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Author: Grafiati

Published: 11 September 2023

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1

Elbeltagi, Ahmed, Nikul Kumari, Jaydeo Dharpure, Ali Mokhtar, Karam Alsafadi, Manish Kumar, Behrouz Mehdinejadiani, et al. "Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches." Water 13, no. 4 (February 20, 2021): 547. http://dx.doi.org/10.3390/w13040547.

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Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.

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Sones, Jenny L., Heinrich E. Lob, Catherine E. Isroff, and Robin L. Davisson. "Role of decidual natural killer cells, interleukin-15, and interferon-γ in placental development and preeclampsia." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 307, no. 5 (September 1, 2014): R490—R492. http://dx.doi.org/10.1152/ajpregu.00176.2014.

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Preeclampsia is a hypertensive, proteinuric disease that affects 5–10% of all pregnancies and is a leading cause of maternal and perinatal morbidity/mortality (Soto et al., J Matern Fetal Neonatal Med 25: 498–507, 2011). The primary treatment for preeclampsia still is delivery of the fetus and placenta. The underlying mechanisms remain elusive. One possibility is inadequate uterine angiogenesis/vascularity (decidualization) at the time of implantation (Torry et al., Am J Reprod Immunol 51: 257–268, 2004). Here, we review evidence for dysregulation of decidual natural killer (dNK) cells, which secrete important angiogenic factors during decidualization, as a contributing factor in preeclampsia.

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3

Armindo, Robson A., Rubens D. Coelho, Marconi B. Teixeira, and Paulo J. Ribeiro Junior. "Spatial variability of leaf nutrient contents in a drip irrigated citrus orchard." Engenharia Agrícola 32, no. 3 (June 2012): 479–89. http://dx.doi.org/10.1590/s0100-69162012000300007.

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This study aimed to evaluate the spatial variability of leaf content of macro and micronutrients. The citrus plants orchard with 5 years of age, planted at regular intervals of 8 x 7 m, was managed under drip irrigation. Leaf samples were collected from each plant to be analyzed in the laboratory. Data were analyzed using the software R, version 2.5.1 Copyright (C) 2007, along with geostatistics package GeoR. All contents of macro and micronutrients studied were adjusted to normal distribution and showed spatial dependence.The best-fit models, based on the likelihood, for the macro and micronutrients were the spherical and matern. It is suggest for the macronutrients nitrogen, phosphorus, potassium, calcium, magnesium and sulfur the minimum distances between samples of 37; 58; 29; 63; 46 and 15 m respectively, while for the micronutrients boron, copper, iron, manganese and zinc, the distances suggests are 29; 9; 113; 35 and 14 m, respectively.

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4

Hossain, SK Safdar, Bamidele Victor Ayodele, and Abdulrahman Almithn. "Predictive Modeling of Bioenergy Production from Fountain Grass Using Gaussian Process Regression: Effect of Kernel Functions." Energies 15, no. 15 (July 31, 2022): 5570. http://dx.doi.org/10.3390/en15155570.

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Experimental studies have shown that bioethanol production from biomass sources has been reported to be influenced by several process parameters. It is not entirely known, however, how the interaction of these factors affects the concentration of bioethanol production. In this study, the use of Gaussian Process Regression (GPR) in predictive modeling of bioethanol production from fountain grass has been investigated. Parametric analysis showing the interaction effect of time, pH, temperature, and yeast extract on the bioethanol production was examined. The effect of kernel functions on the performance of the GPR in modeling the prediction of bioenergy output was also examined. The study shows that the kernel function, namely, rotational quadratic (RQGPR), squared exponential (SEGPR), Matern 5/2 (MGPR), exponential (EGPR), and the optimizable (Opt.GPR.), had varying effects on the performance of the GPR. Coefficients of determination (R2) of 0.648, 0.670, 0.667, 0.762, and 0.993 were obtained for the RQGPR, SEGPR, MGPR, EGPR, OptGPR, respectively. The OptGPR with R2 of 0.993 and RMSE of 45.13 displayed the best performance. The input parameters analysis revealed that the pH of the fermentation medium significantly influences bioethanol production. A proper understanding of how the various process variables affect bioethanol production will help in the real-time optimization of the process in the eventuality of scale-up.

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5

Elbeltagi, Ahmed, Aman Srivastava, Nand Lal Kushwaha, Csaba Juhász, János Tamás, and Attila Nagy. "Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning." Water 15, no. 1 (December 22, 2022): 30. http://dx.doi.org/10.3390/w15010030.

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Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and maize. Climate datasets were collected over the period from 1969 to 2019, including: mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (H), solar radiation (SR), sunshine hours (Ssh), wind speed (WS), and day length (DL). Five machine learning (ML) methods were applied, including random forest (RF), support vector regression (SVM), bagged trees (BT), boosted trees (BoT), and matern 5/2 Gaussian process (MG). Models implemented by MG, including Tmean, SR, WS, and DL (Model 3); Tmax, Tmin, Tmean, SR, Ssh, WS, H, and DL (Model 8); Tmean, and SR (Model 9), were found optimal (r2 = 0.85) for forecasting CWP for wheat. Moreover, results of CWP for maize showed that the BT model, a combination of SR, WS, H, and Tmin data, achieved a high correlation coefficient of 0.82 compared to others. The outcomes demonstrated several high performance ML-based alternative CWP estimation methods in case of limited climatic data supporting decision making for designers, developers, and managers of water resources.

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6

Adebayo, Segun, Francis O. Aweda, Isaac A. Ojedokun, and James A. Agbolade. "Meteorological data prediction over selected stations in Sub-Sahara Africa: Leveraging on Machine Learning Algorithm." Ruhuna Journal of Science 13, no. 2 (December 31, 2022): 129–40. http://dx.doi.org/10.4038/rjs.v13i2.120.

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This study investigated selected meteorological data prediction leveraging on a Machine Learning Algorithm Approach over five selected stations in Nigeria. The algorithm of Machine Learning was explored using weather parameters such as temperature, wind speed, wind direction and relative humidity to predict the rainfall rate. In the results, five Gaussian models (i.e., Rational Quadratic, Squared Exponential, Matern 5/2, Exponential and Optimized GPR) revealed different Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) with prediction speeds ranging from 15000 to 26000 and the training time included 7.936, 1.8923, 2.3701, 3.267 and 282.19, respectively. The predicted response as against the true response for the two models shows a linear graph passing through the origin which confirmed a perfect regression model, where all the points lie on a diagonal line. Therefore, the relationship between MSE, MAE and RMSE for different models revealed that the optimized GPR has a better performance as compared to others. More so, visualizing the relationship between the output variable (rainfall) and each input variable reveals that some input variables (relative humidity, rainfall, pressure, wind speed and direction) have a strong correlation with the output variable (rainfall), with others having a noisy relationship which is not very clear.

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Pensupa, Nattha, Treesukon Treebuppachartsakul, and Suejit Pechprasarn. "Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation." Fermentation 9, no. 3 (March 1, 2023): 239. http://dx.doi.org/10.3390/fermentation9030239.

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In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.

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Ekanayake, Piyal, Lasini Wickramasinghe, and Jeevani W. Jayasinghe. "Development of Crop-Weather Models Using Gaussian Process Regression for the Prediction of Paddy Yield in Sri Lanka." International Journal of Intelligent Systems and Applications 14, no. 4 (August 8, 2022): 52–665. http://dx.doi.org/10.5815/ijisa.2022.04.05.

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This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.

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9

Wegerich, Philipp, and Hartmut Gehring. "An In vitro Laboratory Investigation on Layer Thickness-Independent Prediction of the Hemoglobin Concentration." Current Directions in Biomedical Engineering 4, no. 1 (September 1, 2018): 351–54. http://dx.doi.org/10.1515/cdbme-2018-0084.

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Abstract In the noninvasive determination of the hemoglobin concentration a main challenge is the "optical path". With sensors - fixed on human skin - the optical path cannot be exactly determined, as it is defined as the layer thickness in the Lambert Beer principle. The layer thickness is significantly involved in the optical interactions in the tissue. To circumvent this problem self-learning algorithms were evaluated which provide the hemoglobin concentration from reflection and transmission data without knowledge of the layer thickness. First various regression models were trained based on an high rate data set. To evaluate the six most promising models, a prediction dataset was measured in a prospective randomized and blinded study to guarantee integrity of the results. For both data sets, the transmission and reflection of diluted heparinized erythrocyte concentrate was determined with a double integrating sphere setup (laser diodes with 780 to 1310 nm). The evaluated hemoglobin concentrations ranged from 4 to 16 g/dl at a constant oxygen saturation above 97 %. Optical flow through cuvettes (1, 2, 3 mm) simulated different layer thicknesses of the blood. The evaluation of the predictions yielded that the layer thickness independent prediction of the hemoglobin concentration is feasible with the selected approaches. The mean absolute error (MAE) of the best regression model (GPRM - Matern 5/2) is 0.79 g/dl. In the clinically relevant tHb range of less than 8 g/dl the MAE was as low as 0.52 g/dl.

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10

Carbonell Blanco, Olga Alicia, Laura Estefanía García Rodríguez, and Milton Eduardo Bermúdez-Jaimes. "Estrategias de regulación emocional materna con bebés en situaciones de estrés: el uso del canto materno." Universitas Psychologica 18, no. 5 (December 30, 2019): 1–15. http://dx.doi.org/10.11144/javeriana.upsy18-5.erem.

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El propósito de este estudio fue caracterizar las estrategias regulatorias empleadas por madres en la interacción con sus bebés durante el primer año de vida en las situaciones de estrés que ocurrían en el contexto natural (hogar) de cuidado diario. Así mismo, determinar si el canto materno cumplía una función regulatoria de los estados fisiológicos y emocionales del bebé. Se utilizó un diseño de investigación no experimental de tipo transversal, con una muestra no probabilística por conveniencia de 32 díadas madre-bebé, que pertenecían a estratos socioeconómicos bajos. Los hallazgos de este estudio permitieron establecer dos patrones de cuidado materno: Patrón Regulatorio Contingente y Patrón Regulatorio Poco/No Contingente. También, se pudo identificar que el canto materno cumple, entre otras, una función regulatoria en situaciones de estrés en las rutinas de cuidado cotidiano.

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11

Samyn, Pieter, Gustaaf Schoukens, Francis Verpoort, Jeroen Van Craenenbroeck, and Patrick De Baets. "Macromol. Mater. Eng. 5/2007." Macromolecular Materials and Engineering 292, no. 5 (May 16, 2007): 513. http://dx.doi.org/10.1002/mame.200790008.

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12

Rietveld, Ivo B., Naotoshi Suganuma, Kei Kobayashi, Hirofumi Yamada, and Kazumi Matsushige. "Macromol. Mater. Eng. 5/2008." Macromolecular Materials and Engineering 293, no. 5 (May 15, 2008): 325. http://dx.doi.org/10.1002/mame.200890008.

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13

Haverhals, Luke M., W. Matthew Reichert, Hugh C. De Long, and Paul C. Trulove. "Macromol. Mater. Eng. 5/2010." Macromolecular Materials and Engineering 295, no. 5 (May 18, 2010): n/a. http://dx.doi.org/10.1002/mame.201090008.

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14

Ma, Jun, Ly Truc Bao La, Izzuddin Zaman, Qingshi Meng, Lee Luong, Denise Ogilvie, and Hsu-Chiang Kuan. "Macromol. Mater. Eng. 5/2011." Macromolecular Materials and Engineering 296, no. 5 (May 10, 2011): n/a. http://dx.doi.org/10.1002/mame.201190007.

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15

Choi, Seung-Hoon, Seon-Jin Choi, Byoung Koun Min, Woon Young Lee, Jin Seong Park, and Il-Doo Kim. "Macromol. Mater. Eng. 5/2013." Macromolecular Materials and Engineering 298, no. 5 (May 2013): 465. http://dx.doi.org/10.1002/mame.201370013.

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Cho, Himchan, Sung-Yong Min, and Tae-Woo Lee. "Macromol. Mater. Eng. 5/2013." Macromolecular Materials and Engineering 298, no. 5 (May 2013): 600. http://dx.doi.org/10.1002/mame.201370017.

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17

Lavrenova, Anna, Adrien Holtz, Yoan C. Simon, and Christoph Weder. "Macromol. Mater. Eng. 5/2016." Macromolecular Materials and Engineering 301, no. 5 (May 2016): 489. http://dx.doi.org/10.1002/mame.201670015.

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18

Arjmand, Mohammad, Amir Ameli, and Uttandaraman Sundararaj. "Macromol. Mater. Eng. 5/2016." Macromolecular Materials and Engineering 301, no. 5 (May 2016): 640. http://dx.doi.org/10.1002/mame.201670018.

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Ahmed, Jubair, Rupy Kaur Matharu, Talayeh Shams, Upulitha Eranka Illangakoon, and Mohan Edirisinghe. "Macromol. Mater. Eng. 5/2018." Macromolecular Materials and Engineering 303, no. 5 (May 2018): 1870016. http://dx.doi.org/10.1002/mame.201870016.

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Achtel, Christian, Kerstin Jedvert, Marc Kostag, Omar A. El Seoud, and Thomas Heinze. "Macromol. Mater. Eng. 5/2018." Macromolecular Materials and Engineering 303, no. 5 (May 2018): 1870017. http://dx.doi.org/10.1002/mame.201870017.

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21

Zhu, Chenchen, Anastasia F. Koutsomitopoulou, Stephen J. Eichhorn, Jeroen S. van Duijneveldt, Robert M. Richardson, Rinat Nigmatullin, and Kevin D. Potter. "Macromol. Mater. Eng. 5/2018." Macromolecular Materials and Engineering 303, no. 5 (May 2018): 1870019. http://dx.doi.org/10.1002/mame.201870019.

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22

Fóris, Ágota. "5 éves a Terminológiai Kutatócsoport." Magyar Terminológia 5, no. 1 (June 2012): 179–91. http://dx.doi.org/10.1556/materm.5.2012.1.15.

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23

Godoy, Laura Prado, Anderson Dillmann Groto, and Marise Villas Boas Pescador. "Avaliação do estado nutricional correlacionado ao Aleitamento Materno em crianças de 5 a 10 anos no Município de Cascavel/PR." Research, Society and Development 10, no. 5 (May 13, 2021): e46710514264. http://dx.doi.org/10.33448/rsd-v10i5.14264.

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Introdução: O aleitamento materno apresenta inúmeros benefícios quando correlacionado ao desenvolvimento e crescimento infantil. A amamentação materna exclusiva até os seis meses proporciona inúmeros privilégios para o desenvolvimento do recém nato, benefícios estes que englobam desde o sistema imunológico até o estreitamento de laços familiares entre mãe e filho. Metodologia: Foram analisados, por meio de questionário estruturado, o estado nutricional atual e forma de nutrição durante a lactação de cento e cinquenta (150) crianças entre 5 a 10 anos. Resultados e Conclusão: Foi evidenciado que aleitamento materno exclusivo por pelo menos um mês é fator protetor para problemas relacionados ao peso (como baixo peso para a idade, sobrepeso ou obesidade) (p<0,001) e aleitamento por fórmula por pelo menos um mês é fator agravante para problemas relacionados ao peso (p<0,001).

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Mogrovejo Del Saltó, Viviana Nathalie. "Factores epidemiológicos de la hipertensión en el embarazo." RECIAMUC 5, no. 1 (January 30, 2021): 4–13. http://dx.doi.org/10.26820/reciamuc/5.(1).ene.2021.4-13.

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Los trastornos hipertensivos durante el embarazo tienen una incidencia elevada en la morbimortalidad materno/fetal en todo el mundo, porque puede conducir a disfunciones orgánica materna como la preeclampsia, que puede evolucionar hacia una eclampsia o hacia el síndrome de HELLP, los cuales se consideran la tercera causa de muerte materna. Esto hace que la hipertensión arterial (HTA) en el embarazo sea motivo de muchos estudios epidemiológicos con los que se ha logrado progresos importantes en el conocimiento de la fisiopatología y sobre el riesgo que supone la HTA en el embarazo, con la finalidad de reducirla a un umbral óptimo que disminuya sustancialmente la morbimortalidad materno/fetal. El presente artículo presenta los factores epidemiológicos de la HTA en el embarazo, basados en evidencias consensuadas por la mayoría de la comunidad médico-científica, recogidas y organizadas por las principales instituciones internacionales de salud, con el objetivo de contribuir al conocimiento para la prevención de complicaciones del embarazo por HTA, bajo un enfoque metodológico descriptivo documental.

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Lacerda, Valéria Rodrigues de, Elenir Rose Jardim Cury Pontes, and Cecília Lacerda de Queiroz. "Relação entre senso de coerência materno, condições socioeconômicas e percepção da saúde bucal." Estudos de Psicologia (Campinas) 29, no. 2 (June 2012): 203–8. http://dx.doi.org/10.1590/s0103-166x2012000200006.

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O objetivo desta pesquisa foi verificar a relação entre o senso de coerência materno das mães de pré-escolares e as variáveis idade, renda, escolaridade, trabalho, moradia e percepção da saúde bucal. Seiscentas e quarenta mães de pré-escolares do nível III (4 e 5 anos de idade) do município de Campo Grande (Mato Grosso do Sul) responderam a dois questionários, um para a caracterização socioeconômica e percepção da saúde bucal materna e outro para mensurar o senso de coerência materno. Os dados obtidos foram submetidos a análise estatística por meio do programa Bio Estat, versão 5.0. Para a comparação dos escores do senso de coerência materno foi utilizado o Teste Mann Whitney, ao nível de significância de 5%. Houve associação entre menores valores do senso de coerência materno e condições socioeconômicas desfavoráveis, assim como, em situação de stress, as mães apresentaram uma visão pessimista em relação à própria saúde bucal. Portanto, o senso de coerência materno tem fortes características psicológicas e pode ser um determinante de saúde bucal.

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Trettene, Armando Dos Santos, Thais De Oliveira Maximiano, Carolina Cantatore Beraldo, Juliana Campanati Mendonça, Aline Godoi Luiz, and Beatriz Costa. "Aleitamento materno em lactentes com fissura labiopalatina." Revista de Enfermagem UFPE on line 12, no. 5 (May 1, 2018): 1390. http://dx.doi.org/10.5205/1981-8963-v12i5a230983p1390-1396-2018.

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RESUMOObjetivo: identificar fatores associados à adesão ao aleitamento materno em lactentes com fissura de lábio e/ou palato. Método: estudo quantitativo, transversal, com 121 cuidadores de crianças com fissura de lábio e/ou palato. A coleta de dados foi realizada durante a consulta de Enfermagem pré-operatória de queiloplastia e/ou palatoplastia. Os pais/responsáveis responderam a um questionário referente ao recebimento de informações sobre o aleitamento materno recebidas no pré e pós-natal. Para a análise estatística, utilizou-se o teste Qui-quadrado, com significância de 5%. Resultados: o aleitamento materno exclusivo foi observado em 31% (n=38) dos lactentes. Desses, 63% (n=24) foram amamentados por um mês. Entre os fatores para a não adesão ao aleitamento materno prevaleceu a sucção ineficaz (n=45, 37%). Possuir fissura de lábio e palato influenciou negativamente a prática do aleitamento materno (p<0,001), enquanto receber orientações no pré-natal favoreceu a sua adesão (p=0,042). Conclusão: poucos lactentes foram amamentados exclusivamente e por tempo aquém do recomendado. A complexidade da fissura, evidenciada pelo déficit de sucção, influenciou negativamente a adesão ao aleitamento materno, enquanto o recebimento de informações por profissionais de saúde no pré-natal influenciou positivamente. Descritores: Aleitamento Materno; Cuidados de Enfermagem; Fissura Labial; Fissura Palatina; Alimentação Artificial; Enfermagem.ABSTRACTObjective: to identify factors associated with adherence to breastfeeding in infants with cleft lip and / or palate. Method: quantitative, cross-sectional study with 121 caregivers of children with cleft lip and / or palate. Data collection was carried out during the preoperative nursing visit of queiloplasty and / or palatoplasty. The parents / guardians answered a questionnaire regarding the receipt of information about breastfeeding received in the pre- and postnatal care. For the statistical analysis, the chi-square test was used, with significance of 5%. Results: exclusive breastfeeding was observed in 31% (n = 38) of infants. Of these, 63% (n = 24) were breastfed for one month. Among the factors for non-adherence to breastfeeding, ineffective sucking (n = 45, 37%) prevailed. Posterior cleft lip and palate negatively influenced the practice of breastfeeding (p <0.001), while receiving prenatal guidelines favored its adherence (p = 0.042). Conclusion: few infants were breastfed exclusively and for shorter time than recommended. The complexity of the cleft, evidenced by sucking deficit, negatively influenced adherence to breastfeeding, while the receipt of information by prenatal health professionals influenced positively. Descritores: Breast Feeding; Nursing Care; Cleft Lip; Cleft Palate; Bottle Feeding; Nursing.RESUMENObjetivo: identificar factores asociados a la adhesión a la lactancia materna en lactantes con fisura de labio y / o paladar. Método: estudio cuantitativo, transversal, con 121 cuidadores de niños con fisura de labio y / o paladar. La recolección de datos fue realizada durante la consulta de Enfermería preoperatoria de queiloplastia y / o palatoplastia. Los padres / responsables respondieron un cuestionario referente a la recepción de informaciones referentes a la lactancia materna, recibidas en el pre y post-natal. Para el análisis estadístico, se utilizó el testeo Qui-cuadrado, con significancia de 5%. Resultados: la lactancia materna exclusiva se observó en un 31% (n = 38) de los lactantes. De ellos, 63% (n = 24) fueron amamantados por un mes. Entre los factores para la no adhesión a la lactancia materna prevaleció la succión ineficaz (n = 45, 37%). La posesión de fisura de labio y paladar influenció negativamente la práctica de la lactancia materna (p <0,001), mientras que recibir orientaciones en el prenatal favoreció la su adhesión (p = 0,042). Conclusión: pocos lactantes fueron amamantados exclusivamente y por tiempo por debajo de lo recomendado. La complejidad de la fisura evidenciada por el déficit de succión, influenció negativamente la adhesión a la lactancia materna, mientras que la recepción de informaciones por profesionales de salud en el prenatal influenció positivamente. Descritores: Lactancia Materna; Atención de Enfermaría; Labio Leporino; Fisura del Paladar; Alimentación Artificial; Enfermaría.

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Cordella-Masini, María Patricia, Karina Aedo, and Verónica Ramirez. "Riesgos asociados a los hijos de mujeres con trastornos alimentarios." ARS MEDICA Revista de Ciencias Médicas 43, no. 3 (October 4, 2018): 42–49. http://dx.doi.org/10.11565/arsmed.v43i3.1338.

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Resumen Se analiza literatura sobre la influencia materna que los trastornos alimentarios y de la ingestión de alimentos (TAI) tienen sobre el desarrollo psíquico de los hijos. De 1.096 artículos encontrados en PubMed, PsycINFO, Scielo, ProQuest Health & Medical Collection, 32 aportan información relevante organizada en 6 categorías: 1-Alimentación; 2-Antropometría; 3-Percepción materna de los hijos; 4-Percepción de los hijos hacia sus madres; 5-Estilo parental; 6-Psicopatología de los hijos.La evidencia muestra influencia de TAI materno sobre el psico - desarrollo de los hijos. Se discuten mecanismos involucrados y se sugiere seguir avanzando en investigación y clínica

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Rodriguez Chavez, R. A., C. M. Skinner Taylor, L. Pérez Barbosa, G. A. López-Uriarte, E. Barriga-Maldonado, G. Figueroa-Parra, I. Perez-Onofre, et al. "AB0830 ADVERSE PERINATAL OUTCOMES AND CONGENITAL DEFECTS IN MEXICAN WOMEN WITH AUTOIMMUNE RHEUMATIC DISEASES." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 1439. http://dx.doi.org/10.1136/annrheumdis-2021-eular.3270.

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Background:Pregnancy in women with autoimmune rheumatic diseases (ARD) is associated with an increase in the risk of adverse perinatal outcomes and birth defects. Many factors have been attributed to these including medication employed and maternal autoantibodies. The national prevalence of congenital anomalies in newborns in Mexico is estimated to be 73.9 per 10,000 births but the rate of these in Mexican women with ARD is unknown.Objectives:This study aims to describe the frequency of adverse perinatal outcomes and congenital defects in Mexican women with ARD.Methods:We performed a descriptive and prospective study that included all pregnant patients with ARDs from the clinic of pregnancy and rheumatic diseases in the University Hospital “Dr. José Eleuterio González” who gave birth between February 2019 to November 2020. Demographic information, pregnancy outcome, and congenital defects were prospectively evaluated. Congenital defects were clinically confirmed by a clinical geneticist.Results:A total of 40 women were taken in the final analyses. The ARD diagnosis is shown in graphic 1. Rheumatoid arthritis was the most frequent diagnosis that had an adverse perinatal outcome with 4 (40%) and congenital defects in 2 (40%). No complications or birth defects were reported in 25 (62.5%). The adverse perinatal outcome was reported in 10 (25%) women and congenital defects in 5 (12.5%). The most frequent adverse perinatal outcomes were preterm birth 6 (15%) and the second most frequent miscarriage 3 (7.5%) (Table 1 below).Conclusion:We found a high frequency (12.5%) of adverse perinatal outcomes and congenital defects in Mexican women with ARD. Multidisciplinary groups and clinics are needed to adequately serve this complex population and reduce morbidity and mortality. A multidisciplinary approach before, during, and after pregnancy in patients with ARD may improve morbidity and mortality.References:[1]Vinet, E., Bernatsky, S. (2017). Outcomes in Children Born to Women with Rheumatic Diseases. Rheum Dis Clin N Am. http://dx.doi.org/10.1016/j.rdc.2016.12.006.[2]Krishnan AN, Sable CA, Donofrio MT.(2008). Spectrum of fetal echocardiographic findings in fetuses of women with clinical or serologic evidence of systemic lupus erythematosus. J Matern Fetal Neonatal Med. 21(11):776–82.[3]NAVARRETE HERNANDEZ, Eduardo et al. Prevalencia de malformaciones congénitas registradas en el certificado de nacimiento y de muerte fetal: México, 2009-2010. Bol. Med. Hosp. Infant. Mex. [online]. 2013, vol.70, n.6, pp.499-505.Graphic 1.Percentage of adverse perinatal outcomes and congenital defects due to rheumatic disease.Table 1.Frequency and percentages of adverse perinatal outcomes and congenital defects.Adverse perinatal outcome (n=10) 25%Congenital defects (n=5) 12.5%Preterm birth6 (15%)-Miscarriage3 (7.5%)-Stillbirth1 (2.5%)-Congenital heart disease*-1 (2.5%)Congenital cardiac block with pacemaker placement-1 (2.5%)Krabbe´s disease-1 (2.5%)Preauricular appendix-1 (2.5%)Diabetic fetopathy-1 (2.5%)*Tetralogy of FallotDisclosure of Interests:None declared

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Tipiani Rodríguez, Oswaldo. "¿ES LA EDAD MATERNA AVANZADA UN FACTOR DE RIESGO INDEPENDIENTE PARA COMPLICACIONES MATERNO-PERINATALES?" Revista Peruana de Ginecología y Obstetricia 52, no. 3 (April 30, 2015): 179–85. http://dx.doi.org/10.31403/rpgo.v52i331.

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Objetivo: Determinar si la edad materna igual o mayor de 35 años es un factor de riesgo independiente para complicaciones materno-perinatales. Diseño: Estudio caso-control, comparativo. Lugar: Hospital Nacional Arzobispo Loayza, en Lima, Perú, hospital universitario nivel III. Intervenciones: Estudio en el cual se compara 166 casos de gestantes de 35 años o más con 324 gestantes menores de 35 años. Las pacientes, atendidas durante el año 2005, fueron seleccionadas aleatoriamente. El análisis estadístico incluyó la prueba de x2, con prueba de Mantel-Haenszel. Un valor de p < 0,05 fue considerado para la significancia estadística. Medidas de Principales Resultados: Se realizó una regresión logística múltiple para evaluar la asociación entre la edad materna y las variables significativas, alejando factores de confusión. Resultados: La edad materna avanzada fue asociada independientemente con la alteración de la presentación fetal durante el parto (OR 1,05; IC 95% 1,01, 1,10), la cesárea por hemorragia del tercer trimestre (OR 1,05; IC 95% 1,01, 1,09), el parto pretérmino (OR 1,08; IC 95% 1,02, 1,14), la hipertensión crónica (OR 1,03; IC 95% 1,01, 1,05), el aborto recurrente (OR 3,09; IC 95% 1,49, 6,43) y la gran multiparidad (OR 10,34; IC 95% 3,46, 30,93). Se halló mayor prevalencia de diabetes gestacional en gestantes ‘añosas’. La prevalencia de muerte perinatal, Ápgar menor de 7 a los 5 minutos, peso bajo al nacer y morbilidad puerperal no tuvo influencia por la edad materna. Conclusiones: Después de corregir para factores de confusión, la edad materna avanzada representa un factor de riesgo independiente para complicaciones médicas y obstétricas. El reconocimiento de tales factores es importante para la reducción de la morbimortalidad materno-perinatal en este grupo de pacientes.

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Zenone, Terenzio, Luca Vitale, Daniela Famulari, and Vincenzo Magliulo. "Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops." Ecological Processes 11, no. 1 (September 5, 2022). http://dx.doi.org/10.1186/s13717-022-00400-1.

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Abstract Background The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data. Results Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF. Conclusion ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.

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Shnain, Zainb Y., Alyaa K. Mageed, Hasan Sh Majdi, Majid Mohammadi, Adnan A. AbdulRazak, and Mohammad F. Abid. "Investigating the effect of TiO2-based nanofluids in the stability of crude oil flow: parametric analysis and Gaussian process regression modeling." Journal of Petroleum Exploration and Production Technology, February 25, 2022. http://dx.doi.org/10.1007/s13202-022-01473-6.

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AbstractStudy has shown that the precipitation of asphaltenes could be the most detrimental mechanism that significantly influences well productivity during crude oil processing. The flow of the crude oil could seriously be affected if the effect of the asphaltenes is not inhibited. This study aims to investigate the effect of TiO2-based nanofluid in stabilizing crude oil flow. The effect of the ratio of TiO2/SiO2 nanocomposite in the organic nanofluid, the salinity of the nanofluid, and pH on the amount of organic solvent (n-heptane) added to stabilize the crude oil flow was investigated using Gaussian Process Progression (GPR) with five kernel functions [exponential square kernel (model 1), rotational quadratic (model 2), Matern 5/2 (model 3), exponential (model 4), and non-isotopic rotational quadratic (model 5)]. The GPR using the various kernel function had good modeling of the relationship between the ratio of TiO2/SiO2 nanocomposite in the organic nanofluid, the salinity of the nanofluid, the pH, and the amount of organic solvent (n-heptane) added to stabilize the crude oil flow. This is evidence from the R2 of 0.820, 0.999, 0.999, 0.999, 0.999 for model 1, model 2, model 3, model 4, and model 5, respectively. Each of the models had low prediction errors as indicated by the MSE, RMSE, and MAE. Based on the sensitivity analysis, the ratio of TiO2/SiO2 nanocomposite in the organic nanofluid had the most significant influence on the amount of n-heptane added to stabilize the crude oil.

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"Masthead: (Adv. Mater. 5/2021)." Advanced Materials 33, no. 5 (February 2021): 2170033. http://dx.doi.org/10.1002/adma.202170033.

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"Contents: (Adv. Mater. 5/2021)." Advanced Materials 33, no. 5 (February 2021): 2170032. http://dx.doi.org/10.1002/adma.202170032.

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"Masthead: (Adv. Mater. 5/2022)." Advanced Materials 34, no. 5 (February 2022): 2270037. http://dx.doi.org/10.1002/adma.202270037.

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"Masthead: (Adv. Mater. 5/2023)." Advanced Materials 35, no. 5 (February 2023): 2370031. http://dx.doi.org/10.1002/adma.202370031.

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"Macromol. Mater. Eng. 5/2007." Macromolecular Materials and Engineering 292, no. 5 (May 16, 2007): 515–21. http://dx.doi.org/10.1002/mame.200790009.

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"Macromol. Mater. Eng. 5/2008." Macromolecular Materials and Engineering 293, no. 5 (May 15, 2008): 327–30. http://dx.doi.org/10.1002/mame.200890009.

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Li, Xiangyang, and James Mason. "Macromol. Mater. Eng. 5/2009." Macromolecular Materials and Engineering 294, no. 5 (May 18, 2009). http://dx.doi.org/10.1002/mame.200990008.

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"Macromol. Mater. Eng. 5/2009." Macromolecular Materials and Engineering 294, no. 5 (May 18, 2009): 291–93. http://dx.doi.org/10.1002/mame.200990009.

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"Macromol. Mater. Eng. 5/2010." Macromolecular Materials and Engineering 295, no. 5 (May 18, 2010): 411–14. http://dx.doi.org/10.1002/mame.201090009.

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"Macromol. Mater. Eng. 5/2011." Macromolecular Materials and Engineering 296, no. 5 (May 10, 2011): 387–90. http://dx.doi.org/10.1002/mame.201190006.

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"Macromol. Mater. Eng. 5/2012." Macromolecular Materials and Engineering 297, no. 5 (May 2012): 391–94. http://dx.doi.org/10.1002/mame.201290013.

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Yu, Deng-Guang, Nicholas P. Chatterton, Jun-He Yang, Xia Wang, and Yao-Zu Liao. "Macromol. Mater. Eng. 5/2012." Macromolecular Materials and Engineering 297, no. 5 (May 2012). http://dx.doi.org/10.1002/mame.201290014.

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"Macromol. Mater. Eng. 5/2012." Macromolecular Materials and Engineering 297, no. 5 (May 2012). http://dx.doi.org/10.1002/mame.201290015.

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"Macromol. Mater. Eng. 5/2013." Macromolecular Materials and Engineering 298, no. 5 (May 2013): 466–599. http://dx.doi.org/10.1002/mame.201370014.

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"Macromol. Mater. Eng. 5/2013." Macromolecular Materials and Engineering 298, no. 5 (May 2013): 467–72. http://dx.doi.org/10.1002/mame.201370015.

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Haynie, Donald T., Dhan B. Khadka, Michael C. Cross, Alina Gitnik, and Nicole K. Le. "Macromol. Mater. Eng. 5/2013." Macromolecular Materials and Engineering 298, no. 5 (May 2013). http://dx.doi.org/10.1002/mame.201370016.

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Ye, Qiang, Paulette Spencer, Esra Yuca, and Candan Tamerler. "Macromol. Mater. Eng. 5/2017." Macromolecular Materials and Engineering 302, no. 5 (May 2017). http://dx.doi.org/10.1002/mame.201770013.

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"Contents: (Adv. Mater. 5/2012)." Advanced Materials 24, no. 5 (January 26, 2012): 571–75. http://dx.doi.org/10.1002/adma.201290018.

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"Masthead: (Adv. Mater. 5/2012)." Advanced Materials 24, no. 5 (January 26, 2012): n/a. http://dx.doi.org/10.1002/adma.201290021.

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