117	13	the	the	DET
brj-22480	117	14	neural	neural	ADJ
brj-22480	117	15	network	network	NOUN
brj-22480	117	16	and	and	CCONJ
brj-22480	117	17	the	the	DET
brj-22480	117	18	experimental	experimental	ADJ
brj-22480	117	19	value	value	NOUN
brj-22480	117	20	.	.	PUNCT
brj-22480	118	1	approximately	approximately	ADV
brj-22480	118	2	75	75	NUM
brj-22480	118	3	%	%	NOUN
brj-22480	118	4	of	of	ADP
brj-22480	118	5	the	the	DET
brj-22480	118	6	samples	sample	NOUN
brj-22480	118	7	were	be	AUX
brj-22480	118	8	randomly	randomly	ADV
brj-22480	118	9	selected	select	VERB
brj-22480	118	10	for	for	ADP
brj-22480	118	11	learning	learning	NOUN
brj-22480	118	12	and	and	CCONJ
brj-22480	118	13	the	the	DET
brj-22480	118	14	remaining	remain	VERB
brj-22480	118	15	25	25	NUM
brj-22480	118	16	%	%	NOUN
brj-22480	118	17	of	of	ADP
brj-22480	118	18	the	the	DET
brj-22480	118	19	samples	sample	NOUN
brj-22480	118	20	were	be	AUX
brj-22480	118	21	predicted	predict	VERB
brj-22480	118	22	.	.	PUNCT
brj-22480	119	1	the	the	DET
brj-22480	119	2	blue	blue	ADJ
brj-22480	119	3	line	line	NOUN
brj-22480	119	4	in	in	ADP
brj-22480	119	5	the	the	DET
brj-22480	119	6	figure	figure	NOUN
brj-22480	119	7	is	be	AUX
brj-22480	119	8	the	the	DET
brj-22480	119	9	experimental	experimental	ADJ
brj-22480	119	10	value	value	NOUN
brj-22480	119	11	,	,	PUNCT
brj-22480	119	12	the	the	DET
brj-22480	119	13	red	red	ADJ
brj-22480	119	14	line	line	NOUN
brj-22480	119	15	is	be	AUX
brj-22480	119	16	the	the	DET
brj-22480	119	17	predicted	predict	VERB
brj-22480	119	18	value	value	NOUN
brj-22480	119	19	.	.	PUNCT
brj-22480	120	1	the	the	DET
brj-22480	120	2	absolute	absolute	ADJ
brj-22480	120	3	error	error	NOUN
brj-22480	120	4	range	range	NOUN
brj-22480	120	5	of	of	ADP
brj-22480	120	6	the	the	DET
brj-22480	120	7	simulation	simulation	NOUN
brj-22480	120	8	results	result	NOUN
brj-22480	120	9	and	and	CCONJ
brj-22480	120	10	the	the	DET
brj-22480	120	11	experimental	experimental	ADJ
brj-22480	120	12	values	value	NOUN
brj-22480	120	13	was	be	AUX
brj-22480	120	14	within	within	ADP
brj-22480	120	15	2	2	NUM
brj-22480	120	16	%	%	NOUN
brj-22480	120	17	,	,	PUNCT
brj-22480	120	18	and	and	CCONJ
brj-22480	120	19	the	the	DET
brj-22480	120	20	drying	dry	VERB
brj-22480	120	21	rate	rate	NOUN
brj-22480	120	22	and	and	CCONJ
brj-22480	120	23	longitudinal	longitudinal	ADJ
brj-22480	120	24	cracking	cracking	NOUN
brj-22480	120	25	degree	degree	NOUN
brj-22480	120	26	can	can	AUX
brj-22480	120	27	be	be	AUX
brj-22480	120	28	predicted	predict	VERB
brj-22480	120	29	to	to	ADP
brj-22480	120	30	a	a	DET
brj-22480	120	31	certain	certain	ADJ
brj-22480	120	32	extent	extent	NOUN
brj-22480	120	33	for	for	ADP
brj-22480	120	34	the	the	DET
brj-22480	120	35	conventional	conventional	ADJ
brj-22480	120	36	drying	dry	VERB
brj-22480	120	37	quality	quality	NOUN
brj-22480	120	38	of	of	ADP
brj-22480	120	39	wood	wood	NOUN
brj-22480	120	40	after	after	ADP
brj-22480	120	41	softening	soften	VERB
brj-22480	120	42	and	and	CCONJ
brj-22480	120	43	setting	set	VERB
brj-22480	120	44	treatments	treatment	NOUN
brj-22480	120	45	.	.	PUNCT
brj-22480	121	1	as	as	SCONJ
brj-22480	121	2	indicated	indicate	VERB
brj-22480	121	3	from	from	ADP
brj-22480	121	4	the	the	DET
brj-22480	121	5	two	two	NUM
brj-22480	121	6	sets	set	NOUN
brj-22480	121	7	of	of	ADP
brj-22480	121	8	graphs	graph	NOUN
brj-22480	121	9	,	,	PUNCT
brj-22480	121	10	the	the	DET
brj-22480	121	11	bp	bp	PROPN
brj-22480	121	12	neural	neural	PROPN
brj-22480	121	13	network	network	NOUN
brj-22480	121	14	can	can	AUX
brj-22480	121	15	be	be	AUX
brj-22480	121	16	used	use	VERB
brj-22480	121	17	to	to	PART
brj-22480	121	18	predict	predict	VERB
brj-22480	121	19	the	the	DET
brj-22480	121	20	drying	dry	VERB
brj-22480	121	21	rate	rate	NOUN
brj-22480	121	22	and	and	CCONJ
brj-22480	121	23	the	the	DET
brj-22480	121	24	degree	degree	NOUN
brj-22480	121	25	of	of	ADP
brj-22480	121	26	longitudinal	longitudinal	ADJ
brj-22480	121	27	cracking	cracking	NOUN
brj-22480	121	28	.	.	PUNCT
brj-22480	122	1	fig	fig	NOUN
brj-22480	122	2	.	.	PUNCT
brj-22480	123	1	5	5	X
brj-22480	123	2	.	.	X
brj-22480	123	3	training	training	NOUN
brj-22480	123	4	regression	regression	NOUN
brj-22480	123	5	curve	curve	NOUN
brj-22480	123	6	of	of	ADP
brj-22480	123	7	drying	dry	VERB
brj-22480	123	8	rate	rate	NOUN
brj-22480	123	9	fig	fig	NOUN
brj-22480	123	10	.	.	PUNCT
brj-22480	124	1	6	6	X
brj-22480	124	2	.	.	X
brj-22480	124	3	training	training	NOUN
brj-22480	124	4	regression	regression	NOUN
brj-22480	124	5	curve	curve	NOUN
brj-22480	124	6	of	of	ADP
brj-22480	124	7	longitudinal	longitudinal	ADJ
brj-22480	124	8	crack	crack	NOUN
brj-22480	124	9	degree	degree	NOUN
brj-22480	124	10	fig	fig	NOUN
brj-22480	124	11	.	.	PUNCT
brj-22480	125	1	7	7	X
brj-22480	125	2	.	.	X
brj-22480	125	3	comparison	comparison	NOUN
brj-22480	125	4	of	of	ADP
brj-22480	125	5	predicted	predict	VERB
brj-22480	125	6	and	and	CCONJ
brj-22480	125	7	experimental	experimental	ADJ
brj-22480	125	8	values	value	NOUN
brj-22480	125	9	of	of	ADP
brj-22480	125	10	drying	dry	VERB
brj-22480	125	11	rate	rate	NOUN
brj-22480	125	12	fig	fig	NOUN
brj-22480	125	13	.	.	PUNCT
brj-22480	126	1	8	8	NUM
brj-22480	126	2	.	.	X
brj-22480	126	3	comparison	comparison	NOUN
brj-22480	126	4	of	of	ADP
brj-22480	126	5	predicted	predict	VERB
brj-22480	126	6	and	and	CCONJ
brj-22480	126	7	experimental	experimental	ADJ
brj-22480	126	8	values	value	NOUN
brj-22480	126	9	of	of	ADP
brj-22480	126	10	longitudinal	longitudinal	ADJ
brj-22480	126	11	cracking	cracking	NOUN
brj-22480	126	12	degree	degree	NOUN
brj-22480	126	13	peer	peer	NOUN
brj-22480	126	14	-	-	PUNCT
brj-22480	126	15	reviewed	review	VERB
brj-22480	126	16	article	article	NOUN
brj-22480	126	17	bioresources.com	bioresources.com	X
brj-22480	126	18	chai	chai	NOUN
brj-22480	126	19	&	&	CCONJ
brj-22480	126	20	li	li	PROPN
brj-22480	126	21	(	(	PUNCT
brj-22480	126	22	2023	2023	NUM
brj-22480	126	23	)	)	PUNCT
brj-22480	126	24	.	.	PUNCT
brj-22480	127	1	“	"	PUNCT
brj-22480	127	2	prediction	prediction	NOUN
brj-22480	127	3	of	of	ADP
brj-22480	127	4	wood	wood	NOUN
brj-22480	127	5	drying	dry	VERB
brj-22480	127	6	by	by	ADP
brj-22480	127	7	ann	ann	PROPN
brj-22480	127	8	,	,	PUNCT
brj-22480	127	9	”	"	PUNCT
brj-22480	127	10	bioresources	bioresource	NOUN
brj-22480	127	11	18(4	18(4	NUM
brj-22480	127	12	)	)	PUNCT
brj-22480	127	13	,	,	PUNCT
brj-22480	127	14	8212	8212	NUM
brj-22480	127	15	-	-	SYM
brj-22480	127	16	8222	8222	NUM
brj-22480	127	17	.	.	PUNCT
brj-22480	128	1	8221	8221	NUM
brj-22480	128	2	conclusions	conclusion	NOUN
brj-22480	128	3	in	in	ADP
brj-22480	128	4	this	this	DET
brj-22480	128	5	paper	paper	NOUN
brj-22480	128	6	,	,	PUNCT
brj-22480	128	7	the	the	DET
brj-22480	128	8	bp	bp	PROPN
brj-22480	128	9	neural	neural	PROPN
brj-22480	128	10	network	network	NOUN
brj-22480	128	11	was	be	AUX
brj-22480	128	12	used	use	VERB
brj-22480	128	13	to	to	PART
brj-22480	128	14	simulate	simulate	VERB
brj-22480	128	15	and	and	CCONJ
brj-22480	128	16	predict	predict	VERB
brj-22480	128	17	the	the	DET
brj-22480	128	18	drying	dry	VERB
brj-22480	128	19	rate	rate	NOUN
brj-22480	128	20	and	and	CCONJ
brj-22480	128	21	longitudinal	longitudinal	ADJ
brj-22480	128	22	cracking	cracking	NOUN
brj-22480	128	23	degree	degree	NOUN
brj-22480	128	24	of	of	ADP
brj-22480	128	25	wood	wood	NOUN
brj-22480	128	26	.	.	PUNCT
brj-22480	129	1	the	the	DET
brj-22480	129	2	steaming	steaming	NOUN
brj-22480	129	3	treatment	treatment	NOUN
brj-22480	129	4	time	time	NOUN
brj-22480	129	5	,	,	PUNCT
brj-22480	129	6	steaming	steam	VERB
brj-22480	129	7	temperature	temperature	NOUN
brj-22480	129	8	,	,	PUNCT
brj-22480	129	9	set	set	NOUN
brj-22480	129	10	time	time	NOUN
brj-22480	129	11	,	,	PUNCT
brj-22480	129	12	set	set	VERB
brj-22480	129	13	temperature	temperature	NOUN
brj-22480	129	14	,	,	PUNCT
brj-22480	129	15	initial	initial	ADJ
brj-22480	129	16	moisture	moisture	NOUN
brj-22480	129	17	content	content	NOUN
brj-22480	129	18	of	of	ADP
brj-22480	129	19	wood	wood	NOUN
brj-22480	129	20	,	,	PUNCT
brj-22480	129	21	and	and	CCONJ
brj-22480	129	22	position	position	NOUN
brj-22480	129	23	of	of	ADP
brj-22480	129	24	wood	wood	NOUN
brj-22480	129	25	core	core	NOUN
brj-22480	129	26	and	and	CCONJ
brj-22480	129	27	sapwood	sapwood	NOUN
brj-22480	129	28	were	be	AUX
brj-22480	129	29	the	the	DET
brj-22480	129	30	input	input	NOUN
brj-22480	129	31	quantities	quantity	NOUN
brj-22480	129	32	of	of	ADP
brj-22480	129	33	the	the	DET
brj-22480	129	34	model	model	NOUN
brj-22480	129	35	;	;	PUNCT
brj-22480	129	36	the	the	DET
brj-22480	129	37	drying	dry	VERB
brj-22480	129	38	rate	rate	NOUN
brj-22480	129	39	and	and	CCONJ
brj-22480	129	40	the	the	DET
brj-22480	129	41	longitudinal	longitudinal	ADJ
brj-22480	129	42	cracking	cracking	NOUN
brj-22480	129	43	degree	degree	NOUN
brj-22480	129	44	were	be	AUX
brj-22480	129	45	the	the	DET
brj-22480	129	46	output	output	NOUN
brj-22480	129	47	.	.	PUNCT
brj-22480	130	1	there	there	PRON
brj-22480	130	2	were	be	VERB
brj-22480	130	3	60	60	NUM
brj-22480	130	4	test	test	NOUN
brj-22480	130	5	data	datum	NOUN
brj-22480	130	6	in	in	ADP
brj-22480	130	7	the	the	DET
brj-22480	130	8	training	training	NOUN
brj-22480	130	9	group	group	NOUN
brj-22480	130	10	,	,	PUNCT
brj-22480	130	11	accounting	account	VERB
brj-22480	130	12	for	for	ADP
brj-22480	130	13	75	75	NUM
brj-22480	130	14	%	%	NOUN
brj-22480	130	15	of	of	ADP
brj-22480	130	16	the	the	DET
brj-22480	130	17	total	total	ADJ
brj-22480	130	18	data	datum	NOUN
brj-22480	130	19	,	,	PUNCT
brj-22480	130	20	and	and	CCONJ
brj-22480	130	21	20	20	NUM
brj-22480	130	22	data	datum	NOUN
brj-22480	130	23	in	in	ADP
brj-22480	130	24	the	the	DET
brj-22480	130	25	test	test	NOUN
brj-22480	130	26	group	group	NOUN
brj-22480	130	27	,	,	PUNCT
brj-22480	130	28	accounting	account	VERB
brj-22480	130	29	for	for	ADP
brj-22480	130	30	25	25	NUM
brj-22480	130	31	%	%	NOUN
brj-22480	130	32	of	of	ADP
brj-22480	130	33	the	the	DET
brj-22480	130	34	total	total	ADJ
brj-22480	130	35	data	datum	NOUN
brj-22480	130	36	.	.	PUNCT
brj-22480	131	1	1	1	X
brj-22480	131	2	.	.	PUNCT
brj-22480	131	3	the	the	DET
brj-22480	131	4	results	result	NOUN
brj-22480	131	5	show	show	VERB
brj-22480	131	6	that	that	SCONJ
brj-22480	131	7	when	when	SCONJ
brj-22480	131	8	the	the	DET
brj-22480	131	9	number	number	NOUN
brj-22480	131	10	of	of	ADP
brj-22480	131	11	neurons	neuron	NOUN
brj-22480	131	12	in	in	ADP
brj-22480	131	13	the	the	DET
brj-22480	131	14	hidden	hide	VERB
brj-22480	131	15	layer	layer	NOUN
brj-22480	131	16	was	be	AUX
brj-22480	131	17	9	9	NUM
brj-22480	131	18	,	,	PUNCT
brj-22480	131	19	the	the	DET
brj-22480	131	20	neural	neural	ADJ
brj-22480	131	21	network	network	NOUN
brj-22480	131	22	training	training	NOUN
brj-22480	131	23	error	error	NOUN
brj-22480	131	24	was	be	AUX
brj-22480	131	25	the	the	DET
brj-22480	131	26	smallest	small	ADJ
brj-22480	131	27	,	,	PUNCT
brj-22480	131	28	which	which	PRON
brj-22480	131	29	was	be	AUX
brj-22480	131	30	0.00605	0.00605	NUM
brj-22480	131	31	;	;	PUNCT
brj-22480	131	32	at	at	ADP
brj-22480	131	33	this	this	DET
brj-22480	131	34	time	time	NOUN
brj-22480	131	35	,	,	PUNCT
brj-22480	131	36	the	the	DET
brj-22480	131	37	determination	determination	NOUN
brj-22480	131	38	coefficient	coefficient	NOUN
brj-22480	131	39	r2	r2	PROPN
brj-22480	131	40	of	of	ADP
brj-22480	131	41	the	the	DET
brj-22480	131	42	neural	neural	ADJ
brj-22480	131	43	network	network	NOUN
brj-22480	131	44	training	training	NOUN
brj-22480	131	45	set	set	NOUN
brj-22480	131	46	was	be	AUX
brj-22480	131	47	0.96	0.96	NUM
brj-22480	131	48	(	(	PUNCT
brj-22480	131	49	drying	dry	VERB
brj-22480	131	50	rate	rate	NOUN
brj-22480	131	51	prediction	prediction	NOUN
brj-22480	131	52	)	)	PUNCT
brj-22480	131	53	and	and	CCONJ
brj-22480	131	54	0.99	0.99	NUM
brj-22480	131	55	(	(	PUNCT
brj-22480	131	56	longitudinal	longitudinal	ADJ
brj-22480	131	57	crack	crack	NOUN
brj-22480	131	58	prediction	prediction	NOUN
brj-22480	131	59	)	)	PUNCT
brj-22480	131	60	.	.	PUNCT
brj-22480	132	1	2	2	X
brj-22480	132	2	.	.	X
brj-22480	132	3	the	the	DET
brj-22480	132	4	experimental	experimental	ADJ
brj-22480	132	5	test	test	NOUN
brj-22480	132	6	value	value	NOUN
brj-22480	132	7	was	be	AUX
brj-22480	132	8	in	in	ADP
brj-22480	132	9	good	good	ADJ
brj-22480	132	10	agreement	agreement	NOUN
brj-22480	132	11	with	with	ADP
brj-22480	132	12	the	the	DET
brj-22480	132	13	predicted	predict	VERB
brj-22480	132	14	value	value	NOUN
brj-22480	132	15	,	,	PUNCT
brj-22480	132	16	which	which	PRON
brj-22480	132	17	demonstrated	demonstrate	VERB
brj-22480	132	18	that	that	SCONJ
brj-22480	132	19	the	the	DET
brj-22480	132	20	constructed	construct	VERB
brj-22480	132	21	bp	bp	PROPN
brj-22480	132	22	neural	neural	PROPN
brj-22480	132	23	network	network	NOUN
brj-22480	132	24	achieved	achieve	VERB
brj-22480	132	25	good	good	ADJ
brj-22480	132	26	stability	stability	NOUN
brj-22480	132	27	.	.	PUNCT
brj-22480	133	1	the	the	DET
brj-22480	133	2	absolute	absolute	ADJ
brj-22480	133	3	error	error	NOUN
brj-22480	133	4	range	range	NOUN
brj-22480	133	5	between	between	ADP
brj-22480	133	6	the	the	DET
brj-22480	133	7	simulation	simulation	NOUN
brj-22480	133	8	results	result	NOUN
brj-22480	133	9	and	and	CCONJ
brj-22480	133	10	the	the	DET
brj-22480	133	11	experimental	experimental	ADJ
brj-22480	133	12	values	value	NOUN
brj-22480	133	13	was	be	AUX
brj-22480	133	14	within	within	ADP
brj-22480	133	15	2	2	NUM
brj-22480	133	16	%	%	NOUN
brj-22480	133	17	.	.	PUNCT
brj-22480	134	1	3	3	X
brj-22480	134	2	.	.	X
brj-22480	134	3	in	in	ADP
brj-22480	134	4	general	general	ADJ
brj-22480	134	5	,	,	PUNCT
brj-22480	134	6	the	the	DET
brj-22480	134	7	neural	neural	ADJ
brj-22480	134	8	network	network	NOUN
brj-22480	134	9	model	model	NOUN
brj-22480	134	10	has	have	VERB
brj-22480	134	11	a	a	DET
brj-22480	134	12	good	good	ADJ
brj-22480	134	13	predictive	predictive	ADJ
brj-22480	134	14	ability	ability	NOUN
brj-22480	134	15	for	for	ADP
brj-22480	134	16	drying	dry	VERB
brj-22480	134	17	rate	rate	NOUN
brj-22480	134	18	and	and	CCONJ
brj-22480	134	19	longitudinal	longitudinal	ADJ
brj-22480	134	20	cracking	cracking	NOUN
brj-22480	134	21	degree	degree	NOUN
brj-22480	134	22	.	.	PUNCT
brj-22480	135	1	therefore	therefore	ADV
brj-22480	135	2	,	,	PUNCT
brj-22480	135	3	it	it	PRON
brj-22480	135	4	can	can	AUX
brj-22480	135	5	be	be	AUX
brj-22480	135	6	considered	consider	VERB
brj-22480	135	7	that	that	SCONJ
brj-22480	135	8	although	although	SCONJ
brj-22480	135	9	the	the	DET
brj-22480	135	10	properties	property	NOUN
brj-22480	135	11	of	of	ADP
brj-22480	135	12	wood	wood	NOUN
brj-22480	135	13	vary	vary	VERB
brj-22480	135	14	widely	widely	ADV
brj-22480	135	15	and	and	CCONJ
brj-22480	135	16	the	the	DET
brj-22480	135	17	complex	complex	ADJ
brj-22480	135	18	relationship	relationship	NOUN
brj-22480	135	19	between	between	ADP
brj-22480	135	20	them	they	PRON
brj-22480	135	21	has	have	AUX
brj-22480	135	22	not	not	PART
brj-22480	135	23	been	be	AUX
brj-22480	135	24	fully	fully	ADV
brj-22480	135	25	elucidated	elucidate	VERB
brj-22480	135	26	,	,	PUNCT
brj-22480	135	27	the	the	DET
brj-22480	135	28	network	network	NOUN
brj-22480	135	29	model	model	NOUN
brj-22480	135	30	provides	provide	VERB
brj-22480	135	31	a	a	DET
brj-22480	135	32	reliable	reliable	ADJ
brj-22480	135	33	model	model	NOUN
brj-22480	135	34	and	and	CCONJ
brj-22480	135	35	good	good	ADJ
brj-22480	135	36	prediction	prediction	NOUN
brj-22480	135	37	ability	ability	NOUN
brj-22480	135	38	,	,	PUNCT
brj-22480	135	39	which	which	PRON
brj-22480	135	40	is	be	AUX
brj-22480	135	41	of	of	ADP
brj-22480	135	42	great	great	ADJ
brj-22480	135	43	significance	significance	NOUN
brj-22480	135	44	to	to	ADP
brj-22480	135	45	the	the	DET
brj-22480	135	46	optimization	optimization	NOUN
brj-22480	135	47	of	of	ADP
brj-22480	135	48	the	the	DET
brj-22480	135	49	drying	dry	VERB
brj-22480	135	50	process	process	NOUN
brj-22480	135	51	of	of	ADP
brj-22480	135	52	pine	pine	ADJ
brj-22480	135	53	wood	wood	NOUN
brj-22480	135	54	squares	square	NOUN
brj-22480	135	55	.	.	PUNCT
brj-22480	136	1	acknowledgments	acknowledgment	NOUN
brj-22480	136	2	this	this	DET
brj-22480	136	3	work	work	NOUN
brj-22480	136	4	was	be	AUX
brj-22480	136	5	financially	financially	ADV
brj-22480	136	6	supported	support	VERB
brj-22480	136	7	by	by	ADP
brj-22480	136	8	the	the	DET
brj-22480	136	9	science	science	NOUN
brj-22480	136	10	and	and	CCONJ
brj-22480	136	11	technology	technology	NOUN
brj-22480	136	12	project	project	NOUN
brj-22480	136	13	of	of	ADP
brj-22480	136	14	henan	henan	PROPN
brj-22480	136	15	province	province	PROPN
brj-22480	136	16	(	(	PUNCT
brj-22480	136	17	222102110205	222102110205	NUM
brj-22480	136	18	)	)	PUNCT
brj-22480	136	19	,	,	PUNCT
brj-22480	136	20	and	and	CCONJ
brj-22480	136	21	the	the	DET
brj-22480	136	22	key	key	ADJ
brj-22480	136	23	research	research	NOUN
brj-22480	136	24	project	project	NOUN
brj-22480	136	25	of	of	ADP
brj-22480	136	26	higher	high	ADJ
brj-22480	136	27	education	education	NOUN
brj-22480	136	28	institutions	institution	NOUN
brj-22480	136	29	in	in	ADP
brj-22480	136	30	henan	henan	PROPN
brj-22480	136	31	province	province	PROPN
brj-22480	136	32	(	(	PUNCT
brj-22480	136	33	23a520034	23a520034	NUM
brj-22480	136	34	)	)	PUNCT
brj-22480	136	35	.	.	PUNCT
brj-22480	137	1	references	reference	NOUN
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brj-22480	137	14	.	.	PUNCT
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brj-22480	138	10	,	,	PUNCT
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brj-22480	138	17	)	)	PUNCT
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brj-22480	138	29	l.	l.	PROPN
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brj-22480	138	31	and	and	CCONJ
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brj-22480	138	33	,	,	PUNCT
brj-22480	138	34	s.	s.	PROPN
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brj-22480	138	37	2005	2005	NUM
brj-22480	138	38	)	)	PUNCT
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brj-22480	139	8	artificial	artificial	ADJ
brj-22480	139	9	neural	neural	ADJ
brj-22480	139	10	networks	network	NOUN
brj-22480	139	11	,	,	PUNCT
brj-22480	139	12	”	"	PUNCT
brj-22480	139	13	wood	wood	NOUN
brj-22480	139	14	science	science	NOUN
brj-22480	139	15	and	and	CCONJ
brj-22480	139	16	technology	technology	NOUN
brj-22480	139	17	40	40	NUM
brj-22480	139	18	,	,	PUNCT
brj-22480	139	19	563574	563574	NUM
brj-22480	139	20	.	.	PUNCT
brj-22480	140	1	doi	doi	NOUN
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brj-22480	140	4	/	/	SYM
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brj-22480	140	6	-	-	PUNCT
brj-22480	140	7	006	006	NUM
brj-22480	140	8	-	-	PUNCT
brj-22480	140	9	0096	0096	NUM
brj-22480	140	10	-	-	SYM
brj-22480	140	11	3	3	NUM
brj-22480	140	12	avramidis	avramidi	NOUN
brj-22480	140	13	,	,	PUNCT
brj-22480	140	14	s.	s.	PROPN
brj-22480	140	15	,	,	PUNCT
brj-22480	140	16	and	and	CCONJ
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brj-22480	140	18	,	,	PUNCT
brj-22480	140	19	h.	h.	PROPN
brj-22480	140	20	(	(	PUNCT
brj-22480	140	21	2007	2007	NUM
brj-22480	140	22	)	)	PUNCT
brj-22480	140	23	.	.	PUNCT
brj-22480	141	1	“	"	PUNCT
brj-22480	141	2	artificial	artificial	ADJ
brj-22480	141	3	neural	neural	ADJ
brj-22480	141	4	network	network	NOUN
brj-22480	141	5	and	and	CCONJ
brj-22480	141	6	mathematical	mathematical	ADJ
brj-22480	141	7	modeling	modeling	NOUN
brj-22480	141	8	comparative	comparative	ADJ
brj-22480	141	9	analysis	analysis	NOUN
brj-22480	141	10	of	of	ADP
brj-22480	141	11	nonisothermal	nonisothermal	ADJ
brj-22480	141	12	diffusion	diffusion	NOUN
brj-22480	141	13	of	of	ADP
brj-22480	141	14	moisture	moisture	NOUN
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brj-22480	141	17	,	,	PUNCT
brj-22480	141	18	”	"	PUNCT
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brj-22480	141	24	65(2	65(2	NOUN
brj-22480	141	25	)	)	PUNCT
brj-22480	141	26	,	,	PUNCT
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brj-22480	141	28	-	-	SYM
brj-22480	141	29	93	93	NUM
brj-22480	141	30	.	.	PUNCT
brj-22480	142	1	doi	doi	NOUN
brj-22480	142	2	:	:	PUNCT
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brj-22480	142	4	/	/	SYM
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brj-22480	142	6	-	-	PUNCT
brj-22480	142	7	006	006	NUM
brj-22480	142	8	-	-	PUNCT
brj-22480	142	9	0113	0113	NUM
brj-22480	142	10	-	-	SYM
brj-22480	142	11	0	0	NUM
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brj-22480	142	14	y.	y.	PROPN
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brj-22480	142	19	,	,	PUNCT
brj-22480	142	20	g.	g.	PROPN
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brj-22480	142	22	(	(	PUNCT
brj-22480	142	23	2005	2005	NUM
brj-22480	142	24	)	)	PUNCT
brj-22480	142	25	.	.	PUNCT
brj-22480	143	1	“	"	PUNCT
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brj-22480	143	3	on	on	ADP
brj-22480	143	4	improving	improve	VERB
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brj-22480	143	12	oven	oven	NOUN
brj-22480	143	13	drying	dry	VERB
brj-22480	143	14	method	method	NOUN
brj-22480	143	15	,	,	PUNCT
brj-22480	143	16	”	"	PUNCT
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brj-22480	143	22	(	(	PUNCT
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brj-22480	143	24	)	)	PUNCT
brj-22480	143	25	,	,	PUNCT
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brj-22480	143	27	-	-	SYM
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brj-22480	143	29	.	.	PUNCT
brj-22480	144	1	doi	doi	NOUN
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brj-22480	144	4	:	:	PUNCT
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brj-22480	144	6	:	:	PUNCT
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brj-22480	144	8	-	-	PUNCT
brj-22480	144	9	s1	s1	NOUN
brj-22480	144	10	-	-	PUNCT
brj-22480	144	11	014	014	NUM
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brj-22480	144	13	,	,	PUNCT
brj-22480	144	14	l.	l.	PROPN
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brj-22480	145	16	,	,	PUNCT
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brj-22480	145	20	26(12	26(12	NOUN
brj-22480	145	21	)	)	PUNCT
brj-22480	145	22	,	,	PUNCT
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brj-22480	145	24	-	-	SYM
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brj-22480	145	26	.	.	PUNCT
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brj-22480	146	10	2023	2023	NUM
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brj-22480	147	1	“	"	PUNCT
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brj-22480	147	8	,	,	PUNCT
brj-22480	147	9	”	"	PUNCT
brj-22480	147	10	bioresources	bioresource	NOUN
brj-22480	147	11	18(4	18(4	NUM
brj-22480	147	12	)	)	PUNCT
brj-22480	147	13	,	,	PUNCT
brj-22480	147	14	8212	8212	NUM
brj-22480	147	15	-	-	SYM
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brj-22480	147	17	.	.	PUNCT
brj-22480	147	18	8222	8222	NUM
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brj-22480	147	20	:	:	PUNCT
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brj-22480	147	28	,	,	PUNCT
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brj-22480	147	31	,	,	PUNCT
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brj-22480	147	33	cai	cai	X
brj-22480	147	34	,	,	PUNCT
brj-22480	147	35	y.	y.	PROPN
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brj-22480	147	38	2018	2018	NUM
brj-22480	147	39	)	)	PUNCT
brj-22480	147	40	.	.	PUNCT
brj-22480	148	1	“	"	PUNCT
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brj-22480	148	9	moisture	moisture	NOUN
brj-22480	148	10	content	content	NOUN
brj-22480	148	11	in	in	ADP
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brj-22480	148	13	frequency	frequency	NOUN
brj-22480	148	14	vacuum	vacuum	NOUN
brj-22480	148	15	drying	dry	VERB
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brj-22480	148	21	)	)	PUNCT
brj-22480	148	22	,	,	PUNCT
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brj-22480	148	25	.	.	PUNCT
brj-22480	149	1	doi	doi	NOUN
brj-22480	149	2	:	:	PUNCT
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brj-22480	149	7	,	,	PUNCT
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brj-22480	149	14	and	and	CCONJ
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brj-22480	150	1	“	"	PUNCT
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brj-22480	150	4	internal	internal	ADJ
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brj-22480	150	25	,	,	PUNCT
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brj-22480	150	27	-	-	SYM
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brj-22480	150	29	.	.	PUNCT
brj-22480	151	1	doi	doi	NOUN
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brj-22480	151	6	-	-	PUNCT
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brj-22480	151	8	-	-	PUNCT
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brj-22480	151	10	-	-	PUNCT
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brj-22480	151	17	,	,	PUNCT
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brj-22480	152	13	convolutional	convolutional	ADJ
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brj-22480	152	23	181(1	181(1	NUM
brj-22480	152	24	)	)	PUNCT
brj-22480	152	25	,	,	PUNCT
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brj-22480	153	1	doi	doi	NOUN
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brj-22480	153	10	,	,	PUNCT
brj-22480	153	11	avramidis	avramidis	PROPN
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brj-22480	153	13	s.	s.	PROPN
brj-22480	153	14	,	,	PUNCT
brj-22480	153	15	and	and	CCONJ
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brj-22480	153	17	,	,	PUNCT
brj-22480	153	18	j.	j.	PROPN
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brj-22480	153	20	(	(	PUNCT
brj-22480	153	21	2017	2017	NUM
brj-22480	153	22	)	)	PUNCT
brj-22480	153	23	.	.	PUNCT
brj-22480	154	1	“	"	PUNCT
brj-22480	154	2	artificial	artificial	ADJ
brj-22480	154	3	neural	neural	ADJ
brj-22480	154	4	network	network	NOUN
brj-22480	154	5	modeling	modeling	NOUN
brj-22480	154	6	for	for	ADP
brj-22480	154	7	predicting	predict	VERB
brj-22480	154	8	elastic	elastic	ADJ
brj-22480	154	9	strain	strain	NOUN
brj-22480	154	10	of	of	ADP
brj-22480	154	11	white	white	PROPN
brj-22480	154	12	birch	birch	PROPN
brj-22480	154	13	disks	disk	NOUN
brj-22480	154	14	during	during	ADP
brj-22480	154	15	drying	dry	VERB
brj-22480	154	16	,	,	PUNCT
brj-22480	154	17	”	"	PUNCT
brj-22480	154	18	european	european	ADJ
brj-22480	154	19	journal	journal	PROPN
brj-22480	154	20	of	of	ADP
brj-22480	154	21	wood	wood	NOUN
brj-22480	154	22	and	and	CCONJ
brj-22480	154	23	wood	wood	NOUN
brj-22480	154	24	products	product	NOUN
brj-22480	154	25	75(6	75(6	NUM
brj-22480	154	26	)	)	PUNCT
brj-22480	154	27	,	,	PUNCT
brj-22480	154	28	949	949	NUM
brj-22480	154	29	-	-	SYM
brj-22480	154	30	955	955	NUM
brj-22480	154	31	.	.	PUNCT
brj-22480	155	1	doi	doi	NOUN
brj-22480	155	2	:	:	PUNCT
brj-22480	155	3	10.1007	10.1007	NUM
brj-22480	155	4	/	/	SYM
brj-22480	155	5	s00107	s00107	NOUN
brj-22480	155	6	-	-	PUNCT
brj-22480	155	7	017	017	NUM
brj-22480	155	8	-	-	PUNCT
brj-22480	155	9	1183	1183	NUM
brj-22480	155	10	-	-	PUNCT
brj-22480	155	11	x	x	SYM
brj-22480	155	12	fu	fu	PROPN
brj-22480	155	13	,	,	PUNCT
brj-22480	155	14	z.	z.	PROPN
brj-22480	155	15	y.	y.	PROPN
brj-22480	155	16	,	,	PUNCT
brj-22480	155	17	zhou	zhou	PROPN
brj-22480	155	18	,	,	PUNCT
brj-22480	155	19	f.	f.	PROPN
brj-22480	155	20	,	,	PUNCT
brj-22480	155	21	and	and	CCONJ
brj-22480	155	22	gao	gao	PROPN
brj-22480	155	23	,	,	PUNCT
brj-22480	155	24	x.	x.	NOUN
brj-22480	155	25	(	(	PUNCT
brj-22480	155	26	2019	2019	NUM
brj-22480	155	27	)	)	PUNCT
brj-22480	155	28	.	.	PUNCT
brj-22480	156	1	“	"	PUNCT
brj-22480	156	2	assessment	assessment	NOUN
brj-22480	156	3	of	of	ADP
brj-22480	156	4	mechanical	mechanical	ADJ
brj-22480	156	5	properties	property	NOUN
brj-22480	156	6	based	base	VERB
brj-22480	156	7	on	on	ADP
brj-22480	156	8	the	the	DET
brj-22480	156	9	changes	change	NOUN
brj-22480	156	10	of	of	ADP
brj-22480	156	11	chromatic	chromatic	ADJ
brj-22480	156	12	values	value	NOUN
brj-22480	156	13	in	in	ADP
brj-22480	156	14	heat	heat	NOUN
brj-22480	156	15	treatment	treatment	NOUN
brj-22480	156	16	wood	wood	NOUN
brj-22480	156	17	,	,	PUNCT
brj-22480	156	18	”	"	PUNCT
brj-22480	156	19	measurement	measurement	NOUN
brj-22480	156	20	152(1	152(1	NUM
brj-22480	156	21	)	)	PUNCT
brj-22480	156	22	,	,	PUNCT
brj-22480	156	23	article	article	NOUN
brj-22480	156	24	107215	107215	NUM
brj-22480	156	25	.	.	PUNCT
brj-22480	157	1	doi	doi	NOUN
brj-22480	157	2	:	:	PUNCT
brj-22480	157	3	10.1016	10.1016	NUM
brj-22480	157	4	/	/	SYM
brj-22480	157	5	j.measurement.2019.107215	j.measurement.2019.107215	PROPN
brj-22480	157	6	fu	fu	PROPN
brj-22480	157	7	,	,	PUNCT
brj-22480	157	8	z.	z.	PROPN
brj-22480	157	9	y.	y.	PROPN
brj-22480	157	10	,	,	PUNCT
brj-22480	157	11	cai	cai	PROPN
brj-22480	157	12	,	,	PUNCT
brj-22480	157	13	y.	y.	PROPN
brj-22480	157	14	c.	c.	PROPN
brj-22480	157	15	,	,	PUNCT
brj-22480	157	16	gao	gao	PROPN
brj-22480	157	17	,	,	PUNCT
brj-22480	157	18	x.	x.	NOUN
brj-22480	157	19	,	,	PUNCT
brj-22480	157	20	and	and	CCONJ
brj-22480	157	21	zhou	zhou	PROPN
brj-22480	157	22	,	,	PUNCT
brj-22480	157	23	f.	f.	PROPN
brj-22480	157	24	(	(	PUNCT
brj-22480	157	25	2020	2020	NUM
brj-22480	157	26	)	)	PUNCT
brj-22480	157	27	.	.	PUNCT
brj-22480	158	1	“	"	PUNCT
brj-22480	158	2	simulation	simulation	NOUN
brj-22480	158	3	and	and	CCONJ
brj-22480	158	4	prediction	prediction	NOUN
brj-22480	158	5	of	of	ADP
brj-22480	158	6	wood	wood	NOUN
brj-22480	158	7	drying	dry	VERB
brj-22480	158	8	strain	strain	NOUN
brj-22480	158	9	based	base	VERB
brj-22480	158	10	on	on	ADP
brj-22480	158	11	artificial	artificial	ADJ
brj-22480	158	12	neural	neural	ADJ
brj-22480	158	13	network	network	NOUN
brj-22480	158	14	model	model	NOUN
brj-22480	158	15	,	,	PUNCT
brj-22480	158	16	”	"	PUNCT
brj-22480	158	17	forestry	forestry	NOUN
brj-22480	158	18	science	science	NOUN
brj-22480	158	19	56(06	56(06	NUM
brj-22480	158	20	)	)	PUNCT
brj-22480	158	21	,	,	PUNCT
brj-22480	158	22	7682	7682	NUM
brj-22480	158	23	.	.	PUNCT
brj-22480	159	1	gao	gao	PROPN
brj-22480	159	2	,	,	PUNCT
brj-22480	159	3	m.	m.	NOUN
brj-22480	159	4	,	,	PUNCT
brj-22480	159	5	wang	wang	PROPN
brj-22480	159	6	,	,	PUNCT
brj-22480	159	7	f.	f.	PROPN
brj-22480	159	8	,	,	PUNCT
brj-22480	159	9	liu	liu	PROPN
brj-22480	159	10	,	,	PUNCT
brj-22480	159	11	j.	j.	PROPN
brj-22480	159	12	,	,	PUNCT
brj-22480	159	13	song	song	NOUN
brj-22480	159	14	,	,	PUNCT
brj-22480	159	15	p.	p.	PROPN
brj-22480	159	16	,	,	PUNCT
brj-22480	159	17	chen	chen	PROPN
brj-22480	159	18	,	,	PUNCT
brj-22480	159	19	j.	j.	PROPN
brj-22480	159	20	,	,	PUNCT
brj-22480	159	21	yang	yang	PROPN
brj-22480	159	22	,	,	PUNCT
brj-22480	159	23	h.	h.	PROPN
brj-22480	159	24	,	,	PUNCT
brj-22480	159	25	mu	mu	PROPN
brj-22480	159	26	,	,	PUNCT
brj-22480	159	27	h.	h.	PROPN
brj-22480	159	28	,	,	PUNCT
brj-22480	159	29	qi	qi	PROPN
brj-22480	159	30	,	,	PUNCT
brj-22480	159	31	d.	d.	PROPN
brj-22480	159	32	,	,	PUNCT
brj-22480	159	33	chen	chen	PROPN
brj-22480	159	34	,	,	PUNCT
brj-22480	159	35	m.	m.	NOUN
brj-22480	159	36	,	,	PUNCT
brj-22480	159	37	wang	wang	PROPN
brj-22480	159	38	,	,	PUNCT
brj-22480	159	39	y.	y.	PROPN
brj-22480	159	40	,	,	PUNCT
brj-22480	159	41	and	and	CCONJ
brj-22480	159	42	yue	yue	PROPN
brj-22480	159	43	,	,	PUNCT
brj-22480	159	44	h.	h.	PROPN
brj-22480	159	45	(	(	PUNCT
brj-22480	159	46	2022	2022	NUM
brj-22480	159	47	)	)	PUNCT
brj-22480	159	48	.	.	PUNCT
brj-22480	160	1	“	"	PUNCT
brj-22480	160	2	estimation	estimation	NOUN
brj-22480	160	3	of	of	ADP
brj-22480	160	4	the	the	DET
brj-22480	160	5	convolutional	convolutional	ADJ
brj-22480	160	6	neural	neural	ADJ
brj-22480	160	7	network	network	NOUN
brj-22480	160	8	with	with	ADP
brj-22480	160	9	attention	attention	NOUN
brj-22480	160	10	mechanism	mechanism	NOUN
brj-22480	160	11	and	and	CCONJ
brj-22480	160	12	transfer	transfer	NOUN
brj-22480	160	13	learning	learning	NOUN
brj-22480	160	14	on	on	ADP
brj-22480	160	15	wood	wood	NOUN
brj-22480	160	16	knot	knot	NOUN
brj-22480	160	17	defect	defect	VERB
brj-22480	160	18	classification	classification	NOUN
brj-22480	160	19	,	,	PUNCT
brj-22480	160	20	”	"	PUNCT
brj-22480	160	21	journal	journal	NOUN
brj-22480	160	22	of	of	ADP
brj-22480	160	23	applied	applied	ADJ
brj-22480	160	24	physics	physics	NOUN
brj-22480	160	25	(	(	PUNCT
brj-22480	160	26	23	23	NUM
brj-22480	160	27	)	)	PUNCT
brj-22480	160	28	,	,	PUNCT
brj-22480	160	29	131	131	NUM
brj-22480	160	30	,	,	PUNCT
brj-22480	160	31	article	article	NOUN
brj-22480	160	32	no	no	NOUN
brj-22480	160	33	.	.	PUNCT
brj-22480	161	1	233101	233101	NUM
brj-22480	161	2	.	.	PUNCT
brj-22480	162	1	doi	doi	NOUN
brj-22480	162	2	:	:	PUNCT
brj-22480	162	3	10.1063/5.0087060	10.1063/5.0087060	PROPN
brj-22480	162	4	huang	huang	PROPN
brj-22480	162	5	,	,	PUNCT
brj-22480	162	6	j.	j.	PROPN
brj-22480	162	7	,	,	PUNCT
brj-22480	162	8	lu	lu	PROPN
brj-22480	162	9	,	,	PUNCT
brj-22480	162	10	x.	x.	PROPN
brj-22480	162	11	,	,	PUNCT
brj-22480	162	12	chen	chen	PROPN
brj-22480	162	13	,	,	PUNCT
brj-22480	162	14	l.	l.	PROPN
brj-22480	162	15	,	,	PUNCT
brj-22480	162	16	sun	sun	PROPN
brj-22480	162	17	,	,	PUNCT
brj-22480	162	18	h.	h.	PROPN
brj-22480	162	19	,	,	PUNCT
brj-22480	162	20	wang	wang	PROPN
brj-22480	162	21	,	,	PUNCT
brj-22480	162	22	s.	s.	PROPN
brj-22480	162	23	,	,	PUNCT
brj-22480	162	24	and	and	CCONJ
brj-22480	162	25	fang	fang	X
brj-22480	162	26	,	,	PUNCT
brj-22480	162	27	g.	g.	PROPN
brj-22480	162	28	(	(	PUNCT
brj-22480	162	29	2022	2022	NUM
brj-22480	162	30	)	)	PUNCT
brj-22480	162	31	.	.	PUNCT
brj-22480	163	1	“	"	PUNCT
brj-22480	163	2	accurate	accurate	ADJ
brj-22480	163	3	identification	identification	NOUN
brj-22480	163	4	of	of	ADP
brj-22480	163	5	pine	pine	ADJ
brj-22480	163	6	wood	wood	NOUN
brj-22480	163	7	nematode	nematode	NOUN
brj-22480	163	8	disease	disease	NOUN
brj-22480	163	9	with	with	ADP
brj-22480	163	10	a	a	DET
brj-22480	163	11	deep	deep	ADJ
brj-22480	163	12	convolution	convolution	NOUN
brj-22480	163	13	neural	neural	ADJ
brj-22480	163	14	network	network	NOUN
brj-22480	163	15	,	,	PUNCT
brj-22480	163	16	”	"	PUNCT
brj-22480	163	17	remote	remote	ADJ
brj-22480	163	18	sensing	sense	VERB
brj-22480	163	19	14(4	14(4	NOUN
brj-22480	163	20	)	)	PUNCT
brj-22480	163	21	,	,	PUNCT
brj-22480	163	22	913	913	NUM
brj-22480	163	23	.	.	PUNCT
brj-22480	163	24	doi	doi	NOUN
brj-22480	163	25	:	:	PUNCT
brj-22480	163	26	10.3390	10.3390	NUM
brj-22480	163	27	/	/	SYM
brj-22480	163	28	rs14040913	rs14040913	PROPN
brj-22480	163	29	iliadis	iliadis	PROPN
brj-22480	163	30	,	,	PUNCT
brj-22480	163	31	l.	l.	PROPN
brj-22480	163	32	,	,	PUNCT
brj-22480	163	33	mansfield	mansfield	PROPN
brj-22480	163	34	,	,	PUNCT
brj-22480	163	35	s.	s.	PROPN
brj-22480	163	36	d.	d.	PROPN
brj-22480	163	37	,	,	PUNCT
brj-22480	163	38	and	and	CCONJ
brj-22480	163	39	avramidis	avramidis	PROPN
brj-22480	163	40	,	,	PUNCT
brj-22480	163	41	s.	s.	PROPN
brj-22480	163	42	(	(	PUNCT
brj-22480	163	43	2013	2013	NUM
brj-22480	163	44	)	)	PUNCT
brj-22480	163	45	.	.	PUNCT
brj-22480	164	1	“	"	PUNCT
brj-22480	164	2	predicting	predict	VERB
brj-22480	164	3	douglas	douglas	PROPN
brj-22480	164	4	-	-	PUNCT
brj-22480	164	5	fir	fir	NOUN
brj-22480	164	6	wood	wood	NOUN
brj-22480	164	7	density	density	NOUN
brj-22480	164	8	by	by	ADP
brj-22480	164	9	artificial	artificial	ADJ
brj-22480	164	10	neural	neural	ADJ
brj-22480	164	11	networks	network	NOUN
brj-22480	164	12	(	(	PUNCT
brj-22480	164	13	ann	ann	PROPN
brj-22480	164	14	)	)	PUNCT
brj-22480	164	15	based	base	VERB
brj-22480	164	16	on	on	ADP
brj-22480	164	17	progeny	progeny	PROPN
brj-22480	164	18	testing	testing	NOUN
brj-22480	164	19	information	information	NOUN
brj-22480	164	20	,	,	PUNCT
brj-22480	164	21	”	"	PUNCT
brj-22480	164	22	holzforschung	holzforschung	NOUN
brj-22480	164	23	67(7	67(7	NUM
brj-22480	164	24	)	)	PUNCT
brj-22480	164	25	,	,	PUNCT
brj-22480	164	26	771	771	NUM
brj-22480	164	27	-	-	SYM
brj-22480	164	28	777	777	NUM
brj-22480	164	29	.	.	PUNCT
brj-22480	165	1	doi	doi	NOUN
brj-22480	165	2	:	:	PUNCT
brj-22480	165	3	10.1515	10.1515	NUM
brj-22480	165	4	/	/	SYM
brj-22480	165	5	hf-2012	hf-2012	NOUN
brj-22480	165	6	-	-	PUNCT
brj-22480	165	7	0132	0132	NUM
brj-22480	165	8	ozsahin	ozsahin	NOUN
brj-22480	165	9	,	,	PUNCT
brj-22480	165	10	s.	s.	PROPN
brj-22480	165	11	,	,	PUNCT
brj-22480	165	12	and	and	CCONJ
brj-22480	165	13	murat	murat	NOUN
brj-22480	165	14	,	,	PUNCT
brj-22480	165	15	m.	m.	NOUN
brj-22480	165	16	(	(	PUNCT
brj-22480	165	17	2018	2018	NUM
brj-22480	165	18	)	)	PUNCT
brj-22480	165	19	.	.	PUNCT
brj-22480	166	1	“	"	PUNCT
brj-22480	166	2	prediction	prediction	NOUN
brj-22480	166	3	of	of	ADP
brj-22480	166	4	equilibrium	equilibrium	NOUN
brj-22480	166	5	moisture	moisture	NOUN
brj-22480	166	6	content	content	NOUN
brj-22480	166	7	and	and	CCONJ
brj-22480	166	8	specific	specific	ADJ
brj-22480	166	9	gravity	gravity	NOUN
brj-22480	166	10	of	of	ADP
brj-22480	166	11	heat	heat	NOUN
brj-22480	166	12	treated	treat	VERB
brj-22480	166	13	wood	wood	NOUN
brj-22480	166	14	by	by	ADP
brj-22480	166	15	artificial	artificial	ADJ
brj-22480	166	16	neural	neural	ADJ
brj-22480	166	17	networks	network	NOUN
brj-22480	166	18	,	,	PUNCT
brj-22480	166	19	”	"	PUNCT
brj-22480	166	20	european	european	ADJ
brj-22480	166	21	journal	journal	PROPN
brj-22480	166	22	of	of	ADP
brj-22480	166	23	wood	wood	NOUN
brj-22480	166	24	and	and	CCONJ
brj-22480	166	25	wood	wood	NOUN
brj-22480	166	26	products	product	NOUN
brj-22480	166	27	76(2	76(2	NUM
brj-22480	166	28	)	)	PUNCT
brj-22480	166	29	,	,	PUNCT
brj-22480	166	30	563	563	NUM
brj-22480	166	31	-	-	SYM
brj-22480	166	32	572	572	NUM
brj-22480	166	33	.	.	PUNCT
brj-22480	167	1	doi	doi	NOUN
brj-22480	167	2	:	:	PUNCT
brj-22480	167	3	10.1007	10.1007	NUM
brj-22480	167	4	/	/	SYM
brj-22480	167	5	s00107	s00107	NOUN
brj-22480	167	6	-	-	PUNCT
brj-22480	167	7	017	017	NUM
brj-22480	167	8	-	-	PUNCT
brj-22480	167	9	1219	1219	NUM
brj-22480	167	10	-	-	SYM
brj-22480	167	11	2	2	NUM
brj-22480	167	12	wu	wu	PROPN
brj-22480	167	13	,	,	PUNCT
brj-22480	167	14	h.	h.	PROPN
brj-22480	167	15	w.	w.	PROPN
brj-22480	167	16	,	,	PUNCT
brj-22480	167	17	and	and	CCONJ
brj-22480	167	18	avramidis	avramidis	PROPN
brj-22480	167	19	,	,	PUNCT
brj-22480	167	20	s.	s.	PROPN
brj-22480	167	21	(	(	PUNCT
brj-22480	167	22	2006	2006	NUM
brj-22480	167	23	)	)	PUNCT
brj-22480	167	24	.	.	PUNCT
brj-22480	168	1	“	"	PUNCT
brj-22480	168	2	prediction	prediction	NOUN
brj-22480	168	3	of	of	ADP
brj-22480	168	4	timber	timber	NOUN
brj-22480	168	5	kiln	kiln	NOUN
brj-22480	168	6	drying	dry	VERB
brj-22480	168	7	rates	rate	NOUN
brj-22480	168	8	by	by	ADP
brj-22480	168	9	neural	neural	ADJ
brj-22480	168	10	networks	network	NOUN
brj-22480	168	11	,	,	PUNCT
brj-22480	168	12	”	"	PUNCT
brj-22480	168	13	drying	dry	VERB
brj-22480	168	14	technology	technology	NOUN
brj-22480	168	15	24(12	24(12	NUM
brj-22480	168	16	)	)	PUNCT
brj-22480	168	17	,	,	PUNCT
brj-22480	168	18	1541	1541	NUM
brj-22480	168	19	-	-	SYM
brj-22480	168	20	1545	1545	NUM
brj-22480	168	21	.	.	PUNCT
brj-22480	169	1	doi	doi	NOUN
brj-22480	169	2	:	:	PUNCT
brj-22480	169	3	10.1080/07373930601047584	10.1080/07373930601047584	NUM
brj-22480	169	4	article	article	NOUN
brj-22480	169	5	submitted	submit	VERB
brj-22480	169	6	:	:	PUNCT
brj-22480	169	7	february	february	PROPN
brj-22480	169	8	17	17	NUM
brj-22480	169	9	,	,	PUNCT
brj-22480	169	10	2023	2023	NUM
brj-22480	169	11	;	;	PUNCT
brj-22480	169	12	peer	peer	NOUN
brj-22480	169	13	review	review	NOUN
brj-22480	169	14	completed	complete	VERB
brj-22480	169	15	:	:	PUNCT
brj-22480	169	16	april	april	PROPN
brj-22480	169	17	8	8	NUM
brj-22480	169	18	,	,	PUNCT
brj-22480	169	19	2023	2023	NUM
brj-22480	169	20	;	;	PUNCT
brj-22480	169	21	revised	revise	VERB
brj-22480	169	22	version	version	NOUN
brj-22480	169	23	received	receive	VERB
brj-22480	169	24	and	and	CCONJ
brj-22480	169	25	accepted	accept	VERB
brj-22480	169	26	:	:	PUNCT
brj-22480	169	27	april	april	PROPN
brj-22480	169	28	11	11	NUM
brj-22480	169	29	,	,	PUNCT
brj-22480	169	30	2023	2023	NUM
brj-22480	169	31	;	;	PUNCT
brj-22480	169	32	published	publish	VERB
brj-22480	169	33	:	:	PUNCT
brj-22480	169	34	october	october	PROPN
brj-22480	169	35	19	19	NUM
brj-22480	169	36	,	,	PUNCT
brj-22480	169	37	2023	2023	NUM
brj-22480	169	38	.	.	PUNCT
brj-22480	170	1	doi	doi	NOUN
brj-22480	170	2	:	:	PUNCT
brj-22480	170	3	10.15376	10.15376	NUM
brj-22480	170	4	/	/	SYM
brj-22480	170	5	biores.18.4.8212	biores.18.4.8212	PROPN
brj-22480	170	6	-	-	PUNCT
brj-22480	170	7	8222	8222	NUM
brj-22480	170	8	https://link.springer.com/article/10.1007/s00107-017-1219-2	https://link.springer.com/article/10.1007/s00107-017-1219-2	NOUN