This study investigates seismicity and rockburst control within the Merensky Reef and UG2 orebody of an intermediate-depth platinum mine located in South Africa's Bushveld Complex. Utilising a mine-wide seismic monitoring system, over 1,900 seismic events were recorded between April 2023 and May 2024, revealing complex seismic patterns driven by mining-induced stress changes. The largest event, with a magnitude of ML 2.0, underscores the seismic risks associated with mining at increasing depths. Key findings include the spatial and temporal variability of seismic events, with high seismic activity observed near pillars and geological structures. A significant portion of events correlated with pillar and foundation failures. Moment tensor analysis identified dominant failure modes, such as stress-driven fracture closures and dynamic pillar failures. To mitigate risks, the study emphasises advanced support system redesign and rockburst risk assessment using the rockburst damage potential (RDP) method. The research highlights the importance of monitoring, geotechnical compliance, and adaptive designs in enhancing safety and reducing seismic hazards in underground mining operations. The study applied the RDP framework, classifying damage across R1 to R5 scales, where most instances fell into low-risk categories (R1 and R3), indicating minor to moderate damage. However, data limitations and the tabular stoping method's complexity led to inconclusive results, highlighting the need for more robust data and customised risk assessment tools. Despite these challenges, the RDP approach provided valuable insights into rockburst mechanisms and the effectiveness of existing support systems.

RESUMO Em regiões com baixa disponibilidade hídrica é de grande importância um planejamento mais detalhado sobre o uso de recursos hídricos. A utilização de modelos climáticos destinados às previsões de curto e médio prazo se apresenta como uma estratégia importante para se obter informações antecipadas acerca da demanda hídrica de culturas e do regime hídrico de determinada bacia. Objetivou-se, neste estudo, avaliar o desempenho das previsões climáticas sazonais e sua aplicabilidade na estimativa da demanda de irrigação. Para tanto, utilizou-se o Modelo de Simulação de Estratégias de Irrigação e dados de previsão climática derivados do modelo Eta. Para análise das simulações, foi utilizado um conjunto de cinco membros (dias 13, 14, 15, 16 e 17) de previsão sazonal de precipitação e evapotranspiração de referência para cada mês entre o período de 2001 a 2012. O espalhamento para a evapotranspiração de referência demonstrou que o modelo não foi capaz de reproduzir o comportamento desta variável durante o período seco. Comparação entre previsões com os meses de antecedência não demonstraram diferenças significativas entre previsões da precipitação e da evapotranspiração de referência. Contudo, os resultados obtidos para as previsões com um mês de antecedência apresentaram melhor desempenho. hídricos bacia Objetivouse, Objetivouse Objetivou se, Objetivou-se estudo irrigação tanto utilizouse utilizou Eta simulações dias 13 14 15 1 17 200 2012 seco Contudo 20 201 2

ABSTRACT In regions with water shortages, detailed planning on water resource use is essential. The use of climate models for short- and medium-range forecasts is an important strategy for obtaining early information on the water requirements of crops and the water regime of a particular basin. This study aimed to assess the performance of seasonal climate forecasts and their applicability in estimating irrigation needs. To that end, the Simulation Model for Irrigation Strategies and climate forecast data derived from the Eta model were used. To analyze simulations, five members (days 13, 14, 15, 16 and 17) of the seasonal forecasts of rainfall and reference evapotranspiration for every month between 2001 and 2012 were used. The spread for reference evapotranspiration demonstrated that the model was unable to reproduce the behavior of this variable during the dry period. Comparison between forecasts months in advance showed no significant differences between the rainfall and the reference evapotranspiration forecasts. However, the results obtained for a one-month lead-time forecast exhibited superior performance. shortages essential short mediumrange medium range basin needs end used simulations days 13 14 15 1 17 200 201 period However onemonth one leadtime lead time 20 2

RESUMO: A batata (Solanum tuberosum L.) é uma fonte cada vez mais importante de alimento, de emprego rural e de aumento de renda, contribuindo para a estabilização social do meio rural, principalmente nos países em desenvolvimento. Entretanto, trata-se de uma cultura bastante sensível às doenças, as quais afetam drasticamente a produtividade, além da necessidade de controle elevar o custo de produção e o risco de contaminação ambiental. O objetivo do trabalho foi avaliar o controle da requeima (Phytophthora infestans) da batata cv. Ágata com base no sistema de previsão de doenças Blitecast e sua produtividade de tubérculos em duas safras. Os experimentos foram conduzidos na primavera de 2022 e no outono de 2023, no município de Passo Fundo. Diferentes valores de severidade (VS) acumulada, calculados pelo modelo Blitecast, constituíram os tratamentos VS 18, VS 24 e VS 30, acrescentando o tratamento semanal e a testemunha, sem aplicação. O delineamento foi em blocos casualizados com quatro repetições, sendo cada parcela composta de cinco fileiras de plantas com 3 metros de comprimento. Foram avaliadas a severidade da requeima e a produtividade final de tubérculos. A utilização do sistema de previsão Blitecast para o controle da requeima da batata, permite reduzir o número de aplicações de fungicidas sem afetar a produtividade final de tubérculos. O sistema de previsão deve ser utilizado como ferramenta para o manejo integrado de doenças da batata. RESUMO Solanum L. L alimento renda desenvolvimento Entretanto tratase trata se ambiental Phytophthora infestans cv safras 202 2023 Fundo (VS acumulada 18 2 30 testemunha aplicação repetições comprimento 20 1

ABSTRACT: Potato (Solanum tuberosum L.) is an increasingly important source of food, rural employment, and financial income, contributing to the social stabilization of rural areas, especially in developing countries. However, this crop is very sensitive to diseases, which drastically affect yield, in addition to increasing production costs for their control and the risk of environmental contamination. This study evaluated the control of late blight (Phytophthora infestans) in potato cv. Ágata based on the Blitecast disease forecast system and tuber yield in two growing seasons. The experiments were conducted in the spring of 2022 and autumn of 2023 in the municipality of Passo Fundo. Different accumulated severity values (SV) were calculated by the Blitecast model, which constituted the treatments SV 18, SV 24 and SV 30, in addition to a weekly treatment and a control without application. The experimental design consisted of randomized blocks with four replications, in which each plot had five rows of plants measuring 3 m in length. Late blight severity and the final tuber yield were evaluated. The use of the Blitecast forecast system to control potato late blight allows for a reduction in the number of fungicide applications without affecting the final tuber yield. The forecast system should be used as a tool for the integrated management of potato diseases. ABSTRACT Solanum L. L food employment income areas countries However diseases contamination Phytophthora infestans cv seasons 202 Fundo (SV model 18 2 30 application replications length 20 1

RESUMO Objetivo: Este estudo visa comparar métodos de calibração de pesos na subamostra de Exames Laboratoriais da Pesquisa Nacional de Saúde (PNS) 2013, buscando avaliar sua representatividade e precisão. Métodos: Duas propostas alternativas de construção de pesos calibrados foram realizadas com base nos métodos de pós-estratificação e raking. Uma comparação entre os pesos fornecidos para a subamostra de Exames Laboratoriais e os dois pesos sugeridos foi realizada por meio de estimativas de parâmetros de interesse utilizando os dados da subamostra da PNS 2013. Além disso, foram utilizadas sete medidas para avaliar o desempenho dos sistemas de pesos propostos. Resultados: Os pesos de pós-estratificação e raking alternativos produziram estimativas generalizáveis para a população-alvo da PNS 2013, enquanto os pesos originais não. Os métodos alternativos apresentaram desempenho similar ao método original, com pequena vantagem para o raking em algumas medidas de avaliação. Conclusão: Recomenda-se que os pesos básicos de delineamento sejam documentados e incluídos nos arquivos de dados de uso público da PNS. Além disso, sugere-se o chaveamento das informações entre a amostra e a subamostra da PNS 2013 para possibilitar a exploração de métodos como a imputação de dados, visando obter estimativas mais precisas e representativas. Essas melhorias são essenciais para garantir a qualidade e utilidade dos dados da PNS em estudos epidemiológicos e de saúde coletiva.

ABSTRACT Objective: This study aims to compare calibration methods for weights in the subsample of Laboratory Exams from the 2013 Brazilian National Health Survey (PNS), seeking to assess their representativeness and precision. Methods: Two alternative proposals for constructing calibrated weights were performed based on post-stratification and raking methods. A comparison between the weights provided for the Laboratory Exams subsample and the two suggested weights was conducted through parameter estimates using the 2013 PNS subsample data. Additionally, seven measures were used to assess the performance of the proposed weighting systems. Results: The alternative post-stratification and raking weights produced generalizable estimates for the target population of the 2013 PNS, while the original weights did not. The alternative methods showed similar performance to the original method, with a slight advantage for raking in some evaluation measures. Conclusion: It is recommended that basic design weights be documented and included in the public-use data files of the PNS. Furthermore, it is suggested to cross-reference information between the sample and subsample of the 2013 PNS to enable the exploration of methods such as data imputation, aiming to obtain more accurate and representative estimates. These improvements are essential to ensure the quality and usefulness of PNS data in epidemiological and public health studies.

Abstract The majority of photovoltaic parks installed in the national territory have means of recording and reporting, along with electricity generation, several meteorological variables that influence their performance, in particular, solar radiation, which is measured with pyranometers. These instruments may present variations in their calibration constants after prolonged use, and require a monitoring scheme and periodic calibrations. Below is a preliminary validation method for solar radiation data in photovoltaic parks, which can indicate in which parks erroneous solar radiation values are being reported. The method compares the hourly values reported each day in each park with those estimated by the WRF numerical forecast model and on days where the correlation between both series is greater than 0.985, it then calculates the relationship between the sums of each series. If the value of this ratio is close to 1, it can be considered that the pyranometer of that park is working correctly. The results of the evaluation of solar radiation reports in a set of photovoltaic parks connected to the national electrical grid are presented.

Resumen La mayoría de los parques fotovoltaicos instalados en el territorio nacional cuentan con medios de registro y reporte, junto con la generación eléctrica, de varias variables meteorológicas que tienen influencia en su rendimiento, en particular, la radiación solar, que es medida con piranómetros. Estos instrumentos pueden presentar variaciones en sus constantes de calibración tras un uso prolongado, y requieren de un esquema de monitoreo y calibraciones periódicas. A continuación se presenta un método de validación preliminar de los datos de radiación solar en los parques fotovoltaicos, que puede ir indicando en qué parques se están reportando valores erróneos de radiación solar. El método compara los valores horarios reportados cada día en cada parque con los estimados por el modelo numérico de pronóstico WRF y en los días donde la correlación entre ambas series es superior a 0.985, calcula entones la relación entre las sumas de cada serie. Si el valor esta relación es próximo a 1, se puede considerar que el piranómetro de ese parque funciona correctamente. Se presentan los resultados de la evaluación de los reportes de radiación solar en un conjunto de parques fotovoltaicos conectados a la red eléctrica nacional.

Abstract The unfavorable effects of meteorological conditions on the health of the Cuban population are increasing in frequency and intensity, due to the already visible impacts of climate change on Cuba, a problem that demands an evaluation of the vulnerability of the Cuban population to regional dangers. Associated with the meteorological processes that influence the country. An adaptation of the methodology used in Cuba for studies of danger, vulnerability and risk is carried out, applied in this case to the estimation of meteor-tropic risk, based on establishing the baseline of the general daily excess mortality for the period. 2001-2020, as an indicator of the vulnerability of the populations residing in the western and eastern halves of Cuba; to then estimate the danger associated with global and regional synoptic processes that influence those same portions of the territory. It is demonstrated that there is a well-defined relationship between excess mortality and the hazards analyzed, which allowed us to satisfactorily estimate the magnitude of the daily meteor-tropic risk and arrive at a statistical model that facilitates its short-term forecast. The meteor-tropic genesis of the days with the highest excess mortality is verified, both in winter and summer, demonstrating the relevant role that some meteorological processes and conditions have on the health of the Cuban population, a fact that justifies the need for its forecast and mitigation.

Resumen Los efectos desfavorables de las condiciones meteorológicas sobre la salud de la población cubana están aumentando en frecuencia e intensidad, debido a los impactos ya visibles del cambio climático sobre Cuba, problema que demanda realizar la evaluación de la vulnerabilidad de la población cubana ante los peligros regionales asociados a los procesos meteorológicos que influyen sobre el país. Se realiza una adaptación de la metodología utilizada en Cuba para los estudios de peligro, vulnerabilidad y riesgo, aplicada en este caso a la estimación del riesgo meteoro-trópico, a partir de establecer la línea base de la mortalidad general diaria en exceso para el período 2001-2020, como indicador de la vulnerabilidad de las poblaciones residentes en las mitades occidental y oriental de Cuba; para después estimar el peligro asociado a los procesos sinópticos de escala global y regional que influyen sobre esas mismas porciones del territorio. Se demuestra que existe una relación bien definida entre la mortalidad en exceso y los peligros analizados, lo cual permitió estimar satisfactoriamente la magnitud del riesgo meteoro-trópico diario y llegar a un modelo estadístico que facilita su pronóstico a corto plazo. Se comprueba la génesis meteoro-trópica de los días con mayor mortalidad en exceso, tanto en invierno como en verano, demostrando el relevante papel que tienen algunos procesos y condiciones meteorológicas sobre la salud de la población cubana, hecho que justifica la necesidad de su pronóstico y mitigación.

Abstract The objective of the research is the development of an agrometeorological model for forecasting sugar agricultural yield. The yields by strains and totals, area of each strain and total, and age of the plantation were taken from 13 Management Business Units in the Cienfuegos, Sancti Spíritus and Ciego de Ávila provinces in the period 1980-2015; as well as monthly records of precipitation, average temperature and evapotranspiration from seven meteorological stations. The Productive Potential Index was calculated according to the phenological phase in which it is found to verify its degree of effectiveness. For statistical processing, the Excel tabulator, STATISTICA version 8.0.5 and the SciPy mathematical module were used. The effectiveness of the agricultural yield estimates obtained with the model presented for the 2011 - 2015 harvests was compared using the coefficient of determination R2, analyzing the relationship established between the regressor variables and the predictand. The multiple linear regression model allows agricultural performance forecasts to be made with precision and adjustment, depending on the specific commercial scenarios of the country's sugarcane production. The province of Ciego de Ávila reveals, of the three under study, the highest degree of effectiveness in estimating the agricultural yield of sugar cane.

Resumen La investigación tiene como objetivo la elaboración de un modelo agrometeorológico de pronóstico del rendimiento agrícola azucarero. Se tomaron los rendimientos por cepas y totales, área de cada cepa y total, y edad de la plantación de 13 Unidades Empresariales de Base de las provincias Cienfuegos, Sancti Spíritus y Ciego de Ávila en el período 1980-2015; así como, registros mensuales de precipitación, temperatura media y evapotranspiración de siete estaciones meteorológicas. Se calculó el Índice Potencial Productivo según la fase fenológica en la que se encuentre para comprobar su grado de efectividad. Para el procesamiento estadístico se utilizó el tabulador Excel, STATISTICA versión 8.0.5 y el módulo matemático SciPy. La efectividad de las estimaciones de rendimiento agrícola obtenidas con el modelo presentado para las zafras 2011 - 2015, se comparó usando el coeficiente de determinación R2, analizando la relación establecida entre las variables regresoras y el predictando. El modelo de regresión lineal múltiple permite realizar pronósticos de rendimiento agrícola con precisión y ajuste, en dependencia de los escenarios comerciales concretos de la producción cañera del país. La provincia Ciego de Ávila revela, de las tres en estudio, el mayor grado de efectividad en la estimación del rendimiento agrícola de la caña de azúcar.

Abstract This systematic literature review (SLR) explores current state-of-the-art artificial intelligence (AI) methods for forecasting hotel demand. Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). The study conducted an SLR using the PRISMA model and identified 20 papers indexed in Scopus and the Web of Science. It addresses the gaps in the literature on AI-based demand forecasting, highlighting the need for clarity in model specification, understanding the impact of AI on pricing accuracy and financial performance, and the challenges of available data quality and computational expertise. The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management. Additionally, this study discusses the limitations of AI-based demand forecasting, such as the need for high-quality data. It also suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.

Resumo Esta revisão sistemática da literatura (RSL) visa identificar os mais recentes métodos de previsão de procura de um hotel utilizando inteligência artificial (IA). Considerando a importância que a gestão da receita tem para o sucesso empresarial na industria hoteleira, este estudo visa identificar modelos e métodos eficazes baseados em IA para a previsão da procura, incluindo Machine Learning, Deep Learning e Artificial Neural Networks. Neste estudo foi realizada uma RSL utilizando o modelo PRISMA, tendo sido identificados 20 artigos indexados na Scopus e na Web of Science. No estudo são abordadas as lacunas na literatura sobre a previsão da procura baseada em IA, destacando-se a necessidade de maior clareza na especificação dos modelos; o impacto da IA na precisão do preço e no desempenho financeiro; e os desafios relativos à qualidade dos dados disponíveis e da capacidade computacional. A revisão conclui que a utilização de IA integrada nos modelos de análise pode melhorar significativamente a precisão da previsão, reforçando as decisões orientadas por dados na gestão hoteleira. Além disso, este estudo discute também as limitações associadas à previsão da procura baseada em IA, como a necessidade de dados de elevada qualidade. São sugeridas futuras linhas de investigação visando melhorar ainda mais as técnicas de previsão incluindo IA na indústria hoteleira.

Abstract: During construction, uncontrolled resources impact project performance. Earned Value Management (EVM) is a widespread method used for project management based on time and cost control. Advances in Information Technology (IT) provide options to improve the EVM method. The EVM is a project-level method that excludes detailing the behavior of project parameters at the level of construction operations, they are handled in aggregated economic terms over time. Thus, this work studies the improvement of EVM using IT to express the handling of operational variables. This article uses a road construction project as a case study, to evaluate three approaches (i.e., Bayesian Network (BN), Artificial Neural Network (ANN), and Hybrid EVM-ANN) as improvement options for the EVM method. It was found that the ANN provides the best improvement of EVM results. The use of ANN and project parameters improves the handling of EVM. By mayor forecast effectiveness, is expected to improve the quality and availability of data for decision making, a condition which in turn may improve agility and adaptability of the project as-built outcomes. The model EVM-ANN uses parameters that influence project implementation completion, making it easier to assess project time performance based on various conditions in the field so that the project can obtain the best strategy to ensure project completion on time.

Resumen: Durante la construcción, los recursos no controlados impactan el desempeño del proyecto. La Gestión del Valor Ganado (GVG) es un método ampliamente utilizado para la gestión de proyectos basado en el control de tiempos y costos. Los avances en las Tecnologías de la Información (TI) brindan opciones para mejorar el método GVG. El GVG es un método a nivel de proyecto que excluye detallar el comportamiento de los parámetros del proyecto a nivel de operaciones de construcción, se manejan en términos económicos agregados a lo largo del tiempo. Por lo tanto, este trabajo estudia la mejora del GVG utilizando TI para expresar el manejo de variables operativas. Este artículo utiliza un proyecto de construcción de carreteras como caso de estudio, para evaluar tres enfoques (es decir, Red Bayesiana (RB), Red Neuronal Artificial (RNA) y EVM-RNA Híbrida) como opciones de mejora para el método GVG. Se encontró que la RNA proporciona la mejor mejora de los resultados del GVG. El uso de la RNA y los parámetros del proyecto mejora el manejo del GVG. Al tener una mayor efectividad en los pronósticos, se espera que mejore la calidad y disponibilidad de los datos para la toma de decisiones, una condición que a su vez puede mejorar la agilidad y adaptabilidad de los resultados del proyecto tal como está construido. El modelo EVM-ANN utiliza parámetros que influyen en la finalización de la implementación del proyecto, lo que facilita la evaluación del desempeño del tiempo del proyecto en función de diversas condiciones en el campo para que el proyecto pueda obtener la mejor estrategia para garantizar su finalización a tiempo.

Resumo Introdução: A epidemic intelligence (EI) assegura a deteção precoce, a avaliação e a comunicação das ameaças para a saúde pública para definir prioridades e mobilizar recursos para a investigação, vigilância, prevenção e controlo. Em Portugal, a Direção-Geral da Saúde (DGS) é responsável pela EI e publica semanalmente um relatório de ameaças para a saúde pública (RONDA) que é partilhado com a rede de autoridades de saúde, instituições e profissionais de saúde pública. As alterações nas ameaças comunicadas em relatórios periódicos de ameaças desde a COVID-19 não foram descritas anteriormente. Métodos: Comparámos as ameaças reportadas na RONDA entre 2016 a 2022 em três períodos sequenciais: antes da COVID-19 (janeiro de 2016 - março de 2020), P2 durante as restrições (abril de 2020 - fevereiro de 2022) e P3 na fase pós-aguda da COVID-19 (fevereiro de 2022 - setembro de 2022). Comparamos a frequência média mensal de ameaças não COVID-19 relatadas entre todas as ameaças relatadas nesses períodos, considerando diferentes categorias dentro do grupo de doenças, foco geográfico e fontes de informação. Resultados: As ameaças não-COVID-19 tiveram uma diminuição na frequência média mensal de reporte no Período2 (14,7 vs. 2,3 p < 0.001) em comparação com o Período1, antes da COVID-19. Houve um retorno ao padrão pré-pandêmico de notificação no Período 3 (14,67 vs. 17,63 p < 0.208) para ameaças não COVID-19 com um aumento nas doenças virais emergentes (2,20 vs. 7,62 p < 0.001). O ECDC e a OMS são as fontes de informação mais frequentes, seguidas das fontes nacionais de saúde pública. Conclusões: Durante a COVID-19, houve alterações no reporte de ameaças em Portugal. A COVID-19 possivelmente afetou a EI e os relatórios de ameaças epidémicas globais, possivelmente desviando a atenção e os recursos de outras ameaças para a pandemia. No entanto, a quantidade de ameaças que justificam o acompanhamento e a comunicação pode estar a aumentar. É necessária investigação em comunicação de ameaças detetadas no âmbito da EI a fim de assegurar que todas as ameaças relevantes são avaliadas e comunicadas de acordo com os objetivos da EI, garantindo simultaneamente o investimento em recursos e a preparação para a prevenção e resposta.

Abstract Background: Epidemic intelligence (EI) ensures early detection, assessment, and communication of public health threats. Threat reporting defines priorities and mobilize resources for surveillance, prevention, and control. In Portugal, the Directorate-General of Health (DGS) is responsible for EI and publishes a weekly public health threat report (RONDA). Changes in threats in regular threat reports since COVID-19 have not been previously described. We analysed changes in non-COVID threat reporting in the weekly threat report. Methods: Using the DGS Emergency Operations Centre’s threat reporting database, we compared threats reported in RONDAs from 2016 to 2022 in three sequential periods: P1 before COVID-19 (January 2016-March 2020), P2 during acute COVID-19 restrictions (April 2020-February 2022), and P3 in post-acute COVID-19 phase (February 2022-September 2022). We described the monthly average frequency of reports on non-COVID-19 threats in those periods considering different disease groups, geographical focus, and information sources. We estimated expected non-COVID-19 reports on threats using a forecast model fitted to the time series until March 2020 and compared observed and expected values. Results: Non-COVID-19 threats had a decrease in the monthly average frequency of reporting in period 2 (14,7 vs. 2,3 p < 0.001) compared to period 1. Using the forecast methods, there were 114 fewer non-COVID threats than the 162 expected (−70%) in period 2. In period 3, there were 105 more threats than expected (+256%). The ECDC and the WHO were the most frequent sources of information followed by national Public Health sources. Conclusions: During COVID-19, there was a decrease in reports on non-COVID threats in Portugal. COVID-19 possibly affected global EI, by shifting attention and resources from other threats to the pandemic. However, the number of threats that warrant follow-up and communication is increasing. Further research is necessary to inform the EI research and development agenda, to ensure that all relevant threats are detected, accessed, and communicated according to evolving EI objectives and priorities while resources and preparedness are guaranteed.

ABSTRACT Global solar radiation (Qg) is of paramount importance for several functions, such as photosynthesis and other biological processes, technologies based on solar energy (production of electricity and hot water), estimation of evapotranspiration (irrigation management), and forecast of crops, among other activities, at zero cost. This study aimed to develop a new methodology to estimate the daily and monthly value of global solar radiation based on the power generated by a 10 W photovoltaic solar panel. The methodology performance was assessed by comparing the Qg values estimated by the new method and the Qg values observed in a weather station, and also calculated by the SunDATA software and observed in the NASA POWER system. High r, R2, and Willmott coefficient values (close to 1), a low mean absolute error (MAE) of 0.202 kWh m−2 per day, a low mean bias error (MBE) of −0.146 kWh m−2 per day, a low mean percentage error (MPE) of 3.25%, and a low root mean square error (RMSE) of 0.292 kWh m−2 per day were obtained, confirming the excellent accuracy and reliability of the proposed methodology to estimate Qg. The NASA POWER system and SunDATA software presented similar values for the entire study period compared to the observed data, but not as good as the proposed technique. Thus, the proposed methodology had high performance in estimating Qg. (Qg functions processes production water, water , water) irrigation management, management management) crops activities cost 1 panel station r R2 R close 1, 1) MAE (MAE 0202 0 202 0.20 m2 m 2 m− MBE (MBE 0146 146 −0.14 MPE (MPE 325 3 25 3.25% RMSE (RMSE 0292 292 0.29 obtained data technique Thus 020 20 0.2 014 14 −0.1 32 3.25 029 29 02 0. 01 −0. 3.2 −0 3. −

RESUMO A radiação solar global (Qg) é de suma importância para diversas funções, como fotossíntese e outros processos biológicos, tecnologias baseadas em energia solar (produção de eletricidade e água quente), estimativa da evapotranspiração (manejo da irrigação), previsão de cultivos, entre outras atividades, a custo zero. Este estudo teve como objetivo desenvolver uma nova metodologia para estimar o valor diário e mensal da radiação solar global com base na potência gerada por um painel solar fotovoltáico de 10 W. O desempenho da metodologia foi avaliado pela comparação entre os valores de Qg estimados pelo novo método e os valores de Qg observados em uma estação metereológica, e também pelos valores calculados pelo software SunDATA e os observados no sistema NASA POWER. Altos valores de r, R2 e coeficiente de Willmott (próximos de 1), baixo erro médio absoluto (MAE) de 0,202 kWh m-2 por dia, baixo erro médio da estimativa (MBE) de -0,146 kWh m-2 por dia, baixo erro médio percentual (MPE) de 3,25% e baixa raiz do erro quadrático médio (RMSE) de 0,292 kWh m-2 por dia foram obtidos, confirmando a excelente acurácia e confiabilidade da metodologia proposta para estimar o Qg. O sistema NASA POWER e o software SunDATA apresentaram valores semelhantes para todo o período de estudo em relação aos dados observados, mas os da presente metodologia foram melhores. Assim, a metodologia proposta apresentou alto desempenho na estimativa da Qg. (Qg funções biológicos produção quente, quente , quente) manejo irrigação, irrigação irrigação) cultivos atividades zero 1 W metereológica r R próximos 1, 1) MAE (MAE 0202 0 202 0,20 m2 m 2 m- MBE (MBE 0,146 0146 146 -0,14 MPE (MPE 325 3 25 3,25 RMSE (RMSE 0292 292 0,29 obtidos melhores Assim 020 20 0,2 0,14 014 14 -0,1 32 3,2 029 29 02 0, 0,1 01 -0, 3, -0 -

Higher Education needs to be reimagined considering the exciting developments within virtual reality and artificial intelligence. As a new generation, Generation Alpha, better known as Gen Alpha,1 is preparing for higher education, and educators are challenged to create opportunities to meet their needs. In this study, we explore an existing paradigm, scenario planning, but within an unfamiliar context - the future of higher education. We pose the question: What are the possible future scenarios within higher education that will appeal to the Gen Alpha student and a millennial parent in a post-pandemic world? As a result, we present a scenario matrix with four learning and teaching models forecast to be successful in 2030 in higher education institutions. These four learning and teaching models are based on two axes: Social connect vs social disconnect in learning and teaching and a single career option or multiple career options as the end goal for the student. Respondents of this longitudinal study are millennial parents of Gen Alpha students. Results indicate that the three main themes that will influence the learning and teaching models of the future are learning spaces and places, glass learning, and techceptance. In line with the three themes, the three major findings were that (1) millennial parents are open to hybrid learning approaches, (2) learning on screens is important, but it should be regulated and based on set standards, and lastly (3) millennial parents are accepting technology (techceptance) as a supportive tool and learning place in higher education. Overall, there was a positive shift in perspectives on higher education regardless of the impact of the pandemic. The outcomes of the research will assist higher educational institutions to strategically align the learning and teaching offerings with the needs and resources of the future student, namely Gen Alpha.

South Sudan is expansive and sparsely populated with over 80% of the population living in rural areas. The country has no national grid connecting its cities and towns, thus making rural areas "good candidates" for stand-alone renewable energy systems. This study was conducted to determine the technical feasibility and economic viability of a stand-alone photovoltaic (PV) system compared to a diesel generator. A techno-economic model was developed to forecast the performance of the PV system. The system was initially designed using the IEEE Recommended Practice for Sizing of Stand-Alone Photovoltaic Systems (IEEE P1562-2021) and the IEEE Recommended Practice for Sizing Lead-Acid Batteries for Stand-Alone Photovoltaic Systems (IEEE 1013-2019). The solar radiation data used for modeling were acquired from the Ineichen clear sky model and then transposed to the plane of array irradiation using pvlib python. The system optimization and sensitivity analysis was performed under various diesel fuel costs using the Hybrid Optimization of Multiple Energy Resources (HOMER) software. Results show that at a fuel price of $ 2 per liter, the levelized cost of electricity (LCOE) of the PV system is 64% lower than that of the diesel generator and that the system can earn 11% return on investment (ROI) and recover the investment in about 5.5 years. With a drop in price of diesel fuel to $1 per liter, the payback period increases to about 7 years. These results show that standalone PV systems are technically feasible and economically viable in rural and peri-urban areas of South Sudan.

RESUMEN La tasa de deserción es un indicador clave de la calidad educativa, por lo que es imperativo que las instituciones educativas diseñen estrategias para reducirla y así aumentar la retención estudiantil y alcanzar los logros académicos. Mientras que la investigación sobre la deserción se ha concentrado principalmente en métodos de aprendizaje automático aplicados a conjuntos de datos sobre educación presencial, este artículo introduce un enfoque novedoso al utilizar modelos de series temporales para analizar la tasa de deserción de la Universidad Nacional Abierta y a Distancia (UNAD). En cuanto a la metodología, se adaptó el proceso CRISP-DM en cuatro fases, a saber: F1. Comprensión del negocio y de los datos, F2. Preparación de los datos. F3. Modelado y evaluación y F4. Despliegue del modelo. Respecto a los resultados, se empleó un conjunto de datos abiertos sobre la deserción en la UNAD que abarca desde 1999 hasta 2021, el cual se obtuvo del sistema SPADIES. Mediante el uso de las bibliotecas de Python statsmodels y pandas, se implementó un modelo ARIMA, el cual arrojó excelentes resultados en las medidas de error. Este modelo ARIMA se utilizó para predecir la tasa de deserción futura de la UNAD, la cual se proyecta que oscilará alrededor del 23 %. En conclusión, el modelo ARIMA desarrollado para la UNAD se destaca como una herramienta innovadora y fundamental en el ámbito educativo, capaz de predecir de forma precisa la tasa de deserción de semestres futuros, lo cual le otorga a la UNAD una ventaja única en la toma decisiones estratégicas.

ABSTRACT Dropout rates are a key indicator of educational quality, making it imperative for educational institutions to design strategies to reduce them, thereby contributing to improved student retention and the achievement of academic objectives. While dropout research has primarily focused on machine learning methods applied to in-person education datasets, this article introduces a novel approach based on time series models for dropout rates analysis at the National Open and Distance University (UNAD). Methodologically, an adaptation of the CRISP-DM methodology was undertaken in four phases, namely: F1. Business and data understanding, F2. Data preparation, F3. Model building and evaluation, and F4. Model deployment. In terms of results, an open dataset on UNAD dropout, obtained from the SPADIES system between 1999 and 2021, was employed. Using Python libraries statsmodels and pandas, an ARIMA model was implemented, displaying optimal error metrics. This ARIMA model was utilized to forecast future dropout rates at UNAD, projecting a future dropout rate fluctuating around 23%. In conclusion, the ARIMA model developed for UNAD stands as an innovative and essential tool in the educational realm, capable of accurately anticipating dropout rates for upcoming semesters. This provides UNAD with a unique advantage in strategic decision-making.

RESUMEN Los pronósticos de producción de fruta son una herramienta para planificar la cosecha y mejorar estrategias de mercado. Para su realización es imprescindible contar con información acerca del desarrollo de los frutos a lo largo del tiempo. El objetivo del presente trabajo fue encontrar el modelo matemático-estadístico que mejor describa el patrón de crecimiento de frutos tangor murcott (Citrus reticulata x C. sinensis ‘Murcott’) y analizar cómo es afectado por condiciones medioambientales. En nueve huertos, ubicados en cuatro localidades en la provincia de Corrientes, Argentina, se registró durante cinco temporadas el diámetro ecuatorial de 2053 frutos desde los 82 días después de plena floración hasta el momento de cosecha. Se compararon los modelos no lineales: Logístico, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, y sus respectivas re-parameterizaciones. Como principales criterios de bondad de ajuste se consideraron las magnitudes de medidas de no linealidad, coeficiente de determinación y estimaciones del desvío residual. La combinación modelo-parametrización seleccionada fue la quinta parametrización del modelo Logístico con efectos aleatorios en sus tres parámetros. Un modelo de análisis de la variancia sobre las estimaciones de estos parámetros para cada fruto mostró que los factores huerto y temporada eran una importante fuente de variabilidad, principalmente en los relacionados con el tamaño inicial de los frutos y su tasa de crecimiento. Estos resultados permitirán construir tablas de crecimiento, que además de realizar predicciones de rendimientos, podrán ser utilizadas para estimar distribución de tamaños de fruto a cosecha y mejorar la práctica cultural de raleo.

ABSTRACT Fruit production forecasts are a tool to plan the harvest and improve market strategies. To carry it out, it is essential to have information about the behavior of fruit development over time. The objective of this work was to find the mathematical-statistical model that best describes the growth pattern of tangor murcott fruit (Citrus reticulata x C. sinensis 'Murcott') and analyze how it is affected by environmental conditions. For this, in nine orchards, located in four locations in the province of Corrientes, Argentina, the equatorial diameter of 2,053 fruit from 82 days after full flowering to harvest were periodically registered during five seasons. The nonlinear models were compared: Logistic, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, and their respective re-parameterizations. The magnitudes of nonlinearity measures, coefficient of determination and estimates of residual deviation were considered as the main goodness-of-fit criteria. The selected model-parameterization combination was the fifth parameterization of the Logistic model with random effects on its three parameters. An Analysis of Variance model on the estimates of these parameters for each fruit showed that orchard and season factors were an important source of variability, mainly in those related to the initial size of the fruit and their growth rate. These results will allow the construction of growth tables, which in addition to making yield predictions, can be used to estimate fruit size distribution at harvest and improve the cultural practice of manual fruit thinning.