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Load recognition remains not comprehensively explored in Home Energy Management Systems (HEMSs). There are gaps in current approaches to load recognition, such as enhancing appliance identification and increasing the overall performance of the load-recognition system through more robust models. To address this issue, we propose a novel approach based on the Analysis of Variance (ANOVA) F-test combined with SelectKBest and gradient-boosting machines (GBMs) for load recognition. The proposed approach improves the feature selection and consequently aids inter-class separability. Further, we optimized GBM models, such as the histogram-based gradient-boosting machine (HistGBM), light gradient-boosting machine (LightGBM), and XGBoost (extreme gradient boosting), to create a more reliable load-recognition system. Our findings reveal that the ANOVA-GBM approach achieves greater efficiency in training time, even when compared to Principal Component Analysis (PCA) and a higher number of features. ANOVA-XGBoost is approximately 4.31 times faster than PCA-XGBoost, ANOVA-LightGBM is about 5.15 times faster than PCA-LightGBM, and ANOVA-HistGBM is 2.27 times faster than PCA-HistGBM. The general performance results expose the impact on the overall performance of the load-recognition system. Some of the key results show that the ANOVA-LightGBM pair reached 96.42% accuracy, 96.27% F1, and a Kappa index of 0.9404; the ANOVA-HistGBM combination achieved 96.64% accuracy, 96.48% F1, and a Kappa index of 0.9434; and the ANOVA-XGBoost pair attained 96.75% accuracy, 96.64% F1, and a Kappa index of 0.9452; such findings overcome rival methods from the literature. In addition, the accuracy gain of the proposed approach is prominent when compared straight to its competitors. The higher accuracy gains were 13.09, 13.31, and 13.42 percentage points (pp) for the pairs ANOVA-LightGBM, ANOVA-HistGBM, and ANOVA-XGBoost, respectively. These significant improvements highlight the effectiveness and refinement of the proposed approach.

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This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study's results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

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Objective: This study aims to develop a predictive model using artificial intelligence to estimate the ICU length of stay (LOS) for Congenital Heart Defects (CHD) patients after surgery, improving care planning and resource management. Design: We analyze clinical data from 2240 CHD surgery patients to create and validate the predictive model. Twenty AI models are developed and evaluated for accuracy and reliability. Setting: The study is conducted in a Brazilian hospital's Cardiovascular Surgery Department, focusing on transplants and cardiopulmonary surgeries. Participants: Retrospective analysis is conducted on data from 2240 consecutive CHD patients undergoing surgery. Interventions: Ninety-three pre and intraoperative variables are used as ICU LOS predictors. Measurements and main results: Utilizing regression and clustering methodologies for ICU LOS (ICU Length of Stay) estimation, the Light Gradient Boosting Machine, using regression, achieved a Mean Squared Error (MSE) of 15.4, 11.8, and 15.2 days for training, testing, and unseen data. Key predictors included metrics such as "Mechanical Ventilation Duration", "Weight on Surgery Date", and "Vasoactive-Inotropic Score". Meanwhile, the clustering model, Cat Boost Classifier, attained an accuracy of 0.6917 and AUC of 0.8559 with similar key predictors. Conclusions: Patients with higher ventilation times, vasoactive-inotropic scores, anoxia time, cardiopulmonary bypass time, and lower weight, height, BMI, age, hematocrit, and presurgical oxygen saturation have longer ICU stays, aligning with existing literature.

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This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.

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Background: Identifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903. Methods: A total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms. Results: For both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified. Conclusion: These results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

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PURPOSE: There are currently more than 480 primary immune deficiency (PID) diseases and about 7000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. In a proof-of-concept data mining study, we aimed to predict PID diagnoses using a supervised machine learning algorithm based on classification tree boosting. METHODS: Through a data query at the USIDNET registry we obtained a database of 2396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 286 features and all 12 diagnoses to include in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. After a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight to correct for imbalanced classification. RESULTS: The twelve PID diagnoses were CVID (1098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, PID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46-0.87, Gini 0.07-0.75, and LogLoss 0.09-8.55. DISCUSSION: Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.

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Identifying risk factors associated with COVID-19 lethality is crucial in combating the ongoing pandemic. In this study, we developed lethality predictive models for each epidemiological wave and for the overall dataset using the Extreme Gradient Boosting technique and analyzed them using Shapley values to determine the contribution levels of various features, including demographics, comorbidities, medical units, and recent medical information from confirmed COVID-19 cases in Mexico between February 23, 2020, and April 15, 2022. The results showed that pneumonia and advanced age were the most important factors predicting patient death in all cohorts. Additionally, the medical unit where the patient received care acted as a risk or protective factor. IMSS medical units were identified as high-risk factors in all cohorts, except in wave four, while SSA medical units generally were moderate protective factors. We also found that intubation was a high-risk factor in the first epidemiological wave and a moderate-risk factor in the following waves. Female gender was a protective factor of moderate-high importance in all cohorts, while being between 18 and 29 years old was a moderate protective factor and being between 50 and 59 years old was a moderate risk factor. Additionally, diabetes (all cohorts), obesity (third wave), and hypertension (fourth wave) were identified as moderate risk factors. Finally, residing in municipalities with the lowest Human Development Index level represented a moderate risk factor. In conclusion, this study identified several significant risk factors associated with COVID-19 lethality in Mexico, which could aid policymakers in developing targeted interventions to reduce mortality rates.

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This paper proposes and analyzes an immune-structured population model of tilapia subject to Tilapia Lake Virus (TiLV) disease. The model incorporates within-host dynamics, used to describe the interaction between the pathogen, the immune system and the waning of immunity. Individuals infected with a low dose acquire a low immunity level and those infected with a high dose acquire a high level of immunity. Since individuals' immune status plays an important role in the spread of infectious diseases at the population level, the within-host dynamics are connected to the between-host dynamics in the population. We define an explicit formula for the reproductive number [Formula: see text] and show that the disease-free equilibrium is locally asymptotically stable when [Formula: see text], while it is unstable when [Formula: see text]. Furthermore, we prove that an endemic equilibrium exists. We also study the influence of the initial distribution of host resistance on the spread of the disease, and find that hosts' initial resistance plays a crucial role in the disease dynamics. This suggests that the genetic selection aiming to improve hosts' initial resistance to TiLV could help fight the disease. The results also point out the crucial role played by the inoculum size. We find that the higher the initial inoculum size, the faster the dynamics of infection. Moreover, if the initial inoculum size is below a certain threshold, it may not result in an outbreak at the between-host level. Finally, the model shows that there is a strong negative correlation between heterogeneity and the probability of pathogen invasion.

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A cellulose microfluidic pH boosting layer adapts a non-enzymatic copper oxide glucose sensor strip for neutral pH samples. This adaptation allows the non-enzymatic technology to realize in-situ glucose measurements. A three-electrode system is constructed to test samples in a classical electrochemical cell, and in a sensing strip to test the microfluidic system. The system consists of copper oxide as working electrode, and silver and carbon paints as reference, and counter electrodes, respectively. The fabrication of the pH-boosting layer is made with natural cellulose. Within this layer are NaOH crystals, grown by a drying processes after immersion of cellulose in a concentrated solution of NaOH. The microfluidic layer is placed on top of the sensing electrodes, and while it transports the fluid sample to the sensing electrodes, the fluid dissolves the NaOH crystals, increasing the pH of the sample. This change allows the non-enzymatic mechanism to sense the glucose concentration in the fluid. Our system shows the capability to measure glucose in samples with neutral pH and human blood with a sensitivity of 70 µA/mM cm2, enough to distinguish between hypoglycemia and hyperglycemia.

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Despite the current advances in global vaccination against SARS-CoV-2, boosting is still required to sustain immunity in the population, and the induction of sterilizing immunity remains as a pending goal. Low-cost oral immunogens could be used as the basis for the design of affordable and easy-to-administer booster vaccines. Algae stand as promising platforms to produce immunogens at low cost, and it is possible to use them as oral delivery carriers since they are edible (not requiring complex purification and formulation processes). Herein, a Chlamydomonas-made SARS-CoV-2 RBD was evaluated as an oral immunogen in mice to explore the feasibility of developing an oral algae-based vaccine. The test immunogen was stable in freeze-dried algae biomass and able to induce, by the oral route, systemic and mucosal humoral responses against the spike protein at a similar magnitude to those induced by injected antigen plus alum adjuvant. IgG subclass analysis revealed a Th2-bias response which lasted over 4 months after the last immunization. The induced antibodies showed a similar reactivity against either Delta or Omicron variants. This study represents a step forward in the development of oral vaccines that could accelerate massive immunization.

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The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

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The surging demand for commodity crops has led to rapid and severe agricultural frontier expansion globally and has put producing regions increasingly under pressure. However, knowledge about spatial patterns of agricultural frontier dynamics, their leading spatial determinants, and socio-ecological trade-offs is often lacking, hindering contextualized decision making towards more sustainable food systems. Here, we used inventory data to map frontier dynamics of avocado production, a cash crop of increasing importance in global diets, for Michoacán, Mexico, before and after the implementation of the North American Free Trade Agreement (NAFTA). We compiled a set of environmental, accessibility and social variables and identified the leading determinants of avocado frontier expansion and their interactions using extreme gradient boosting. We predicted potential expansion patterns and assessed their impacts on areas important for biodiversity conservation. Avocado frontiers expanded more than tenfold from 12,909 ha (1974) to 152,493 ha (2011), particularly after NAFTA. Annual precipitation, distance to settlements, and land tenure were key factors explaining avocado expansion. Under favorable climatic and accessibility conditions, most avocado expansion occurred on private lands. Contrary, under suboptimal conditions, most avocado expansion occurred on communal lands. Large areas suitable for further avocado expansion overlapped with priority sites for restoration, highlighting an imminent conflict between conservation and economic revenues. This is the first analysis of avocado frontier dynamics and their spatial determinants across a major production region and our results provide entry points to implement government-based strategies to support small-scale farmers, mostly those on communal lands, while trying to minimize the socio-environmental impacts of avocado production. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10113-022-01883-6.

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Currently, the whole world is facing a life-threatening novel coronavirus 2019 (COVID-19) pandemic. Natural products are well-known for their potential role against viral disease, and some anti-viral agents have been developed to combat these diseases. Herein, the authors investigated the possible effects of this Holy plant Nigella sativa L. (NS), against coronavirus, using evidence-based and mechanistic approaches to conclude the immune-boosting and alleviation of respiratory systemeffects of NS. The pharmacological studies established a prominent role in treating various respiratory, immune systems, cardiovascular, skin, and gastrointestinal disorders. Literature supported the significant anti-viral role and showed an inhibitory role for NS against MHV-A59 CoV (mouse-hepatitis virus­A59) infected Hela, i.e., HeLaCEACAM1a (HeLa-epithelial carcinoembryonic antigen-related cell adhesion molecule 1a) cell. NS is a safe herbal product or dietary supplement and could be an effective and affordable community adjuvant treatment for coronavirus in the current scenario.

Actualmente, el mundo entero se enfrenta a una pandemia del nuevo coronavirus 2019 (COVID-19) que amenaza la vida. Los productos naturales son bien conocidos por su papel potencial contra las enfermedades virales, y se han desarrollado algunos agentes antivirales para combatir estas enfermedades. En este documento, los autores investigaron los posibles efectos de esta planta sagrada Nigella sativa L. (NS), contra el coronavirus, utilizando enfoques mecanicistas y basados en la evidencia para concluir el refuerzo inmunológico y el alivio de los efectos del SN en el sistema respiratorio. Los estudios farmacológicos establecieron un papel destacado en el tratamiento de diversos trastornos respiratorios, del sistema inmunológico, cardiovasculares, cutáneos y gastrointestinales. La literatura apoyó el importante papel antivírico y mostró un papel inhibidor de NS contra células Hela infectadas con MHV-A59 CoV (virus de la hepatitis de ratón-A59), es decir, HeLaCEACAM1a (molécula de adhesión celular 1a relacionada con el antígeno carcinoembrionario epitelial de HeLa). NS es un producto a base de hierbas o un suplemento dietético seguro y podría ser un tratamiento adyuvante comunitario eficaz y asequible para el coronavirus en el escenario actual.

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It was reported that the Brazilian city, Manaus, likely exceeded the herd immunity threshold (presumably 60-70%) in November 2020 after the first wave of COVID-19, based on the serological data of a routine blood donor. However, a second wave started in November 2020, when an even higher magnitude of deaths hit the city. The arrival of the second wave coincided with the emergence of the Gamma (P.1) variant of SARS-CoV-2, with higher transmissibility, a younger age profile of cases, and a higher hospitalization rate. Prete et al. (2020 MedRxiv 21256644) found that 8 to 33 of 238 (3.4-13.9%) repeated blood donors likely were infected twice in Manaus between March 2020 and March 2021. It is unclear how this finding can be used to explain the second wave. We propose a simple model which allows reinfection to explain the two-wave pattern in Manaus. We find that the two waves with 30% and 40% infection attack rates, respectively, and a reinfection ratio at 3.4-13.9%, can explain the two waves well. We argue that the second wave was likely because the city had not exceeded the herd immunity level after the first wave. The reinfection likely played a weak role in causing the two waves.

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The process of forest degradation, along with deforestation, is the second greatest producer of global greenhouse gas emissions. A key challenge that remains unresolved is how to quantify the critical threshold that distinguishes a degraded from a non-degraded forest. We determined the critical threshold of forest degradation in mature stands belonging to the temperate evergreen rain forest of southern Chile by quantifying key forest stand factors characterizing the forest degradation status. Forest degradation in this area is mainly caused by high grading, harvesting of fuelwood, and sub-canopy grazing by livestock. We established 160 500-m2 plots in forest stands that represented varied degrees of alteration (from pristine conditions to obvious forest degradation), and measured several variables related to the structure and composition of the forest stands, including exotic and native species richness, soil nutrient levels, and other landscape-scale variables. In order to identify classes of forest degradation, we applied multivariate and machine-learning analyses. We found that richness of exotic species (including invasive species) with a diameter at breast height (DBH) < 10 cm and tree density (N, DBH > 10 cm) were the two composition and structural variables that best explained the forest degradation status, e.g., forest stands with five or more exotic species were consistently found more associated with degraded forest and stands with N < 200 trees/ha represented degraded forests, while N > 1,000 trees/ha represent pristine forests. We introduced an analytical methodology, mainly based on machine learning, that successfully identified the forest degradation status that can be replicated in other scenarios. In conclusion, here by providing an extensive data set quantifying forest and site attributes, the results of this study are undoubtedly useful for managers and decision makers in classifying and mapping forests suffering various degrees of degradation.

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Investigating the factors that affect broiler chicken prices in Turkey is vital for understanding market formation. The parameters and factors likely to influence the price of broiler chicken were analyzed for the period between 2010-2020 in Turkey. The study adopted the boosting regression model to predict the correlation between broiler chicken consumer price and variable factors like broiler feed, corn, soybean meal, wheat prices, the dollar exchange rate, producer price index (PPI), and agricultural PPI. The accuracy of the estimation of the regression model created according to the results of the analysis was calculated as 86%. The producer price index variable caused the highest relative impact (25.63%) on broiler chicken meat prices. The highest positive correlation was obtained between the producer price index and the agricultural PPI (r = 0.984). Thus, it was determined that chicken prices were affected by feed raw material prices and the general economic conditions in Turkey. In addition to improving the prevailing economic conditions, an effective price control mechanism is required to prevent excessive price fluctuations in the sector. Simultaneously, it is essential to create policies to reduce input costs.(AU)

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Abstract Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray. However, a sufficient number of radiologists are needed to interpret the X-ray images. High rates of child deaths due to pneumonia have been encountered. Using this type of system, a diagnosis can be made quickly, and then the treatment process can be started rapidly. This study aims to diagnose pneumonia using boosting techniques by the automatic tool. With this tool, the workload of the doctors/radiologists is reduced. The boosting techniques are a family of machine learning techniques. Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are used for the study. These techniques are chosen because of their simulation duration for modeling and convenience for real-time applications. L2 normalization and feature selection are applied to the data before applying the techniques. Random Forest Classifier is used for feature selection estimator. After the modeling, Categorical Boosting algorithm is observed as faster than the other techniques. Simulation duration is obtained as 0.7 seconds. By using this automatic tool, the user can be able to upload the desired X-ray image to the system and get the result easily from the screen without any radiologist/doctor.

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The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

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This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De Carvalho, F.A.T. Like the previously mentioned methods, the proposed regression models consider the midpoints and half of the length of the intervals as additional variables. We considered various methods to fit the regression models, including tree-based models, K-nearest neighbors, support vector machines, and neural networks. The approaches proposed in this paper were applied to a real dataset and to synthetic datasets generated with linear and nonlinear relations. For an evaluation of the methods, the root-mean-squared error and the correlation coefficient were used. The methods presented herein are available in the the RSDA package written in the R language, which can be installed from CRAN.

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A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.(AU)

Um sistema de monitoramento de comportamento de vacas baseado na Internet das Coisas (IoT) foi projetado e implementado através do uso de acelerômetro tri-axial, Microcontrolador MSP430, módulo de rádio, frequência sem fio (RF), e um portátil. O sistema implementado mediu o comportamento do movimento da vaca e transmitiu dados de aceleração ao portátil através do módulo RF sem fio. Os resultados foram exibidos no portátil em um gráfico 2D, através do qual os padrões de comportamento das vacas foram previstos. Os dados medidos do sistema foram analisados usando o Multi-retropropagação-Adaptativa algoritmo de Boosting para determinar o estado comportamental específico das vacas. O sistema desenvolvido pode ser usado para aumentar a classificação de desempenho de vaca comportamento através da detecção de aceleração de dados. A precisão excedeu 90% de todas as categorias de classificação de comportamento e a especificidade do andar normal atingiu 96.98%. A sensibilidade foi boa para todos os padrões de comportamento, exceto em pé e deitada, com um máximo de 87.23% para ficar de pé. No geral, o sistema baseado em IoT fornece medição precisa e remota do comportamento da vaca, e o algoritmo de conjunto de classificação pode efetivamente reconhecer vários padrões de comportamento em vacas leiteiras. Pesquisas futuras irão melhorar os parâmetros do algoritmo de classificação e aumentar a quantidade de vacas matriculadas. Uma vez que a funcionalidade e confiabilidade do sistema foram confirmadas em larga escala, a comercialização pode se tornar possível.(AU)

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