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Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices' transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet's network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.

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The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software developed in R language for implementing six (generalized boosted machines, generalized linear models, support vector machines, random forest, Bayesian regression models and deep neural networks) of the most popular supervised machine learning algorithms with the optional use of sparse kernels. The SKM focuses on user simplicity, as it does not try to include all the available machine learning algorithms, but rather the most important aspects of these six algorithms in an easy-to-understand format. Another relevant contribution of this package is a function for the computation of seven different kernels. These are Linear, Polynomial, Sigmoid, Gaussian, Exponential, Arc-Cosine 1 and Arc-Cosine L (with L = 2, 3, … ) and their sparse versions, which allow users to create kernel machines without modifying the statistical machine learning algorithm. It is important to point out that the main contribution of our package resides in the functionality for the computation of the sparse version of seven basic kernels, which is indispensable for reducing computational resources to implement kernel machine learning methods without a significant loss in prediction performance. Performance of the SKM is evaluated in a genome-based prediction framework using both a maize and wheat data set. As such, the use of this package is not restricted to genome prediction problems, and can be used in many different applications.

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Genomic enabled prediction is playing a key role for the success of genomic selection (GS). However, according to the No Free Lunch Theorem, there is not a universal model that performs well for all data sets. Due to this, many statistical and machine learning models are available for genomic prediction. When multitrait data is available, models that are able to account for correlations between phenotypic traits are preferred, since these models help increase the prediction accuracy when the degree of correlation is moderate to large. For this reason, in this chapter we review multitrait models for genome-enabled prediction and we illustrate the power of this model with real examples. In addition, we provide details of the software (R code) available for its application to help users implement these models with its own data. The multitrait models were implemented under conventional Bayesian Ridge regression and best linear unbiased predictor, but also under a deep learning framework. The multitrait deep learning framework helps implement prediction models with mixed outcomes (continuous, binary, ordinal, and count, measured on different scales), which is not easy in conventional statistical models. The illustrative examples are very detailed in order to make the implementation of multitrait models in plant and animal breeding friendlier for breeders and scientists.

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Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.

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Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as "genomic images." In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

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When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2-17.45% (datasets 1-3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.

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BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. MAIN BODY: We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. CONCLUSIONS: The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

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Objective.This work sought to validate and propose an instrument to measure the performance of tutors in promoting self-directed learning in students involved in processes of problem-based learning. Methods. Confirmatory factor analysis (CFA) was applied to validate the instrument composed of 60 items and six factors (self-assessment of learning gaps within the United Nations specific context: self-assessment, reflexion, critical thinking, administration of information, group skills), using a sample of 207 students from a total of 279, which comprise the student population of the Faculty of Nursing at Universidad de Colima in Mexico. (2007). Results. The CFA results demonstrated that the instrument is acceptable to measure performance of tutors in promoting self-directed learning, given that all the indicators, variances, covariances, and thresholds are statistically significant. Conclusion. The instrument permits obtaining students' opinions on how much professors contribute for them to develop each of the 60 skills described in the scale. Lastly, the results could report if professors are placing more emphasis in some areas than in other areas they should address during the problem-based learning (PBL) process, or if definitely their actions are removed from the premises of PBL, information that will be useful for school management in decision making on the direction of teaching as a whole.

Objetivo.Validar y proponer un instrumento que mide el desempeño de los tutores en la promoción del aprendizaje autodirigido en los estudiantes involucrados en procesos de ABP. Métodos. Se aplicó el Análisis Factorial Confirmatorio (AFC) para validar el instrumento compuesto por 60 ítems y seis factores (Autoevaluación de las Brechas de Aprendizaje dentro de las Naciones Unidas Contexto Especifico: Autoevaluación, Reflexión, Pensamiento Crítico, Administración de la Información, Habilidades de Grupo), utilizando una muestra de 207 estudiantes de un total de 279, que conforman la población estudiantil de la Facultad de Enfermería de la Universidad de Colima, México. (2007). Resultados. Los resultados del AFC demostraron que el instrumento es aceptable para medir el desempeño de los tutores en la promoción del aprendizaje autodirigido ya que todos los indicadores, las varianzas, covarianzas y thresholds son estadísticamente significativos. Conclusión. El instrumento permite obtener la opinión de los estudiantes sobre cuánto el profesor contribuye para que ellos desarrollen cada una de las 60 habilidades descritas en la escala. Al final, los resultados podrían informar si el profesor está haciendo más énfasis en una área que debe atender durante el proceso del ABP o en otra, o si definitivamente su actuación se aleja de las premisas del ABP, información que será útil para la administración escolar en la toma de decisiones sobre el rumbo de la docencia en su conjunto.

Objetivo.Validar e propor um instrumento que meça o desempenho dos tutores na promoção da aprendizagem autodirigido nos estudantes envolvidos em processos de ABP. Métodos. Se aplicou uma Análise Fatorial Confirmatório (AFC) para validar o instrumento composto por 60 itens e seis fatores (Auto-avaliação das Brechas de Aprendizagem dentro das Nações Unidas Contexto Especifico: Auto-avaliação, Reflexão, Pensamento Crítico, Administração da Informação, habilidades de Grupo), utilizando uma amostra de 207 estudantes de um total de 279, que conformam a população estudantil da Faculdade de Enfermagem da Universidad de Colima, México. (2007). Resultados. Os resultados do AFC demostraram que o instrumento é aceitável para medir o desempenho dos tutores na promoção da aprendizagem autodirigido já que todos os indicadores, as variâncias, covariâncias e thresholds são estatisticamente significativos. Conclusão. O instrumento permite obter a opinião dos estudantes sobre quanto o professor contribui para que eles desenvolvam cada uma das 60 habilidades descritas na escala. Ao final, os resultados poderiam informar se o professor está fazendo mais ênfase em uma do que em outras das áreas que deve atender durante o processo do ABP, ou se definitivamente sua atuação se afasta e as premissas do ABP, informação que será útil para a administração escolar na toma de decisões sobre o rumo da docência em seu conjunto.

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OBJECTIVE: This work sought to validate and propose an instrument to measure the performance of tutors in promoting self-directed learning in students involved in processes of problem-based learning. METHODS: Confirmatory factor analysis (CFA) was applied to validate the instrument composed of 60 items and six factors (self-assessment of learning gaps within the United Nations specific context: self-assessment, reflexion, critical thinking, administration of information, group skills), using a sample of 207 students from a total of 279, which comprise the student population of the Faculty of Nursing at Universidad de Colima in Mexico. (2007). RESULTS: The CFA results demonstrated that the instrument is acceptable to measure performance of tutors in promoting self-directed learning, given that all the indicators, variances, covariances, and thresholds are statistically significant. CONCLUSION: The instrument permits obtaining students' opinions on how much professors contribute for them to develop each of the 60 skills described in the scale. Lastly, the results could report if professors are placing more emphasis in some areas than in other areas they should address during the problem-based learning (PBL) process, or if definitely their actions are removed from the premises of PBL, information that will be useful for school management in decision making on the direction of teaching as a whole.

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OBJETIVO: Describir la importancia de los modelos matemáticos en la comprensión de la dinámica de transmisión de las enfermedades infecciosas, así como en el diseño de medidas eficaces de control. MATERIAL Y MÉTODOS: Se revisaron las publicaciones internacionales sobre el tema a través de medios digitales; se identificaron alrededor de 60 artículos, aunque sólo se revisaron 27 de éstos por su estrecha relación con el tema. RESULTADOS: Este trabajo explica de manera sinóptica los antecedentes, importancia y clasificación de los modelos matemáticos en padecimientos infecciosos. De modo adicional se describen con detalle algunos modelos comunes de transmisión de enfermedades y otros de uso más reciente que se utilizan en la modelación de trastornos infecciosos. CONCLUSIONES: El empleo de modelos matemáticos ha crecido en grado significativo en los últimos años y son de gran ayuda para idear medidas eficaces de control y erradicación de las enfermedades infecciosas.

OBJECTIVE: To describe the importance of mathematical models in the understanding of infectious disease transmission dynamics, as well as in the design of effective strategies for control. MATERIAL AND METHODS: International literature was reviewed on the subject through digital means. Around 60 papers about the subject were identified; nevertheless, this study is based on only 27 of these, due to the fact that they were directly related to the subject. RESULTS:This work presents a brief explanation of the antecedents, importance and classification of mathematical models for infectious diseases. In addition, a detailed description of some classical models is discussed as well as other more recent models used in the modeling of infectious disease. CONCLUSIONS: The use of mathematical models for infectious diseases has grown significantly in the last few years and has proven to be of great help in designing efficient strategies for control and eradication of infectious diseases.

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OBJECTIVE: To describe the importance of mathematical models in the understanding of infectious disease transmission dynamics, as well as in the design of effective strategies for control. MATERIAL AND METHODS: International literature was reviewed on the subject through digital means. Around 60 papers about the subject were identified; nevertheless, this study is based on only 27 of these, due to the fact that they were directly related to the subject. RESULTS: This work presents a brief explanation of the antecedents, importance and classification of mathematical models for infectious diseases. In addition, a detailed description of some classical models is discussed as well as other more recent models used in the modeling of infectious disease. CONCLUSIONS: The use of mathematical models for infectious diseases has grown significantly in the last few years and has proven to be of great help in designing efficient strategies for control and eradication of infectious diseases.

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