Search Results (2,435)

City-scale microscopic traffic simulation is increasingly used for policy evaluation, operational planning, and disruption analysis, where repeated scenario runs must retain vehicle-level trajectories rather than only aggregate traffic indicators. This requirement creates an efficiency–consistency trade-off: single-node simulators preserve centralized state ownership but become inefficient for million-vehicle tasks, whereas distributed execution reduces runtime but may disrupt vehicle updates at partition boundaries. Cross-partition movement can cause trajectory breaks, duplicate or missing updates, and inconsistent local interaction states if boundary events, vehicle context, and update ownership are not coordinated. To address this problem, this study proposes DiCoSim, a distributed coordination framework for boundary-consistent large-scale microscopic traffic simulation. DiCoSim integrates incremental spectral-clustering partitioning, spatio-temporal event aggregation, and acknowledgment-controlled state handoff to coordinate workload balance, boundary communication, and vehicle handoff. Experiments on a 483 km${}^2$ Tianjin network with 1.5 million agents show that DiCoSim achieved a $14.49\times$ strong-scaling speedup on 16 compute nodes while maintaining close agreement with centralized execution. For the fixed boundary-crossing evaluation cohort, the trajectory interruption rate was reduced to 0.06%. In addition, a single 72 h continuous high-load run achieved 99.96% availability. These results indicate that, under the tested Tianjin conditions, coordinated boundary management supports efficient million-agent microscopic simulation while maintaining vehicle-state continuity across partitions. Full article

Slow earthquakes represent an important mode of fault slip transitional between stable creep and dynamic rupture. Their occurrence is jointly controlled by mineral composition, pore-fluid pressure, effective normal stress, system stiffness, and microstructural evolution. Because the internal state of natural faults cannot be directly observed, studies of slow slip, tectonic tremor, and low-frequency earthquakes have long been challenged by weak signals, complex noise, and discrepancies in observational scales. Building on the physical foundations of rock friction and slip stability, this review summarizes recent applications of deep learning to laboratory friction and acoustic data, natural seismic waveforms, Global Navigation Satellite System (GNSS) observations, and strain measurements, with particular emphasis on event detection, fault-state estimation, rate-and-state friction parameter inversion, and forecasting of slip evolution. Existing studies have progressed from event identification to the reconstruction of shear stress, estimation of frictional parameters, and prediction of future fault states. Nevertheless, applications to natural faults remain dominated by event detection and catalog construction, whereas parameter inversion and forecasting still rely largely on laboratory experiments or synthetic data. Physics-informed neural networks, transfer learning, reduced-order modeling, and data assimilation provide promising pathways for integrating laboratory experiments, numerical simulations, and natural observations; however, their reliability remains limited by constitutive-model dependence, parameter non-uniqueness, domain shift, and insufficient independent validation. Future work should strengthen multi-observation integration, cross-region validation, and uncertainty quantification, while developing a bidirectional framework linking laboratory experiments, numerical simulations, and natural fault observations. At present, deep learning is better suited to fault-state characterization and probabilistic assessment of slip trends than to deterministic prediction of the exact timing of slow earthquakes. Full article

Large-scale public events generate concentrated passenger demand during egress periods, often overwhelming urban transit systems. This paper proposes a multi-modal evacuation framework that coordinates in-service buses temporarily diverted from existing lines and dedicated shuttle vehicles pre-positioned at depots. The problem is formulated as a two-layer stochastic optimization under travel time uncertainty: the upper layer determines pre-event shuttle fleet sizing, while the lower layer makes real-time dispatching decisions for both modes. We propose an Uncertainty-Aware Reinforcement Learning framework with Categorical DQN (UARL-CD) that learns a robust dispatching policy through a reward function aligned with the lower-level objective, explicitly accounting for travel time uncertainty via distributional value representation and stochastic training, with an action masking mechanism enforcing operational constraints. Simulation experiments based on a realistic stadium evacuation scenario demonstrate that the proposed framework significantly outperforms deterministic optimization and rule-based strategies, achieving a 31.6% reduction in evacuation completion time and a 48.4% reduction in average passenger waiting time compared to shuttles alone, while maintaining robustness to travel time uncertainty with only 4.0% performance degradation and online decisions executed within the 2-min decision interval. Full article

Coronary heart disease represents a great economic expense, especially coronary artery calcification (CAC), which is associated with worse clinical outcomes and increased technical complexity of percutaneous coronary intervention. Red cell distribution width (RDW) is a marker of dysregulated erythropoiesis associated with inflammation and iron accumulation in macrophages that is mediated by hepcidin. Research has indicated that higher RDW levels are linked to an increased risk of adverse cardiovascular events, and some studies have shown an association with CAC. This narrative review aims to summarize the current evidence on the mechanisms responsible for elevated RDW and its relationship with inflammation and CAC. Systemic inflammation induces the expression of hepcidin, which alters iron levels; macrophages, depending on iron levels, can help to resolve inflammation or trigger inflammation in atherosclerotic cardiovascular disease (ASCVD) and can induce vascular calcification. This is known as macrophage polarization. Evidence from large-scale clinical trials has established the involvement of immune-mediated inflammation in the pathogenesis of ASCVD. Thus, systemic inflammation is a novel cardiovascular risk factor that can be treated with anti-cytokine therapy. RDW could be a useful marker of inflammation associated with CAC and a risk marker for experiencing adverse cardiovascular events. Full article

Wildfire monitoring using synthetic aperture radar (SAR) provides critical capabilities under challenging atmospheric conditions where optical sensors are limited by smoke and cloud cover. We evaluated Sentinel-1 C-band SAR interferometric coherence for Burned-area detection of the 2017 “Las Máquinas” megafire (Maule, Chile), comparing Ascending (Asc) and Descending (Dsc) orbital geometries processed with the AMSTer InSAR software. Multi-temporal RGB Coherent Change Detection composites were constructed using two interferometric pairs per orbit: the Normalised Differential Activity Index (NDAI, R channel), pre-fire coherence (G channel), and co-event coherence (B channel), clearly delineating the fire scar through red and orange signatures reflecting fire-induced vegetation loss and soil exposure. Seven machine-learning classifiers (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and XGBoost) were trained on the three-band coherence feature space. For the Ascending orbit, XGBoost achieved the highest performance (OA = 0.9328; F1 = 0.9195) and mapped 143,950 ha (76.2%) as Burned. For the Descending orbit, XGBoost also performed best (OA = 0.9221; F1 = 0.9055) and mapped 144,475 ha (76.5%) as Burned. In this case study, the Ascending geometry performed marginally better than the Descending one; however, the leading classifiers were statistically indistinguishable, indicating that the Burned and Unburned classes are close to linearly separable in the coherence feature space. These results confirm the effectiveness of coherence-based SAR analysis for large-scale wildfire mapping under adverse atmospheric conditions. Full article

Cardiovascular diseases remain the leading cause of global mortality, yet conventional diagnostics are episodic and clinic-centered, missing the dynamic physiological events that unfold between encounters. Wearable devices offer continuous, real-world monitoring and, together with artificial intelligence, digital biomarkers, and telecardiology, increasingly support a shift toward predictive, preventive, personalized, and participatory (P4) cardiovascular care. This narrative review synthesizes the contemporary evidence base for wearable cardiovascular technology and organizes it around the four pillars of P4 medicine, with the explicit aim of distinguishing what is clinically proven from what remains aspirational. The wearable ecosystem now spans consumer smartwatches, medical-grade ECG patches, smart textiles, and emerging soft bioelectronics, generating an expanding repertoire of digital biomarkers. Evidence is strongest where validation is most mature: atrial fibrillation screening, supported by large-scale studies, and structured heart-failure telemonitoring, associated with reductions in heart-failure hospitalization of 18–32% in structured programs. For acute coronary syndrome triage, cuffless blood pressure, cardiac rehabilitation, and AI-derived prognostic markers, the supporting evidence is growing but rests largely on analytical and early clinical validation rather than on demonstrated improvements in hard cardiovascular outcomes. Across all four pillars, translation is constrained by accuracy variability across demographic subgroups, regulatory fragmentation, data privacy concerns, interoperability deficits, and inequitable access for elderly, low-income, and low- and middle-income populations. Realizing the P4 promise will require harmonized validation standards, demographic-stratified accuracy reporting, equitable access strategies, and a clinical infrastructure capable of converting continuous wearable data into actionable decisions. Full article

Advanced seismic hazard assessment frameworks rely on stochastic models which include aftershock production in the form of branching or self-exciting point processes. Such empirical constructs are based on debated statistical laws observed across catalogs of natural seismicity, which lack a derivation from first principles. Here, we derive the statistics of aftershock production in a generalized mean-field model of avalanche dynamics with static random thresholds and bimodal relaxation. The number of direct aftershocks is statistically characterized as a renewal counting process accounting for Borel-distributed refractory intervals. At the large-number limit, the expected number of aftershocks is proportional to the size of the parent event with a characteristic scale linearly depending only on the branching parameter governing refractory intervals, whereas the variance follows a distinct parabolic dependence with the same parameter. This model provides a rationale for the overdispersion in aftershock production observed in field data with respect to the Poissonian offspring numbers of standard Hawkes models, but it cannot explain the ubiquity of self-similar aftershock production found in catalogs and lab experiments. Full article

Background: Pharmacists, as healthcare professionals of public trust, operate within a complex legal environment involving multiple forms of professional liability, particularly civil and criminal liability. Although these forms of responsibility are formally regulated by statutory law, the practical distinction between them remains largely dependent on judicial interpretation and case-specific circumstances. Objective: The aim of this study was to assess the scale and nature of judicial proceedings involving pharmacists and to identify the most common factors leading to civil and criminal liability. Additionally, the study sought to highlight the largely under-recognized phenomenon of legal actions involving pharmacists and to determine which areas of pharmacy practice generate the greatest legal risk. Methods: The study employed a mixed-methods design combining qualitative case law analysis with descriptive quantitative assessment. A total of 146 judicial decisions involving pharmacists were reviewed, including 43 civil and 19 criminal cases constituting the principal subject of the analysis. The examined judgments were assessed with regard to factual background, legal qualification, judicial reasoning, applied legal provisions, and type of liability imposed. Cases were additionally categorized according to the nature of the violation and the protected legal interest affected. Results: A total of 146 judicial decisions involving pharmacists were identified and analyzed, including 43 civil cases and 19 criminal cases. The findings demonstrate that legal proceedings involving pharmacists are not isolated events but represent a recurring phenomenon within professional practice. Civil cases were dominated by disputes related to reimbursement claims, prescription processing, and regulatory compliance, accounting for over half of all analyzed civil proceedings. In contrast, criminal cases were less frequent but displayed substantial heterogeneity, including unlawful medicine distribution, reimbursement fraud, breaches of professional duties, and other criminal offences. Importantly, only a minority of cases involved direct patient harm, while the majority arose from administrative, organizational, and regulatory obligations associated with pharmacy practice. The analysis identified prescription reimbursement procedures, medicine dispensing regulations, and pharmacy management responsibilities as the principal sources of legal risk for pharmacists. Conclusions: Published case law demonstrates the recurrent presence and considerable diversity of legal proceedings involving pharmacists. Most cases do not arise from direct patient harm but rather from regulatory, administrative, and organizational obligations associated with pharmacy practice. Civil proceedings are predominantly linked to reimbursement and prescription-related disputes, whereas criminal cases are less common but more diverse in nature. The results highlight the need for greater legal awareness among pharmacists, enhanced risk-management education, and stronger integration of legal competencies into undergraduate and postgraduate pharmacy training. Increased recognition of legal risks may contribute to both professional protection and improved patient safety. Full article

Precipitation nowcasting aims to predict short-term precipitation evolution over forecast lead times of 1–6 h and can support hydrological-risk and disaster-prevention applications when near-real-time observations are available. However, precipitation forecasting remains challenging because of rapid spatiotemporal evolution, spatial displacement, and the difficulty of representing localized high-intensity precipitation. To address these issues, this study proposes STAMP-GAN, a spatiotemporal attention-modulated generative adversarial network for regional precipitation sequence prediction. STAMP-GAN combines an AM-ConvLSTM temporal evolution module with spatial attention, efficient channel attention, large-receptive-field context modeling, and temporal-index-conditioned feature modulation. A spatially aligned two-dimensional digital elevation model (DEM) field is retained as static auxiliary geographical information. The STAMP-Net generator uses hierarchical multi-scale feature extraction to reconstruct precipitation structures at different spatial scales while a dual-branch temporal PatchGAN provides adversarial supervision for both the complete forecast sequence and the final three forecast frames. A hybrid objective combines regression, event-based, structural, temporal, and adversarial constraints. Experiments on the ERA5 and CMA-S datasets show that, compared with the best-performing baseline for each metric, STAMP-GAN achieves relative CSI improvements of approximately 6.5% and 9.5%, respectively. The proposed framework provides a data-driven approach for retrospective hourly regional precipitation sequence prediction under gridded meteorological-data conditions, rather than a fully validated operational real-time nowcasting system. Full article

Reservoirs are increasingly recognized as active components of the global carbon cycle, acting as both sinks and sources of organic carbon while providing essential drinking-water services. Yet, the spatiotemporal behavior of organic carbon fractions and their coupled controls by hydrological extremes, algal blooms, and reservoir operations remain poorly resolved in large, regulated water-supply reservoirs. Here, we quantified the distribution, drivers, and transport balance of organic carbon in Danjiangkou Reservoir (China) through monthly monitoring from March to December 2024 across the main reservoir, downstream reach, and major tributaries. Dissolved organic carbon (DOC), particulate organic carbon (POC), and total organic carbon (TOC) were measured together with key water-quality parameters and hydrological indicators to identify the dominant controls. Across the reservoir–tributary network, DOC, POC, and TOC ranged from 1.03 to 13.17 mg/L, 0.03 to 45.40 mg/L, and 1.34 to 48.57 mg/L, respectively, with tributaries generating the largest extremes. Reservoir waters exhibited substantially lower concentrations than tributaries and showed pronounced seasonal heterogeneity, with DOC generally higher in autumn than in summer and spring. Event-scale analyses revealed two distinct regimes: precipitation-driven runoff primarily intensified POC and TOC through terrestrial particle inputs, whereas bloom periods increased DOC and, under severe conditions, elevated TOC. POC and TOC were positively correlated with inflow, indicating that inflow dynamics govern the delivery of particulate carbon. The annual DOC transport flux was about 61 kt, with seasonal alternation between sink and source months, indicating net interception of upstream DOC under specific hydrological and operational conditions. These findings clarify how tributary forcing and reservoir regulation jointly govern carbon routing in large drinking-water reservoirs, with implications for carbon budgeting and protection of source water. Full article

The global atmospheric CO₂ concentration continues to rise, leading to increasingly severe greenhouse effects, ocean acidification, and extreme climate events. Therefore, the development of efficient CO₂ capture materials is urgently needed. Aerogels, a class of three-dimensional nanoporous solid materials formed by the crosslinking of nanoparticles or polymer molecular chains via the sol–gel process, exhibit outstanding advantages in CO₂ capture due to their high specific surface area, tunable nanopores, and abundant surface functionalizable sites. This paper systematically summarizes the preparation methods, including supercritical drying, ambient pressure drying, and freeze drying, reviews the classification and design strategies of aerogel materials, and analyzes the application status of aerogels in scenarios ranging from direct air capture, post-combustion flue gas capture, and natural gas purification to carbon sequestration. Finally, future development trends are prospected, aiming to provide a reference for the design and large-scale application of high-performance aerogel-based CO₂ adsorbents. Full article

In recent decades, there has been a marked increase in the frequency of natural disasters on a global scale. This increase is particularly notable in the context of climatological disasters, such as wildfires, which have become increasingly prevalent and intense in past years. It is evident that as the planet experiences the repercussions of climate change, the severity of these fires will intensify. The present study focuses on the analysis of wildfires that have occurred in Spain in recent years, both in terms of their number and the area affected, using data collected from the national and international databases. Since the beginning of this century, there has been an increasing trend in the number of large wildfires (>500 ha) in this country. In contrast, there has been a decline in the overall number of wildfires. However, when analyzing a more extended period, spanning from 1970 onward, these trends become less discernible. The study also analyzes the differences between some of these databases and notes that, despite the fact that certain databases offer exhaustive documentation of burned areas, others exhibit specific limitations due to a variety of factors. These limitations may include the nature of the recorded data, the resolution of wildfire detection or wildfire perimeter identification detection systems, or the recent initiation of data collection for such events. The development of strategies based on historical data and predictive models is necessary for anticipating future scenarios and mitigating the impacts of wildfires. Full article

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia with a marked age-related increase in prevalence. Current guidelines recommend left atrial appendage closure (LAAC) as an alternative to long-term oral anticoagulation in selected patients at high-risk of both thromboembolic and bleeding events. Although the use of LAAC has increased substantially in recent years, its effects on cardiac function and post-procedural functional recovery remain incompletely understood. Therefore, this study aimed to evaluate changes in periprocedural cardiac function and functional parameters in patients with AF who underwent LAAC while receiving guideline-directed medical therapy. A retrospective cohort study was performed in 250 patients who underwent percutaneous LAAC at Lanzhou University Second Hospital between May 2019 and July 2025. A total of 162 patients were included in the final analysis. Echocardiographic parameters, NYHA functional class, and Barthel Index scores were compared before and after the procedure. Significant improvements were observed in several echocardiographic parameters. Post-procedural changes in NYHA functional class distribution and Barthel Index scores indicated improved functional status, with no evidence of deterioration in cardiac structure or function. These findings support the safety of LAAC with respect to cardiac functional recovery and demonstrate that the procedure does not adversely affect cardiac performance in patients with AF receiving guideline-directed medical therapy. Nevertheless, further large-scale, multi-center prospective studies are warranted to clarify the mechanisms underlying the observed improvements in cardiac function following LAAC. Full article

Battery-powered IoT sensor nodes require energy-efficient Medium Access Control (MAC) protocols to extend network lifetime while supporting priority-sensitive traffic. This paper proposes a Priority-Aware Packet Aggregation/Piggybacking-Based (Priority + Packet Aggregation) Hybrid Energy-Efficient MAC ($P^2$A-HMAC) protocol that integrates bit-mapped priority signaling with lightweight packet aggregation/piggybacking to reduce control overhead. The protocol classifies traffic into emergency, buffer-overflow, priority, and normal categories, providing guaranteed access to delay-sensitive data while minimizing radio activity, idle listening, and contention. A mathematical energy model is developed for both priority- and contention-based operations. MATLAB-based evaluation demonstrates that $P^2$A-HMAC consistently achieves lower energy consumption than TDMA, EA-TDMA, ASHMAC, E-BMA, and $P^2$A-HMAC across varying network sizes, packet sizes, and event-generation probabilities. The proposed protocol provides substantial energy savings, particularly in large-scale and moderate-traffic networks, while maintaining priority-aware communication and QoS, making it suitable for low-power IoT and wireless sensor network applications. Full article

Background/Objectives: Low-back pain (LBP) is a common musculoskeletal disorder, with a lifetime prevalence reported to be as high as 84%. Chuna manipulative therapy (CMT), a Korean style of manual therapy for correcting joint misalignment, has rarely been evaluated in randomized controlled trials (RCTs). This study primarily aimed to evaluate the feasibility of a future large-scale, multicenter RCT comparing thrust and non-thrust CMT; preliminary clinical and safety data were collected as secondary, exploratory objectives. Methods: This multicenter, assessor-blinded pilot RCT randomized 30 participants with non-acute LBP and pelvic deviation into thrust (n = 15) or non-thrust (n = 15) CMT groups for a 4-week treatment. Feasibility outcomes (eligibility, recruitment, adherence, retention, data completeness, and treatment fidelity) were the primary endpoints. Preliminary clinical effects (Numeric Rating Scale (NRS), Oswestry Disability Index (ODI)) and adverse events were assessed over 24 weeks as exploratory outcomes. Results: Of the 35 screened, 30 (85.7%) were randomized, and the planned 15 per center was achieved at both sites. Twenty-nine (96.7%) received at least the minimum number of sessions and were retained to week 24, with complete outcome data and no protocol deviations in intervention delivery. In exploratory analyses, both groups improved from baseline and between-group differences favored thrust CMT, but as the trial was not powered to compare effectiveness, these findings are preliminary only. Nine mild adverse events resolved spontaneously. Conclusions: A large-scale, multicenter RCT comparing thrust and non-thrust CMT in pelvic deviation is feasible. The observed feasibility outcomes can inform its progression criteria, whereas the exploratory clinical findings require confirmation in an adequately powered definitive trial. Full article