Search Results (24,207)

Falls in older adults and individuals with balance impairments remain a major concern because they are closely associated with injury, reduced mobility, and loss of independence. This study presents a preclinical proof-of-concept for a cognitive smart walker architecture that combines user-compatible walking assistance with active safety intervention. The system integrates a 2D LiDAR sensor for contactless lower-limb monitoring, a six-degree-of-freedom (6-DOF) force/torque sensor to measure user–walker interaction, and an inertial measurement unit (IMU) for dynamic monitoring, with all data processed in real time on a Raspberry Pi/ROS-based platform. Normal walking assistance is provided through a command-level variable admittance-based controller that converts interaction forces into a smoothed signed duty-cycle command rather than a rigid speed-control signal. Safety decisions are managed by a Hierarchical State Machine (HSM). Early-risk conditions are handled through motor-based dynamic braking, whereas severe physical crises additionally deploy lateral support legs to enlarge the base of support. Within this framework, the system can detect and manage foot entanglement, grip loss, forward fall, vertical collapse, lateral fall, successive crises, and recovery-abort events. In experiments across multiple scenarios, the system correctly detected all 50 crisis cases and did not issue unnecessary interventions in 30 non-crisis cases. These findings show that the proposed architecture can preserve transparent walking assistance during normal gait while providing graded, context-sensitive active safety when risk emerges. Full article

Identifying critical stations is fundamental to improving the resilience and operational safety of urban rail transit networks. However, most existing identification methods—especially dynamic node removal approaches—assume that station failures affect only the failed node itself, thereby overlooking the cascading impacts caused by train turnback adjustments under bidirectional service interruptions. This simplification leads to systematic underestimation of stations with strong operational dependencies. To address this gap, this study proposes a framework for identifying critical station that explicitly incorporates bidirectional operational disruptions and the indirect failures they induce within turnback sections. This study is among the first to explicitly model turnback-related failure propagation within operational sections in critical station identification, providing a closer alignment with real-world rail transit operations. A comprehensive evaluation system is then constructed by integrating dynamic network connectivity indicators, network topology characteristics, and station attributes. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), combined with objectively determined indicator weights, is employed to synthesize multidimensional indicators and rank station importance. The method is applied to the Chengdu Metro network (12 lines and 282 stations). Results indicate that considering turnback related indirect failures substantially amplifies the measured impact of station disruptions on network connectivity. Critical stations are highly concentrated at intersections between the loop line and major radial lines, while several non-interchange stations within key turnback sections—such as Lijiatuo Station and Wannianchang Station—exhibit pronounced increases in importance rankings. Comparative analysis shows that the rankings of some stations change by more than 50% relative to the conventional node removal method, indicating that traditional approaches may significantly underestimate operationally critical stations associated with turnback sections. More importantly, the proposed method enables a direct comparison between structurally important stations and operationally critical stations under disruption scenarios. Overall, the proposed framework provides a more realistic and operation oriented identification of critical stations by explicitly accounting for train operation dependencies under bidirectional interruptions, offering practical insights for resilience assessment and emergency management of large scale urban rail transit networks. Full article

Prosumer communities, aggregations of residential and commercial entities equipped with distributed energy resources (DER), including photovoltaic systems, battery storage, and flexible loads, are emerging as critical organizational units in decarbonising smart grid architectures. Managing these communities effectively requires balancing economic efficiency with equity, autonomy, and environmental sustainability, objectives that conventional centralized control methods and existing multi-agent reinforcement learning (MARL) implementations fail to address simultaneously. This article proposes a value-aligned hierarchical multi-agent reinforcement learning (VA-HMARL) framework as a formally unified architecture that embeds equity (Jain’s Fairness Index J ≥ 0.90), individual autonomy, and carbon sustainability as hard constraints within the MARL reward structure. The framework integrates: a multi-objective Value Alignment Module (VAM) combining economic, fairness, sustainability, and comfort objectives; attention-based implicit coordination for scalable agent interaction; and differentially private federated policy aggregation (ε = 1.0, δ = 10⁻⁵) for GDPR-compliant collaborative learning. Simulation on a 20-prosumer community modelled on the IEEE 33-bus feeder over 10 Monte Carlo runs (300 episodes each) demonstrates: a 6.2% energy cost reduction versus the Rule-Based baseline (p = 0.0004); a Jain’s Fairness Index of 0.912 ± 0.031 at policy convergence (final 50 episodes), satisfying the J ≥ 0.90 community equity floor; and an 18.0% reduction in CO₂ emissions. The economic efficiency trade-off relative to performance-optimized MARL baselines is limited to 2.4%, within the 5% design target. These results establish VA-HMARL as a technically feasible and ethically grounded paradigm for autonomous decentralized energy governance. Full article

Colorectal cancer (CRC) is a multifactorial disease arising from dynamic interactions between gut microbiota, inflammatory processes, metabolic reprogramming, and dysregulated host signaling pathways. Increasing evidence highlights the potential of synbiotics—combinations of probiotics and prebiotics—as promising modulators of these processes. This review explores the mechanisms by which synbiotics influence CRC development and progression, integrating data from preclinical and clinical studies. Synbiotics exert beneficial effects by restoring microbial balance, enhancing the production of short-chain fatty acids (SCFAs), strengthening intestinal barrier integrity, and reducing chronic inflammation and oxidative stress. These functional changes converge on key molecular pathways, including Wnt/β-catenin, NF-κB, and PI3K/Akt/mTOR, which regulate tumor cell proliferation, survival, and immune responses. Preclinical studies consistently demonstrate anti-tumor effects, including reduced tumor growth, increased apoptosis, and modulation of the tumor microenvironment. Clinical evidence suggests that synbiotics may improve postoperative outcomes, reduce chemotherapy-related toxicity, and positively influence microbiome composition, although results remain heterogeneous. Emerging approaches focusing on microbiome profiling and personalized synbiotic interventions offer new opportunities for precision medicine in CRC. Overall, synbiotics represent a promising adjunctive strategy in colorectal cancer management, with potential to enhance therapeutic efficacy and improve patient outcomes. Further large-scale clinical studies are needed to validate their long-term benefits and establish standardized treatment protocols. Full article

Despite the rapid expansion of photovoltaic (PV) installations over the past decade, challenges such as curtailments of renewable energy sources (RESs) and grid constraints continue to limit the capacity of Cyprus’ power system to accommodate higher solar penetration. In this context, grid reliability, defined as the ability to maintain stable operation by balancing supply and demand, minimizing curtailment, and reducing stress on the island network, has emerged as a critical concern. The deployment of PV-plus-storage systems offers a viable solution to enhance grid reliability while alleviating operational constraints. This paper presents a real-world case study of the first commercially deployed grid-connected PV-powered, battery-integrated electric vehicle (EV) charging station in Cyprus. Commissioned in May 2025, the system integrates a 60.32 kW_p rooftop PV array, a 100 kW/97 kWh battery energy storage system (BESS), and a 160 kW DC fast charger. A custom cloud-based energy management platform enables real-time monitoring, forecasting, and optimization under a zero-export scheme. High-resolution operational and weather data were collected between 15 May and 30 November 2025. Over this period, the integrated PV-battery system supplied 29% of the site’s total energy demand (self-sufficiency rate of 28.97%) and achieved a self-consumption rate of 98.69%. Such rates would not have been attainable with a pure PV system, given the depot’s evening-concentrated EV charging demand profile, which requires the BESS to time-shift daytime solar generation. The system reduced depot electricity costs by approximately 29%, generating €16,010 in savings and avoiding 26.47 tonnes of carbon dioxide (CO₂) emissions compared to a grid-only baseline. Beyond site-level performance, the system contributed to grid stress reduction by absorbing excess PV generation that would otherwise have been curtailed/wasted. Operational insights indicate minimal temperature-related issues, highlight the importance of automated fault detection and alerting to minimize downtime, and demonstrate how periodic operation strategies can optimize system performance and mitigate curtailment in Cyprus’s isolated grid. Full article

Effective management of hazardous materials (hazmat) and chemical, biological, radiological, and nuclear (CBRN) incidents depends not only on technical capabilities but also on human behaviour. A critical challenge in mass decontamination operations is the potential for casualties to leave the scene before receiving treatment, increasing personal risk and the likelihood of secondary contamination. Despite its operational significance, little is known about the behavioural variables that influence whether casualties remain on scene. This paper presents a structured scenario development methodology, grounded in expert elicitation, to identify the key factors affecting casualty compliance during mass decontamination. A modified Delphi-inspired approach was used to design realistic scenarios that will inform future behavioural studies. The findings contribute to a more robust evidence base for emergency planning by integrating psychosocial variables into operational assumptions for hazmat/CBRN response. Full article

The exceptional longevity of bats challenges classical theories of inflammaging and suggests an alternative that improved resilience in responding to pathogens and cellular damage can increase longevity. Accordingly, we have developed the Core Longevity State Vector (CLSV-6) to characterize an expanded explanation for inflammaging that can be predictive of successful aging and used to develop potential strategies for successful aging. Despite high metabolic rates and persistent viral exposure, many bat species have much longer lifespans than would be predicted for mammals of their size. The increased longevity of many bat species is achieved through damage tolerance, regulated inflammasome activity, constitutive basal antiviral defenses, enhanced autophagy–mitophagy, and efficient resolution of inflammation, rather than through heightened inflammatory immunity. The CLSV-6 is introduced as a multidimensional immunotype framework integrating six conserved mechanisms that link bat immunity to bat longevity and to human healthy aging: (1) damage tolerance, (2) autophagy–mitophagy, (3) proteostasis (management of degraded proteins), (4) basal immune readiness without activation, (5) inflammasome regulation, and (6) inflammatory resolution capacity. Together, these mechanisms enable a robust antiviral defense when needed without chronic inflammation. Notably, centenarians converge toward this bat-like configuration. Studies suggest that centenarians often preserve more functional NK cells, better macrophage regulation, and improved anti-inflammatory control, with both bats and humans exhibiting reduced activation of the NLRP3 inflammasome, resulting in greater immune resilience. Building on this framework, functional foods—including polyphenols, fermented foods, and herbal extracts—are proposed as practical strategies to shift human immunity toward bat-like, CLSV-6 immunotype by enhancing cellular quality control, regulating inflammasome activity, strengthening basal antiviral readiness, and supporting inflammatory resolution, thereby redirecting longevity strategies from immune stimulation toward damage containment and repair. This review reframes longevity as an emergent property of integrated immune damage management and provides a mechanistic roadmap for nutritional interventions to engineer healthier human aging inspired by bat immunity. Full article

Large language model (LLM) serving has emerged as a major source of energy consumption in modern AI infrastructure. In current deployments, graphics processing units (GPUs) are typically operated at default high-frequency settings to maximize performance. However, under practical service-level objectives (SLOs), peak performance is often unnecessary, especially during the memory-bound decode stage, resulting in substantial power redundancy and avoidable energy waste. Existing studies that apply GPU dynamic voltage and frequency scaling (DVFS) to improve the energy efficiency of LLM serving have shown promising results. However, they generally rely on coarse-grained control, accurate output length prediction, or request-level resource management, which limits their effectiveness under highly dynamic workloads and strict SLO constraints. We present EcoInfer, a fine-grained DVFS framework for energy-efficient LLM serving. EcoInfer performs iteration-level, workload-aware GPU frequency control that adapts to the current inference phase and system state while preserving latency guarantees. It comprises three tightly integrated modules: a machine-learning-based frequency–latency predictor that estimates iteration latency across candidate GPU frequencies using lightweight iteration-level features; an SLO-aware frequency controller that selects the minimum feasible frequency within a sweet-spot-guided candidate range; and a low-overhead runtime optimization layer that combines adaptive decision caching with asynchronous execution to reduce and hide the overhead of online control. Implemented on top of vLLM, EcoInfer achieves up to 25.4% energy savings and 21.5% average energy savings and improves energy efficiency by 1.28× on average in terms of Tokens/J while maintaining a nearly unchanged SLO attainment rate compared with the default vLLM baseline. Full article

Over 350,000 patients experience out-of-hospital cardiac arrest annually in the United States. Patients presenting to the emergency department (ED) in cardiac arrest are critically ill and require emergent clinical decisions and treatment. Point-of-care ultrasound (POCUS) is useful as a rapid, bedside diagnostic imaging tool to help elucidate possible causes of cardiac arrest and guide management. Using a validated systematic approach such as the SHoC and CASA protocols, clinicians can safely perform POCUS with shortened pauses during pulse checks. POCUS is key in evaluating cardiac arrhythmias such as ventricular fibrillation (VF), which has higher survival rates with defibrillation, versus cardiac standstill, which has higher mortality per recent research studies. When performed by experienced users, POCUS also facilitates identification of reversible causes for improved cardiac arrest patient outcomes and can guide decisions in resuscitative efforts. In summary, this manuscript is a narrative review that illustrates identifiable POCUS findings and synthesizes them to guide potential emergent intervention in patients in cardiac arrest including: ventricular fibrillation, cardiac standstill, pericardial effusion, cardiac tamponade, right heart strain such as from acute pulmonary embolism, reduced cardiac function, wall motion abnormality, ruptured ventricular pseudoaneurysm, and aortic dissection. Full article

In this study, we present a machine-learning approach—a land use random forest (LURF) model—to produce daily PM₁₀ concentration maps at a 1 km resolution across the Campania region for the year 2022. The model combines daily measurements from 13 ARPA Campania monitoring stations with a wide set of spatial and atmospheric information. The predictors include population, land cover, road network, ERA5 meteorological data, satellite aerosol observations from MODIS, output from the CHIMERE chemistry transport model, and a flag identifying days affected by Saharan dust transport. The model is trained and validated using a station-based cross-validation scheme that accounts for spatial correlation between sites. Under this scheme, the LURF reproduces observed concentrations with substantially smaller errors than the raw CHIMERE output (RMSE of 11.0 vs. 23.6 $\mathsf{\mu }$g m⁻³). CHIMERE concentrations and ERA5 meteorology emerge as the most informative predictors, while the dust flag specifically improves the representation of episodic high-PM₁₀ events. The resulting 1-km maps reveal clear urban–rural contrasts. They identify pollution hotspots in the Naples metropolitan area and along major motorways that are not visible in coarser model outputs. Probabilistic exceedance maps further show that meeting the future 2030 EU limit value of 20 $\mathsf{\mu }$g m⁻³ will be challenging across much of the metropolitan area. Overall, the proposed framework provides a low-cost, practical tool for high-resolution PM₁₀ exposure assessment, supporting epidemiological studies, environmental justice analyses, and air quality management in regions with complex terrain and limited monitoring coverage. Full article

Exophiala endophthalmitis of exogenous origin is an exceptionally rare but severe ocular infection, characterized by diagnostic delays, limited therapeutic guidance, and frequently poor outcomes. Herein, we report one new case of an 80-year-old woman who presented with severe fungal keratitis progressing to endophthalmitis two years after an uncomplicated cataract surgery. The condition was initially misdiagnosed and treated with topical antibiotics and corticosteroids. By cultivation, microscopy, histopathological, and PCR analysis of the samples, Exophiala dermatitidis was identified as the causative agent. Despite targeted antifungal therapy with voriconazole, the disease rapidly progressed, resulting in corneal perforation and evisceration of the affected eye. The number of confirmed cases of this infection remains very limited. To address this gap, we conducted a structured review of all reported instances of exogenous Exophiala endophthalmitis, in which Exophiala dermatitidis emerged as the predominant causative species. Common predisposing factors included corneal barrier disruption, ocular surgery, diabetes mellitus, and corticosteroid use. Diagnostic confirmation was frequently delayed, and treatment outcomes varied. Amphotericin B-based regimens were associated with poor results, whereas voriconazole, particularly when combined with surgical intervention, demonstrated more favorable outcomes. Exogenous Exophiala endophthalmitis remains underrecognized, with limited evidence to guide management. This entity should be considered in postoperative or trauma-associated intraocular inflammation, and current evidence supports azole-based therapy combined with surgical intervention when indicated. Full article

In 2023, Tropical Cyclone Freddy caused severe damage in southern Malawi, flooding much of the lowland area near Lake Chilwa and displacing many residents. This study evaluates long-term, region-specific mitigation strategies to lessen future risks, using a novel approach that combines drone and satellite data, building footprints, and 3D simulations to analyze how building elevation affects flood damage and assess Property-Level Flood Risk Adaptation measures. Results show a significant difference in ground elevation between affected and unaffected buildings, with damaged structures generally at lower levels. The 3D simulation confirmed a water-level rise of approximately 3.0 m caused by Freddy. Scenario analysis indicates that elevating buildings by 2.0, 2.5, and 3.0 m could reduce direct flood exposure and 64%, 76%, and 91% of damage, respectively. These insights can inform the development of targeted regional risk-mitigation strategies through Property-Level Flood Risk Adaptation in high-risk areas. Full article

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

Pediatric sepsis is a life-threatening systemic infectious response syndrome. Its core pathophysiological process involves a systemic imbalance between oxygen delivery and demand, coupled with cellular energy metabolism dysfunction, which collectively contribute to high mortality rates. Parameters of oxygen metabolism serve as critical indicators reflecting tissue perfusion and cellular oxygen utilization. Consequently, these parameters hold significant value for the early identification, severity stratification, therapeutic guidance, and prognostic evaluation of pediatric sepsis. This review systematically elucidates the pathophysiological mechanisms underlying oxygen metabolism disorders in pediatric sepsis. Furthermore, it highlights the current clinical applications and significance of key monitoring indices, including blood lactate, central venous oxygen saturation, oxygen delivery, and oxygen consumption. By integrating recent research advancements, this paper also explores therapeutic strategies aimed at optimizing oxygen metabolism, such as blood purification, microcirculation-targeted therapies, and extracorporeal membrane oxygenation. Finally, we provide future perspectives on emerging biomarkers and metabolomic approaches, aiming to establish a theoretical foundation for the optimized clinical management of pediatric sepsis. Full article

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article