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Registro Completo |
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
05/03/2012 |
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Data da última atualização: |
06/07/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LIMA, A. A. de; SILVA, D. V. da; MAIA, A. G.; SILVA, I. H. L. da; BEBER, P. M.; PRADO, R. de M.; WADT, P. G. S. |
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Afiliação: |
ALINY ALENCAR DE LIMA, UFAC; DÉBORAH VERÇOZA DA SILVA, UFAC; ALTENIRA GALVÃO MAIA, UFAC; IGOR HONORATO LEDUINO DA SILVA, UFAC; PAULO MÁRCIO BEBER, UFAC; RENATO DE MELLO PRADO, UNESP; PAULO GUILHERME SALVADOR WADT, CPAF-AC. |
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Título: |
Determinação da matéria seca e dos teores de macronutrientes da grama batatais pelos métodos de secagem em forno de microondas e estufa. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Ciência e Cultura, Barretos, v. 7, n. 2, p. 31-34, nov. 2011. |
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ISSN: |
1980-0029 |
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Idioma: |
Português |
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Conteúdo: |
O uso do forno de microondas para secagem de folhas pode ser método alternativo a estufa pelo menor tempo de secagem se não afetar os teores de nutrientes e o diagnóstico nutricional da cultura. Objetivou-se avaliarem dois métodos de secagem, em forno microondas e estufa, para determinação de matéria seca e teores foliares de macronutrientes da grama batatais. A coleta das amostras no campo foi realizada na Unesp, Câmpus Jaboticabal, em ziguezague, a partir de 200 folhas recém-maduras (sadias), cortadas a 6 cm acima do nível do solo. Os tratamentos foram constituídos de dois métodos de secagem em forno microondas e em estufa de circulação de ar forçada e com 10 repetições. Avaliaram-se a massa da matéria seca e os teores foliares de macronutrientes. Os métodos de secagem em estufa e forno microondas foram semelhantes na determinação da matéria seca das folhas e dos teores dos macronutrientes, exceto potássio e cálcio. O emprego da secagem das folhas da grama batatais pelo forno microondas é adequado para a determinação da matéria seca e não influencia no diagnóstico nutricional da cultura. |
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Palavras-Chave: |
Análise de variância; Análisis de varianza; Análisis estadístico; Análisis químico; Dry matter; Drying oven; Equipamiento para secado; Estufa de secado; Hornos microondas; Nutrición de las plantas; Pastos forrajeros; Teste de Tukey. |
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Thesagro: |
Análise estatística; Análise foliar; Análise química; Área Foliar; Estufa; Folha; Forno de microondas; Grama Batatais; Gramínea forrageira; Macroelemento; Matéria Seca; Método estatístico; Nutrição vegetal; Paspalum notatum; Secagem artificial; Valor nutritivo. |
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Thesaurus Nal: |
Analysis of variance; Chemical analysis; Drying equipment; Forage grasses; Leaf area; Microwave ovens; Plant nutrition; Statistical analysis. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/161285/1/24157.pdf
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Marc: |
LEADER 02996naa a2200637 a 4500
001 1917516
005 2021-07-06
008 2011 bl uuuu u00u1 u #d
022 $a1980-0029
100 1 $aLIMA, A. A. de
245 $aDeterminação da matéria seca e dos teores de macronutrientes da grama batatais pelos métodos de secagem em forno de microondas e estufa.$h[electronic resource]
260 $c2011
520 $aO uso do forno de microondas para secagem de folhas pode ser método alternativo a estufa pelo menor tempo de secagem se não afetar os teores de nutrientes e o diagnóstico nutricional da cultura. Objetivou-se avaliarem dois métodos de secagem, em forno microondas e estufa, para determinação de matéria seca e teores foliares de macronutrientes da grama batatais. A coleta das amostras no campo foi realizada na Unesp, Câmpus Jaboticabal, em ziguezague, a partir de 200 folhas recém-maduras (sadias), cortadas a 6 cm acima do nível do solo. Os tratamentos foram constituídos de dois métodos de secagem em forno microondas e em estufa de circulação de ar forçada e com 10 repetições. Avaliaram-se a massa da matéria seca e os teores foliares de macronutrientes. Os métodos de secagem em estufa e forno microondas foram semelhantes na determinação da matéria seca das folhas e dos teores dos macronutrientes, exceto potássio e cálcio. O emprego da secagem das folhas da grama batatais pelo forno microondas é adequado para a determinação da matéria seca e não influencia no diagnóstico nutricional da cultura.
650 $aAnalysis of variance
650 $aChemical analysis
650 $aDrying equipment
650 $aForage grasses
650 $aLeaf area
650 $aMicrowave ovens
650 $aPlant nutrition
650 $aStatistical analysis
650 $aAnálise estatística
650 $aAnálise foliar
650 $aAnálise química
650 $aÁrea Foliar
650 $aEstufa
650 $aFolha
650 $aForno de microondas
650 $aGrama Batatais
650 $aGramínea forrageira
650 $aMacroelemento
650 $aMatéria Seca
650 $aMétodo estatístico
650 $aNutrição vegetal
650 $aPaspalum notatum
650 $aSecagem artificial
650 $aValor nutritivo
653 $aAnálise de variância
653 $aAnálisis de varianza
653 $aAnálisis estadístico
653 $aAnálisis químico
653 $aDry matter
653 $aDrying oven
653 $aEquipamiento para secado
653 $aEstufa de secado
653 $aHornos microondas
653 $aNutrición de las plantas
653 $aPastos forrajeros
653 $aTeste de Tukey
700 1 $aSILVA, D. V. da
700 1 $aMAIA, A. G.
700 1 $aSILVA, I. H. L. da
700 1 $aBEBER, P. M.
700 1 $aPRADO, R. de M.
700 1 $aWADT, P. G. S.
773 $tCiência e Cultura, Barretos$gv. 7, n. 2, p. 31-34, nov. 2011.
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Registro original: |
Embrapa Acre (CPAF-AC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
27/06/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
SANTOS, F. F. dos; DOMINGUES, M. A.; SUNDERMANN, C. V.; CARVALHO, V. O. de; MOURA, M. F.; REZENDE, S. O. |
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Afiliação: |
FABIANO FERNANDES DOS SANTOS, ICMC/USP; MARCOS AURÉLIO DOMINGUES, UEM; CAMILA VACCARI SUNDERMANN, ICMC/USP; VERONICA OLIVEIRA DE CARVALHO, Unesp Rio Claro; MARIA FERNANDA MOURA, CNPTIA; SOLANGE OLIVEIRA REZENDE, ICMC/USP. |
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Título: |
Latent association rule cluster based model to extract topics for classification and recommendation applications. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Expert Systems with Applications, New York, v. 112, n. 1, p. 34-60, Dec. 2018. |
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DOI: |
https://doi.org/10.1016/j.eswa.2018.06.021 |
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Idioma: |
Inglês |
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Conteúdo: |
The quality of any text mining technique is highly dependent on the features that are used to represent the document collection. A classical form of document representation is the vector space model (VSM), according to which the documents are represented as vectors of weights that correspond to the features of the documents. The bag-of-words model is the most popular VSM approach due to its simplicity and general applicability, but this model does not include term dependency and has a high dimensionality. In the literature, several models for document representation have been proposed in order to capture the dependency of terms. Among them, the topic model representation is one of the most interesting approaches - since it describes the collection of documents in a way that reveals their internal struc- ture and the interrelationships therein, and also provides a dimensionality reduction. However, even for topic models, the efficient extraction of information concerning the relations among terms for document representation is still a major research challenge. In order to address this issue, we proposed the latent association rule cluster based model (LARCM). The LARCM is a non-probabilistic topic model that makes use of association rule clustering to build a document representation with low dimensionality in such a way that each feature (i.e., topic) is comprised of information concerning relations among the terms. We evaluated the interpretability of the topics obtained by using our proposed model against the ones provided by the traditional latent dirichlet allocation (LDA) model and the LDA model using a document representation that includes correlated terms (i.e., bag-of-related-words). The experimental results indi- cated that the LARCM provides topics with better interpretability than the LDA models. Additionally, we used the topics obtained by the LARCM in two different applications: text classification and page recommendation. With respect to text classification, the topics were used to improve document collection representation. Concerning page recommendation, topics were used as contextual information in context- aware recommender systems. Results have shown that the topics provided by the LARCM can be used to improve both applications. MenosThe quality of any text mining technique is highly dependent on the features that are used to represent the document collection. A classical form of document representation is the vector space model (VSM), according to which the documents are represented as vectors of weights that correspond to the features of the documents. The bag-of-words model is the most popular VSM approach due to its simplicity and general applicability, but this model does not include term dependency and has a high dimensionality. In the literature, several models for document representation have been proposed in order to capture the dependency of terms. Among them, the topic model representation is one of the most interesting approaches - since it describes the collection of documents in a way that reveals their internal struc- ture and the interrelationships therein, and also provides a dimensionality reduction. However, even for topic models, the efficient extraction of information concerning the relations among terms for document representation is still a major research challenge. In order to address this issue, we proposed the latent association rule cluster based model (LARCM). The LARCM is a non-probabilistic topic model that makes use of association rule clustering to build a document representation with low dimensionality in such a way that each feature (i.e., topic) is comprised of information concerning relations among the terms. We evaluated the interpretability of the topics obtained by ... Mostrar Tudo |
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Palavras-Chave: |
Association rules; Clustering; Clusterização; Context-aware recommender systems; Document representation; Mineração de textos; Regras de associação; Text classification; Text mining; Topic model. |
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Thesaurus NAL: |
Cluster analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03339naa a2200325 a 4500
001 2092838
005 2020-01-07
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.eswa.2018.06.021$2DOI
100 1 $aSANTOS, F. F. dos
245 $aLatent association rule cluster based model to extract topics for classification and recommendation applications.$h[electronic resource]
260 $c2018
520 $aThe quality of any text mining technique is highly dependent on the features that are used to represent the document collection. A classical form of document representation is the vector space model (VSM), according to which the documents are represented as vectors of weights that correspond to the features of the documents. The bag-of-words model is the most popular VSM approach due to its simplicity and general applicability, but this model does not include term dependency and has a high dimensionality. In the literature, several models for document representation have been proposed in order to capture the dependency of terms. Among them, the topic model representation is one of the most interesting approaches - since it describes the collection of documents in a way that reveals their internal struc- ture and the interrelationships therein, and also provides a dimensionality reduction. However, even for topic models, the efficient extraction of information concerning the relations among terms for document representation is still a major research challenge. In order to address this issue, we proposed the latent association rule cluster based model (LARCM). The LARCM is a non-probabilistic topic model that makes use of association rule clustering to build a document representation with low dimensionality in such a way that each feature (i.e., topic) is comprised of information concerning relations among the terms. We evaluated the interpretability of the topics obtained by using our proposed model against the ones provided by the traditional latent dirichlet allocation (LDA) model and the LDA model using a document representation that includes correlated terms (i.e., bag-of-related-words). The experimental results indi- cated that the LARCM provides topics with better interpretability than the LDA models. Additionally, we used the topics obtained by the LARCM in two different applications: text classification and page recommendation. With respect to text classification, the topics were used to improve document collection representation. Concerning page recommendation, topics were used as contextual information in context- aware recommender systems. Results have shown that the topics provided by the LARCM can be used to improve both applications.
650 $aCluster analysis
653 $aAssociation rules
653 $aClustering
653 $aClusterização
653 $aContext-aware recommender systems
653 $aDocument representation
653 $aMineração de textos
653 $aRegras de associação
653 $aText classification
653 $aText mining
653 $aTopic model
700 1 $aDOMINGUES, M. A.
700 1 $aSUNDERMANN, C. V.
700 1 $aCARVALHO, V. O. de
700 1 $aMOURA, M. F.
700 1 $aREZENDE, S. O.
773 $tExpert Systems with Applications, New York$gv. 112, n. 1, p. 34-60, Dec. 2018.
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