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This set of files contains ocean heat content (OHC) and steric height derived from satellite altimetry and gravimetry, along with related uncertainties, correction factors, and other diagnostics. The …

This set of files contains ocean heat content (OHC) and steric height derived from satellite altimetry and gravimetry, along with related uncertainties, correction factors, and other diagnostics. The source altimetry data used are the NASA-SSH version 1 gridded sea surface height fields, and the source gravimetry data are the JPL RL06.3_v04 mascon fields. Data are presented either on a 0.5x0.5-degree lat/lon grid, or as globally-integrated OHC/global mean steric height, with the mask used in the global integration provided.   Acknowledgments: The authors would like to express their gratitude to the NASA GRACE-Follow On Science Team and the NASA Physical Oceanography Program for supporting this work. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).

Мазкур мақолада жамоат хавфсизлигини таъминлаш доирасида ички ишлар органларининг профилактик фаолиятини проактив механизмларга трансформация қилишнинг илмий-ҳуқуқий асослари тадқиқ этилган. Жумладан,…

Мазкур мақолада жамоат хавфсизлигини таъминлаш доирасида ички ишлар органларининг профилактик фаолиятини проактив механизмларга трансформация қилишнинг илмий-ҳуқуқий асослари тадқиқ этилган. Жумладан, қонунчиликдаги «жамоат жойи» тушунчасини легаллаштириш, жиноят қонунчилигидаги мавҳум ва баҳоловчи атамаларни чиқариб ташлаш ҳамда кибермакондаги безорилик (виртуал безорилик) учун жавобгарликни норматив мустаҳкамлаш бўйича асослантирилган қонун лойиҳалари таклифлари ишлаб чиқилган. Шунингдек, жиноятларни квалификация қилишда такрорийлик ва хавфли рецидивни дифференциациялаш, шу билан бирга, мураккаб тергов вазиятларида рақамли криминалистика ютуқларини (полиграф) процессуал амалиётга жорий этиш масалалари илмий асослаб берилган.

Summary  Introduction: Algorithmic bias in systematic reviews that use automatic screening is a major challenge in the application of AI in health sciences. This article presents preliminary…

Summary  Introduction: Algorithmic bias in systematic reviews that use automatic screening is a major challenge in the application of AI in health sciences. This article presents preliminary findings from the project titled “Identification, Reporting, and Mitigation of Algorithmic Bias in Systematic Reviews with AI-Assisted Screening: Systematic Review and Development of a Checklist for its Evaluation” registered in PROSPERO with the registration number CRD420251036600 (https://www.crd.york.ac.uk/PROSPERO/view/CRD420251036600). The results presented here are preliminary and part of ongoing work. Objective: To synthesize knowledge about the taxonomies of algorithmic bias, reporting, relationships with classical biases, and use of visualizations in AI-supported systematic reviews in health sciences. Methods: A specific literature review was conducted, focusing on systematic reviews, conceptual frameworks, and reporting standards for bias in AI in healthcare, as well as studies cataloguing detection and mitigation strategies, with an emphasis on taxonomies, transparency practices, and visual/illustrative tools. Results: A mature body of work describes stage-based taxonomies and mitigation methods for algorithmic bias in general clinical AI. Common improvements in reporting and transparency (e.g. CONSORT-AI, SPIRIT-AI) are described. However, there is a notable absence of direct application to AI-automated screening of systematic reviews or empirical analyses of the interactions of biases with classical biases at the review level. Visualization techniques, such as bias heatmaps and pipe diagrams, are available, but have not been adapted to review workflows. Conclusions: There are fundamental methodologies to identify and mitigate algorithmic bias in AI in health, but significant gaps remain in the understanding and operationalization of these frameworks within AI-assisted systematic reviews. Future research should address this translational gap to ensure transparency, fairness, and methodological rigor in the synthesis of evidence. Keywords Algorithmic bias; Artificial intelligence; Systematic Review; Health Sciences; Equity; Reporting Standards

Mazkur maqolada jahon tilshunosligida belgi ifodalovchi leksemalarning o‘rganilishi, ularning semantik va funksional xususiyatlari hamda turli tillardagi namoyon bo‘lish jihatlari tahlil q…

Mazkur maqolada jahon tilshunosligida belgi ifodalovchi leksemalarning o‘rganilishi, ularning semantik va funksional xususiyatlari hamda turli tillardagi namoyon bo‘lish jihatlari tahlil qilinadi. Belgi bildiruvchi leksemalarning predmet va hodisalarning rang, hajm, shakl, holat, xususiyat va boshqa belgilarini ifodalashdagi imkoniyatlari yoritiladi. Shuningdek, ushbu birliklarning semantik tarkibi, ma’no taraqqiyoti, milliy-madaniy xususiyatlari va lingvokulturologik mazmuni jahon tilshunosligidagi mavjud tadqiqotlar asosida ko‘rib chiqiladi. Tadqiqotda belgi ifodalovchi leksemalarning til tizimidagi o‘rni hamda ularning milliy dunyoqarash va olamning lisoniy manzarasini aks ettirishdagi ahamiyati ochib beriladi.

Complementary materials (planimetries of the fortresses) for the paper "Fortresses of the people, fortresses of the state. New insights on local agency and rural fortifications from the Alpujarra (Gra…

Complementary materials (planimetries of the fortresses) for the paper "Fortresses of the people, fortresses of the state. New insights on local agency and rural fortifications from the Alpujarra (Granada and Almeria, Spain)" in Journal of Medieval Iberian Studies.

ELT is in the midst of a tremendous transformation as the use of technology continues to evolve and influence classroom practices. AI is one of the most powerful innovations that are currently ha…

ELT is in the midst of a tremendous transformation as the use of technology continues to evolve and influence classroom practices. AI is one of the most powerful innovations that are currently having a significant impact on English Language Teaching (ELT). Machine learning, including chatbots, adaptive learning systems, speech recognition systems, and generative AI, creates opportunities for personalized, engaging language learning. These tools provide real-time feedback, foster autonomous learning, facilitate differentiated instruction, and create opportunities for authentic language use beyond the classroom. All of these aspects are a hot topic of interest among researchers, educators, and policymakers. Recent studies have found that AI can actually play a very positive role in developing a learner's language skills (speaking, listening, reading, writing, vocabulary, and grammar), for example: conversational AI lets learners interact in the learning process without feeling scrutinized by their peers in the classroom, and can be used to build confidence in fluency; writing tools can be used to give learners instant feedback, helping them correct mistakes and learn from them more rapidly; and in the same way, writingbots can give learners instant correction, helping them correct common grammatical errors and learn from them. In a multilingual classroom, such as the classroom of India, where learners may switch back and forth between different languages as they learn English, the potential of AI is even more apparent. With AI, teachers can support learners through personalized support, translation, and customized learning experiences to help learners reach their goals. It can be beneficial to have said technical assistance to help support language acquisition for multilingual learners, help with translation, and have adaptive learning that takes into consideration students' different language backgrounds. But even with its immense potential, the integration of AI into the classroom has presented challenges: digital inequity, teacher readiness, privacy issues, bias in algorithms, academic integrity, and technology dependence. The goal of this paper is to review the current literature on AI in ELT, consider the pedagogical implications of AI, and examine both the opportunities and obstacles to using AI in language learning. The suggestion being made is that AI should not be considered as a tool to replace teachers but a tool to work alongside them to facilitate teaching and learning. The paper concludes by emphasizing the importance of responsible AI use within ELT, the importance of raising awareness around ethical considerations, formulating guidelines, and adopting studentcentered approaches in order to unlock the potential of AI in education. Keywords: Artificial Intelligence, English Language Teaching, Digital Pedagogy, Multilingual Education, Language Learning Technologies

Archival copy of the print article in Journal of Islamic Studies, Prince of Songkla University, Vol. 4 No. 1 (June 2008–May 2009), pp. 123–142. The PDF was exported from the journal's OJS …

Archival copy of the print article in Journal of Islamic Studies, Prince of Songkla University, Vol. 4 No. 1 (June 2008–May 2009), pp. 123–142. The PDF was exported from the journal's OJS editorial archive; the issue is not yet publicly released on OJS.

This repository contains the underlying data and R-scripts required to generate the models and results as reported in the research article "Generation length estimates for nearly 10,000 species of fre…

This repository contains the underlying data and R-scripts required to generate the models and results as reported in the research article "Generation length estimates for nearly 10,000 species of freshwater fish". The uploaded zip-file is organised into two principal parts: data and scripts. It also provides a 'guide.md' file which contains further instructions and information about the purpose of the individual components of this repository and how they are meant to be utilised. The scripts are numbered by their order of intended usage.

Ушбу мақолада глобал иқлим ўзгариши шароитида пахта етиштиришда сув тежовчи технологияларнинг аҳамияти, сув тежовчи технологияларни қўллаш бўйича имкониятлар ҳақида маълумотлар келтирилган.