聚变科学与工程资源库
检索物理代码、实验数据、代理模型、工程几何、技术文档与验证资料，并核对来源、版本和使用条件。
Twisted Shell Topology and Spectral Closure: An Eight-State Phase-Return Construction
ZenodoThis preprint develops a reduced geometric model in which a four-phase compact progression undergoes an orientation reversal after one complete traversal and returns to its full original state after a second traversal. The construction produces an eight-state phase-return orbit from four phase quadratures combined with two return orientations. Removing the uniform phase mode leaves a three-dimensional active sector, and projecting onto the return-restored sector produces a rank-three subspace within the eight-state shell. This gives a normalized active-shell fraction of three-eighths directly from the geometry rather than by fitting. The same three-dimensional active structure is represented through a tetrahedral tight frame and connected to the first nontrivial spherical harmonic sector. This sector has three active components and supplies a natural degree-one map on the sphere, giving a common geometric and spectral origin for several normalization factors used in later shell-based constructions. The paper also studies spectral selection on the twisted compact loop. Once the inactive periodic zero mode is excluded, the first twisted mode becomes the lowest admissible nonconstant branch and has one quarter of the quadratic gradient energy of the first nonzero periodic mode. This provides a spectral distinction between twisted and ordinary periodic closure within the reduced construction. A further result is the separation of distinct primitive closure classes. The paper shows that repeated powers of a single projector cannot generate all of the different coefficients appearing at different perturbative orders. The second-order return contribution and the fourth-order curvature contribution therefore represent distinct reduced structural classes rather than repeated copies of the same operation. The work is deliberately structural rather than phenomenological. It does not derive a measured coupling constant or claim that the reduced topology alone fixes a unique physical stability value. Instead, it establishes the finite-state architecture, projector structure, angular sector, topological degree, and spectral mode relations, while leaving the canonical observable bridge and continuous modulus as open problems for subsequent work.
A new diagnostic method based on support vector machine for short circuit winding faults in induction motors
University of Souk AhrasInter-turn short circuits (ITSCs) in induction motor (IM) windings are among the most critical and frequent faults in industrial environments, as they can rapidly evolve into severe damage, leading to unplanned downtime and costly maintenance. To enhance the reliability of IMs, this paper proposes a machine learning–based diagnosis method dedicated to ITSC failures. The developed diagnostic tool combines a support vector machine (SVM) classifier with Fisher’s ratio (FR)-based feature selection. The proposed framework uses experimentally acquired current signals under healthy conditions and five ITSC fault severity levels (1%–5%), evaluated across four load conditions (25%, 50%, 75%, and 100%). Each signal is segmented into 200 non-overlapping segments (500 samples each), from which nine time-domain features are extracted to capture fault-related characteristics. These features are then used for training and testing a SVM classifier capable of distinguishing between healthy states and levels of severity of ITSC faults. To optimize the classification process, the FR algorithm is employed to select the most informative features while discarding those with low relevance. Our findings unveiled that the proposed hybrid FR-SVM-based diagnosis achieves high diagnostic accuracy ranging from 99.54% to 100%. Furthermore, the outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
Blood Differential Count effects of Diplazium polypodioides Blume, Diplazium maximum (D.Don) C. Chris. and Stenochlaena palustris edible fiddlehead ferns from Northern Luzon Philippines on ICR mice (Mus musculus)
College of Medicine, Cagayan State University, Carig, Tuguegarao City, Cagayan, PhilippinesDiplazium polypodiodes, Diplazium maximum locally called sarabat 1 and 2 respectively and Stenochlaena palustris or red-fern fiddlehead extracts were evaluated on blood cells of ICR mice. Thirty-two (32) twelve-week-old mice were divided into eight treatments with four per treatment along with positive and negative controls. Mice were fed daily for four weeks with regular food, water plus extracts in low dose (40mg/kg) and high dose (80mg/kg) of sarabat1, sarabat2, and red-fern. Blood was extracted by tail tipping on Day 0, 14, 28 and complete blood count (CBC) with platelets was analyzed. Hematocrit and red blood cells (RBC) was elevated in the positive control but not significantly different with fern extracts with a p-value of 0.119 and 0.208, respectively. White blood cells indicated the negative control and low dose fern extract with mean values of 11.75 and 13.07, respectively, were slightly higher but not significantly different with a p-value of 0.185. Effects on neutrophil and absolute lymphocyte counts follow the same trend with a p-value of 0.249 and 0.119, respectively. Platelet seems elevated in low dose sarabat 1 and 2 as well as low dose red fern but not significant with a 0.370 p-value. Comparison of pre and post-test treatments with extracts of Sarabat 1, 2 and red fern on blood counts at two dosages showed that a mong the parameters tested, only WBC showed a highly significant (0.002) variation at post-treatment which seemed to indicate WBC stimulation which alerts us to caution, while the rest of the differentials are within safe reference parameter ranges. published by the International Journal of Biosciences | IJB
RISK ASSESSMENT OF FUEL OIL BUNKERING OPERATIONS USING AN INTEGRATED HIRARC–HAZOP FRAMEWORK: A CASE STUDY AT A MARINE JETTY TERMINAL
Universitas Negeri MalangIntegrated Terminal Makassar's fuel oil bunkering operations at Jetty 1 and Jetty 2 present significant, intertwined occupational and process hazards. This study analyzed these risks by integrating Hazard Identification, Risk Assessment, and Risk Control (HIRARC) with Hazard and Operability (HAZOP) studies, a novel approach for conventional fuel bunkering. HIRARC identified nine hazards across preparation, transfer, and completion stages, initially rated as low risk, subsequently reduced to low or very low via predominantly administrative controls. HAZOP analysis revealed deeper process deviations underlying these occupational hazards, including communication failures, excessive pressure and flow rates, hose integrity degradation, slippery deck conditions, and inadequate residual fuel handling during completion. The integrated HIRARC-HAZOP framework demonstrates that occupational risks directly correlate with process deviations, a connection often overlooked by single-method analyses. While existing controls are functional, three critical areas require reinforcement: formalizing pre-transfer communication checklists, establishing hose integrity documentation beyond visual inspection, and procedurally mandating the draining and disconnection sequence at operational completion. These refinements aim to enhance safety by moving beyond reliance on spontaneous compliance towards a robust, procedurally driven safety management system.
THE MEDIATING ROLE OF RISK MANAGEMENT ON TECHNOLOGICAL UNDERSTANDING, FINANCIAL LITERACY, AND SME PERFORMANCE IN MEDAN
Universitas Sumatera Utara, MedaThis study examines the impact of technological understanding and financial literacy on Small and Medium Enterprise (SME) performance, with risk management practices as a mediating variable among SMEs in Medan. An explanatory sequential mixed-methods approach combining quantitative and qualitative methods was employed. The population comprised culinary SMEs registered with the Medan City Department of Cooperatives, SMEs, Industry, and Trade. Data were collected via questionnaires and in-depth interviews. Quantitative analysis used Structural Equation Modeling-Partial Least Square (SEM-PLS), while qualitative data utilized a thematic approach to strengthen quantitative results. The findings show that technological understanding and financial literacy both positively affect SME performance and risk management practices. Additionally, risk management practices positively influence SME performance and successfully mediate the effects of technological understanding and financial literacy on performance. These results indicate that SMEs' capability to comprehend technology and manage finances enhances business performance more effectively when supported by sound risk management. Ultimately, integrating technological understanding, financial literacy, and risk management serves as a strategic driver to boost SME competitiveness, resilience, and sustainability.
PENGEMBANGAN VIDEO ANIMASI PEMBUATAN POLA DASAR DENGAN METODE DANCKAERTS UNTUK MATA KULIAH KONSTRUKSI POLA BUSANA
Pendidikan GaneshaPenelitian ini memiliki tujuan untuk (1) mengembangkan media pembelajaran berupa video animasi pembuatan pola dasar metode Danckaerst pada mata kuliah Konstruksi Pola Busana serta (2) mengetahui kualitas media video animasi yang telah dikembangkan berdasarkan penilaian ahli materi, ahli media, dan uji coba kelompok kecil. Penelitian ini menggunakan metode penelitian dan pengembangan (Research and Development) dengan menggunakan model ADDIE (Analysis, Design, Development, Implementation, Evaluation). Penelitian ini menggunakan 2 orang ahli materi, dua orang ahli media, dan enam orang mahasiswa Prodi Pendidikan Kesejahteraan Keluarga, Kosentrasi Tata Busana sebagai subjek penelitian. Pengumpulan data dalam penelitian ini dilakukan dengan observasi dan penyebaran angket. Sedangkan teknik analisis data dalam penelitian ini menggunakan analisis deskriptif kuantitatif dan deskriptif kualitatif. Hasil penelitian ini menunjukkan bahwa (1) media pembelajaran video animasi pembuatan pola dasar metode Danckaerst berhasil dikembangkan melalui tahapan model ADDIE, sehingga menghasilkan media pembelajaran berupa video animasi yang dikemas dalam format MP4 dengan materi yang telah disesuaikan dengan tujuan dan capaian pembelajaran mata kuliah Konstruksi Pola Busana. (2) Hasil uji kualitas produk melalui uji validitas oleh ahli materi, ahli media, dan uji coba kelompok kecil memperoleh persentase produk media pembelajaran video animasi pembuatan pola dasar metode Dankaerst sebesar 99,8% dari ahli materi, 98,8% dari ahli media, dan 94,4% dari uji coba kelompok kecil mahasiswa dengan kategori sangat baik. Berdasarkan hasil penelitian tersebut, media pembelajaran pembuatan pola dasar metode Danckaerst memiliki kualitas sangat baik sehingga media ini dapat digunakan dalam pembelajaran mata kuliah konstruksi pola busana untuk meningkatkan motivasi belajar dan mendukung proses pembelajaran secara mandiri.
Barrier-Aware Merge Trees Reveal Ultrametric Organization in Nonlinear p-Spin Energy Landscapes: Code and Processed Data
ZenodoThis record contains the Python code, fixed random seeds, processed numerical results, validation tables, and publication figures accompanying the manuscript “Barrier-Aware Merge Trees Reveal Ultrametric Organization in Nonlinear p-Spin Energy Landscapes.” It includes results from 36 spherical p=3 disorder realizations, matched spherical-null analyses, sampling-convergence and path-sensitivity tests, analytic geodesic validation, spherical-string calibration, a structure-preserving node-label permutation null, and processed metrics for the external Cambridge LJ13 transition-state-network benchmark. The raw Cambridge LJ13 archive is third-party material and is not redistributed in this record. Its official source and complete processing provenance are documented in the included THIRD_PARTY_DATA.md file.
Bowman County hybrid geothermal-wind-solar power system model (MATLAB)
University of North DakotaMATLAB model underlying a techno-economic assessment of low-enthalpy geothermal as firming capacity in a wind-dominant rural hybrid power system for Bowman County, North Dakota. The model sizes a wind, solar photovoltaic, geothermal ORC and battery portfolio against measured county demand, dispatches it hourly, and computes levelized cost, lifecycle emissions, a no-geothermal counterfactual and a cost-of-firmness frontier. Included are the sizing and dispatch routines, the ORC energy balance and its validation against a demonstration binary plant on the same resource, one-at-a-time sensitivity analysis, a seed ensemble, a geothermal outage-timing test, and a run across all 33 complete years of the station record. Resource inputs are transcribed from the North Dakota Agricultural Weather Network (NDAWN) Bowman station: 403 monthly observation records covering January 1993 to July 2026, and hourly series for 2023 including paired 3 m and 10 m wind measurements. NDAWN data are provided by North Dakota State University and are used here with attribution. Run main_bowman for the base case, or bowman_run_2023 for the measured hourly case.
The life-cost cycle-based sizing of complementary energy storage technologies in DC microgrids
Mustansiriyah UniversityHybrid energy storage system (HESS) configurations have the potential to mitigate the detrimental effects of photovoltaic power generation oscillations on DC microgrid safety and reliability. The economic efficiency of HESS can be improved through the utilization of batteries and their complementary attributes through an energy management strategy. This will allow for the full utilization of the benefits of superconducting magnetic energy storage (SMES), such as high efficiency, lossless energy storage, elevated power density, and rapid response. The battery-SMES HESS is subject to a life cycle cost (LCC) model, along with its associated constraints. System expenditures can be drastically cut by optimizing the HESS capacity layout. The goal function is the lowest LCC, presuming that the power demands of the system are met. Particle swarm optimization takes acceleration into account when designing the capacity of the system. In order to prove that the suggested method of configuring capacity works, a microgrid model is created and tested using numerical data.
A review of field-programmable gate array-based biomedical signal processing for public health applications
Universitas Ahmad DahlanBiomedical 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.
adwitiyashukla/sec-rag-platform
adwitiyashuklaEvaluation-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.
Knowledge Management Capabilities and SMES Performance in Computer Village Ikeja, Lagos
Department Department of Business Administration, Chrisland University, AbeokutaIn 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.
input4MIPs Controlled Vocabularies (CVs)
ZenodoAnnouncements 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
reunios2024/cortex-sentinel-trading-nexus
reunios2024Self-Tuning Multi-Agent AI Trading System 2026: 8-Source Signal Fusion & Kronos Model
GiriNeoSepsisMimicTM
Graphic Era UniversityGiriNeoSepsisMimic™ 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.
moneeshreddych/Stockprizepredictor
moneeshreddychA 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.
ORNL-Fusion/OEDGE
ORNL-FusionOEDGE/DIVIMP/LIM plasma edge simulation codes for fusion reactor applications
THE EFFECT OF EVENT ACTIVATION ON BRAND AWARENESS AND ITS IMPLICATIONS ON PURCHASE INTENTION OF PROMAG HERBAL PRODUCTS IN BANDUNG
STIE STAN-IMIndonesia'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.
noorabdalla04/wearable-fusion-auth-benchmark
noorabdalla04Honest 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.
api-evangelist/alif-semiconductor
api-evangelistAlif 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.
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 CaldasThis 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.
etelford32/Hydrogen_plasma_fusion_2D
etelford32Hydrogen isotope machine learning simulation
api-evangelist/earth-ai
api-evangelistEarth AI is a vertically integrated, AI-powered mineral exploration company that discovers, develops, and owns critical mineral mines for the emerging hardware economy — data centers, humanoid robots, eVTOL aircraft, fusion energy, space, and defense.
ArefVakili/DD-fusion-neutron-diagnostics
ArefVakiliGeant4-based Monte Carlo simulation of neutron detector response and efficiency for 2.45 MeV DD fusion neutrons. Essential for converting counts to reaction rates and fusion power, enabling accurate plasma diagnostics and performance optimization in every fusion reactors.