Search Results (3,873)

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

Background: Multimodal Deep learning has emerged as a crucial method for automated patient-specific quality assurance (PSQA) in radiotherapy research. Integrating image-based dose matrices with tabular plan complexity metrics enables more accurate prediction of quality indicators, including the Gamma Passing Rate (GPR) and dose difference (DD). However, modality imbalance remains a significant challenge, as tabular encoders often dominate training, suppressing image encoders and reducing model robustness. This issue becomes more pronounced under task heterogeneity, with GPR prediction relying more on tabular data, whereas dose difference prediction (DDP) depends heavily on image features. Methods: We propose BMMQA (Balanced Multi-modal Quality Assurance), a novel framework that achieves modality balance by adjusting modality-specific loss factors to control convergence dynamics. The framework introduces four key innovations: (1) task-specific fusion strategies (softmax-weighted attention for GPR regression and spatial cascading for DD prediction); (2) a balancing mechanism supported by Shapley values to quantify modality contributions; (3) a fast network forward mechanism for efficient computation of different modality combinations; and (4) a modality-contribution-based task weighting scheme for multi-task multimodal learning. A large-scale multimodal dataset comprising 1370 IMRT plans was curated in collaboration with Peking Union Medical College Hospital (PUMCH). Results: Experimental results demonstrate that, under the standard 2%/3 mm GPR criterion, BMMQA outperforms existing fusion baselines. Under the stricter 2%/2 mm criterion, it achieves a 15.7% reduction in mean absolute error (MAE). The framework also enhances robustness in critical failure cases (GPR < 90%) and achieves a peak SSIM of 0.964 in dose distribution prediction. Conclusions: Explicit modality balancing improves predictive accuracy and strengthens clinical trustworthiness by mitigating overreliance on a single modality. This work highlights the importance of addressing modality imbalance for building trustworthy and robust AI systems in PSQA and establishes a pioneering framework for multi-task multimodal learning. Full article

Plastic pollution has emerged as a pervasive and systemic driver of ecological and biogeochemical disruption in freshwater and marine environments. Unlike natural materials that circulate within closed biogeochemical loops, synthetic polymers predominantly follow unidirectional and irreversible trajectories, a phenomenon we describe as “irreversible plastic transport.” These flows culminate in sedimentary entrapment, where plastics persist as long-term ecological stressors and potential vectors of contaminant transfer. Recent global syntheses indicate that sedimentary microplastic loads can exceed 27,000 particles/kg dry weight in certain river systems, highlighting the urgency of sediment-inclusive risk assessments. This review synthesizes interdisciplinary findings to conceptualize plastics as both pollutants and governance challenges. We highlighted the dominant transport pathways of micro- and nanoplastics and emphasize that sedimentary sinks are critical long-term retention zones. Current monitoring frameworks often underestimate sedimentary burdens by focusing on surface water and overlooking subsurface ecological legacies. We propose an integrated governance approach combining cross-media monitoring, Earth system modeling, and adaptive policies to address these persistent synthetic agents. Embedding plastic dynamics within comprehensive risk assessment frameworks is essential for sustainable water management during the Anthropocene. Our synthesis supports risk-based decision-making and encourages proactive, transdisciplinary global governance strategies that integrate sediment-focused monitoring and long-term ecological risk management. Full article

Following restrictions on antibiotic growth promoters in poultry production, there is growing interest in natural feed additives that support health and productivity. Among these, black cumin (Nigella sativa) has emerged as a promising candidate due to its antioxidant, antimicrobial, and immunomodulatory properties. Most studies report that black cumin, in the form of whole seeds, seed meal, or seed oil, improves body weight gain and feed conversion ratio, enhances antioxidant and immune status, and provides additional benefits on lipid profiles, liver enzymes, and cecal microbial balance. This review provides a focused synthesis of recent studies (2014–2025) on black cumin supplementation in broiler chickens, considering its various forms (whole seeds, seed meal, seed oil, and nano-formulations) and production contexts (healthy, heat-stressed, and disease-challenged birds). Specifically, this review compares responses across different forms and doses, evaluates effects on growth performance, immune function, gut health, antioxidant status, liver metabolism, and meat and carcass quality, and highlights inconsistencies among studies. Additionally, it identifies key research gaps to guide future investigations, including optimal dosing, long-term safety, and practical applications in commercial production. Overall, black cumin shows potential as a natural alternative to antibiotics, but further standardized, large-scale studies are needed to confirm its efficacy and feasibility in sustainable poultry farming. Full article

Demand electrification is creating new operating conditions in distribution networks—such as congestion, overloads, and voltage issues—that have traditionally been addressed through network expansion planning (NEP). As an alternative, this work proposes the use of non-wire alternatives (NWAs) based on hybrid photovoltaic–storage (ESS) plants and analyzes their siting and sizing under uncertainty conditions. To this end, a MINLP model with a DistFlow representation is formulated to determine generation and storage locations and capacities, minimizing investment while satisfying current and voltage limits. Different uncertainty management methodologies are compared: robust optimization, equivalent probabilistic profile, weighted multi-scenario, and multi-scenario with penalty. The results on the CIGRE MV network show that the robust approach guarantees feasibility in the worst case, albeit with a high investment cost. In contrast, methods based on averages or simple weightings fail to adequately capture adverse conditions, while the multi-scenario optimization with expected penalty emerges as the most effective option, balancing investment and overload reduction. In conclusion, the explicit consideration of uncertainty in NWA planning is essential to obtaining realistic and adaptable solutions, with the expected penalty formulation standing out as the most efficient alternative for network operators. Full article

Associative learning is a fundamental neural mechanism in human memory and cognition. It has attracted considerable attention in neuromorphic system design owing to its multimodal integration, fault tolerance, and energy efficiency. However, prior studies mostly focused on single inputs, with limited attention to multibit pairs or recall under non-orthogonal input patterns. To address these issues, this study proposes a bidirectional associative learning system using paired multibit inputs. It employs a synapse–neuron structure based on spiking neural networks (SNNs) that emulate biological learning, with simple circuits supporting synaptic operations and pattern evaluation. Importantly, the update and read functions were designed by drawing inspiration from the operational characteristics of emerging synaptic devices, thereby ensuring future compatibility with device-level implementations. The proposed system was verified through Cadence-based simulations using CMOS neurons and Verilog-A synapses. The results show that all patterns are reliably recalled under intact synaptic conditions, and most patterns are still robustly recalled under biologically plausible conditions such as partial synapse loss or noisy initial synaptic weight states. Moreover, by avoiding massive data converters and relying only on basic digital gates, the proposed design achieves associative learning with a simple structure. This provides an advantage for future extension to large-scale arrays. Full article

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios. Full article

Industrial image anomaly detection (IAD) is crucial for quality control, but its performance often degrades when training data contain label noise. To circumvent the reliance on potentially flawed labels, unsupervised methods that learn from the data distribution itself have become a mainstream approach. Among various unsupervised techniques, student–teacher frameworks have emerged as a highly effective paradigm. Student–Teacher Feature Pyramid Matching (STPM) is a powerful method within this paradigm, yet it is susceptible to such noise. Inspired by STPM and aiming to solve this issue, this paper introduces Meta-Learning Guided STPM (MLG-STPM), a novel framework that enhances STPM’s robustness by incorporating a guidance mechanism inspired by meta-learning. This guidance is achieved through an Evolving Meta-Set (EMS), which dynamically maintains a small high-confidence subset of training samples identified by their low disagreement between student and teacher networks. By training the student network on a combination of the current batch and the EMS, MLG-STPM effectively mitigates the impact of noisy labels without requiring an external clean dataset or complex re-weighting schemes. Comprehensive experiments on the MVTec AD and VisA benchmark datasets with synthetic label noise (0% to 20%) demonstrate that MLG-STPM significantly improves anomaly detection and localization performance compared to the original STPM, especially under higher noise conditions, and achieves competitive results against other relevant approaches. Full article

Background & Aim: Malnutrition and frailty are prevalent among older adults following discharge from acute care, including emergency departments. This transition period presents a critical window for targeted nutrition interventions. This systematic review synthesises evidence on the effectiveness of nutrition interventions for malnourished, frail older adults and incorporates analyses of stakeholders’ perspectives, including those of patients, caregivers, and healthcare professionals. By integrating clinical outcomes with stakeholder experiences, the review aims to identify strategies that can optimise nutritional care and support recovery in the post-acute setting. Methods: Searches were conducted in Scopus, CINAHL, EBSCO, EMBASE, and PubMed for randomised controlled trials (RCTs) of nutrition interventions in participants ≥65 years living with frailty and identified as malnourished on discharge from acute care. The primary outcome was assessing the effects of nutrition interventions on malnutrition, nutrition status, physical function and frailty, food intake, and quality of life. Secondary outcomes were hospital readmission and mortality. The quality of studies was assessed using the Cochrane Risk of Bias Tool (V2). Results: Five RCTs with 551 participants were included. Nutrition interventions, including counselling, oral nutrition supplements, and multidisciplinary strategies, improved dietary intake, weight, frailty, physical function, BMI, and quality of life in older adults post-discharge. Some studies also reported reduced hospital stays, readmissions, and mortality. However, none explored stakeholder perspectives, highlighting a gap in person-centred transitional care design. Conclusion: This systematic review highlights a critical gap in evidence for nutrition interventions targeting frail older adults at hospital discharge. While short-term benefits were observed, long-term sustainability and real-world feasibility remain uncertain. The absence of stakeholder involvement further limits person-centred design. These findings underscore the need for integrated nutrition care pathways that embed effective interventions into transitional care models. Full article

Semaglutide-induced facial changes, or “Ozempic face” popularized by media, have gained increasing recognition since the widespread and growing use of Ozempic (semaglutide) for weight loss. It refers to facial volume depletion and soft tissue laxity following rapid weight loss associated with this medication. Semaglutide use can also cause gastrointestinal side effects, volume loss, and decrease skin quality not only in the face but globally. As the use of Ozempic becomes increasingly popular, more patients are presenting to cosmetic clinics for these undesirable esthetic changes. While cosmetic changes following rapid weight loss is not new, such as those following bariatric interventions, the accessibility and ease of GLP-1, Glucose-like protein-1, makes this a growing concern among the community. It is important for clinicians to recognize these potential effects, counsel patients appropriately, and give options for treatment. This emerging esthetic concern highlights the need for further investigation into underlying causes, risk factors, and potential interventions. Full article

Background/Objectives: Innovative intranasal delivery systems have emerged as a strategy to overcome the limitations of conventional COVID-19 vaccines, including suboptimal mucosal immunity, limited antigen retention, and vaccine hesitancy. This study aimed to evaluate physicochemical properties and murine safety of a novel COVID-19 intranasal vaccine candidate based on CHIVAX 2.1 (CVX)-loaded chitosan nanoparticles (CNPs). Methods: The CVX recombinant protein was encapsulated into CNPs using the ionic gelation method. The nanoparticles were characterized by their physicochemical properties (mean size, zeta potential, morphology, and encapsulation efficiency) and spectroscopic profiles. Mucin adsorption and in vitro release profiles in simulated nasal fluid were also assessed. In vivo compatibility was evaluated through histopathological analysis of tissues in male C-57BL/6J mice following intranasal administration. Results: CNPs exhibited controlled size distribution (38.5–542.5 nm) and high encapsulation efficiency (65.4–92.2%). Zeta potential values supported colloidal stability. TEM analysis confirmed spherical morphology and successful CVX encapsulation, and immunogenic integrity was also demonstrated. Mucin adsorption analysis demonstrated effective nasal retention, particularly in particles ≈90 nm. In vitro release studies revealed a biphasic protein profile, where ≈80% of the recombinant protein was released within 2 h. Importantly, histopathological analyses and weight monitoring of intranasally immunized mice revealed no signs of adverse effects related to toxicity. Conclusions: The ionic gelation encapsulation process preserved the physical and immunological integrity of CVX antigen. Furthermore, the intranasal administration of the CVX-loaded CNPs demonstrated a favorable safety profile in vivo. These findings support the potential of the CVX intranasal vaccine formulation for further immunogenicity studies, with no apparent biosafety concerns. Full article

Background/Objectives: Qualitative child-centered research on pediatric obesity treatment and body image remains limited. This study aimed to explore children’s and adolescents’ experiences with hospital-based obesity treatment and how these experiences relate to body image. Methods: A full-day workshop including three main participatory tasks was conducted in two groups of children (9–13 years) and adolescents (14–18 years), focusing on their experiences with obesity treatment and body image. Data were audiotaped, transcribed verbatim, and analyzed using reflexive thematic analysis. Results: Four main themes emerged, reflecting different aspects of participants’ experiences. The first theme, Talk with me and not my parents, encompassed participants’ desire for greater agency, as children described lacking information and feeling excluded from consultations. The second theme, Experiences of communication with healthcare professionals (HCPs) about obesity, concerned participants’ perceptions of trust, support, and non-judgmental communication, with some adolescents expressing a need for additional psychological support. The third theme, Internalization of lifestyle advice, indicated that healthy diet was viewed as the primary focus of obesity treatment, while physical activity received less attention. The final theme, Perceptions of the body, conveyed mixed experiences with weighing and most participants perceived weight loss as success in treatment and weight gain as failure. The participants shared experiences of weight-based bullying, perceived stigma, and challenges with maintaining a positive body image in a society with stereotypical thin and muscular body ideals. Conclusions: Body image was influenced by HCPs’ emphasis on health and body size, and by their own internalized perceptions, influenced by societal ideals and experiences of stigma. Full article

Anti-PD-1 therapies have transformed cancer treatment by restoring antitumor T cell activity. Despite their broad clinical use, variability in treatment response and immune-related adverse events underscore the need for therapeutic optimization. This article provides an integrative overview of the pharmacokinetics (PKs) of anti-PD-1 antibodies—such as nivolumab, pembrolizumab, and cemiplimab—and examines pharmacokinetic–pharmacodynamic (PK-PD) relationships, highlighting the impact of clearance variability on drug exposure, efficacy, and safety. Baseline clearance and its reduction during therapy, together with interindividual variability, emerge as important dynamic biomarkers with potential applicability across different cancer types for guiding individualized dosing strategies. The review also discusses established biomarkers for anti-PD-1 therapies, including tumor PD-L1 expression and immune cell signatures, and their relevance for patient stratification. The evidence supports a shift from traditional weight-based dosing toward adaptive dosing and therapeutic drug monitoring (TDM), especially in long-term responders and cost-containment contexts. Notably, the inclusion of clearance-based biomarkers—such as baseline clearance and its reduction—into therapeutic models represents a key step toward individualized, dynamic immunotherapy. In conclusion, optimizing anti-PD-1 therapy through PK-PD insights and biomarker integration holds promise for improving outcomes and reducing toxicity. Future research should focus on validating PK-based approaches and developing robust algorithms (machine learning models incorporating clearance, tumor burden, and other validated biomarkers) for tailored cancer treatment. Full article

Platelet-rich plasma (PRP) has gained attention in regenerative medicine due to its bio-active proteins with tissue-healing potential. However, heterogeneity in PRP composition remains a major challenge for reproducibility and standardization. Given that body mass index (BMI) affects systemic blood parameters, we investigated whether BMI affects the cellular and molecular composition of PRP. Seventy-three participants were stratified into normal weight, overweight, and obese groups. PRP was prepared using a double-syringe system, and platelet activation was induced by freeze–thaw cycles. Whole blood and PRP cell counts were analyzed, and IL6, IGF1, HGF, and PDGF-BB levels in PRP were quantified by ELISA. Platelet enrichment and levels of IGF1, HGF, and PDGF-BB in PRP did not significantly differ between BMI groups. In contrast, IL6 concentrations were higher in normal-weight compared to overweight and obese individuals. Moreover, BMI-related associations emerged between platelet counts and PDGF-BB, and between PRP proteins and sex or caffeine intake, suggesting a more complex BMI-specific modulation of PRP composition. In conclusion, our findings support considering BMI as a relevant factor in PRP therapy. Incorporating BMI into PRP standardization strategies could improve reproducibility and support personalized regenerative approaches. Full article