This work developed an effective and eco-friendly analytical method to support quality control for Brazilian Bill proposal No. 5332/2009. The bill advocates the creation of the “Brazilian bread” by mandating the incorporation of cassava byproducts into wheat flour, with the aim of reducing Brazil’s external dependency on wheat imports and promoting domestic cassava family farming. For this, a handheld near-infrared (NIR) spectrophotometer was utilized for on-site determination of cassava starch in commercial wheat flour. The quantitative analysis, which utilized NIR spectra pre-processed with offset correction (OFF) combined with linear baseline correction (LBC), alongside the successive projections algorithm for interval selection in partial least squares (iSPA-PLS), exhibited superior predictive capability, achieving a relative error of prediction below 8%. Furthermore, the classification process, which involved NIR spectra pre-processed with LBC and iSPA-PLS for discriminant analysis (iSPA-PLS-DA), yielded an impressive 94% overall accuracy, with only 3 misclassifications in the test set. ecofriendly eco friendly No 53322009 5332 2009 5332/2009 bread flour Brazils Brazil s farming this nearinfrared near infrared (NIR onsite site preprocessed pre processed OFF (OFF LBC, , (LBC) iSPAPLS, iSPAPLS iSPA PLS (iSPA-PLS) capability 8 8% Furthermore process iSPAPLSDA, iSPAPLSDA DA (iSPA-PLS-DA) 94 accuracy set 5332200 533 200 5332/200 (LBC (iSPA-PLS (iSPA-PLS-DA 9 533220 53 20 5332/20 53322 5 2 5332/2 5332/

ABSTRACT The estimation of evapotranspiration (ET) and crop water requirements are crucial for the proper management and allocation of water resources in terms of quantity, quality, and timeliness. Therefore, remote sensing estimation of ET using the SEBAL algorithm (Surface Energy Balance Algorithm for Land) can provide spatio-temporal, non-punctual data, unlike traditional calculations relying on the nearest meteorological station. This research analyzed ET using SEBAL, based on ten Landsat 8 satellite images processed with a program developed in the Model Builder of ArcGis® version 10.2. The analysis was conducted during the vegetative period of starchy corn from May to October 2016. Validation of the results happened with a drainage lysimeter installed in a monitoring plot. Additionally, the statistical indices - such as percentage relative error (PRE) (0,09), root mean square error (RMSE) (0,30), R2 (0,92), and Nash-Sutcliffe efficiency (NASH) (0,91) - indicated a good correlation of ET for starchy corn in the central highlands of Peru. The ET identified at ten monitoring points ranged from 1,05 to 7,79 mm d-1. (ET quantity quality timeliness Therefore Surface Land spatiotemporal, spatiotemporal spatio temporal, temporal spatio-temporal nonpunctual non punctual data station ArcGis 102 10 2 10.2 2016 plot Additionally PRE (PRE 0,09, 009 0,09 , 0 09 (0,09) RMSE (RMSE 0,30, 030 0,30 30 (0,30) R 0,92, 092 0,92 92 (0,92) NashSutcliffe Nash Sutcliffe NASH (NASH 0,91 091 91 (0,91 Peru 105 1 05 1,0 779 7 79 7,7 d1. d1 d 1. d-1 10. 201 00 0,0 (0,09 03 0,3 3 (0,30 0,9 9 (0,92 (0,9 1, 77 7, d- 20 0, (0,0 (0,3 (0, (0 (

RESUMO As pimenteiras ornamentais apresentam variabilidade genética que pode ser acessada por meio de caracteres morfológicos e moleculares. A seleção de genótipos para formação de população base para o melhoramento pode ser realizada por meio de análise conjunta de vários tipos de dados, proporcionando maior precisão na seleção. Nessa perspectiva, este estudo teve como objetivo avaliar a diversidade entre acessos de pimenta com base na análise de características fenotípicas e marcadores moleculares e selecionar os melhores para serem utilizados como parentais em programas de melhoramento. O trabalho foi realizado no Centro de Ciências Agrárias da Universidade Federal da Paraíba, Paraíba, Brasil. Foram utilizados 16 genótipos de pimenteiras ornamentais, e caracterizados quanto a oito caracteres quantitativos, nove qualitativos e 18 pares de iniciadores microssatélites. As análises simultâneas das variáveis foram realizadas usando o método de agrupamento de Tocher, agrupamento do algoritmo de Ward e a matriz de dissimilaridades. Os métodos de agrupamentos foram eficientes na separação dos genótipos, identificando variabilidade genética e precisão na seleção por meio da análise conjunta de dados quantitativos, qualitativos e moleculares. Diferentes grupos foram formados entre os genótipos pelo método de Tocher (seis grupos) e pelo método de Ward (três grupos). Existe variabilidade genética entre os genótipos de pimenteiras ornamentais, considerando análise conjunta de dados quantitativos, qualitativos e moleculares. Características qualitativas são importantes na identificação de divergência genética entre acessos de pimenteira ornamental. Os genótipos UFPB 46, 134, 137, 443 e 449, o mini pimentão Akamu e a cultivar Calypso são indicados para seleção, podem ser utilizados para realizar cruzamentos e dar continuidade ao programa de melhoramento genético. perspectiva Paraíba Brasil 1 quantitativos microssatélites dissimilaridades seis três grupos. . ornamental 46 134 137 44 449 genético 4 13

ABSTRACT Ornamental pepper plants have genetic variability, which can be accessed through morphological and molecular traits. Genotype selection to form the base population for breeding can be performed through the joint analysis of several types of data, providing greater selection accuracy. From this perspective, this study aimed to evaluate the diversity among pepper accessions based on analysis of phenotypic traits and molecular markers and to select the best ones to use as parents in breeding programs. The study was carried out at the Centro de Ciências Agrárias of the Universidade Federal da Paraíba, Paraíba, Brazil. Sixteen ornamental pepper genotypes were used and characterized for eight quantitative traits, nine qualitative traits, and 18 pairs of microsatellite primers. Simultaneous variable analyses were performed using Tocher’s clustering method, Ward’s clustering algorithm, and the dissimilarity matrix. The clustering methods were efficient in separating the genotypes, identifying genetic variability, and accuracy in the selection through the joint analysis of quantitative, qualitative, and molecular data. Different groups were formed among the genotypes by Tocher’s method (six groups) and Ward’s method (three groups). There is genetic variability among ornamental pepper genotypes considering the joint analysis of quantitative, qualitative, and molecular data. Qualitative traits are important in the identification of genetic divergence among ornamental pepper accessions. The UFPB genotypes 46, 134, 137, 443 and 449, the mini pepper Akamu, and the cultivar Calypso are indicated for selection and can be used to carry out crosses and continue the breeding program. data perspective programs Paraíba Brazil 1 primers Tochers Tocher s Wards Ward algorithm matrix six three groups. . 46 134 137 44 449 Akamu program 4 13

Abstract Introduction: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD. Methods: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil. The sample included adult volunteers classified according to the presence of mild or severe CKD. For MS tracking, the k-nearest neighbors (KNN) classifier algorithm was used with the following inputs: gender, smoking, neck circumference, and waist-to-hip ratio. Results were considered significant at p < 0.05. Results: A total of 196 adult patients were evaluated with a mean age of 44.73 years, 71.9% female, 69.4% overweight, and 12.24% with CKD. Of the latter, 45.8% had MS, the majority had up to 3 altered metabolic components, and the group with CKD showed statistical significance in: waist circumference, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The KNN algorithm proved to be a good predictor for MS screening with 79% accuracy and sensitivity and 80% specificity (area under the ROC curve – AUC = 0.79). Conclusion: The KNN algorithm can be used as a low-cost screening method to evaluate the presence of MS in people with CKD. Introduction (CKD (MS factors Methods crosssectional cross sectional Luís MA Brazil tracking knearest k nearest (KNN inputs gender smoking circumference waisttohip hip ratio 005 0 05 0.05 19 4473 44 73 44.7 years 719 71 9 71.9 female 694 69 4 69.4 1224 12 24 12.24 latter 458 45 8 45.8 components pressure glucose 79 80 area 0.79. 079 0.79 . 0.79) Conclusion lowcost low cost 00 0.0 1 447 7 44. 71. 6 69. 122 2 12.2 45. 07 0.7 0. 12.

Resumo Introdução: A doença renal crônica (DRC) e a síndrome metabólica (SM) são reconhecidas como problemas de saúde pública relacionados ao excesso de peso e a fatores cardiometabólicos. O objetivo deste estudo foi desenvolver um modelo para prever a SM em pessoas com DRC. Métodos: Este foi um estudo transversal prospectivo de pacientes de um centro de referência em São Luís, MA, Brasil. A amostra incluiu voluntários adultos classificados de acordo com a presença de DRC leve ou grave. Para o rastreamento da SM, o algoritmo de classificação k-nearest neighbors (KNN) foi utilizado com os seguintes dados: sexo, tabagismo, circunferência do pescoço e relação cintura-quadril. Os resultados foram considerados significativos com p < 0,05. Resultados: Foram avaliados 196 pacientes adultos com média de idade de 44,73 anos, 71,9% do sexo feminino, 69,4% com sobrepeso e 12,24% com DRC. Desses últimos, 45,8% apresentaram SM, a maioria tinha até 3 componentes metabólicos alterados, e o grupo com DRC apresentou significância estatística em: circunferência da cintura, pressão arterial sistólica, pressão arterial diastólica e glicemia de jejum. O algoritmo KNN comprovou ser um bom preditor para a triagem de SM com acurácia e sensibilidade de 79% e especificidade de 80% (área sob a curva ROC – AUC = 0,79). Conclusão: O algoritmo KNN pode ser usado como um método de triagem de baixo custo para avaliar a presença de SM em pessoas com DRC. Introdução (DRC (SM cardiometabólicos Métodos Luís MA Brasil grave knearest k nearest (KNN dados tabagismo cinturaquadril. cinturaquadril cintura quadril. quadril cintura-quadril 005 0 05 0,05 Resultados 19 4473 44 73 44,7 anos 719 71 9 71,9 feminino 694 69 4 69,4 1224 12 24 12,24 últimos 458 45 8 45,8 alterados sistólica jejum 79 80 área 0,79. 079 0,79 . 0,79) Conclusão 00 0,0 1 447 7 44, 71, 6 69, 122 2 12,2 45, 07 0,7 0, 12,

Abstract: The purpose of this article is to achieve optimal coordination for timed overcurrent relays modeled in a test system. The traditional adjustment that is usually used in the field is taken as the base criterion to determine the improvements achieved with the implemented optimization methods. Optimal parameters are determined for two timed overcurrent relays modeled on the IEEE 13-node test system using PowerFactory software. The optimization algorithms are coded in Python. PowerFactory links with Python to generate interoperability between the algorithm and the electrical system. Satisfactory results are achieved from the COA and GSA algorithms with a reduction of more than 50% in relay operating times.

Resumen: El objeto de este artículo es conseguir la coordinación óptima para los relés de sobrecorriente temporizados, modelados en un sistema de prueba. El ajuste tradicional que se emplea usualmente en el campo se toma como criterio base para determinar las mejoras conseguidas con los métodos de optimización implementados. Se determinan parámetros óptimos para dos relés de sobrecorriente temporizados modelados en el sistema de prueba de 13 nodos de la IEEE mediante el software PowerFactory. Los algoritmos de optimización son codificados en Python. Gracias a la interfaz que presenta PowerFactoy con Python, cada algoritmo se ejecuta dentro del sistema eléctrico. Se consiguen resultados satisfactorios de los algoritmos COA y GSA con una reducción de más del 50 % en los tiempos de operación de los relés.

Abstract: This investigation proposes to develop a methodology based on the mean-variance mapping optimization (MVMO) algorithm to reduce the active power losses of the subtransmission system of the Electric Company Quito (EEQ), determining the best operating point of the system, considering the state of taps of the transformers, the capacitor banks and the contribution of reagents from the generation plants that the company currently has. EEQ. The same ones that, when operating in an adequate manner, lead to reducing the value of active power losses in the network, thus improving the operating conditions of the EEQ Electrical Power System.

Resumen: Esta investigación propone desarrollar una metodología basada en el algoritmo de optimización de mapeo media varianza (MVMO) para reducir las pérdidas de potencia activa del sistema de subtransmisión de la Empresa Eléctrica Quito (EEQ), determinando el mejor punto de operación del sistema, considerando el estado de tap’s de los transformadores, los bancos de capacitores y del aporte de reactivos de las centrales de generación con las que cuenta actualmente la EEQ. Los mismos que al operar de una manera adecuada llevan a reducir el valor de las pérdidas de potencia activa en la red, mejorando así las condiciones operativas del Sistema Eléctrico de Potencia de la EEQ.

ABSTRACT This study analyzed the leaf spectral response of three native timber forest species in the tropical dry forest: Diomate (Astronium graveolens Jacq.), Choibá (Dipteryx oleifera Benth.), and Algarrobo (Hymenaea courbaril L.). The study was conducted at the León Morales Soto Arboretum and Palmetum, at the Universidad Nacional de Colombia in Medellín, Antioquia, Colombia. Spectral data from the leaves were collected in situ using the portable spectroradiometer ASD FieldSpec HandHeld-2, which operates with a spectral resolution of 1 nm (resampled to 10 nm) and covers a spectral range between 325 and 1,075 nm (limited to 400-900 nm). Based on the measurements, the behavior and spectral variability of the species were evaluated. One-factor Analysis of Variance and Mann-Whitney-Wilcoxon U-test were implemented in reflectance spectra to select the optimal narrow bands for species discrimination. The classification capacity of the selected narrow bands was assessed using the K-nearest neighbors' algorithm. It was found that A. graveolens and H. courbaril exhibited spectral signatures typical of healthy vegetation, while D. oleifera showed spectral changes during the early stages of senescence. Regarding spectral separability, 23 narrow bands in the visible region and near-infrared region were identified as optimal for distinguishing the plant species. The supervised classification algorithm applied to these 23 narrow bands achieved an overall accuracy of 95.8%. In conclusion, these findings provide valuable insights into the spectral response of important tropical species and contribute to their conservation efforts by enhancing understanding of their unique spectral characteristics in diverse and heterogeneous ecosystems like tropical forests.

RESUMEN Esta investigación analizó la respuesta espectral foliar de tres especies forestales maderables nativas del bosque seco tropical, Diomate (Astronium graveolens Jacq.), Choibá (Dipteryx oleifera Benth.) y Algarrobo (Hymenaea courbaril L.). El estudio se efectuó en el Arboretum y Palmetum León Morales Soto de la Universidad Nacional de Colombia, sede Medellín, Antioquia, Colombia. Los datos espectrales de las hojas se colectaron in situ con el espectrorradiómetro portátil ASD FieldSpec HandHeld-2, que trabaja con una resolución espectral de 1 nm (remuestreada a 10 nm) y un rango espectral entre 325 y 1.075 nm (acotado entre 400 y 900 nm). A partir de las mediciones, se evaluó el comportamiento y la variabilidad espectral de las especies. Se implementó el Análisis de Varianza de un factor y la prueba U de Mann - Whitney - Wilcoxon en los espectros de reflectancia con el fin de seleccionar las bandas estrechas óptimas para discriminar las especies. Se evaluó la capacidad de clasificación de las especies en las bandas estrechas seleccionadas, utilizando el algoritmo de los K vecinos más cercanos. Encontrando que A. graveolens e H. courbaril presentaron firmas espectrales típicas de la vegetación saludable y D. oleifera evidenció cambios espectrales en el período inicial de la senescencia. Respecto a la separabilidad espectral, se encontraron 23 bandas estrechas en la región visible e infrarrojo cercano óptimas para diferenciar las especies vegetales. El algoritmo de clasificación supervisada aplicado en las 23 bandas estrechas, tuvo una precisión general del 95,8%. En conclusión, estos hallazgos proporcionan valiosos conocimientos sobre la respuesta espectral de importantes especies tropicales y contribuyen a sus esfuerzos de conservación al mejorar la comprensión de sus características espectrales únicas en ecosistemas diversos y heterogéneos como el bosque tropical.

ABSTRACT Colombia is the ninth largest producer of dragon fruit in the world and the department of Boyacá is the largest with 440 hectares planted. Bacterial phytopathogens can cause pitahaya fruit losses and low yields by producing stem soft rot. This research aimed to study and identified possible agents of bacterial diseases of pitahaya in Boyacá, Colombia. Thirteen farms in the region were selected to take 20 samples by means of a targeted and random sampling of stem and fruit tissues with soft rot symptoms in the early stages of the disease. A process of microbiological isolation, biochemical and molecular taxonomic identification of the isolated bacteria. The 16s rRNA gene sequences of the V2-V5 region were edited by removing the primers, assembling and obtaining the consensus sequence of the primers 1100R-337F and 800R -518F. The phylogenetic analysis was performed by BLAST at NCBI, using the "Classifier" and "SeqMatch" tools from the RDP website, and phylogenetic trees were created by multiple alignment using the MUSCLE algorithm and the Tamura Nei distance method was performed. Signs like blisters and mucilaginous fluids, symptoms like yellow and brown chlorotic spots, soft rot, and liquefaction were identified, and possible vectors like flies and ants were report. Twenty-five bacterial morphotypes were identified classifieds in 4 phyla, 9 families and 13 genera. The analysis of the 16S rRNA gene sequences of the bacterial strains showed a 98 to 100% identity with Enterobacter cloacae, Pectobacterium carotovora and Paenibacillus glucanolyticus, reported in other studies as causing the soft rot of the pitahaya stem and fruit. New species were reported as possible pathogenic bacteria of pitahaya: Pantoea cypripedii, Kluyvera intermedia and Klebsiella oxytoca. Infectivity assays did not have positive results. The microbiota identified in the stages of the necrotrophic phase or final stage of soft rot of the stem belong to the genera Achromobacter, Sphingobacterium, Pseudomonas, Klebsiella, Paenibacillus, Bacillus, Stenotrophomonas and Microbacterium. In the fly Leptoglossus zonatus associated with the crop were identified Pseudomonas fulva and Lysinibacillus fusiformis were identified. This is also the first official report of a complex of possible phytopathogen bacteria of the order Enterobacterales (Enterobacteriacea, Erwiniaceae and Pectobacteriaceae family) in symptoms by bacterial disease on pitahaya Colombian crops. Identification of the bacteria in a pathogenic system can guide chemical and biological control practices in order to increase the productive and export potential of exotic and orphan crops from small local farmings.

RESUMEN Colombia es el noveno productor de pitahaya amarilla en el mundo y el Departamento de Boyacá es el mayor productor con 440 hectáreas plantadas. Los fitopatógenos bacterianos pueden causar pérdidas de frutos de pitahaya y bajos rendimientos al producir la pudrición blanda de los tallos. El objetivo de esta investigación fue estudiar e identificar posibles agentes de enfermedades bacterianas de la pitahaya en Boyacá, Colombia. Trece (13) fincas de la región fueron seleccionadas para la toma de 20 muestras mediante un muestreo dirigido y al azar de tejidos de tallo y frutos con síntomas de pudrición blanda en estadios iniciales de la enfermedad. Se realizó un proceso de aislamiento microbiológico, identificación bioquímica y taxonómica molecular de las bacterias aisladas. Las secuencias del gen ARNr 16s de la región V2-V5 fueron editadas mediante la eliminación de los primers, ensamblaje y obtención de la secuencia consenso de los primers 1100R-337F y 800R -518F. El análisis filogenético fue hecho por BLAST en el NCBI, por las herramientas "Classifier" y "SeqMatch", del sitio Web del RDP y se realizaron árboles filogenéticos mediante alineamiento múltiple usando el algoritmo MUSCLE y el método de distancias de Tamura Nei. Se registraron signos como ampollas y fluidos mucilaginosos, síntomas como manchas cloróticas amarillas y marrones, pudrición blanda y licuefacción, y se reportaron posibles vectores como moscas y hormigas. Veinticinco (25) morfotipos de bacterias fueron clasificados en 4 filos, 8 familias y 13 géneros. El análisis de las secuencias del gen ARNr 16S de las cepas bacterianas mostró una identidad del 98 al 100% con Enterobacter cloacae, Pectobacterium carotovora y Paenibacillus glucanolyticus, reportados en otros estudios como causantes de la pudrición blanda del tallo y fruto de pitahaya. Se reportaron nuevas especies como posibles bacterias patógenas de pitahaya: Pantoea cypripedii, Kluyvera intermedia y Klebsiella oxytoca. Los ensayos de infectividad no arrojaron resultados positivos. La microbiota identificada en los estadios de la fase necrotrofa o final de la pudrición blanda del tallo podría estar conformada por los géneros Achromobacter, Sphingobacterium, Pseudomonas, Klebsiella, Paenibacillus, Bacillus, Stenotrophomonas y Microbacterium. En la mosca Leptoglossus zonatus asociada al cultivo se identificó Pseudomonas fulva y Lysinibacillus fusiformis. Este es el primer registro oficial de un complejo de posibles bacterias fitopatógenas del orden Enterobacterales (familia Enterobacteriaceae, Erwiniaceae y Pectobacteriaceae) en síntomas por enfermedad bacteriana en cultivos colombianos de pitahaya. La identificación de las bacterias de un pato-sistema puede orientar las prácticas de control químico y biológico con el fin de incrementar el potencial productivo y de exportación de los cultivos exóticos y huérfanos de las pequeñas fincas locales.

ABSTRACT Blueberries are a fruit that is an important source of bioactive components beneficial to the human diet, such as anthocyanins and total phenolics, which are altered by the use of high temperatures during processing. This study aimed to evaluate the use of artificial neural networks in the optimization of sucrose concentration and time for the osmotic pre-treatment of blueberries of the Biloxi variety, to retain the greatest amount of anthocyanins and total phenolics in the subsequent preparation of jam. Artificial neural networks of the feedforward type were used, with a Backpropagation training algorithm with Levenberg-Marquardt weight adjustment, to achieve the optimal predicted combination that maximizes the retention of these bioactive components. The model achieved its best performance with 11 neurons in the hidden layer, achieving an R2 coefficient of 0.98 and a mean square error of 4.76, indicating a strong ability for generalization. Artificial neural networks allowed to obtain the best optimal combination of predicted multiple responses of factors consisting of a sucrose concentration of 1.64 M and a time of 211.52 min, which retained a higher content of total monomeric anthocyanins with 70.98 mg cyanidin-3-O-glucoside 100 g-1 of jam and total phenolics with 110.54 mg GAE g-1 of jam. On the other hand, through single-response optimization was obtained that the combination of experimental factors that maximized total anthocyanins (71.59 mg cyanidin-3-O-glucoside 100 g-1 of jam) was 1.54 M of sucrose and 232.73 min and for total phenols (111.06 mg GAE g-1 of jam) 1.79 M of sucrose and 196.36 min. The use of artificial neural networks is an excellent alternative for modeling phenomena, compared to traditional methods.

RESUMEN El arándano es un fruto que posee una fuente importante de componentes bioactivos beneficiosos para la dieta humana, como las antocianinas y fenoles totales, que se ven alterados por el uso de temperaturas altas durante el procesamiento. El objetivo de este estudio fue evaluar el uso de redes neuronales artificiales en la optimización de la concentración de sacarosa y el tiempo para el pretratamiento osmótico de arándanos de la variedad Biloxi, con la finalidad de retener la mayor cantidad de antocianinas y componentes fenólicos totales en la elaboración posterior de mermelada. Se utilizó redes neuronales artificiales del tipo feedfoward, con algoritmo de entrenamiento de Backpropagation con ajuste de pesos de Levenberg-Marquardt para lograr la combinación óptima predicha que maximice la retención de estos componentes bioactivos. El modelo logró su mejor rendimiento con 11 neuronas en la capa oculta, logrando un coeficiente R2 de 0,98 y un error cuadrático medio de 4,76; lo que indica una gran capacidad de generalización. Las redes neuronales artificiales permitieron obtener la mejor combinación de los factores experimentales -concentración de sacarosa (1,64 M) y tiempo (211,52 min)- que maximizaron los contenidos de antocianinas monoméricas totales (70,98 mg cianidina-3-O-glucósido 100 g-1) y fenoles totales (110,54 mg AGE g-1) presentes en mermelada. En cambio, mediante optimización de respuesta se obtuvo que la combinación de factores experimentales que maximizó las antocianinas totales (71,59 mg cianidina-3-O-glucósido 100 g-1 de mermelada) fue 1,54 M de sacarosa y 232,73 min y para fenoles totales (111,06 mg GAE g-1 de mermelada) 1,79 M y 196,36 min. El uso de redes neuronales artificiales es una excelente alternativa para modelar fenómenos, en comparación con los métodos tradicionales.

Abstract Introduction: The Aedes vittatus mosquito is an important vector of yellow fever in Africa, with vectorial competence for dengue, chikungunya and Zika. Its presence has been reported in some places in Africa, Asia, Europe, and -recently- America. However, information on its distribution is fragmented, with limited descriptions of the specific characteristics of its habitats. Objective: To compile records of its occurrence, describe the ecological characteristics of its habitat and estimate its current and future global potential invasion. Methods: We employed both first-record data and global occurrence records to describe its habitat. Additionally, we used an ecological niche model, specifically the MaxEnt algorithm, with bioclimatic variable layers to estimate potential invasion areas. Since the native range of A. vittatus is unknown, we calibrated accessible areas using two hypotheses, Africa and Asia, based on available genetic information. Results: Regardless of its native area, A. vittatus appears to be distributed in tropical and subtropical areas in all continents with potential to reach even currently colder climates as global climate change. It is found mainly in tropical and urban areas, likely through transcontinental and terrestrial passive transport. Conclusions: The mosquito can be found on all continents, ranging from sea level to 2 500 m.a.s.l., at temperatures between 15 and 30 °C, and has the potential for further expansion.

Resumen Introducción: El mosquito Aedes vittatus es un importante vector de la fiebre amarilla en África, con competencia vectorial para el dengue, la chikungunya y el Zika. Se ha informado de su presencia en algunas áreas de África, Asia, Europa y, recientemente, América. Sin embargo, la información sobre su distribución es fragmentada, con descripciones limitadas de las características específicas de sus hábitats. Objetivo: Recopilar registros de su presencia, describir las características ecológicas de su hábitat y estimar su potencial de invasión actual y futuro a nivel mundial. Métodos: Utilizamos datos de primeros registros y registros de presencia a nivel global para describir su hábitat. Además, empleamos un modelo de nicho ecológico, específicamente el algoritmo MaxEnt, con capas de variables bioclimáticas para estimar las áreas de potencial invasión. Dado que se desconoce el área nativa de A. vittatus, calibramos áreas accesibles bajo dos hipótesis, África y Asia, basadas en la información genética disponible. Resultados: Independientemente de su área nativa, A. vittatus parece distribuirse en regiones tropicales y subtropicales en todos los continentes, con el potencial de expandirse incluso hacia climas actualmente más fríos debido al cambio climático global. Se encuentra principalmente en áreas tropicales y urbanas, posiblemente a través del transporte pasivo transcontinental y terrestre. Conclusiones: El mosquito se puede encontrar en todos los continentes, desde el nivel del mar hasta altitudes de 2 500 m.s.n.m., a temperaturas que oscilan entre 15 y 30 °C, y presenta el potencial para una mayor expansión geográfica.

Livestock farms in Kenya face pressure to increase productivity amid rising global population. Cattle farming dominates, but small to medium-sized farms struggle with cattle insemination. Currently, visual observation is used for heat detection, with farmers maintaining farm journals. Modern methods utilizing sensors to improve estrus prediction are time-consuming, costly and need constant internet connection. This research proposes a novel approach-the use of an on-controller machine learning algorithm- for estrus prediction in cattle. Motion and temperature data was collected from two zero-grazed multiparous Holstein Friesian cows in Kiambu County, Kenya for 11 months. The data was cleaned and stored. Movement intensity profiles were derived by root-mean-squaring directional accelerometer values and averaging this over time. Validation was performed by observing cow behavior for indicators such as restlessness, mounting, and vulva swelling, with farmer predictions documented in their records. The collected data was then used to train a machine learning algorithm, with several models tested, and a neural network emerged as the best fit. The TensorFlow library facilitated the implementation of the algorithm on a microcontroller, allowing for the development of an animal tag featuring the ML algorithm. Results demonstrated 83.9% sensitivity, 89.0% specificity and 89.5% accuracy in detecting estrus, compared to farmer's visual observation, which had only 37% sensitivity. These findings underscore the potential to integrate machine learning into Precision Livestock Farming for estrus prediction, with prediction occurring directly on the animal tag offline. This integration holds promise for farmers, notably heightened insemination success rates, without necessitating significant financial investment.

Abstract: The primary objective of this research study is to assess the factors that influence student’s preference of professors. Preferring the same professor multiple times is associated with factors such as time dedicated to the subject taught by the professor in question, reason for enrollment, and preparation of classes. Through the J48 algorithm of the Weka program, the primary variables students prioritize when repeatedly selecting the same professor are predicted. Training with 50% of the data and validating with the remaining 50% achieve this prediction with effectiveness greater than 83%. The results show that the most influential variables are: 1) the professor did everything possible for students to learn, 2) depth of learning, 3) pleasant work style, 4) preparation of classes, 5) encouragement on the topics covered, and 6) endeavors to help students learn. It is concluded that identifying these variables facilitates the design of educational policies focused on students and professors.

Resumen: El principal objetivo de esta investigación es evaluar los factores que influyen en los estudiantes para preferir un docente. Preferir varias veces un mismo docente está asociado con factores como el tiempo dedicado a la asignatura impartida por el profesor en cuestión, el motivo de la inscripción y la preparación de clases. Por medio del algoritmo J48 del programa Weka, se predicen las principales variables que priorizan los estudiantes al preferir un mismo docente varias veces. Entrenando con el 50% de los datos y validando con el 50% restante, se logra realizar esta predicción con una efectividad superior al 83%. Los resultados muestran que las variables más influyentes son: 1) hizo lo posible porque aprendiera, 2) aprender con profundidad, 3) trabajar agradablemente, 4) preparación de clases, 5) motivar temas tratados y 6) preocuparse porque aprendan. Se concluye que la identificación de estas variables facilita el diseño de políticas educativas enfocadas en los estudiantes y los docentes.

SUMMARY: In the present study, it is aimed to reveal the performances of different classification algorithms in sex determination from first cervical vertebra, that is, atlas measurements. The classification success of 4 different machine learning algorithms was comparatively examined for the purpose of sex determination by evaluating 22 atlas measurements on cone beam computed tomography (CBCT) images of 145 female and 145 male adults. Logistic regression (LR), classification and regression tree (CART), support vector machine (SVM) and neural network (NN) algorithms were used for sex diagnosis. Area under the ROC curve (AUC), classification accuracy (CA), F1-ratio, Precision and Recall indexes were used for model performances. Except for 2 measurements, there was a significant difference between men and women in terms of 20 other parameters (p<0.05). The adjusted effects of these parameters on sex determination were examined with multivariate models and algorithms, and the success of all 4 algorithms was quite good. The success of the NN algorithm (Accuracy 91.3 %; 0.87 Specificity, 0.85 Sensitivity) in correctly classifying male and female was the highest, followed by the LR algorithm (Accuracy 90.9 %; 0.86 Specificity, 0.83 Sensitivity). It was found that the machine learning algorithm applied to the variables of the atlas gave high accuracy regarding sex and the NN model was highly effective in sex determination. In addition, a large morphometric database of atlas was presented in our results.

RESUMEN: El objetivo de estudio fue revelar el desempeño de diferentes algoritmos de clasificación en la determinación del sexo a partir de la primera vértebra cervical, el atlas. El éxito de la clasificación de 4 algoritmos diferentes de aprendizaje automático se examinó comparativamente con el fin de determinar el sexo mediante la evaluación de 22 mediciones de atlas en imágenes de tomografía computarizada de haz cónico (CBCT) de 145 mujeres y 145 hombres adultos. Para el diagnóstico de sexo se utilizaron algoritmos de clasificación y árbol de regresión (CART), máquina de vectores de soporte (SVM) y redes neuronales (NN). Para el rendimiento del modelo se utilizaron el área bajo la curva ROC (AUC), la precisión de clasificación (CA), la relación F1, los índices de precisión y recuperación. Excepto en dos mediciones, hubo una diferencia significativa entre hombres y mujeres en otros 20 parámetros (p<0,05). Los efectos ajustados de estos parámetros en la determinación del sexo se examinaron con modelos y algoritmos multivariados, y el éxito de los cuatro algoritmos fue bastante bueno. El éxito del algoritmo NN (Precisión 91,3 %; 0,87 Especificidad, 0,85 Sensibilidad) en clasificar correctamente a hombres y mujeres fue el más alto, seguido por el algoritmo LR (Precisión 90,9 %; 0,86 Especificidad, 0,83 Sensibilidad). Se encontró que el algoritmo de aprendizaje automático aplicado a las variables del atlas dio una alta precisión con respecto al sexo, y el modelo NN fue altamente efectivo en la determinación del sexo. Además, en nuestros resultados se presentó una importante base de datos morfométricos del atlas.

SUMMARY: The determination of sex differences in anatomical structures is critical in establishing gold standard morphometric data in basic medical sciences, and in surgical and internal sciences in selecting the right area during invasive intervention and applying the correct intervention methodology appropriate to the area. The aim of this study is to determine the sex difference using Machine learning (ML) algorithms and Artificial neural networks (ANN) with parameters obtained from basilar artery. The study was performed on computed tomography angiography images of 63 women and 94 men. The following parameters were measured on the images: initial width of the right vertebral artery, initial width of the left vertebral artery, termination width of the right vertebral artery, termination width of the left vertebral artery, basilar artery width, and basilar artery length. The measurements were used in ML algorithms and ANN input to determine sex differences. As a result of the study, a sex difference rate of 0.84 was determined with the ML algorithms Random Forest (RF), Quadratic Discriminant Analysis (QDA), Extra Tree Classifier (ETC) and 0.84 with the Multilayer Perceptron Classifier (MLCP) algorithm of ANN. As a result of the study, sex difference was found with an accuracy rate of 0.84 using ML algorithms and ANN with parameters obtained from basilar artery. In this context, we think that this study will shed light on basic and clinical medical sciences.

RESUMEN: La determinación de las diferencias de sexo en las estructuras anatómicas es fundamental para establecer datos morfométricos de gold estándar, en las ciencias médicas básicas y en las ciencias quirúrgicas e internas para seleccionar el área correcta durante la intervención invasiva y aplicar la metodología de intervención correcta y apropiada. El objetivo de este estudio fue determinar la diferencia de sexo utilizando algoritmos de aprendizaje automático (ML) y redes neuronales artificiales (ANN) con parámetros obtenidos de la arteria basilar. El estudio se realizó sobre imágenes de angiografía por tomografía computarizada de 63 mujeres y 94 hombres. Se midieron en las imágenes los siguientes parámetros: ancho inicial de la arteria vertebral derecha, ancho inicial de la arteria vertebral izquierda, ancho de terminación de la arteria vertebral derecha, ancho de terminación de la arteria vertebral izquierda, ancho de la arteria basilar y longitud de la arteria basilar. Las mediciones se utilizaron en algoritmos de ML y entradas de ANN para determinar las diferencias de sexo. Como resultado del estudio, se determinó una tasa de diferencia de sexo de 0,84 con los algoritmos ML Random Forest (RF), Análisis Discriminante Cuadrático (QDA), Extra Tree Classifier (ETC) y 0,84 con el algoritmo Multilayer Perceptron Classifier (MLCP) de ANA. Como resultado del estudio, se encontró una diferencia de sexo con una tasa de precisión de 0,84 utilizando algoritmos ML y ANN con parámetros obtenidos de la arteria basilar. En este contexto, pensamos que este estudio será de utilidad en las ciencias médicas básicas y clínicas.

Early diagnosis and prompt treatment initiation are essential in the management of axial spondyloarthritis (axSpA), also known as ankylosing spondylitis, remembering that underdiagnosis and overdiagnosis of axSpA are common. These South African guidelines offer screening tools and details of useful investigations, including imaging. Care of the axSpA patient requires a multidisciplinary holistic approach emphasising lifestyle interventions, particularly exercise, smoking cessation and psychosocial support. The ankylosing spondylitis disease activity score is recommended as a measure of disease activity, and a stepwise algorithm for therapy is provided. Screening for comorbidities and vaccination is advised.