R programming language

In this work, we used a new statistic method that modified Beta-Weibull
distribution to evaluate the predictive capacity of cumulative incidences. According to the works of Cabarrou and al. (2016) who uses a parametric model
(that of Gompertz) and motivated by the cert of having a new parametric
more flexible and adaptable, we proposed a modified Beta-Weibull model,
because having more parameters than the Gompertz model and offers obviously the possibility to generate many other forms of risks function and which
are more flexible. The new distribution generalises many existants in literature.

In linear models, the ordinary least squares (OLS) estimators of parameters have always turned out to be the best linear unbiased estimators. However, if the data contain outliers, this may affect the least-squares estimates. So, an alternative approach; the so-called robust regression methods, is needed to obtain a better fit of the model or more precise estimates of parameters. In this article, various robust regression methods have been reviewed. The focus is on the presence of outliers in the y-direction (response direction). Comparison of the properties of these methods is done through a simulation study. The comparison's criteria were the efficiency and breakdown point. Also, the methods are applied to a real data set.

This study was conducted to determine students' engagement in a Google classroom as well as their satisfaction and difficulties encountered in utilizing the said application. This study involved 29 students, 12 from the Bachelor of Secondary Education major in Biological Science (BSEd) 3C and 17 from the Bachelor of Science in Information Technology (BSIT) 3G, of Iloilo Science and Technology University Miagao Campus. Students' engagement was based on their participation in the eight (8) assigned activities. Students' satisfaction was based on their responses to the items on the instrument prepared by the researchers. The instrument used contained 18 items based on the ISO/IEC 25010 model. While the difficulties encountered was based on the problems they listed down. BSEd 3C students' engagement was found to be higher than that of the BSIT 3G students. Both groups of students had at least high satisfaction on the Google classroom's functional suitability and usability. Poor internet connection was the mostly identified difficulty encountered by them.

This paper presents the power network reconfig-uration algorithm HATSGA with a " R " modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context. The modelling of the power distribution network with the language " R " is used to represent the network and support computation of distinct algorithm configurations towards the evaluation of new reconfiguration topologies. The HATSGA algorithm adopts hybrid Tabu Search and Genetic Algorithm strategy and can be configured in different ways to compute network reconfiguration solutions. The evaluation of power loss with HATSGA uses the IEEE 14-Bus topology as the power test scenario. The evaluation of reconfiguration topologies with minimum power loss with HATSGA indicates that an efficient solution can be reached with a feasible computational time. This suggests that HATSGA can be potentially used for computing reconfiguration network topologies...

Abiotic and biotic stress responses are traditionally thought to be regulated by discrete signaling mechanisms. Recent experimental evidence revealed a more complex picture where these mechanisms are highly entangled and can have synergistic and antagonistic effects on each other. In this study, we identified shared stress-responsive genes between abiotic and biotic stresses in rice (Oryza sativa) by performing meta-analyses of microarray studies. About 70% of the 1,377 common differentially expressed genes showed conserved expression status, and the majority of the rest were down-regulated in abiotic stresses and up-regulated in biotic stresses. Using dimension reduction techniques, principal component analysis, and partial least squares discriminant analysis, we were able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of differentially expressed genes. Furthermore, using a random forests decision tree model, eight out of 10 stress conditions were classified with high accuracy. Comparison of genes contributing most to the accurate classification by partial least squares discriminant analysis, recursive-support vector machine, and random forests revealed 196 common genes with a dynamic range of expression levels in multiple stresses. Functional enrichment and coexpression network analysis revealed the different roles of transcription factors and genes responding to phytohormones or modulating hormone levels in the regulation of stress responses. We envisage the top-ranked genes identified in this study, which highly discriminate abiotic and biotic stresses, as key components to further our understanding of the inherently complex nature of multiple stress responses in plants.

The need for clean energy in areas without grid connection has become a significant focus. Hence, hybrid solar, wind microgrids combined with Battery Energy Storage Systems (BESS) and intelligent Energy Management Systems (EMS) are considered potential solutions. This research is about the design of an optimized, lightweight solar, and reliable hybrid system for Neduntheevu, Sri Lanka, which also includes techno, economic optimization based on HOMER, MATLAB/Simulink dynamic simulations, and PSCAD transient analysis. The proposed system are making use of the sister nature of solar and wind resources, also BESS and EMS have been used to power a population of approximately 5, 000 continuously, stably, and cost, effectively. Reliability assessments (LOLP, EENS), sensitivity studies, economic and environmental evaluations result in a very low dependence on fossil fuels, carbon emissions, and operational costs, and keeping the system strong even if the weather changes and the load condition vary. This study offers a practical framework for implementing sustainable, resilient, and economically viable hybrid microgrids in remote off, grid communities.

Penelitian ini untuk mengetahui Hubungan Antara Gaya Belajar Terhadap Minat Belajar Mahasiswa. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian korelasi. Penelitian ini direncanakan akan dilaksanakan pada tanggal 4 Desember 2025, yang jatuh pada hari Kamis. Lokasi penelitian bertempat di
Universitas Haluoleo. Berdasarkan analisis data dari 132 responden mahasiswa dan uji hipotesis yang telah dilakukan, penelitian ini menyimpulkan bahwa terdapat hubungan yang positif, kuat, dan signifikan antara gaya belajar terhadap minat belajar mahasiswa. Hal ini ditunjukkan oleh koefisien korelasi Pearson sebesar r = 0,678
dengan nilai signifikansi p = 0,000 (p < 0,05). Lebih lanjut, analisis regresi linear sederhana menunjukkan bahwa gaya belajar secara signifikan memengaruhi minat belajar, di mana 46% variasi dalam minat belajar mahasiswa dapat dijelaskan oleh variasi dalam gaya belajar (R² = 0,460). Persamaan regresi Ŷ = 28,45 + 0,59X
mengindikasikan bahwa setiap kenaikan satu satuan pada skor gaya belajar, minat belajar mahasiswa akan meningkat sebesar 0,59 satuan. Distribusi gaya belajar menunjukkan bahwa gaya visual adalah yang paling dominan (39,4%) di antara responden, diikuti oleh gaya auditorik (31,1%) dan kinestetik (29,5%). Temuan ini

This study aimed at suggesting a syllabus named "Introduction to Computational Linguistics" to be taught within the programs of Language Teaching and Translation in the Yemeni Universities. This course could be a threshold for scientific study of language through dehumanizing the routinely-repeated processing, analyzing and even production of natural human languages to save time and effort, and avoid bias and subjectivity. The study method relied on a review for several similar programs from well-known universities which included this course within their study syllabi. It also used a field survey through which academics and professionals who gave their insights about the feasibility of including CL within the programs of language departments in the Yemeni Universities. A review to previous research papers and books highlighted the importance of teaching language processing and programming at language programs as well that highlight the importance of giving training to students of Language programs to introduce students to Natural Language Processing (NLP) skills such as tagging, tokenizing, parsing, sentiment analyzing, machine translation, text production, analysis and summarization that could be handled in later courses. The study concluded with a strong recommendation for tradition language schools to include CL course within their academic language programs either within the BA and/or MA program syllabi either as ready-made software track or as a programming track. Later appraisal might give insightful feedback for the development phase to the concerned Language program.

The need for clean energy in areas without grid connection has become a significant focus. Hence, hybrid solar, wind microgrids combined with Battery Energy Storage Systems (BESS) and intelligent Energy Management Systems (EMS) are considered potential solutions. This research is about the design of an optimized, lightweight solar, and reliable hybrid system for Neduntheevu, Sri Lanka, which also includes techno, economic optimization based on HOMER, MATLAB/Simulink dynamic simulations, and PSCAD transient analysis. The proposed system are making use of the sister nature of solar and wind resources, also BESS and EMS have been used to power a population of approximately 5, 000 continuously, stably, and cost, effectively. Reliability assessments (LOLP, EENS), sensitivity studies, economic and environmental evaluations result in a very low dependence on fossil fuels, carbon emissions, and operational costs, and keeping the system strong even if the weather changes and the load condition vary. This study offers a practical framework for implementing sustainable, resilient, and economically viable hybrid microgrids in remote off, grid communities.

Remote islands like Neduntheevu face persistent reliance on diesel power due to grid extension infeasibility, creating econom ic, logistical, and environmental vulnerabilities. Assessing hybrid microgrids, we focus on small islands aiming for low-carbon power. With Neduntheevu as a benchmark-peaking at 250 kW at night, drawing 93,907 kWh yearly, the study looks at solar-wind-battery mixes, how they work together, and how energy is dispatched. It probably reduces battery size without raising loss risks much. The system rel ies on solid AC, DC, or hybrid power paths tested against real conditions like rust, rough transport, and weak structures. Power management de cisions shape how long units run and stay stable. Simulations handle reliability checks instead of live testing. Long-term environmental damage is reviewed regularly. Battery size matches usage peaks tightly. EMS strategies, spanning rule-based, optimization, and AI-assisted frameworks, are evaluated for dynamic stability, predictive dispatch, and battery life extension. Techno-economic analysis, emphasizing Net Present Cost (NPC) and Levelized Cost of Energy (LCOE), is combined with a cradle-to-grave Life Cycle Assessment (LCA) that accounts for transport, installation, operation, and end-of-life battery impacts. Results indicate that solar-wind hybrid microgrids with robust EMS can substantially reduce diesel dependence, lower emissions, enhance supply reliability, and maintain cost-effectiveness in remote island contexts. This synthesis provides a design framework and research agenda for practical low-carbon electrification of isolated communities.

Circular or directional data is used for various disciplines, where analysis of angular data requires special methods that are available in some statistical packages. However, it seems that these tools analyze only the angular values and do not include the vector modules, assuming unit vectors in all cases. Los datos circulares o direccionales se utilizan para varias disciplinas, donde el análisis de datos angulares requiere métodos especiales que están disponibles en algunos paquetes estadísticos. Sin embargo, parece que estas herramientas analizan solo los valores angulares y no incluyen los módulos vectoriales, asumiendo vectores unitarios en todos los casos.

Distributed energy resources are promising solutions for household energy affordability and resilience as weather extremes and ageing infrastructure intensify grid-reliability risks. Here we present a comprehensive nationwide assessment of over 500,000 US households, evaluating economic and back-up viability of solar–battery systems. We find that 60% of households could reduce electricity costs with average savings of 15%, whereas 63% of households could achieve affordable back-up power during power outages covering an average of 51% of their essential energy needs. However, these benefits show limited alignment with areas of greatest need, particularly in regions facing high outage risks. We also identify notable disparities in access to solar and battery, with less-populated and disadvantaged communities showing consistently lower viability. These findings demonstrate the need for targeted policy interventions to ensure equitable access to solar–battery benefits, especially as states transition from net energy metering to other electricity tariff policies.

The integration of artificial intelligence (AI) with adaptive crop monitoring has emerged as a crucial element in the environmental modeling of dynamic agricultural landscapes. AI-driven mapping has fundamentally transformed cartographic solutions across engineering, natural, and technical sciences by embedding automation into methodologies. This is particularly vital for Geographic Information Systems (GIS) where the automation of spatial data processing is critical. Given that agricultural landscapes undergo seasonal and yearly transformations, precise environmental forecasting becomes increasingly important. This study presents an overview of recent methodological advancements in three interdisciplinary areas: the environmental monitoring of agricultural landscape dynamics in soil studies, the application of AI in GIS through machine learning (ML) and deep learning (DL) techniques, and bibliometric analysis. The tools utilize the R-based libraries including Bibliometrix, Treemap, and Wordcloud, alongside the Mendeley reference system. The research explores the deployment of novel AI and ML methodologies in scalable data-driven analysis within agriculture and soil studies, addressing associated issues with their application. This review draws upon a comprehensive selection of over 100 papers from recognized databases such as Scopus, Web of Science (WoS), PubMed, and Google Scholar, providing an in-depth examination of AI applications in soil and environmental studies. Additionally, the study outlines future perspectives for AI in environmental analysis by identifying best practices for AI implementation in GIS and advocating for systematic benchmarking in remote sensing pertinent to soil studies.

For the last 20 years, Argentina has been proposed as an emerging market with great potential for pecan production, with an estimated annual production of around 3000 tons. In Argentina, this species can be classified into two large groups: improved material and wild material. The latter may present interesting allelic variants that may allow to expand the genetic base of the species, facilitating the establishment of new high-quality cultivars. The objective of this work was to characterize the genetic diversity and genetic structure of 73 wild pecan individuals, distributed in the provinces of Jujuy, Entre Ríos, Corrientes, Mendoza, Buenos Aires and Río Negro, in Argentina. Eight microsatellite markers developed for the species were amplified by PCR and resolved in polyacrylamide gels stained with silver nitrate. The average genetic diversity (UHe) for all the individuals analyzed was moderate (0.406; ds = 0.031), with clear variations between the origins analyzed, showing a maximum of UHe = 0.587 for the material from Entre Ríos and a minimum value of UHe = 0.261 for the individuals from Corrientes. Ten exclusive alleles (Ae) were found in seven individuals from three different origins. The genetic structure observed showed the presence of two genetic groups (K), distributed heterogeneously among the analyzed origins and related to the UHe and Ae values. No grouping by origin nor geographical location were observed. These results, although preliminary, may guide the selection of new materials for the breeding program of the species carried out at INTA (Argentina).

The growing frequency and intensity of extreme weather events, along with the mounting challenges posed by climate change, make hydrological models that incorporate cutting-edge technologies and innovation vital for sustainable water management strategies and efficient flood detection systems. This paper investigates the effectiveness of deep learning models, specifically Temporal Fusion Transformer (TFT), Transformer, and Long Short-Term Memory (LSTM), using a linear model as baseline, for predicting water levels using meteorological data as covariates. Hourly data from three weather stations (Aminteo, Vevi, Zazari) spatially distributed in the catchment were used to forecast water levels, for the next 24 hours, of the Amintas stream. The study employs key evaluation metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Kling-Gupta Efficiency (KGE), to assess model performance. Results reveal satisfactory outcomes across all models, with the Temporal Fusion Transformer (TFT) model demonstrating superior performance in RMSE, MAPE, NSE, and KGE metrics. The discussion underscores the models' ability to outperform linear models by extracting hidden information from weather data. The goal of this paper is to enhance water level prediction capabilities in the face of climate-induced hydrological challenges by leveraging advanced deep learning models and innovative techniques. CCS Concepts: • Computing methodologies → Artificial intelligence.

This mixed-methods study investigates the integration of Artificial Intelligence (AI) tools in IELTS Writing Task 2 preparation among EFL learners (n = 18). Employing a descriptive cross-sectional survey design, the research combines quantitative Likert-scale data with qualitative thematic analysis to examine learner perceptions of AI-mediated feedback, idea generation, and the associated risks of cognitive dependency. The findings reveal a dualistic relationship with technology: while students demonstrate strong reliance on AI for surface-level linguistic accuracy (grammar correction: M = 3.56; vocabulary enhancement: 77.8% usage), they remain skeptical about AI's capacity to replace human pedagogical guidance (M = 2.94 for AI as a teacher substitute). Qualitative data further exposes critical tensions, including inconsistent scoring reliability, linguistic over-complexity, and the phenomenon of "cognitive inertia"—a psychological dependency that undermines learner autonomy. Despite these challenges, AI emerges as a valuable scaffold for autonomous practice, eliminating feedback waiting periods and providing immediate linguistic support. The study concludes that AI functions optimally as a complementary "micro-level" editor rather than a substitute for human instruction, proposing a hybrid pedagogical model that integrates technological efficiency with critical human mentorship. Recommendations for students, educators, and institutions emphasize the development of AI literacy and structured reflection to preserve authentic intellectual development in high-stakes writing contexts.

Cuando comenzó la pandemia de COVID 19, las plataformas sociales tuvieron un rol central en la producción y acceso a la información. Este estudio identifica los tópicos de mayor interés y sus sentimientos asociados en Twitter en la conversación pública en lengua española en ese periodo. Asimismo, analiza el rol de Twitter como plataforma social involucrada en la conversación pública, como medio para la autocomunicación de masas y para amplificar la voz de un conjunto reducido de actores de alta visibilidad. Se recolectaron 231.375 tuits en España y América Latina durante dos meses. Se midieron indicadores de frecuencia y sentimientos, y se agruparon términos para identificar tópicos y determinar el interés de los usuarios sobre estos, mediante métodos digitales y de lenguaje computacional en R. La frecuencia de los principales términos es dinámica a lo largo del período estudiado, lo que sugiere diferentes percepciones de la pandemia. Los tópicos principales refieren a conversacione...

Resumen. El COVID-19, según la Organización Mundial de Salud (OMS), es la "enfermedad infecciosa causada por el coronavirus que se ha descubierto más recientemente". Para el tratamiento del COVID-19 se han informado por diferentes medios diversos productos, entre ellos se tiene al dióxido de cloro; se dice que puede curar y prevenir el COVID-19 porque incrementa los niveles de oxígeno en la sangre; también expertos en la salud indicaron que es falso y, por el contrario, esta sustancia puede oxidar la hemoglobina y desencadenar más problemas de salud. Se planteó el objetivo de desarrollar un modelo de minería de datos sobre el análisis de sentimiento en Twitter basado en lexicones sobre el uso de dióxido de cloro para el tratamiento del COVID-19, para su desarrollo se usó la metodología CRISP-DM y la herramienta Orange Canvas. En los resultados se obtuvo que la palabra dióxido de cloro está asociada a las palabras sí, tomar y COVID; por otro lado, la opinión de las personas en relación al dióxido de cloro, con tendencia a negatividad y neutralidad, dan como resultado calificaciones compuestas, lo que significa que 4500 tuits de los usuarios de Twitter tienen opiniones de neutralidad y rechazo en distintas proporciones sobre el tratamiento del dióxido de cloro para el COVID-19. Palabras clave: análisis de sentimiento / dióxido de cloro / Twitter / COVID-19

Inspired by the groundbreaking investigation of ice patch archaeology in Yukon Territory, the authors began exploring the Mackenzie, Selwyn, and Richardson Mountains for ice patch archaeological sites in 2000. Through remote sensing analysis, followed by intensive field surveys in the Selwyn and Mackenzie Mountains, we documented eight ice patch sites containing well-preserved archaeological artifacts and biological specimens. Twenty additional ice patches exhibit the key indicators of ice patch archaeological sites (permanent or intermittent ice and snow lenses containing caribou fecal matter, faunal material, or both), but so far these patches have not yielded artifacts. Collections from ice patches in the Selwyn Mountains include examples of three precontact hunting technologies: throwing dart (atlatl), bow-and-arrow, and snare. Atlatl technology, represented by the distal ends of two darts dating to 2410 and 2310 14 C yr BP, predates bow-and-arrow technology, represented by two complete arrows, two distal shaft fragments, and a partial bow dating between 850 and 270 14 C yr BP. A ground squirrel snare dates to 970 14 C yr BP. Caribou dominates the faunal remains recovered from the ice patches. These data suggest that hunting on ice patches was part of a broader-spectrum summer subsistence economy focused on a broad alpine valley, known locally as K'atieh, and that hunters tended to target ice patches close to other subsistence locations in this area. Key words: ice patch archaeology, mountain woodland caribou, Selwyn Mountains, dart-throwing technology, bow and arrow, ground squirrel snare RÉSUMÉ. Inspiré par les travaux révolutionnaires de l'archéologie des névés menés dans le territoire du Yukon, les auteurs ont commencé en l'an 2000 l'exploration des montagnes du Mackenzie, de Selwyn et de Richardson afin d'y trouver des sites archéologiques associés à des névés. L'analyse des données de la télédétection suivie d'un programme de prospection intensif dans les montagnes de Selwyn et du Mackenzie a permis de documenter huit sites de névés qui contiennent des objets archéologiques et des spécimens biologiques bien conservés. Vingt autres sites présentent les caractéristiques des sites archéologiques de névés (soit des lentilles de glace et de neige permanentes ou semi-permanentes contenant des matières fécales et (ou) de la faune), mais n'ont livré pour l'instant aucun artefact. Les collections des névés des montagnes Selwyn comprennent des exemplaires de trois systèmes d'armes de chasse antérieures au contact. La technique du tir au propulseur est représentée par les extrémités distales de deux lances datant de 2410 et 2310 années radiocarbones BP et est antérieure à celle de l'arc et de la flèche qui consiste en deux flèches complètes, deux fragments distaux de hampe et un arc incomplet datés entre 850 et 270 années radiocarbones BP. Un piège à spermophile date de 970 années radiocarbones BP. Les ossements de caribou dominent les restes de faune trouvés dans les névés. Ces données laissent entendre que la chasse dans les zones alpines faisait partie d'une économie de subsistance estivale dans laquelle un large éventail d'espèces était chassé. Ces activités de subsistance se concentraient dans les larges vallées alpines, connues localement sous le nom de K'atieh et les chasseurs avaient tendance à cibler les névés localisés à proximité des autres ressources de la région. Mots clés : archéologie des névés, caribous des bois des montagnes, chaîne de Selwyn, utilisation de propulseurs, arcs et flèches, piège à écureuil terrestre Révisé pour la revue Arctic par Nicole Giguère.

Este artículo presenta un estudio interdisciplinario en el campo de las humanidades digitales, proponiendo un análisis de sentimientos mediante minería de texto, aplicado a un conjunto de nueve novelas históricas contemporáneas que buscan ser representaciones de la Edad Media. El objetivo principal es examinar cómo la literatura actual utiliza la autenticidad histórica y la adaptación para ofrecer visiones del pasado, empleando para ello herramientas computacionales que permiten una "lectura distante" de las obras.

This article describes and evaluates the application of the supervised sentiment analysis in political communication through a real-time classifier of political opinions in Spanish tweets using machine learning techniques, both on a local computer and using distributed computing for big data problems. We describe the associated emerging methods and techniques and analyze the opportunities that these innovations represent for political communication

Doctor en Comunicación, Cultura y Educación y licenciado en Economía por la Usal. Ha desarrollado su trabajo en investigación de audiencias, metodologías de investigación, estructura del sistema audiovisual y las industrias culturales, televisión, educación y comunicación, publicando artículos y libros a nivel nacional e internacional.