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We survey some lesser-known facts concerning properties of embeddings and projections between forcing notions. We will also state some generalizations of Easton’s lemma. To our knowledge, many of these facts have not b...

This paper explores the perceptions of first year Business students to embedding sessions and additional support workshops offered through a collaboration between learning advisors and lecturers in a first year foundatio...

Abstract Cross-lingual information retrieval limits global exchange of data because of the high diversity in the methods to classify, document and encode medical procedures. Traditional keyword-based or single-language s...

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning ...

Abstract Neural Machine Translation (NMT) for low-resource languages such as Assamese faces significant challenges due to the scarcity of parallel corpora. To address this, we propose a novel zero-shot translation framew...

Protein aggregation plays a central role in the pathogenesis of many neurodegenerative diseases and poses major challenges in protein engineering. A key driver of this process is the presence of aggregation-prone regions...

Starting university is challenging. Students require resilience to face the inevitable challenges of university life, yet many may not be equipped with the strategies they need. In this research, we explored changes in r...

Amid escalating environmental challenges and rapid digital transformation, sustained green innovation (SGI) has become a strategic imperative for manufacturing firms. Drawing on the knowledge-based view, this study exami...

We study complex-valued word embeddings where each word is represented by a magnitude and phase. Using a skip-gram objective, the real component captures symmetric similarity while the imaginary component induces directi...

Chandrasekhar’s H ( μ ) function forms the foundation of radiative transfer theory for semi-infinite, isotropically scattering atmospheres under external illumination. However, the classical formulation does not accou...

Subjects / keywords: AGREGACIÓN DE PROTEÍNAS, REGIONES APR, EMBEDDINGS, PROT-T5, MODELOS DE LENGUAJE DE PROTEÍNAS, APRENDIZAJE AUTOMÁTICO PROFUNDO, REDES NEURONALES, BIOINFORMÁTICA, PROTEÍNAS AMILOIDES, MUTACIONES PUNTUALES, PROTEÍNAS...

In the context of global warming, heavy precipitation is a frequent and impactful hazardous event. Timely identification of meteorological conditions associated with heavy rainfall is critical for urban flood risk manage...

Knowledge extraction has several approaches such as traditional approaches that rely on lexical representation capabilities, one of which is TF-IDF whose implementation can be combined with several classic similarity met...

IntroductionVision-based fall analysis has advanced rapidly, yet a key bottleneck remains: visually similar motions can correspond to markedly different physical outcomes, as subtle differences in contact mechanics and p...

Abstract A prompt and precise diagnosis is necessary to lessen the worldwide burden of asthma, a common chronic respiratory condition. Spirometry is one of the resource-intensive conventional methods that frequently misc...

Column type annotation (CTA) is a critical task in semantic table interpretation (STI), where meaningful semantic labels are automatically assigned to table columns. This process enriches tabular data with essential sema...

We demonstrate the facile fabrication of metal-wire-embedded microtrenches interconnected with semiconducting ZnO nanowires (ZNWs) through the continuous mechanical machining of micrograting trenches, the mechanical embe...

Organizations have become highly reliant on a range of data sources that span structured, semi-structured, and unstructured data types. These repositories allow large-scale storage for faster ingestion and analytics but ...

Opinion mining, a critical subfield of natural language processing, seeks to extract and analyze user-generated expressions. This paper proposes a novel Multi-Task Opinion Mining framework that integrates psychological i...

Abstract Traffic flow prediction is a fundamental building block for Intelligent Transportation Systems (ITS), enabling proactive congestion mitigation, adaptive traffic signal control, and reliable route guidance. Howev...

Abstract Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches. Traditional ex...

Mobile health (mHealth) apps are increasingly deployed for evidence-based mental health interventions, broadening access to care. While effective, Internet-based Cognitive Behavioural Therapy, delivered via web or app, f...

Abstract Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accel...

Abstract This study introduces an intelligent, large language model (LLM)–driven feedback system designed to assess and enhance students’ programming tasks through semantic comparison and pedagogically contextualized...