Search Results (232)

In response to the evolving dynamics of global supply chains, business-to-business (B2B) sharing economy models within the logistics industry have gained importance for innovation and sustainability over the last few years. According to a literature review, the sharing economy has become a pivotal innovation in the business environment, especially for resource utilisation efficiency and the potential to advance sustainable development policies. Despite the known positive impact on the economy and environment, integrating sharing economy models into logistics and supply chains remains limited. This highlights a key research area that requires a thorough examination of the barriers and opportunities for business-to-business (B2B) sharing economy platforms in logistics and supply chains that reflect environmental policy goals and promote cleaner, more efficient logistics systems. This paper outlines the significance of B2B sharing economy platforms as a crucial part of smart and resource-efficient supply chains. Using a system theory approach, B2B sharing economy platforms in logistics and SC were identified and systematically and comprehensively analysed across four critical aspects: sharing storage, sharing parking space, shared labour, and collaborative transportation. The scope of the research is limited to the smart and sustainable dimensions of logistics and supply chains, with a particular focus on the analysis of B2B sharing economy platforms. The novelty of this study lies in its empirical and theory-informed analysis of B2B sharing platforms as a key driver for smart and resource-efficient logistics. While prior studies have largely focused on consumer-facing sharing models or conceptual frameworks, this paper systematically evaluates operational B2B platforms. The analysis reveals that while B2B platforms offer valuable solutions in collaborative transport, storage, labour, and parking, they are underutilised and insufficiently aligned with environmental and digital objectives. The study introduces a spider chart analysis grounded in system theory to evaluate platforms against six dimensions, uncovering trade-offs between flexibility and sustainability. These insights contribute to understanding the strategic positioning of such platforms and propose a direction for smarter, resource-efficient supply chains. Full article

Power transformers are critical assets in power systems, and their reliable operation is essential for grid stability. Conventional fault diagnosis methods suffer from delayed response and limited adaptability, while existing artificial intelligence-based approaches face challenges related to data heterogeneity, limited interpretability, and weak integration of physical mechanisms. To address these issues, this paper proposes a physics-informed enhanced transformer-based framework for power transformer fault diagnosis. A unified temporal representation scheme is developed to integrate heterogeneous monitoring data using Dynamic Time Warping and physics-guided feature projection. Physical priors derived from thermodynamic laws and gas diffusion principles are embedded into the attention mechanism through multi-physics coupling constraints, improving physical consistency and interpretability. In addition, a multi-task diagnostic strategy is adopted to jointly perform fault classification, severity assessment, and fault localization. Experiments on 3000 samples from 76 power transformers demonstrate that the proposed method achieves high diagnostic accuracy and superior robustness under noise and interference, indicating its effectiveness for practical predictive maintenance applications. Full article

Automatically generated chains-of-thought (gCoTs) have become common as large language models adopt deliberative behaviors. Prior work emphasizes fidelity to internal processes, leaving explanatory properties underexplored. Our central hypothesis is that these traces, produced by highly capable reasoning models, are not arbitrary by-products of decoding but exhibit stable and practically valuable textual properties beyond answer fidelity. We apply a multidimensional text-evaluation framework that quantifies four axes—structural coherence, logical–factual consistency, linguistic clarity, and coverage/informativeness—that are standard dimensions for assessing textual quality, and use it to evaluate five reasoning models on the GSM8K arithmetic word-problem benchmark (~1.3 k–1.4 k items) with reproducible, normalized metrics. Logical verification shows near-ceiling self-consistency, measured by the Aggregate Consistency Score (ACS ≈ 0.95–1.00), and high final-answer entailment, measured by Final Answer Soundness (FAS0 ≈ 0.85–1.00); when sound, justifications are compact, with Justification Set Size (JSS ≈ 0.51–0.57) and moderate redundancy, measured by the Redundant Constraint Ratio (RCR ≈ 0.62–0.70). Results also show consistent coherence and clarity; from gCoT to answer implication is stricter than from question to gCoT support, indicating chains anchored to the prompt. We find no systematic trade-off between clarity and informativeness (within-model slopes ≈ 0). In addition to these automatic and logic-based metrics, we include an exploratory expert rating of a subset (four raters; 50 items × five models) to contextualize model differences; these human judgments are not intended to support dataset-wide generalization. Overall, gCoTs display explanatory value beyond fidelity, primarily supported by the automated and logic-based analyses, motivating hybrid evaluation (automatic + exploratory human) to map convergence/divergence zones for user-facing applications. Full article

Background: Many Canadian medical schools have introduced equity-focused admissions pathways for Black and Indigenous applicants, yet little is known about how current medical students perceive these policies. Understanding these perceptions is critical to ensuring equity initiatives are effective and well-supported. Methods: We conducted a cross-sectional survey of 95 undergraduate medical students at McMaster University. The survey included Likert-scale, multiple-choice, and open-ended questions assessing attitudes toward Black and Indigenous facilitated admissions pathways. Educational background was categorized by the number of humanities/social science courses taken prior to medical school. Quantitative data were summarized descriptively; qualitative responses were thematically analyzed. Results: Most students supported diversity in medicine and agreed that equity pathways address barriers faced by Black and Indigenous applicants. However, fewer than half felt informed about the purpose of these pathways. Responses highlighted concerns about transparency, fairness, and the possibility that pathways may disproportionately benefit higher-socioeconomic-status applicants. Subgroup trends did not show consistent support among students with greater exposure to humanities/social sciences; some expressed stronger skepticism regarding fairness. Qualitative themes emphasized the need for clearer communication, recognition of socioeconomic barriers, and expansion of equity initiatives. Interpretation: Students broadly valued equity-focused admissions but questioned their implementation and transparency. Concerns about socioeconomic privilege and unclear standards indicate a need for better institutional communication and more inclusive eligibility criteria. Equity pathways should be paired with structured education and clear messaging to foster trust, improve understanding, and align admissions policies with the social accountability mandate of medical education. Full article

Background/Objective: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have received widespread attention as effective obesity treatments. However, limited research has examined the perspectives of patients contemplating GLP-1RAs. This study explored perceptions, motivations, and barriers among individuals considering GLP-1RA therapy for obesity treatment, with the goal of informing patient-centered care and enhancing clinician engagement. Methods: Adults completed surveys and interviews between June and November 2025. In this pragmatic mixed-methods study, both survey and interview questions explored perceived benefits, barriers, and decision-making processes. Qualitative data, describing themes based on the Health Belief Model, were analyzed using Dedoose (version 9.0.107), and quantitative data were analyzed using SAS (version 9.4). Participant characteristics included marital status, income, educational attainment, employment status, insurance status, age, race/ethnicity, and sex. Anticipated length on GLP-1RA medication and selected self-reported health conditions (depression, anxiety, hypertension, heart disease, back pain, joint pain), reported physical activity level, and perceived weight loss competency were also recorded. Results: Among the 31 non-diabetic participants who were considering GLP-1RA medication for weight loss, cost emerged as the most significant barrier. Life course events, particularly (peri)menopause among women over 44, were commonly cited as contributors to weight gain. Participants expressed uncertainty about eligibility, long-term safety, and treatment expectations. Communication gaps were evident, as few participants initiated discussions and clinician outreach was rare, reflecting limited awareness and discomfort around the topic. Conclusions: Findings highlight that individuals considering GLP-1RA therapy face multifaceted emotional, financial, and informational barriers. Proactive, empathetic clinician engagement, through validation of prior efforts, clear communication of risks and benefits, and correction of misconceptions, can support informed decision-making and align treatment with patient goals. Full article

Background: Hypertensive disorders of pregnancy (HDP) have significant implications for women’s long-term health, including at least a twofold increased lifetime risk of cardiovascular disease (CVD). The Blood Pressure Postpartum (BP²) Study was a three-arm randomised trial evaluating follow-up and lifestyle behaviour change strategies during the first year after HDP. Methods: This qualitative sub-study, embedded within the BP² Study, explored women’s experiences of life in the first year following HDP. Semi-structured telephone interviews were conducted with 34 women, approximately 10–12 months postpartum. Interviews were transcribed verbatim and a thematic analysis was undertaken. Results: Participants reflected on their experiences post-HDP; three major themes were identified: Navigating life with a newborn, The value of support, and Processing and Moving forward. Some women felt informed and empowered to make positive lifestyle changes; others were still processing their HDP experience and/or feeling overwhelmed by the demands of early motherhood. Responses were influenced by their HDP experience, available support, prior experience with healthy behaviours, and financial stability. Conclusions: The findings highlight that postpartum women who experienced HDP face unique challenges, including physical recovery, emotional processing, and intensive infant care. It often takes time for these women to begin prioritising their own health, as they navigate these challenges. The insights generated from women’s experiences suggest that flexible, accessible, and individually tailored support may facilitate postpartum health, promote lifestyle change, and help reduce long-term CVD risk. Full article

Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology. Full article

Video super-resolution methods based on convolutional kernels or optical flow often face challenges such as limited utilization of multi-frame detail information or strong reliance on accurate optical flow estimation. To address these issues, this paper proposes a novel super-resolution reconstruction network named Dual Motion Compensation and Multi-scale Structure–Texture Prior (DCST-Net). Dual motion compensation performs direct and progressive motion mapping in parallel, effectively mitigating estimation bias in motion modeling. A multi-scale structure–texture prior is introduced to enhance high-frequency details through feature fusion, alleviating over-smoothing caused by warping and fusion processes. The proposed DCST-Net method is validated on datasets containing both large and small targets, demonstrating its effectiveness and robustness. Full article

Background/Objectives: Caregivers of infants with congenital muscular torticollis (CMT) frequently seek information online, although the accuracy, clarity, and safety of web-based content remain variable. As large language models (LLMs) are increasingly used as health information tools, their reliability for caregiver education requires systematic evaluation. This study aimed to assess the reproducibility and quality of ChatGPT-5.1 responses to caregiver-centered questions regarding CMT. Methods: A set of 17 questions was developed through a Delphi process involving clinicians and caregivers to ensure relevance and comprehensiveness. ChatGPT generated responses in two independent sessions. Reproducibility was assessed using TF–IDF cosine similarity and embedding-based semantic similarity. Ten clinical experts evaluated each response for accuracy, readability, safety, and overall quality using a 4-point Likert scale. Results: ChatGPT demonstrated moderate lexical consistency (mean TF–IDF similarity 0.75) and high semantic stability (mean embedding similarity 0.92). Expert ratings indicated moderate to good performance across domains, with mean scores of 3.0 for accuracy, 3.6 for readability, 3.1 for safety, and 3.1 for overall quality. However, several responses exhibited deficiencies, particularly due to omission of key cautions, oversimplification, or insufficient clinical detail. Conclusions: While ChatGPT provides fluent and generally accurate information about CMT, the observed variability across topics underscores the importance of human oversight and content refinement prior to integration into caregiver-facing educational materials. Full article

High-fidelity 3D face reconstruction from a single image is challenging, owing to the inherently ambiguous depth cues and the strong entanglement of multi-scale facial textures. In this regard, we propose a hierarchical multi-resolution self-supervised framework (HMR-Framework), which reconstructs coarse-, medium-, and fine-scale facial geometry progressively through a unified pipeline. A coarse geometric prior is first estimated via 3D morphable model regression, followed by medium-scale refinement using a vertex deformation map constrained by a global–local Markov random field loss to preserve structural coherence. In order to improve fine-scale fidelity, a learnable Gabor-aware texture enhancement module has been proposed to decouple spatial–frequency information and thus improve sensitivity for high-frequency facial attributes. Additionally, we employ a wavelet-based detail perception loss to preserve the edge-aware texture features while mitigating noise commonly observed in in-the-wild images. Extensive qualitative and quantitative evaluation of benchmark datasets indicate that the proposed framework provides better fine-detail reconstruction than existing state-of-the-art methods, while maintaining robustness over pose variations. Notably, the hierarchical design increases semantic consistency across multiple geometric scales, providing a functional solution for high-fidelity 3D face reconstruction from monocular images. Full article

Hyperspectral unmixing (HU) aims to extract constituent information from mixed pixels and is a fundamental task in hyperspectral remote sensing. Deep non-negative matrix factorization (DNMF) has recently attracted attention for HU due to its hierarchical representation capability. However, existing DNMF-based methods are often sensitive to noise and outliers, and face limitations in incorporating prior knowledge, modeling feature structures, and enforcing sparsity constraints, which restrict their robustness, accuracy, and interpretability. To address these challenges, we propose a sparse deep NMF model with adversarial graph regularization for hyperspectral unmixing, termed DNMF-AG. Specifically, we design an adversarial graph regularizer that integrates local similarity and dissimilarity graphs to promote intraclass consistency and interclass separability in the spatial domain, thereby enhancing structural modeling and robustness. In addition, a Gram-based sparsity constraint is introduced to encourage sparse abundance representations by penalizing inner product correlations. To further improve robustness and computational efficiency, a truncated activation function is incorporated into the iterative update process, suppressing low-amplitude components and promoting zero entries in the abundance matrix. The overall model is optimized using the alternating direction method of multipliers (ADMM). Experimental results on multiple synthetic and real datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of estimation accuracy and robustness. Full article

Background/Objectives: Cardiovascular (CV) diseases remain the leading cause of morbidity and mortality across Europe. Despite substantial progress in prevention, diagnostics, and therapeutics, outpatient cardiology care continues to face systemic challenges, including limited consultation time, workforce constraints, and incomplete clinical information at the point of care. The primary objective of this study is threefold. First, to evaluate whether AI-enhanced telemedicine improves clinical control of hypertension, dyslipidemia, and heart failure compared to standard ambulatory care. Second, to assess the impact on physician workflow efficiency and documentation burden through AI-assisted clinical documentation. Third, to determine patient satisfaction and safety profiles of integrated telemedicine–AI systems. Clinical control will be measured by a composite endpoint of disease-specific targets assessed at the 12-month follow-up visit. Methods: The TeleAI-CVD Concept Study aims to evaluate the integration of telemedicine and artificial intelligence (AI) to enhance the efficiency, quality, and individualization of cardiovascular disease management in the ambulatory setting. Within this framework, AI-driven tools will be employed to collect structured clinical histories and current symptomatology from patients prior to outpatient visits using digital questionnaires and conversational interfaces. Results: Obtained data, combined with telemonitoring metrics, laboratory parameters, and existing clinical records, will be synthesized to support clinical decision-making. Conclusions: This approach is expected to streamline consultations, increase diagnostic accuracy, and enable personalized, data-driven care through continuous evaluation of patient trajectories. The anticipated outcomes of the TeleAI-CVD study include the development of optimized, AI-assisted management protocols for cardiology patients, a reduction in unnecessary in-person visits through effective telemedicine-based follow-up, and accelerated attainment of therapeutic targets. Ultimately, this concept seeks to redefine the paradigm of outpatient cardiovascular care by embedding advanced digital technologies within routine clinical workflows. Full article

Parkinson’s Disease (PD) is a neurodegenerative disorder in which gait abnormalities serve as key indicators of motor impairment and disease progression. Although wearable sensor-based gait analysis has advanced, existing methods still face challenges in modeling multi-sensor spatial relationships, extracting adaptive multi-scale temporal features, and effectively integrating time–frequency information. To address these issues, this paper proposes a multi-sensor gait neural network that integrates biomechanical priors with time–frequency collaborative learning for the automatic assessment of PD gait severity. The framework consists of three core modules: (1) BGS-GAT (Biomechanics-Guided Graph Attention Network), which constructs a sensor graph based on plantar anatomy and explicitly models inter-regional force dependencies via graph attention; (2) AMS-Inception1D (Adaptive Multi-Scale Inception-1D), which employs dilated convolutions and channel attention to extract multi-scale temporal features adaptively; and (3) TF-Branch (Time–Frequency Branch), which applies Real-valued Fast Fourier Transform (RFFT) and frequency-domain convolution to capture rhythmic and high-frequency components, enabling complementary time–frequency representation. Experiments on the PhysioNet multi-channel foot pressure dataset demonstrate that the proposed model achieves 0.930 in accuracy and 0.925 in F1-score for four-class severity classification, outperforming state-of-the-art deep learning models. Full article

Background: Frail patients undergoing AF ablation face elevated periprocedural risks. However, prior studies often examined composite or long-term outcomes and did not stratify acute complication risks by frailty severity. Objective: The objective of this study was to assess the impact of frailty, measured by the Hospital Frailty Risk Score (HFRS) on in-hospital outcomes after AF ablation, and to delineate the risk of specific acute complications across frailty levels. Methods: We analyzed a national inpatient cohort of AF ablation hospitalizations (2016–2021). Patients were stratified into low-, intermediate-, and high-frailty groups by HFRS. In-hospital mortality and major complications (stroke, respiratory failure, sepsis, acute dialysis, cardiac arrest, cardiogenic shock) were compared across frailty groups, and multivariable logistic regression identified independent predictors of these outcomes. Results: Among an estimated 42,830 AF ablation admissions, 80.0% were low-frailty, 15.0% intermediate, and 5.0% high-frailty. High-frailty patients had markedly higher complication rates than low-frailty patients. In-hospital mortality was 6.1% in high frailty vs. 1.0% in low frailty, and stroke occurred in 4.0% vs. 0.3%, respectively. Rates of respiratory failure (18.0% vs. 3.5%), sepsis (8.0% vs. 1.2%), and acute dialysis (4.0% vs. 0.5%) were also significantly higher in the high-frailty group (all p < 0.001). In multivariate analyses, frailty remained a strong independent predictor of complications; high frailty conferred over four-fold higher odds of in-hospital mortality and five-fold higher odds of stroke compared to low frailty. Conclusions: Frailty is a powerful predictor of periprocedural complications and mortality in AF ablation patients. Even after accounting for age and comorbidities, patients with higher frailty scores experienced substantially worse in-hospital outcomes. These findings highlight the importance of frailty assessment to identify high-risk patients and inform clinical decision-making for AF ablation. Full article

Background: Adolescents’ engagement with social media and emerging metaverse platforms has become nearly universal, creating environments rich in opportunities for learning, creativity, and social connection. However, these same spaces also enable a range of risky behaviors (RBs) with potential impacts on mental health, safety, and development. Recent research (2022–2025) has documented rising concerns over cyberbullying, online sexual exploitation, self-harm content, problematic use, and new risks specific to immersive VR. Aims: This Perspective uses a narrative synthesis of recent empirical and theoretical literature, including four key articles provided by the author and over 40 additional peer-reviewed and institutional sources, to (i) map the most prevalent and emergent RBs in adolescent social media and metaverse use, (ii) clarify the neurodevelopmental and socio-technical mechanisms that link these behaviors to individual and contextual factors, and (iii) propose a multi-level framework for intervention, policy, and future research aligned with adolescent development. Methods: A narrative synthesis approach was adopted, which is appropriate for integrating heterogeneous study designs and rapidly evolving evidence. The review emphasizes studies published from 2022 to 2025, with a focus on large-scale surveys, longitudinal cohorts, systematic reviews, and scoping reviews relevant to adolescent online risk. Results: Evidence indicates small but consistent associations between high-intensity platform use and internalizing symptoms, with gendered pathways and cultural moderators. Algorithmic amplification contributes to the spread of harmful content, while immersive environments increase the salience and emotional impact of interactions. Certain groups—those with prior trauma, low SES, or marginalized identities—face heightened vulnerability. Conclusions: RBs in digital spaces emerge from the interplay of adolescent neurodevelopment, platform affordances, and socio-cultural context. This Perspective synthesizes recent evidence via narrative review to articulate these mechanisms and to inform an integrated, multi-level framework for harm mitigation that aligns research, platform design, and policy with adolescent developmental needs, while preserving the benefits of digital engagement. Full article