Search Results (7,102)

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

Despite the rapid digitization of intangible cultural heritage (ICH), the complex mechanisms governing how users interact and co-create knowledge in digital spaces remain underexplored. Understanding the internal dynamics and engagement logic of these interactive environments is therefore essential to developing sustainable heritage knowledge ecosystems. Conceptualizing the Zhihu community as such an ecosystem, this study investigates ICH thematic structures, knowledge demands, and user participation. By employing an LLM-refined BERTopic framework, this study identified 36 core topics and mapped them onto a four-layer architecture (Cultural Resource Layer, Action Subject Layer, Social Support Layer, and External Interaction Layer) and five knowledge demand dimensions (Basic Knowledge, Cultural Experience, Professional Development, Protection and Inheritance, and Modern Application) through weighted semantic similarity and Spearman correlation analysis. The results reveal a structural configuration dominated by the External Interaction Layer. A dual-track demand mechanism was identified, comprising a professionalized ability-oriented pathway and an affective experience-driven mode. Furthermore, deep engagement was primarily catalyzed by topics that integrate technology, action, and narrative, rather than structural prominence alone. The ICH knowledge ecosystem was characterized by an outward-looking and emotion-driven orientation. This research study contributes an ecosystem framework to heritage information while providing insights for practitioners to optimize digital ICH information services through multi-dimensional semantic integration and public co-creation. Full article

Contemporary problems ranging from allergies, myopia, and obesity to chronic anxiety, loneliness, and ultralow fertility can be understood as consequences of evolutionary mismatch intensified by the polycrisis, in which accelerating technological and socioeconomic changes push human adaptations beyond what they evolved to handle. We sought to provide a conceptual review that maps these problems to adaptive needs that are disrupted in highly modernized environments. We then introduce the social evolutionary mismatch and competition hypothesis, which proposes that social aspects of evolutionary mismatch—e.g., increasing population sizes, fragmented communities, rising socioeconomic inequality, constant exposure to inflated social status cues—have a distinct effect of heightening both real and perceived competition. In turn, this perspective can help us make sense of predictable variation in psychosocial outcomes, including obsessive status pursuit, hostility, and social withdrawal. Finally, we outline strategies to lessen the impact of these dynamics by reducing sources of evolutionary mismatch. In sum, we contribute (1) an exposition of how the polycrisis exacerbates evolutionary mismatch and the adaptive needs that are impacted, (2) a theoretical advance identifying mismatch-driven competition as a predictor of multiple problematic outcomes, and (3) a translational framework showing how evolutionary insights can inform interventions to promote well-being in a time of profound societal strain. Full article

This article presents the design and development of an interactive architecture oriented toward the management of traditional vallenato, a musical genre recognized as an Intangible Cultural Heritage of Humanity by UNESCO. Architecture combines the principles of contextual awareness and the use of massive open online courses (MOOCs) to face the current challenges of preservation, dissemination, and teaching of this cultural expression, threatened by commercialization and the loss of its traditional roots. Through a modular structure, adaptive technological tools are integrated to capture, process, and use contextual information, personalizing learning experiences and strengthening the link between communities and their cultural heritage. The proposal consists of several functional layers, including context management, user profiles, educational resources, and a persistence unit, each designed to ensure the interoperability and sustainability of cultural data. In addition, the capacity of architecture to be used in other cultural contexts is highlighted, expanding its impact on different artistic manifestations and heritages worldwide. This article includes a comparative analysis with other existing models, highlighting the advantages of this solution in terms of customization and adaptability. Finally, opportunities for improvement and expansion are explored, as well as the pending challenges in the implementation of this technological tool in educational and cultural environments. Full article

Purpose: This study provides a critical examination of the literature on applying artificial intelligence (AI) and Extended Reality (XR) in restaurant settings and related foodservice operations. It focuses on how AI and XE influence consumer nutrition awareness and decision-making about food choices, and their implications for customer satisfaction, loyalty, and service delivery in foodservice environments. Design/methodology/approach: The study adopts a systematic literature review (SLR) approach following the PRISMA method. An initial search identified over 3900 academic papers published between 2016 and 2025. Studies were selected on the basis of predetermined inclusion and exclusion criteria, and 26 peer-reviewed articles were analyzed. The review provides a conceptual synthesis and develops propositions for practical applications and future research directions. Findings: The review reveals a shift from static systems that rely on optimization, toward adaptive and user-centered solutions that are behavior-oriented. AI applications predominate in the case of calorie tracking, personalized recommendations, and menu planning. Though deployment of XR technologies (e.g., AR and VR) is less prevalent, they offer potential for immersive, and real-time interventions. A key distinction emerges between studies demonstrating empirical effectiveness (e.g., improved understanding and healthier choices) and those focused on technical and/or conceptual developments. To date, there has been limited validation of behavioral impacts in foodservice settings. Originality: This study offers a theory-informed conceptualization of AI and XR applications in restaurant and foodservice contexts by integrating three perspectives: hospitality (menus and dining experience), nutrition (dietary awareness and healthier choices), and human–technology interaction (technology acceptance and user engagement). The study reconceptualizes AI- and XR-enabled systems as behavioral intervention tools and outlines a focused research agenda for advancing nutritional communication in foodservice environments. Full article

Rural communities face significant healthcare access barriers that contribute to persistent health disparities. Mobile health clinics (MHCs) have emerged as a promising strategy for expanding healthcare access, yet their effectiveness in rural settings remains understudied. The aim of this review was to examine the literature to determine what is known about access, health outcomes, and the cost-effectiveness of rural MHCs, specifically with regard to their impact on patient access and outcomes, return on investment (ROI)/financial, and program sustainability. We conducted a comprehensive search of peer-reviewed and grey literature sources. Systematic screening yielded 34 documents for full analysis. Thematic analysis was conducted across three domains: patient access, patient outcomes, and ROI/sustainability. All 34 documents provided data on patient access, with common themes including expanded service utilization, multi-service integration, overcoming geographic and transportation barriers, and improved healthcare affordability. Thirty-two documents addressed patient outcomes, reporting improvements in preventive care delivery, chronic disease management, and high patient satisfaction. Twenty-eight documents included ROI/sustainability information, with evidence suggesting cost-effectiveness particularly through emergency department visit avoidance and multi-service integration. Across the literature reviewed, the quality of evidence varied considerably, yet we concluded mobile health clinics demonstrate promise for expanding healthcare access and improving outcomes in rural populations. Key success factors include multi-service integration, diverse funding partnerships, technological integration, and strong community engagement. More rigorous research with longitudinal clinical outcome measures and robust economic analyses is needed. Full article

The digital preservation and dissemination of historical and cultural heritage is a pivotal area at the intersection of digital humanities and geographic information science. To address the challenges of multi-source heterogeneity, limited dimensionality, and inadequate public engagement, this study designed and implemented an interactive visualization platform using modern Web technologies. Taking the Leshan Confucian Temple (religious heritage) and the former site of Wuhan University (educational heritage) as case studies, the platform integrates four types of heterogeneous data (geospatial coordinates, architectural attributes, visitor behavioral records, and multimedia imagery) into a unified spatiotemporal information model. Core technical implementations are built upon a lightweight front-end stack including the Gaode Map JavaScript API for geographic visualization, ECharts for dynamic statistical charting, and the Tailwind CSS framework for a fully responsive front-end interface. Key interactive features encompass linked map markers with contextual information windows, user-driven chart filtering, and paginated loading of cultural relic cards. Evaluation results demonstrate that the platform achieves cross-device response delay ≤3 s, supports spatially grounded, dynamic, and presentation of cultural heritage information, and attains a System Usability Scale (SUS) score of 82.5. This work offers a lightweight, scalable technical solution for advancing digital recording and public communication of historical and cultural heritage, while contributing to the theoretical discourse on spatial narrative and multi-source data integration in digital humanities. Full article

Structured radiology reporting can improve clinical decision support by standardizing clinical findings into hierarchical formats. However, thousands of questions in structured report templates about clinical findings are prohibitively time-consuming, which can limit clinical adoption. Furthermore, early medical VQA datasets primarily focused on free-text and independent question–answer pairs while a recent dataset, Rad-ReStruct, introduced a hierarchical VQA, but the accompanying model still relies heavily on flattened embedding representations and single-path text–image fusion mechanisms that inadequately handle complex hierarchical dependencies in responses. In this paper, we propose DPA-HiVQA (Dual-Path Cross-Attention for Hierarchical VQA), addressing these limitations through two key contributions: (1) multi-scale image embedding representing global semantic embeddings with patch-level spatial features from domain-specific BioViL encoder; (2) dual-path cross-attention mechanism enabling simultaneous holistic semantic understanding and fine-grained spatial reasoning. Evaluated on the Rad-ReStruct benchmark, the model substantially outperforms the established benchmark baseline with an overall F1-score and Level 3 F1-score improvement by 21.2% and 31.9%, respectively. The proposed model demonstrates that dual-path cross-attention architectures can effectively connect holistic semantic understanding and fine-grained spatial detail, paving the way for practical AI-assisted structured reporting systems that reduce radiologist burden while maintaining diagnostic accuracy. Full article

Ultrafast fiber lasers operating near 2 μm have emerged as a critical platform for advancing mid-infrared photonics due to their narrow pulse durations, high peak powers, and broad tunability. These sources exploit the rich energy-level structures of Tm³⁺ and Ho³⁺ doped fibers and reside within an atmospheric transmission window, enabling applications spanning nonlinear microscopy, precision micromachining, optical frequency metrology, biophotonics, and free-space optical communication. Recent progress in low-loss fiber fabrication, dispersion-engineered cavity design, and mode-locking technologies has significantly expanded the performance boundaries of 2 μm ultrafast fiber lasers. This review systematically examines the underlying pulse-formation mechanisms and categorizes state-of-the-art mode-locking approaches. Representative laser architectures are compared with respect to pulse duration, energy scalability, repetition-rate enhancement, spectral characteristics, and environmental stability. Key application pathways in high-resolution spectroscopy, biomedical diagnostics, and mid-IR supercontinuum generation are highlighted. Finally, the remaining challenges and prospective research directions are discussed to inform the development of next-generation ultrafast photonic sources in the 2 μm band. Full article

Background: Minimally invasive interventional radiology (IR) offers effective, uterus-preserving treatments for several gynecologic and obstetric conditions such as uterine fibroids, adenomyosis and postpartum hemorrhage. Despite their efficacy, these methods remain underused, partly to limited awareness among clinicians and patients. Large language models (LLMs) may help bridge this gap by providing accessible, reliable information. Objective: To evaluate how current LLMs address knowledge gaps and promote awareness of minimally invasive IR methods in gynecology and obstetrics. Methods: A structured ten-question instrument was used to query three publicly available LLMs (OpenEvidence, ChatGPT, and Google Gemini). Responses were analyzed for accuracy, completeness, safety considerations, and patient-centered communication. Results: All three models accurately identified a range of medical, minimally invasive, and surgical treatments for uterine fibroids, adenomyosis, and postpartum hemorrhage, with OpenEvidence and ChatGPT providing more detailed and clinically nuanced responses. OpenEvidence achieved the highest scores overall, closely followed by ChatGPT, while Google Gemini scored lower, particularly in completeness and patient-centered communication. In more complex scenarios, performance differences became more pronounced, with OpenEvidence again leading, ChatGPT performing strongly, and Google Gemini lagging behind. Overall, OpenEvidence and ChatGPT demonstrated higher accuracy, completeness, and safety considerations, whereas Google Gemini showed comparatively weaker and less consistent performance. Conclusions: LLMs may endorse the promotion of minimally invasive IR methods in gynecology and obstetrics, but their outputs vary considerably in quality. Ongoing refinement and integration of evidence-based sources are essential before routine use in clinical practice. Therefore, effective collaboration between artificial intelligence (AI) developers and medical professionals is essential to harness this technology’s full potential. Full article

Background: Assessing food and nutrient intake is an important yet challenging component of nutrition research, particularly in populations at higher risk for dietary underreporting. Objective: To evaluate the feasibility, acceptability, and preliminary measurement characteristics of augmenting interviewer-administered 24 h dietary recalls with a photo-based mobile food record application (mCC: my Circadian Clock). Design: This was a randomized cross-over feasibility study in which each participant completed two sets of three 24 h dietary recalls. One set consisted of standard interviewer-administered recalls, while the other incorporated dietary intake captured via the mCC app during the 24 h preceding the recall to guide the interview. Participants: Participants (n = 10) were adults aged 18–65 years with obesity (BMI > 30 kg/m²) and less than a college-level education, recruited from a general community setting. Main Outcome Measures: Primary feasibility outcomes included recall adherence, protocol completion, participant burden, and usability of the mobile application. Secondary and exploratory outcomes included average energy intake (kcal/day), number of food items and eating occasions reported, Healthy Eating Index (HEI)-2015 scores, and recall duration. Statistical Analyses: Descriptive statistics and paired t-tests were used to explore differences between methods; analyses were considered exploratory and hypothesis-generating. Results: All enrolled participants completed every scheduled recall, resulting in 100% adherence and protocol completion. Most participants (70%) rated the mCC app as easy or very easy to use, although 60% reported greater burden with the Augmented Recalls. Average energy intake was 274 kcal/day lower with the augmented method compared with Standard Recalls (95% CI: −597, 50; p = 0.09), with no clear differences observed in reported food items, eating occasions, HEI-2015 scores, or recall duration. Conclusions: Augmenting interviewer-administered 24 h dietary recalls with a photo-based mobile food record is feasible and acceptable in adults with obesity, though it did not demonstrate clear improvements in dietary intake capture in this small feasibility sample. These findings provide practical guidance for refining technology-assisted recall protocols and informing the design of future, adequately powered studies. Full article

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

This study explores the barriers that hinder the acquisition of digital skills in women with basic education, as well as their relationship with socio-communicative needs in contexts of exclusion. A validated questionnaire (α = 0.970), based on the DigCompEdu framework, was applied to a sample of 575 women in Granada (Spain). Using non-parametric analyses (Kruskal–Wallis test), significant differences were identified according to variables such as age, educational level, employment status and income. The results reveal that older women, women with low incomes, lower educational levels and unemployed women have greater difficulties in accessing, searching for information, creating content, and solving problems with ICT. However, a positive attitude towards technology was observed in all profiles, which constitutes an opportunity for intervention. It is concluded that the digital divide in women with basic training is conditioned by structural factors that generate specific socio-communicative needs. We propose the implementation of training policies with an intersectional and gender focus that favor digital equity and the active inclusion of these women in the digital society. Full article

The rapid development of cyber–physical technologies has led to enhanced observability and controllability of shipboard power systems. However, the reliance of shipboard power systems on information networks undermines the traditional security provided by physical isolation; under malicious attacks, faults in the information domain can propagate rapidly, causing physical power outages and reducing the resilience of shipboard power systems. To address this issue, this paper investigates the cascading failure reconstruction and resilience enhancement in shipboard cyber–physical systems (SCPSs) under uncertain network attacks. First, a cascading failure propagation model is established to capture the interaction between attack paths and system vulnerabilities, revealing how cyberattacks spread through communication links and infiltrate the power topology. Then, a reinforcement learning-based load recovery strategy is developed, in which a masked proximal policy optimization (masked-PPO) algorithm is employed to optimize reconfiguration decisions under operational constraints. The proposed approach enables adaptive and efficient recovery actions in complex cross-domain environments. Case studies based on representative SCPS scenarios demonstrate that the proposed method improves cascading-failure reconfiguration capability by 13.21% and reduces the average decision time by 18.6%, validating its effectiveness, real-time performance, and scalability. Full article

Precipitation forecasting is essential for disaster prevention, water resource management, and socio-economic resilience. The field has evolved from numerical weather prediction (NWP) and optical-flow-based methods toward data-driven deep learning approaches that can exploit larger observational datasets and model complex nonlinear relationships. Against this background, this study evaluates multi-station temporal forecasting models within a single-year, station-based proof-of-concept benchmark under unified data conditions. We adapt the Transformer and Informer architectures to this meteorological setting, rigorously preprocess the AWS dataset to avoid data leakage, and select predictive variables using complementary linear and nonlinear relevance criteria. Model performance is assessed using continuous and categorical precipitation metrics, including the Critical Success Index (CSI). The results show that the Informer outperforms the recurrent neural network (RNN) baselines and achieves the lowest mean MAE and RMSE together with the highest mean CSI among the evaluated models while using substantially fewer parameters than the standard Transformer. However, its sample-wise absolute error distribution remains statistically comparable to that of the standard Transformer. Overall, this study establishes a single-year, station-based proof-of-concept benchmark for comparing architectures in very-short-term (1–5 h ahead) precipitation forecasting. Full article