Search Results (8,702)

Distention of the urinary bladder wall during filling stretches the urothelium and induces the release of chemical mediators, including adenosine 5′-triphosphate (ATP) and prostaglandins (PGs), that transmit signals between cells within the bladder wall. The urothelium also releases soluble nucleotidases (s-NTDs) that control the availability of ATP and its metabolites at receptor sites in umbrella cells and cells deeper in the bladder wall, as well as in the urine. This study investigated whether PGs regulate the intravesical breakdown of ATP by s-NTDs. Using a murine decentralized mucosa-only bladder model and an HPLC technology with fluorescence detection, we evaluated the decrease in ATP and increase in ADP, AMP, and adenosine (ADO) in intraluminal solutions (ILS) collected at the end of physiological bladder filling. PGD₂, PGE₂, and PGI₂, but not PGF_2α, inhibited the conversion of AMP (produced from ATP) to ADO, likely due to a suppressed intravesical release of s-AMPases. The effects of exogenous PGD₂, PGE₂, and PGI₂ were mediated by DP1/DP2, EP2, and IP prostanoid receptors, respectively. Activation of either DP1 or DP2 receptors by endogenous PGD₂ also led to AMP increase and ADO decrease in ILS-containing ATP substrate. Finally, PGs produced by either COX-1 or COX-2 inhibited the hydrolysis of AMP to ADO. Together, these observations suggest that (1) endogenous PGs (chiefly PGD₂, and to lesser degree PGE₂ and PGI₂) allow release of s-NTDs like s-ATPases and s-ADPases but impede the formation of ADO from intravesical ATP by inhibiting the release of s-NTDs/s-AMPases; (2) it is possible that high concentrations of PGD₂, PGE₂ and PGI₂, as anticipated in inflammation or bladder pain syndrome, delay the ADO production and prolong the action of excitatory purine mediators; and (3) either COX-1 and COX-2 are constitutively expressed in the mouse bladder mucosa or COX-2 is induced by distention of the urothelium during bladder filling. Full article

With the growing adoption of connected and autonomous vehicles (CAVs), their market penetration is expected to rise. This study investigates the mixed traffic flow dynamics of human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs) at airport terminal curbsides. A two-lane parking simulation model is developed, integrating the intelligent driver model (IDM), PATH-calibrated cooperative adaptive cruise control (CACC), and a degraded adaptive cruise control (ACC) model to capture different driving behaviors. The model accounts for varying time headways among HDV drivers based on their information acceptance levels and imposes departure constraints to enhance safety. Simulation results show that the addition of CAVs can significantly increase the average speed of vehicles and reduce the average delay time. Two metrics are inversely proportional. Specifically, as illustrated by a curbside length of 400 m and a parking demand of 1300 pcph, when the CAV penetration rate p is 10%, 30%, 50%, 70%, and 100%, respectively, compared to p = 0, the average traffic flow speed increases by 1.7%, 6.4%, 15.0%, 27.2%, and 48.7%, respectively. The average delay time decreases by 2.8%, 6.4%, 10.5%, 13.5%, and 20.0%, respectively. Meanwhile, CAVs and HAVs exhibit consistent patterns in terms of parking space utilization: the first stage (0–30% of parking spaces) showed a stable and concentrated trend; the second stage (30–70% of parking spaces) showed a slow downward trend but remained at a high level; the third stage (70–100% of parking spaces) showed a rapid decline at a steady rate. Full article

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict miner locations using simulated DTN-based movement data. The model was trained on a simulated dataset of 1,048,575 miner movement entries, predicting miner locations across 26 pillar classes. It achieved an 89% accuracy, an 89% recall, and an 83% F1-score, demonstrating strong performance for real-time underground miner localization. These results demonstrate the model’s potential for the real-time localization of trapped miners in GPS-denied environments, supporting enhanced self-escape and rescue operations. Future work will focus on validating the model with real-world data and deploying it for operational use in mines. Full article

With the advancement of global climate governance, public attention—an emerging form of social capital—has played an increasingly important role in the carbon emission effects of green technological innovation. Based on panel data from 267 prefecture-level cities in China from 2012 to 2022, this study employed a two-way fixed-effects model to identify the nonlinear relationship between green innovation and carbon emissions, incorporated interaction terms to examine the moderating effect of public attention, and applied a spatial Durbin model to analyze the spatial spillover effects of green innovation. The results reveal an inverted U-shaped relationship between green innovation and carbon emissions, with the inflection point corresponding to 8.58 authorized green patents per 10,000 people—a threshold that most cities have yet to reach. Public attention significantly altered the shape of the carbon effect curve by making it steeper; in cities with a higher share of secondary industry, it delayed the inflection point, whereas in cities dominated by the tertiary industry, the turning point appeared earlier. In addition, green innovation had significant spatial spillover effects, and its impact on carbon emissions in neighboring cities displayed a U-shaped pattern. This paper proposes an analytical framework of “socially empowered innovation” to reveal the nonlinear moderating mechanism through which public attention influences the carbon effects of green innovation. The findings offer important policy implications: efforts should focus on long-term innovation, promote regional coordination, guide rational public participation, and avoid short-sighted and unsustainable mitigation practices. Full article

High-precision satellite clock bias (SCB) prediction is essential for GNSS applications, including real-time precise point positioning (RT-PPP), Earth observation, planetary exploration, and spaceborne geodetic missions. However, during communication outages or when real-time SCB products are unavailable, RT-PPP may fail due to missing clock corrections. This underscores the necessity of reliable short-term SCB prediction in data-denied environments. To address this challenge, a hybrid model that integrates wavelet transform, a particle swarm optimization-enhanced gray model, and a first-order weighted local method is proposed for short-term SCB prediction. First, the novel model employs the db1 wavelet to perform three-level multi-resolution decomposition and single-branch reconstruction on preprocessed SCB, yielding one trend term and three detailed terms. Second, the particle swarm optimization algorithm is adopted to globally optimize the parameters of the traditional gray model to avoid falling into local optima, and the optimization-enhanced gray model is applied to predict the trend term. For the three detailed terms, the embedding dimension and time delay are calculated, and they are constructed in phase space to establish a first-order weighted local model for prediction. Third, the final SCB prediction is obtained by summing the predicted results of the trend term and the three detailed terms correspondingly. The BDS-3 SCB products from the GNSS Analysis Center of Wuhan University (WHU) are selected for experiments. Results indicate that the proposed model surpasses conventional linear polynomial (LP), quadratic polynomial (QP), gray model (GM), and Legendre (Leg.) polynomial models. The average precision and stability improvements reach (80.00, 79.16, 82.14, and 72.22) % and (36.36, 41.67, 41.67, and 61.11) % for 30 min prediction, (79.31, 78.57, 80.65, and 76.92) % and (44.44, 44.44, 47.37, and 74.36) % for 60 min prediction, and the average precision of the predicted SCB products is better than 0.20 ns and 0.21 ns for 30 min and 60 min, respectively. Furthermore, the proposed model exhibits strong robustness and is less affected by changes in clock types and the amount of modeling data. Therefore, in practical applications, the short-term SCB products predicted by the novel model are fully capable of satisfying the requirements of centimeter-level RT-PPP for clock bias precision. Full article

Background: Armed conflicts impose complex logistical and behavioral challenges on healthcare systems, particularly in managing acute conditions such as ST-elevation myocardial infarction (STEMI) and ischemic stroke. Although both diagnoses require timely intervention, their clinical pathways differ significantly. Few studies have systematically compared their management during active warfare, particularly within the warzone. Methods: This retrospective cohort study was conducted at Soroka University Medical Center (SUMC), the sole tertiary hospital in southern Israel and the main referral center for cardiovascular and neurological emergencies in the region. We included all adult patients (≥18 years) admitted with new-onset STEMI or ischemic stroke during three-month periods of wartime (October–December 2023) and matched routine periods in 2021 and 2022. Patients with in-hospital events, inter-hospital transfers, or foreign citizenship were excluded. Data on demographics, comorbidities, arrival characteristics, treatment timelines, and outcomes were extracted from electronic medical records. Categorical variables were compared using Chi-squared or Fisher’s exact test, and continuous variables using t-tests or Mann–Whitney U tests, as appropriate. Multivariable logistic and linear regression models were adjusted for age, sex, Charlson Comorbidity Index (CCI), and mode of arrival. Interaction terms assessed whether wartime modified the associations differently for STEMI and stroke. Results: A total of 410 patients were included (193 with STEMI and 217 with stroke). Patients with STEMI were significantly more likely to arrive by self-transport during the war (38, 57.6% vs. 32, 25.2%, p < 0.001) and had higher rates of late arrival beyond 12 h (19, 28.8% vs. 13, 10.2%, p = 0.002). These findings support the conclusion that patients were more prone to delayed and unstructured presentations during a crisis. In contrast, patients with stroke showed a reduction of 354 min in symptom-to-door times during the war [median 246 (30–4320 range) vs. 600 min (12–2329 range), p = 0.026]. Regression models revealed longer delays for stroke vs. STEMI in routine settings [β = 543.07 min (239.68–846.47 95% CI), p < 0.001], along with significantly lower in-hospital (OR = 0.39, 95% CI= 0.15–0.97, p = 0.05) and 30-day mortality (OR = 0.43, 95% CI= 0.19–0.94, p = 0.04). However, these differences were no longer significant during wartime. Patients with STEMI showed a trend toward lower 180-day mortality during the war (OR = 0.33, 95% CI = 0.09–0.99; p = 0.07), although this difference did not reach statistical significance. Conclusions: During wartime, patients with stroke arrived earlier and in greater numbers, while patients with STEMI showed reduced admissions and delayed, self-initiated transport. Despite these shifts, treatment timelines and short-term outcomes were maintained. These diagnosis-specific patterns highlight the importance of reinforcing EMS access for STEMI and preserving centralized protocol-based coordination for stroke during crises. Full article

Intelligent video analysis tools have advanced significantly, with numerous cameras installed in various locations to enhance security and monitor unusual events. However, the effective detection and monitoring of violent incidents often depend on manual effort and time-consuming analysis of recorded footage, which can delay timely interventions. Deep learning has emerged as a powerful approach for extracting critical features essential to identifying and classifying violent behavior, enabling the development of accurate and scalable models across diverse domains. This study presents the Int.2D-3D-CNN architecture, which integrates a two-dimensional convolutional neural network (2D-CNN) and 3D-CNNs for video-based violence recognition. Compared to traditional 2D-CNN and 3D-CNN models, the proposed Int.2D-3D-CNN model presents improved performance on the Hockey Fight, Movie, and Violent Flows datasets. The architecture captures both static and dynamic characteristics of violent scenes by integrating spatial and temporal information. Specifically, the 2D-CNN component employs lightweight MobileNetV1 and MobileNetV2 to extract spatial features from individual frames, while a simplified 3D-CNN module with a single 3D convolution layer captures motion and temporal dependencies across sequences. Evaluation results highlight the robustness of the proposed model in accurately distinguishing violent from non-violent videos under diverse conditions. The Int.2D-3D-CNN model achieved accuracies of 98%, 100%, and 98% on the Hockey Fight, Movie, and Violent Flows datasets, respectively, indicating strong potential for violence recognition applications. Full article

This study investigates the mechanisms of gas-containing tectonic coal outbursts by modeling tectonic coal and gas as analogous to soil and pore water. Analytical methods from soil mechanics, specifically those related to quicksand and seismic liquefaction, are employed to classify these outbursts into two types: “quicksand type” and “fluidization type.” Their formation mechanisms are elucidated based on a fracture network model and a one-dimensional seepage failure criterion developed for tectonic coal. The findings indicate that “quicksand type” outbursts result from the continuous detachment of tectonic coal slices within the pressure relief zone under gas seepage pressure. The thickness-to-radius ratio of these coal slices increases with rising gas pressure but decreases with increasing coal strength and normal geostress. A larger thickness-to-radius ratio signifies a more pronounced granular characteristic and accelerates the development of coal and gas outbursts. “Fluidization type” outbursts occur when the effective stress drops to zero, resulting in a complete loss of coal strength. These outbursts represent a specific case of “quicksand type” outbursts and can be triggered by vibrations. The susceptibility of tectonic coal to outbursts is attributed to its low mechanical strength and the presence of dense fractures, which increase the acting area of seepage pressure and, consequently, raise the overall seepage force. According to this analysis, the depth of outburst cavities is generally less than the width of the pressure relief zone, which can result in delayed outbursts. This study enhances the understanding of quicksand and seismic liquefaction theories in soil mechanics and provides valuable guidance for predicting and mitigating coal and gas outbursts. Full article

The ignition and combustion characteristics of the afterburner directly affect the engine performance. In this study, a numerical simulation model was created for both the novel gliding arc assisted combustion system and the conventional spark plug system. The ignition and combustion characteristics of the afterburner were then numerically investigated. Results indicate that gliding arc can enhance ignition and combustion compared to traditional spark plug. In terms of ignition characteristics, gliding arc extends the lean ignition limit by 50% and reduces ignition delay time by up to 33.8%. Regarding combustion performance, gliding arc improves combustion efficiency by up to 7.6% and increases combustor outlet temperature by up to 7%. However, due to more intense combustion dynamics within the chamber, gliding arc reduces the total pressure recovery coefficient by approximately 8% compared to baseline. Full article

This paper addresses stability issues in modern power grids arising from extensive integration of power electronic converters, which introduce complex multi-time-scale interactions. A symbolic simplification method is proposed to accurately model grid-connected converter dynamics, significantly reducing computational complexity through transfer function approximations and yielding efficient reduced-order models. An impedance-based approach utilizing impedance ratio (IR) is developed for stability assessment under active-reactive (PQ) and active power-AC voltage (PV) control strategies. The impacts of Phase-Locked Loop (PLL) and proportional-integral (PI) controllers on system stability are analysed, with a particular focus on quantifying remote converter interactions and delineating stability boundaries across varying network strengths and configurations. Furthermore, time-scale separation effectively simplifies Multi-Voltage Source Converter (MVSC) systems by minimizing inner-loop dynamics. Validation is conducted through frequency response evaluations, IR characterizations, and eigenvalue analyses, demonstrating enhanced accuracy, particularly with the application of lead–lag compensators within the critical 50–250 Hz frequency band. Time-domain simulations further illustrate the adaptability of the proposed models and reduction methodology, providing an effective and computationally efficient tool for stability assessment in converter-dominated power networks. Full article

Federated learning (FL) has emerged as a powerful framework for decentralized model training, preserving data privacy by keeping datasets localized on distributed devices. However, data heterogeneity, characterized by significant variations in size, statistical distribution, and composition across client datasets, presents a persistent challenge that impairs model performance, compromises generalization, and delays convergence. To address these issues, we propose FedDPA, a novel framework that utilizes dynamic prototypical alignment. FedDPA operates in three stages. First, it computes class-specific prototypes for each client to capture local data distributions, integrating them into an adaptive regularization mechanism. Next, a hierarchical aggregation strategy clusters and combines prototypes from similar clients, which reduces communication overhead and stabilizes model updates. Finally, a contrastive alignment process refines the global model by enforcing intra-class compactness and inter-class separation in the feature space. These mechanisms work in concert to mitigate client drift and enhance global model performance. We conducted extensive evaluations on standard classification benchmarks—EMNIST, FEMNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet 200—under various non-identically and independently distributed (non-IID) scenarios. The results demonstrate the superiority of FedDPA over state-of-the-art methods, including FedAvg, FedNH, and FedROD. Our findings highlight FedDPA’s enhanced effectiveness, stability, and adaptability, establishing it as a scalable and efficient solution to the critical problem of data heterogeneity in federated learning. Full article

Oncolytic herpes simplex virus (oHSV) is a promising cancer immunotherapy that induces tumor cell lysis and stimulates anti-tumor immunity. Our previous single-cell RNA sequencing analysis of oHSV-treated medulloblastoma tumors revealed expansion and activation of tumor-infiltrating dendritic cells (DCs), and direct oHSV infection of DCs within the brain. While the therapeutic effects of oHSVs have been primarily attributed to tumor cell infection, we hypothesize that direct infection of DCs also contributes to therapeutic efficacy by promoting DC maturation and immune activation. Although the oHSV infection in DCs was abortive, it led to increased expression of major histocompatibility complex (MHC) class I/II and co-stimulatory molecules. oHSV-infected DCs activated naïve CD4⁺ and CD8⁺ T cells, inducing expression of CD69 and CD25. These primed T cells exhibited enhanced cytotoxicity against CT-2A glioma cells. Adoptive transfer of oHSV-infected DCs via subcutaneous injection near inguinal lymph nodes delayed tumor growth in a syngeneic CT-2A glioma model, independent of tumor viral replication and lysis. Mechanistically, our in vitro studies demonstrate that oHSV can directly infect and functionally activate DCs, enabling them to prime effective anti-tumor T cell responses. This study highlights the anti-tumor potential of leveraging oHSV-infected DCs to augment viroimmunotherapy as a cancer therapeutic. Full article

The Loess Plateau frequently endures droughts, and the propagation process has grown more intricate due to the interplay of climate change and human activities. This study developed the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSMI) on a 3-month scale and examined the spatiotemporal characteristics and driving mechanisms of drought propagation from meteorological to agricultural drought utilizing cross-wavelet analysis, grey relational analysis, and the optimal parameter-based geographical detector (OPGD) model. The results demonstrate a substantial seasonal correlation between meteorological and agricultural droughts in spring, summer, and autumn, as evidenced by cross-wavelet coherence analysis (wavelet coherence > 0.8, p < 0.05). Lag analysis utilizing grey relational degree (>0.8) indicates that drought propagation generally manifests with a temporal delay of 1–3 months, with the shortest lag observed in spring (average 1.2 months) and the longest in winter (average 3.1 months). Distinct spatial heterogeneity is seen within geomorphological divisions: the loess wide valley hills and loess beam hills divisions exhibit the highest propagation rates (0.64 and 0.59), whereas the loess tableland and soil–stone hills divisions have lower propagation (around 0.50). The OPGD results reveal that precipitation, soil moisture, and temperature are the principal contributing factors, although their effects differ among geomorphological types. Interactions among components exhibit synergistic enhancement effects. This study improves our comprehension of seasonal and geomorphological heterogeneity in drought propagation from meteorological to agricultural droughts and provides quantitative evidence to support early drought warnings across various divisions, agricultural risk assessment, and water security strategies in the Loess Plateau. Full article

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

This study presents a comprehensive simulation-driven approach to identify and mitigate bottlenecks in the supply flows of an automotive manufacturing system. The supply process relies on a Kanban-based assembly line replenishment, supported by multiple material transports per shift using tugger trains. To analyze flow constraints, the research combines time–motion studies with data from a Real-Time Locating System (RTLS), enabling spatial-temporal mapping of material movements. Based on this data, digital-twin simulation models of the supply process were developed and validated. These models allowed testing of improvements aimed at increasing throughput and efficiency. The proposed methodology demonstrates effective bottleneck resolution and provides a structured framework for data collection and simulation integration. Unlike traditional observation-based approaches, the use of RTLS enables precise detection of routing deviations and operator behaviors. The simulation model was tested under real operating conditions and validated against ground-truth data. Results showed a reduction in collisions and delivery delays, as well as measurable productivity and financial gains. This approach offers a practical and transferable method for optimizing intralogistics in modern production environments. Full article