Search Results (2,387)

Circular economy (CE) claims in fashion aim to mobilize consumer participation in reuse and recycling, yet the interpretative flexibility of “circular” language can also enable vague messaging and skepticism. This study investigates how consumers assess CE fashion claims in terms of (a) claim substantiation quality (CSQ) and (b) claim support credibility (CSC), and how these assessments influence perceived green authenticity (PGA), green trust (GTR), and circular purchase intention (CPI) in Greece and the United Kingdom. A cross-national online stimulus-based survey utilizing standardized e-commerce product-card claims for a fictitious circular fashion brand gathered data from Greece (n = 640) and the UK (n = 572). PLS-SEM and multi-group analysis evaluated a model distinguishing CSQ and CSC as complementary message properties. In the overall sample, both CSQ and CSC exhibited a positive correlation with CPI, whereas PGA and GTR emerged as the most significant proximal predictors, with authenticity demonstrating the most substantial impact. Indirect-effect tests showed that CSQ affected CPI through both authenticity and trust. On the other hand, CSC was only effective through authenticity, and there was no clear pathway for CSC trust intention. The multi-group results also showed context sensitivity: Greece exhibited a stronger trust-based path to intention, while the UK had a stronger authenticity-based path to intention. Overall, the results support a dual-route theory of CE claim persuasion. Additionally, they suggest that effective CE fashion communication should combine clear, specific content with credible, externally checkable support cues. Full article

This study investigates the pressing issue of credit card fraud in the context of evolving e-commerce platforms and the necessity for improved fraud detection mechanisms. Since the advent of credit cards, the surge in their usage has led to a corresponding increase in fraud rates, highlighting the need to establish strong detection systems to prevent such activities. This research proposes a novel approach by integrating two distinct credit card datasets and a comparative evaluation of four machine learning imputation techniques to address missing values. By leveraging machine learning algorithms and imputation methods, we aim to enhance the accuracy and reliability of fraud detection. Our findings reveal significant improvements in model performance, with the accuracy of the integrated dataset reaching 100%, representing a 6.05% improvement over the original datasets; this improvement was confirmed to be statistically significant. Using the CBPM method, we selected the model that best balances accuracy and time efficiency. This result emphasizes the importance of effective data integration and imputation in combating financial fraud. It has direct practical implications for financial institutions, regulators, fraud analysts, and financial policymakers, who can use this approach to increase detection efficiency, reduce false positives, and optimize decision-making processes. Consequently, the method also helps protect consumers and enhances the overall resilience and credibility of financial markets. Full article

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies—local development, simulated cloud testing, and limited cloud usage for benchmark capture—enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows. Full article

System logs generated in IoT-based and sensor-driven cloud environments encode execution traces and complex relationships among services, functions, and data stores. In many IoT deployments, telemetry is pre-processed at the edge and then integrated into backend services (e.g., application servers and databases) for analytics and operations. During this integration, service executions record relational dependencies (e.g., function-to-data-store interactions) as operational logs (or aggregated statistics), which constitute key evidence for operating sensor-driven services. We therefore evaluate TLV using publicly reproducible backend execution logs as a representative backend model and discuss the generality and limitations of this choice. However, most existing retrieval-augmented generation (RAG) approaches remain document-centric, representing logs as flat textual chunks that fail to preserve execution flow and entity relationships, which are critical for diagnosing complex service execution pipelines in sensor-driven cloud backends. In this study, we propose Trace-LogVector (TLV), a relational log representation that transforms system logs into trace-level retrieval units while explicitly preserving execution order and entity interactions. TLV is constructed based on the Chunk as Relational Data (CARD) design principle, which represents execution flows using entity-centric multi-chunk structures rather than single aggregated text chunks. To evaluate the impact of relational log representation, we conduct controlled experiments comparing single-chunk and CARD-based multi-chunk TLV under identical embedding and retrieval settings. Retrieval performance is quantitatively assessed using Hit@5 and Mean Reciprocal Rank at 5 (MRR@5). Experimental results show that the proposed multi-chunk TLV achieves a Hit@5 of 1.000 and an MRR@5 of 0.900, consistently outperforming the single-chunk baseline across all evaluation queries. These findings demonstrate that preserving execution contexts and entity relationships as relational retrieval units is a key factor in improving RAG-based system log analysis for monitoring and diagnosing large-scale sensor networks and cloud systems. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a primary malignancy often driven by metabolic syndrome, fatty liver disease, and chronic hepatitis. These conditions foster a pro-inflammatory microenvironment that promotes tumor progression. Viniferin, a natural oligostilbene, has gained attention for its potential bioactivity. This study utilized an in silico network pharmacology approach to elucidate the pharmacokinetic properties and molecular mechanisms of ε- and δ-viniferin against HCC within the context of metabolic and inflammatory liver pathologies. Methods: ADMET profiles were characterized using SwissADME and pkCSM. Therapeutic targets were identified by intersecting viniferin-associated molecules with disease genes from GeneCards. A protein–protein interaction (PPI) network was constructed, supplemented by GO and KEGG enrichment analyses. Molecular docking and 200 ns of molecular dynamics (MD) simulations evaluated the binding affinity and structural stability between viniferin isomers and identified hub proteins. Results: Both ε- and δ-viniferin showed favorable drug-like properties, including high gastrointestinal absorption and low hepatotoxicity. We identified 247 overlapping targets, with network analysis highlighting ten essential hub genes, including AKT1, HSP90AA1, ESR1, HIF1A, NFKB1, GSK3B, PTGS2, APP, MTOR, and PIK3CA. Enrichment analysis confirmed their involvement in critical oncogenic pathways. Molecular docking showed strong interactions with APP, HSP90AA1, and AKT1, while MD simulations validated the long-term stability of ε-viniferin within the APP binding pocket. Conclusions: These findings provide mechanistic insights into viniferin as a multi-target agent for HCC, justifying further experimental validation in pre-clinical models. Full article

Background: Contemporary guidelines recommend rapid initiation of four classes of guideline-directed medical therapy (GDMT) for heart failure (HF) with reduced ejection fraction (HFrEF); however, real-world persistence, adherence, and dose optimization remain suboptimal. Methods: We analysed a predefined subregistry within the prospective Cardiology Research Dubrava (CaRD) registry, a real-world HF registry at a tertiary centre that includes patients across the ejection-fraction spectrum in whom contemporary HF therapy, including sodium-glucose cotransporter 2 inhibitors (SGLT2i), is introduced or optimised in routine practice. For this analysis, we included patients with newly diagnosed HFrEF (left ventricular ejection fraction (LVEF) ≤ 40%) who were discharged on all four GDMT classes; 167 of 179 patients with newly diagnosed HFrEF during the study period had an available 6-month medication assessment and comprised the final analytic cohort. The four GDMT pillars (beta-blocker; angiotensin-converting enzyme inhibitor (ACEi), angiotensin receptor blocker (ARB), or angiotensin receptor-neprilysin inhibitor (ARNI); mineralocorticoid receptor antagonist (MRA); and SGLT2i) were initiated within 4 days when clinically feasible. Medication adherence and target-dose attainment were assessed at 6, 12, and 24 months using a structured self-report questionnaire. Major adverse events (MAE) and all-cause mortality were recorded over 24 months. Patients were classified as adherent if they reported regular intake (≥80% of prescribed doses) of all four drug classes at 6 months; otherwise, they were classified as nonadherent. Results: Among the 167 analysed patients (median age 64 years, 74% men, median LVEF 30%), regular adherence at 6, 12, and 24 months was 65%, 55%, and 59% for beta-blockers; 66%, 50%, and 49% for ACEi/ARB/ARNI; 62%, 52%, and 49% for MRAs; and 84%, 57%, and 68% for SGLT2i. Target doses were achieved in 25–33% for beta-blockers, 42–50% for ACEi/ARB/ARNI, and 73–78% for MRAs. At 24 months, 56 survivors (37%) were adherent to all four drug classes. Over 24 months, all-cause mortality was 9.0% and MAE 18.6%, occurring less frequently in adherent vs. nonadherent patients (mortality 0% vs. 13.5%; MAE 8.9% vs. 23.4%). Conclusions: In this real-world, non-randomized HFrEF subregistry, in-hospital initiation of quadruple GDMT was feasible, yet maintaining long-term adherence and achieving target doses remained challenging. These data underscore the gap between guideline recommendations and routine practice and support structured follow-up and protocol-driven titration to optimize implementation. Full article

This practice-based implementation report describes the adoption of the My Abilities First (MAF) initiative for children with developmental delays in Taiwan. Grounded in the International Classification of Functioning, Disability and Health (ICF) framework, MAF emphasizes a strengths-based, participatory, and human rights-oriented approach to early childhood intervention. The purpose of this report is to describe the development of the MAF framework and the details of its innovative, culturally sensitive implementation in Taiwan, using implementation science principles to support the national adoption of My Abilities ID Cards (ABIDs). Central to the MAF initiative is the ABID, a tool that empowers children to express their abilities, preferences, and support needs using their own voice or preferred mode of communication. Guided by implementation science, the MAF team in Taiwan engaged stakeholders in urban and rural centers, developed training programs, and integrated ABID into early intervention and special education systems. Preliminary outcomes indicate that from 2021 to 2025, 140 training sessions reached a total attendance of 6961. Notably, satisfaction with training was high (>95%), and practitioner subjective competence adopting positive language improved. The number of children under age 12 creating ABIDs grew to approximately 700. Preliminary evidence suggests that ABIDs might increase systematic adoption of children’s opinions in assessments and interventions. Qualitative feedback from parents and professionals highlights the contribution of ABIDs, ensuring self-expression, motivation, and meaningful participation. The pioneering Taiwanese experience demonstrates the feasibility and impact of MAF and ABIDs in promoting children’s rights and participation, offering practical insights for global adaptation in diverse contexts. Full article

In the realm of high-speed Ethernet networks, alien crosstalk (AXT) significantly undermines the integrity and efficiency of data transmission. While existing works mostly focus on modeling and physical-layer mitigation techniques such as PAM16/DSQ128 modulation and LDPC coding, there is a lack of experimental evidence on how severe AXT affects commodity 10GBASE-T equipment in realistic, densely cabled installations. In this study, we assemble and evaluate the experimental testbed that emulates a highly adverse AXT environment by tightly bundling up to seven 60 m twisted-pair Ethernet cables and using only off-the-shelf 10GBASE-T network cards. We quantitatively characterize how increasing cable density leads to automatic speed downgrades, connection failures, and non-linear saturation of the aggregate throughput, and relate these effects to the observed link quality on individual ports. Our results demonstrate that, even in the presence of standard crosstalk mitigation and error-correction mechanisms, severe AXT can force commodity 10GBASE-T links to fall back from 10 Gbit/s to 1 Gbit/s or below. Based on these findings, we derive practical guidelines for dense-cabling deployments and identify key requirements for experimental testbeds that can more reliably quantify AXT severity and its impact on commodity 10GBASE-T link stability (rate fallback and link loss) under realistic conditions. Full article

This study examined how pedagogical strategies can support student-coaches’ (SCs) empowerment and promote preservice teachers’ (PSTs) professional learning within Sport Education (SE) seasons. Sixty-seven tenth-grade students (aged 15–18) participated in SE units taught by three PSTs (two males, one female, aged 22–25) enrolled in a master’s degree program in Teaching of Physical Education in Primary and Secondary Education in northern Portugal. Data were collected through participant observation, informal and focus group interviews, and PSTs’ reflective diaries within a Collaborative Action Research (CAR) framework and analyzed thematically. Three CAR cycles addressed key challenges: (1) encouraging SCs to assume responsibility for their role, (2) fostering inclusive and supportive team interactions, (3) strengthening SCs’ sport-specific and instructional knowledge. Guided by a facilitator, PSTs implemented strategies including pre-lesson meetings, structured communication routines, task-modification and feedback cards, accountability systems, and visual identification of SCs. Findings suggest that SCs’ empowerment was progressively constructed through interconnected psychological, relational and pedagogical processes, supported by structured mediation and iterative reflection. Simultaneously, engagement in CAR cycles enabled PSTs to develop adaptive instructional decision-making and mediation strategies. The study highlights how empowerment in SE is shaped through relational and pedagogical conditions and illustrates how CAR can foster reciprocal learning between SCs and PSTs in authentic teacher education contexts. Full article

In this study, we comparatively assessed short-read (Illumina), long-read (Oxford Nanopore Technologies, ONT), and hybrid (Illumina + ONT) sequencing strategies for bacterial genome analysis using the aquaculture-derived isolate 160P. Genomic DNA was extracted and sequenced on Illumina paired-end and ONT long-read platforms, and de novo assemblies were generated using SPAdes, Canu, Flye, and Unicycler under short-read-only, long-read-only, and hybrid workflows, followed by evaluation with QUAST assembly metrics. Among the tested approaches, the hybrid Unicycler assembly provided the highest contiguity, yielding seven contigs and a dominant 4.55 Mb contig consistent with near-complete chromosomal representation. Downstream analyses included functional genome annotation and in silico screening of antimicrobial resistance determinants (CARD), virulence-associated genes (VFDB), and secondary metabolite biosynthetic gene clusters (antiSMASH). Comparative genomic relatedness based on Average Nucleotide Identity (ANI) and digital DNA–DNA Hybridization (dDDH) indicated that 160P is most closely related to Aeromonas sobria CECT 4245^T yet falls below commonly applied species-level thresholds, supporting its placement as a genomically distinct lineage warranting further taxonomic investigation. Collectively, these findings underscore the value of hybrid sequencing for improving assembly continuity, enhancing annotation completeness, and strengthening taxonomic resolution in bacterial pathogen genomics. Full article

Transmissible viral proventriculitis (TVP) is an emerging disease in chickens, linked to chicken proventricular necrosis virus (CPNV), a recently identified birnavirus. Here, we provide the first molecular confirmation of TVP in Bangladesh from a coloured meat-type parent stock (PS) flock, while documenting a contemporaneous white layer flock with consistent clinical signs and characteristic gross lesions. Affected birds exhibited growth retardation, diarrhoea, and increased mortality, alongside hallmark gross changes in proventricular enlargement and wall thickening. From the meat-type PS, proventricular samples were collected for histopathology and molecular diagnostics. Histological analysis revealed severe glandular epithelial damage, necrosis, mononuclear infiltration, epithelial hyperplasia, and metaplasia. Using RT-PCR on nucleic acid extracted from FTA card samples, CPNV was detected. In addition, infectious bronchitis virus (IBV), infectious bursal disease virus (IBDV), and avian reovirus (ARV) nucleic acids were also identified. The amplified CPNV VP1 fragment was sequenced, and phylogenetic analysis placed the Bangladeshi strain within clades of previously reported CPNV isolates. This study represents the first molecularly confirmed report of CPNV associated with TVP in Bangladesh, highlighting the need for active surveillance in commercial and breeder poultry flocks to understand the virus’s epidemiology and support the development of control strategies. Full article

Authenticating cultural heritage artifacts such as Matryoshka Nesting Dolls (MNDs) is increasingly complicated by high-fidelity replicas that successfully mimic surface textures and palettes, leading traditional 2D computer vision models to exhibit dangerous overconfidence in false-positive classifications. To address this, we propose an auditable multimodal framework that transitions from appearance-only detection to a robust verification system based on the following three technical pillars: (1) a 2D visual stream utilizing a ConvNeXt-Tiny backbone for fine-grained style recognition; (2) a 3D geometric stream employing a custom 2D-to-3D reconstruction pipeline based on the Blum Medial Axis (BMA) and surfaces of revolution to capture axisymmetric structural fidelity; and (3) a semantic stream leveraging the Qwen3-VL vision-language model to generate human-interpretable evidence cards. To support this framework, we introduce a novel multimodal dataset comprising 168 unique physical MND sets and 27,387 labeled frames, archived for reproducibility. Our experimental results demonstrate that while 2D-only baselines achieve 77.9% authenticity accuracy, they suffer from a high Expected Calibration Error (ECE) of 0.121. The integrated multimodal framework achieves a superior authenticity accuracy of 96.7% and reduces the ECE to 0.041, representing a 66% improvement in calibration reliability. Crucially, the system shifts the mean confidence for incorrect replica classifications from a high-risk 0.82 to a safe 0.45. Full article

The ovaries are crucial reproductive organs that regulate the menstrual cycle and support pregnancy through the production of steroid hormones. They are highly susceptible to various environmental pollutants, which can lead to ovarian disorders. Luteal phase defect (LPD) and premature ovarian failure (POF) are common ovarian disorders in women. In this study, we integrate network toxicology with molecular docking and molecular dynamics simulations to elucidate the toxicological mechanisms of Benzo(a)pyrene (BaP), a widespread endocrine disruptor, in LPD and POF. Through systematic data mining of the GeneCards and OMIM databases, we identified 1336 targets associated with LPD and 2066 targets related to POF, as well as 220 BaP targets. Venn diagram analysis revealed 36 potential targets for BaP-induced LPD and 43 for BaP-induced POF. GO and KEGG enrichment analyses suggest that BaP-induced LPD and POF may share toxicological mechanisms. PPI network visualization indicated that EGFR, ESR1, and STAT3 are critical common targets for BaP-induced LPD and POF. Molecular docking and molecular dynamics simulations revealed that BaP exhibits strong binding affinity with all three core genes. In KGN cells modeling LPD and POF phenotypes, cellular experiments confirmed that BaP downregulated EGFR and ESR1 expression while upregulating STAT3 expression, thereby supporting the reliability of these targets in BaP-induced ovarian dysfunction. These findings provide insights into BaP-induced reproductive toxicity and offer a foundation for targeted clinical interventions to mitigate the effects of environmental pollutants on women’s reproductive health. Full article

Food insecurity (FI) may lead to lower academic achievement, yet college students with inadequate food underutilize campus food pantries. This research aimed to identify predictors of academic success among pantry shoppers (PSs) to inform outreach. Data from AY 2021–2022 (N = 847) and 2022–2023 (N = 951) were derived from swipes of student identification cards, merged with university student-provided data, and de-identified. Multiple regression, logistic regression, and Least Absolute Shrinkage and Selection Operator (LASSO) were employed to create and validate models using Machine Learning. Grade Point Averages (GPAs) were compared by two-sample t tests. The PSs demonstrated higher GPAs in the fall term than non-pantry shoppers (p = 0.04). Validation of the models indicated strong performance. Multiple regression yielded a low prediction error (0.05), and logistic regression achieved 71% accuracy (AUC = 0.776). LASSO identified positive predictors of academic success, including graduate and honors status, junior and senior classification, females, international residency, and frequency of pantry shopping. Negative predictors included part-time status, first-year status, Black or Hispanic ethnicity, and Pell Grant eligibility. Findings underscore the complex interplay between sociodemographic and academic factors that should be considered when planning pantry outreach programs and highlight the need for standardized measures of student pantry utilization, which may aid resource allocation and sustainability. Full article

Background/Objectives: A persistent mismatch between organ supply and transplant demand affects healthcare systems worldwide, particularly in underdeveloped and transitional systems. Intensive care units (ICUs) represent the primary setting for donor identification following brain death, placing intensive care physicians at the center of organ donation pathways. This nationwide cross-sectional survey aimed to evaluate Romanian intensivists’ knowledge, attitudes, and reported clinical practices regarding brain death determination, communication with families, and system-level barriers to organ donation, to identify modifiable factors relevant to transplant policy development. Methods: A prospective, nationwide, questionnaire-based survey was conducted among intensive care physicians in Romania. The structured questionnaire explored their knowledge and attitudes regarding brain death, communication with families, involvement in donation processes, ethical perceptions, and views on the organization of the transplant system. The survey was distributed through the Romanian Society of Anesthesia and Intensive Care, and descriptive exploratory analyses were performed. Results: A total of 117 ICU physicians participated (mean age 41.0 ± 9.9 years). Although 84.6% agreed with the current brain death diagnostic criteria, and 83% considered the protocol sufficiently clear. The mean number of brain-dead patients managed annually was 8.25 ± 12.90. 69.3% of respondents perceived communication competencies as insufficient. 77.8% considered family consent decisive in donation decisions, while 87% supported the establishment of a national donor registry and 77% favored a donor card system. Organ procurement was reported as a priority in only 38.5% of ICUs. Institutional prioritization of organ procurement and structured training was inconsistent. Conclusions: This nationwide survey identifies key educational, organizational, and systemic barriers limiting organ donation performance in Romania. Targeted training, improved communication strategies, integration of donation pathways into routine intensive care practice, and the adoption of national consent instruments represent essential clinical and policy priorities for low-performing transplant systems. Full article