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Tree diseases contribute to significant economic and food losses in the agricultural sector. Early detection of phytosanitary problems in trees with non-destructive methods is essential to guarantee sustainable orange production. This study presents the findings of a designed methodology conducted to identify diseased orange trees in an orchard situated in the citrus belt of Mexico, specifically in the Rioverde region of San Luis Potosi. To accomplish this, we captured images using a multispectral camera with very high spatial resolution, which was mounted on an unmanned aerial vehicle. These images were used to construct a georeferenced orthomosaic of the orchard. Six thematic classes were established to distinguish various health levels among the trees. We employed several supervised classification algorithms at the pixel level, including Random Forest (RF), K-Nearest Neighbor (KNN), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Maximum Likelihood (ML). Considering the classification accuracy achieved by each algorithm, they can be ranked as follows: Maximum Likelihood (ML) with 88.10%, Support Vector Machine (SVM) with 77.38%, Spectral Angle Mapper (SAM) with 76.19%, K-Nearest Neighbor (KNN) with 64.68%, and Random Forest (RF) with 61.90%. These results successfully identified the phytosanitary status of all the trees in the orchard with an acceptable level of accuracy, providing valuable management information for the grower. [ABSTRACT FROM AUTHOR]

A growing body of empirical research in Latin America reveals a correlation between skin colour and disadvantages in income, health, education, and employment. The impact of skin colour in the political sphere, however, remains severely understudied. Is there any evidence to suggest racialised political underrepresentation in Latin America? To analyse the status of ethno-racial representation in Mexico, this paper uses the Classification Algorithm for Skin Colour (CASCo) to collect skin colour data of the (3,000) members of the last six legislatures of the Mexican Chamber of Deputies. The findings suggest that, while legislatures tend to be relatively diverse, positions of legislative leadership are unproportionally restricted to legislators with light skin colour. This article not only provides much-needed empirical data on the political impact of skin colour in Latin America, but it reveals the sociocultural complexities of the political underrepresentation of racialised groups in Mexico. [ABSTRACT FROM AUTHOR]

The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID‐19 signs in CT scans included ground‐glass opacity (GGO), followed by crazy‐paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. [ABSTRACT FROM AUTHOR]

Summary: Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features. [ABSTRACT FROM AUTHOR]

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Bimodal precipitation is a globally observed and regionally significant event that has a significant influence on the agriculture, public health, and insurance needs of associated regions. Many studies have focused on the mechanisms behind the generation and development of this event; however, little research into its characteristics exists due to a lack of a widely accepted method for accurate detection and quantification. Using a function collection containing various methods, different methods can be compared in terms of their performance in the detection and quantification of bimodal precipitation signals, allowing the proposal of appropriate criteria for method choice in various study types. Five methods (Mosiño and García, 1966; Curtis, 2002; Angeles et al., 2010; Karnauskas et al., 2013; Zhao et al., 2020) are adapted to the Climate Prediction Centre data during 1979–2017 in the domain of southern Mexico and Central America, and their performances are evaluated and compared. While outputs from the five methods reach general consistence for strong bimodal features over the Pacific side of Central America and Yucatán Peninsula, some biases are identified, specifically shown by the fact that methods using monthly climatological data demonstrates bimodal precipitation over the Caribbean side of Central America, while those using daily annual data indicate the existence of bimodal precipitation over the Pacific side of southern Mexico. By comparing two typical algorithms, we determined that this bias was induced by the limitation of temporal resolution in monthly climatological data and the nature of algorithms applying daily annual data. As part of a case study, a cluster algorithm was applied to outputs from an algorithm using daily annual precipitation, and a classification algorithm was used to test clustering performance. The resultant general high accuracy shows that annual bimodal signals offer good adaption to cluster and other potential machine learning algorithms. [ABSTRACT FROM AUTHOR]

The determination of impact significance is a critical step in Environmental Impact Assessment (EIA). Yet, the notion of impact significance is inherently subjective, immersed in uncertainty, and influenced by competing interests. This article addresses the difficult task of collaboratively determining the significance of the environmental impacts of projects in dispute. We introduce an approach centered on analytic deliberation that entails three steps: analysis, translation, and deliberation. Analysis combines and synthesizes impact significance into a single index using a fuzzy pattern classification algorithm, which also integrates the inherent uncertainty of impact assessment. Translation transforms the index into understandable and accessible spatial representations through geovisualization. Deliberation promotes safe spaces for collaboration in which stakeholders examine evidence openly and reflect on the conflicting values underlying their perspectives. The outcome of the approach is a compromise on the most meaningful representation of impact significance, if one exists, as the basis for agreement on the appropriate prevention, mitigation, and compensation measures of a project. Hence, it helps to reach agreement on the impact prevention, mitigation, and compensation strategy prior to the consultation process required by law in most countries. We illustrate our approach through an example from Mexico. • Analytic deliberation enhances collaborative decision making on projects in dispute. • Impact significance should be determined through collaboration. • Collaboration should include an open examination of conflicting perspectives. • Geovisualization is effective for eliciting impact significance. • Safe spaces for collaboration are essential for building consensus. [ABSTRACT FROM AUTHOR]

Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well-known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4-6 weeks of the course and trustworthy enough to be used in an early warning system. [ABSTRACT FROM AUTHOR]