Biomedical signal processing is essential for modern diagnostics, monitoring, and preventive healthcare in public health and mobile health (mHealth) systems. Signals such as electroencephalography (EEG), electromyography (EMG), and heart rate variability (HRV) offer vital insights into brain, muscle, and cardiovascular health. However, achieving real-time, energy-efficient, and scalable processing remains challenging for conventional hardware such as central-processing units (CPUs), graphicsprocessing units (GPUs), and application-specific integrated circuits (ASICs). Field-programmable gate arrays (FPGAs) provide a promising alternative through their reconfigurability, parallelism, and adaptability to dynamic biomedical workloads. This review examines FPGA-based implementations for EEG, EMG, and HRV processing, focusing on key metrics including latency, throughput, and power efficiency. It also discusses design strategies such as low-power optimization, hardware– software co-design, and FPGA-based machine learning acceleration, with attention to data integrity and security in medical contexts. Integration with wearable, portable, and telemedicine platforms is explored, alongside comparative analyses with traditional computing architectures. The paper identifies challenges in power–performance trade-offs, design complexity, and clinical validation, and highlights emerging directions such as artificial intelligence (AI)-driven FPGA platforms, neuromorphic design, and sustainable low-cost solutions for large-scale health monitoring. Overall, FPGA-based biomedical signal processing emerges as a foundation for intelligent, efficient, and accessible next-generation public-health technologies.

1.0核验于 2026-08-13

工具软件来源条款

adwitiyashukla/sec-rag-platform

adwitiyashukla

Evaluation-driven RAG over SEC filings. Hybrid retrieval (dense + BM25 + SPLADE) with RRF fusion and cross-encoder reranking, financial figures computed from filed XBRL rather than generated, groundedness verification on every answer, and a CI quality gate that fails the build on regression.

1.0核验于 2026-08-13

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Knowledge Management Capabilities and SMES Performance in Computer Village Ikeja, Lagos

Department Department of Business Administration, Chrisland University, Abeokuta

In today’s knowledge-driven economy, effective management of knowledge resources is critical for the competitiveness and sustainability of small and medium-sized enterprises (SMEs). Despite SMEs’ significant contributions to employment and economic development, particularly in Nigeria, many still face constraints such as inadequate infrastructure, limited technology adoption, and informal knowledge-sharing practices. This study investigates the relationship between knowledge management capabilities—comprising knowledge acquisition, application, storage, and creation—and SME performance within the dynamic context of Computer Village, Ikeja, Lagos. Employing a quantitative survey research design, data were collected from 375 SMEs using structured questionnaires and analyzed through descriptive and regression analyses. The results demonstrate that all four dimensions of knowledge management significantly and positively impact SMEs’ performance, influencing areas such as innovation, operational efficiency, and customer satisfaction. Findings highlight the importance of structured knowledge practices over informal methods to prevent knowledge loss and enhance competitive advantage. The study recommends that SMEs invest in digital tools for knowledge storage, cultivate cultures that encourage sharing and innovation, and leverage external collaborations to enrich knowledge bases. These insights are crucial for business owners, policymakers, and stakeholders aiming to harness knowledge as a strategic asset to drive sustainable growth and resilience in Nigeria’s SME sector.

1.0核验于 2026-08-13

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input4MIPs Controlled Vocabularies (CVs)

Zenodo

Announcements Updated metagrid related links to point at metagrid.esgf-west.org as this seems to provide a more stable interface Changelog 📚 Improved Documentation Updated metagrid related links to point at metagrid.esgf-west.org as this seems to provide a more stable interface ( #468 ) Changes 764ce2ce bump: version 6.7.52a1 -> 6.7.52 6367d12b Revert changes to show ESGF JSON updates 94248f18 CHANGELOG 45d53b8e Revert direct download link change 6dab203d Update links 9cd47f09 bump(pre-release): version 6.7.51 > 6.7.52a1

1.0核验于 2026-08-13

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reunios2024/cortex-sentinel-trading-nexus

reunios2024

Self-Tuning Multi-Agent AI Trading System 2026: 8-Source Signal Fusion & Kronos Model

1.0核验于 2026-08-13

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GiriNeoSepsisMimicTM

Graphic Era University

GiriNeoSepsisMimic™ is an evidence-informed, algorithmic clinical decision-support reference designed to help clinicians systematically differentiate neonatal sepsis from infectious and non-infectious conditions that may present with similar clinical features. The CDSS uses a safety-first, structured approach beginning with immediate assessment and stabilisation, followed by clinical classification, targeted differential diagnosis, rational investigations, serial reassessment and antimicrobial-stewardship considerations. It covers common neonatal conditions as well as selected uncommon but clinically important infectious, metabolic, neurological, gastrointestinal, endocrine, cardiovascular and inflammatory mimics. It also recognises that sepsis and a mimic may coexist. The tool includes bedside discriminators, investigation pathways, management principles, counselling guidance, evidence grading, references, frequently asked questions, limitations and an explicit AI-assistance and governance statement. Its responsive, self-contained HTML format supports bedside use on computers, tablets and smartphones without external software dependencies. GiriNeoSepsisMimic™ is intended to complement—not replace—clinical judgement, senior neonatal consultation, local antimicrobial policies and established neonatal sepsis protocols. It must never delay stabilisation, microbiological sampling or empiric antimicrobial treatment when neonatal sepsis is clinically suspected. The tool is evidence-informed but has not yet undergone prospective clinical validation as a diagnostic or prescribing system.

1.0核验于 2026-08-13

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moneeshreddych/Stockprizepredictor

moneeshreddych

A machine learning-based stock price prediction system that combines financial news sentiment analysis (FinBERT) with historical market data using a Temporal Fusion Transformer (TFT) to forecast stock price movements. The project integrates NLP and time-series forecasting to improve prediction accuracy and support data-driven investment decisions.

1.0核验于 2026-08-13

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ORNL-Fusion/OEDGE

ORNL-Fusion

OEDGE/DIVIMP/LIM plasma edge simulation codes for fusion reactor applications

1.0核验于 2026-08-13

工具软件来源条款

THE EFFECT OF EVENT ACTIVATION ON BRAND AWARENESS AND ITS IMPLICATIONS ON PURCHASE INTENTION OF PROMAG HERBAL PRODUCTS IN BANDUNG

STIE STAN-IM

Indonesia's herbal pharmaceutical industry faces challenges in increasing brand awareness and purchase intention through effective event activation strategies. This study analyzes the effect of event activation on brand awareness and its implications for purchase intention of Promag Herbal products at PT. Kalbe Farma Bandung Branch, with brand awareness as a mediating variable. Internal data paradox shows event activation intensity reaches 126.88% of target, yet event sales only achieve 71.32% of event sales target. The study uses quantitative descriptive-verification approach with 110 respondents and SEM-PLS analysis. Test results show: (1) event activation has positive significant effect on brand awareness (β = 0.694; T = 11.016; p < 0.001); (2) brand awareness has a positive significant effect on purchase intention (β = 0.648; T = 8.202; p < 0.001); and (3) brand awareness partially mediates the effect of event activation on purchase intention (VAF = 68.9%). These findings confirm that the quality of event activation is more important than quantity in forming strong brand awareness as transmission mechanism toward consumer purchase intention.

1.0核验于 2026-08-13

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noorabdalla04/wearable-fusion-auth-benchmark

noorabdalla04

Honest cross-session benchmark for fast multi-signal wearable biometric authentication (PPG/ECG/GSR/ACC fusion). Within-session accuracy is near-perfect but does not transfer across sessions/days; the honest operating point is orders of magnitude short of a device unlock.

1.0核验于 2026-08-13

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api-evangelist/alif-semiconductor

api-evangelist

Alif Semiconductor is a fabless semiconductor company headquartered in Pleasanton, California with engineering operations in Grenoble, France. It designs secure, AI/ML-enabled 32-bit microcontrollers and fusion processors for edge computing, built around Arm Cortex-M55 cores and dedicated Arm Ethos-U55 Neural Processing Units.

1.0核验于 2026-08-13

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Analysis code and derived data for: Artificial Intelligence Across the Wildfire Management Lifecycle — A PRISMA-Guided Survey of Techniques, Validation Practices, and the Global-South Gap

Universidad Distrital Francisco José de Caldas

This deposit contains the complete analysis pipeline and the derived datasets behind the review article "Artificial Intelligence Across the Wildfire Management Lifecycle: A PRISMA-Guided Survey of Techniques, Validation Practices, and the Global-South Gap" . The review follows a reproducible PRISMA-2020 protocol. A single Scopus query, executed on 29 June 2026 and published verbatim in this deposit, returned 4,463 records, reduced to an eligible corpus of 2,086 journal articles (2015–2025). The article characterises that corpus bibliometrically, formalises how the surveyed methods work, and quantifies validation practice on the corpus itself: fewer than 1% of eligible abstracts mention spatially blocked validation, and only one of the 41 primary studies synthesised reports a spatially blocked protocol. Contents code/ — Python and R scripts that regenerate every reported count, table and figure: PRISMA flow, bibliometric profile, stratified selection pool, supplementary tables, and the keyword co-occurrence network of Figure 3. data/corpus/ — the corpus accession list identifying all 2,086 eligible records (DOI, Scopus EID, year, source, document type, open-access status, citation count, author keywords), plus the exact query string and retrieval date. data/derived/ — annual production, source counts, author-keyword frequencies, the top-50 co-occurrence matrix, Louvain community assignments, PRISMA exclusion logs and the abstract-level term scan results. data/supplementary_tables/ — supplementary tables S1–S3 as CSV. docs/ESM_1.pdf — the article's supplementary material, sections S1–S5. On the raw Scopus export. The raw export is deliberately not included: it carries the Abstract and References fields, which are copyrighted content owned by the individual publishers and licensed to the authors through an institutional Scopus subscription. The accession list identifies the corpus record-for-record, so it can be reconstructed exactly by anyone with Scopus access. Every analysis in the article is reproducible from the files shipped here, with one exception: the abstract-level term scan searches abstract text and therefore requires the licensed export. Its results are included; re-running it requires supplying your own export. See README.md , Section 3. Licences. Code is released under MIT; derived data and documentation under CC BY 4.0. Changes in version 1.1.0. The supplementary material (docs/ESM_1.pdf) is replaced by the version accompanying the manuscript submitted to Environmental Modelling & Software. Section S5 now publishes verbatim the three regular expressions used for the corpus-level term scan, together with the terms they do not match and the direction of that bias. The risk-of-bias wording is aligned with the manuscript, and the reported relation in spread modelling is stated as reported score tracking the breadth of the evaluation rather than its rigour, with the counterexample in the table declared explicitly. The code and the derived data are unchanged from version 1.0.0.

1.0核验于 2026-08-13

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etelford32/Hydrogen_plasma_fusion_2D

etelford32

Hydrogen isotope machine learning simulation

1.0核验于 2026-08-13