Search Results (8,901)

Postoperative wound complications remain a major cause of morbidity, prolonged hospitalization, increased healthcare costs, and reduced quality of life. While traditional wound dressings functioned primarily as passive barriers against contamination and exudate, advances in wound biology have transformed surgical wound management. Tissue repair is now recognized as a dynamic immunometabolic process involving coordinated interactions among immune cells, stromal populations, extracellular matrix remodeling, mechanotransduction, mitochondrial function, redox balance, microbial ecology, and bioelectrical signaling. Consequently, modern wound dressings are increasingly designed as bioactive systems capable of actively modulating the wound microenvironment. Recent developments in biomaterials science, immunoengineering, nanotechnology, extracellular vesicle biology, bioelectronics, and artificial intelligence have enabled the creation of advanced wound platforms, including stimuli-responsive hydrogels, immunomodulatory biomaterials, nanozyme-based dressings, conductive scaffolds, oxygen-generating matrices, extracellular vesicle-loaded systems, and biosensor-integrated interfaces. Therapeutic strategies are progressively shifting from antimicrobial-focused approaches toward immune-regenerative modulation targeting chronic inflammation, mitochondrial dysfunction, ferroptosis, cellular senescence, and impaired mechanobiological signaling. This review examines emerging surgical wound dressings from mechanistic, translational, and biomaterial perspectives, highlighting current innovations, translational challenges, and future directions. Collectively, these technologies may enable intelligent therapeutic systems capable of sensing and directing tissue regeneration in real time. Full article

Basic-region leucine zipper (bZIP) transcription factors are crucial for plant stress responses, but their characterization in the wild species Juglans mandshurica remains limited. Here, we identified 80 bZIP genes in the J. mandshurica genome and classified them into 13 subgroups, with notable enrichment in subgroups S, A, D, and I. All subgroup D members contain both bZIP and DELAY OF GERMINATION 1 (DOG1) domains, forming characteristic dual-module fusion proteins. Evolutionary analysis detected three orthologous gene pairs under positive selection since divergence from Juglans regia. Promoter cis-elements, especially MYB and MYC motifs, are abundant in JmbZIP genes. Protein–protein interaction networks suggest potential functional specialization and coordination among JmbZIP members. Expression profiling revealed distinct patterns across subgroups, with S, A, and D showing high activity across various physiological processes and light stress responses. qRT-PCR validated the dynamic expression of six ABA pathway marker genes, the ABRE-rich JmbZIP41 and JmbZIP42 genes, together with the highly expressed JmbZIP12 gene under salt and drought stress. Our genome-wide analysis enabled the functional screening of bZIP members across subgroups. The key genes identified in this study provide valuable genetic resources for stress-resistance breeding in forest trees, with JmABI5 (JmbZIP40) and JmbZIP42 serving as prime candidates for enhancing tree stress tolerance. Full article

Coordinating agents in dynamic and uncertain environments remains a fundamental challenge in multi-agent systems (MAS) research, particularly in contexts where the composition of agent organizations directly affects overall system performance. While significant effort has focused on task allocation and individual agent planning, predicting the systemic impact of organizational changes and selecting optimal organizational structures under uncertainty remain less explored in MAS. This paper addresses this challenge by introducing a decision-making framework that models structural reorganization as a Markov Decision Process (MDP), where actions represent organizational structures rather than individual agent behaviors, and organizational selection is guided by the anticipated impact on the overall system state. The proposed model captures the stochastic dynamics of multi-agent intervention and diverse agent capabilities through a probabilistic transition function, while a reward function guides the selection of coalition structures that maximize operational effectiveness. The framework is solved using value iteration and evaluated on the RoboCup Rescue simulation platform. Results show that the derived optimal policy identifies, at each decision step, an appropriate coalition structure that reduces system degradation while efficiently utilizing available agents. Full article

It is widely acknowledged that General Matrix Multiplication (GEMM) serves as a foundational kernel across numerous application domains. Complex numbers exhibit distinctive mathematical properties that enable their widespread adoption across engineering computing scenarios, including signal processing and signal transformation. This study investigates high-efficiency CGEMM, namely, complex-valued GEMM, for NPU hardware, broadening the application scope of NPUs beyond mainstream low-precision AI computation workloads. The major contributions of this study are as follows: (i) numerical precision and hardware utilization of the 3M and 4M decomposition schemes on Ascend NPUs are analyzed, and the 4M method is selected as the preferred CGEMM implementation under our tested hardware constraints to fit the bandwidth limitations of modern accelerators for both precision-sensitive and performance-critical matrix computation scenarios; (ii) a complete high-performance CGEMM design based on the 4M scheme tailored for Ascend NPUs is proposed, with an AIC/AIV dual-stream pipeline scheduling strategy equipped to coordinate padding operations, matrix–matrix multiplications, and element-wise instructions across multi-level memory hierarchies and compute units; (iii) a fine-grained task scheduling and assignment mechanism is implemented to maximize Cube core occupancy across diverse matrix dimensions, improving hardware utilization for various computation workloads. Our experimental measurements show that the proposed CGEMM achieves a competitive hardware utilization rate of 83.6% across all tested matrix configurations, enabling efficient exploitation of available computing resources. Meanwhile, we observe a measured average speedup of 1.14× relative to the AscendSipBoost implementation tested on an identical Ascend NPU, alongside a measured 3.17× speedup compared with cuBLAS running on the Nvidia GPU platform adopted in our experiments across all evaluated matrix sizes. These results reflect the promising capability of Ascend NPUs for high-precision complex-valued computing workloads within the tested experimental setup. Full article

Water level monitoring is closely linked to the safety of production and daily activities along riverbanks, making real-time and high-precision water level measurement an urgent technical demand. The feature extraction backbone of the Unet model is modified, and the lightweight MobileNet V2 network is adopted in this paper. The constructed network achieves significantly higher computational efficiency than standard convolutions, effectively overcoming the limited real-time performance of conventional water level measurement methods. Furthermore, the coordinate attention (CA) mechanism is integrated into the skip connections of Unet to strengthen the network’s capability to extract key features for water level segmentation, thereby further improving the accuracy of water level detection. A novel piecewise linear fitting method for water level line measurement based on monocular vision is proposed, and field-measured water level data are adopted to verify the calculation results. The main achievements of the improved model include the following: (1) Compared with the baseline model, the improved model MCUnet (MobileNet V2 + CA + Unet) achieves a 5.77% increase in accuracy and a 25.71% improvement in inference speed on the experimental water surface recognition dataset. (2) Taking the field-observed water level as the reference, the mean absolute error of the proposed image-based water level monitoring method reaches approximately 1.69 cm. (3) In comparison with DeepLab, U2net and Unet, the MCUnet model gains accuracy improvements of 4.47%, 2.81% and 5.77% respectively, with the detection frame rate increased by 12 FPS, 15 FPS and 11 FPS correspondingly. Through this work, the paper can provide some theoretical support and technical references for overcoming the limitations of conventional water level measuring devices, including strict installation requirements, limited measurement precision, high deployment and maintenance costs, and cumbersome data processing. Full article

Localized stress concentration and defect accumulation are prone to occurring during metal spinning because of the coupled effects of complex loading and interfacial friction. In this study, a molecular dynamics model of metal spinning was established to investigate the effects of process parameters and temperature on the mechanical response, material flow, contact loading, and dislocation evolution behavior within the contact zone. The results indicate that the optimal deformation coordination is achieved with an arc radius of 25 Å, an indentation depth of 8 Å, and a tangential velocity of 1.5 Å/ps. Analysis of the normal and tangential forces shows that the normal load is rapidly established during the indentation stage, whereas the tangential load continuously increases with material shear transport. Both loads decrease significantly with increasing temperature. Elevated temperature effectively suppresses dislocation accumulation and simplifies the dislocation structure, causing the plastic deformation behavior to gradually transition toward a dominant primary slip-system mode. This study reveals the local deformation and dislocation evolution mechanisms during spinning and provides theoretical guidance for the process optimization of thin-walled spinning components. Full article

This paper presents a distributed coordination framework for simultaneous multi-target tracking using a mobile wireless sensor network (MWSN) based on discrete-event-system principles. The proposed framework employs a finite-state-machine architecture, where autonomous mobile sensors sequentially process detection and tracking events. Unlike passive tracking approaches that react to target loss after it occurs, the proposed strategy implements predictive handover through Extended-Kalman-Filter-based uncertainty propagation. This enables sensors to anticipate target loss and to reposition auxiliary sensors in advance, acquiring targets along their predicted trajectories. A bidding-based allocation mechanism coordinates sensor assignments by evaluating four competing objectives: network preservation, spatial proximity to handover points, temporal mission feasibility, and estimation uncertainty. The proposed framework integrates four components: EKF-convergence-triggered proactive handover, multi-objective competitive bidding, distributed min–max conflict resolution, and fusion-driven proportional navigation. Unlike existing methods, auxiliary sensors navigate using confidence-weighted EKF estimates shared by neighboring sensors rather than their own measurements. An ablation study over ten Monte Carlo trials confirms that each component contributes independently, with EKF-based predictive triggering identified as the dominant performance driver. Full article

Brown bear populations in Greece face multiple threats, and parasitic infections may pose an additional risk to these vulnerable animals. This study represents the first comprehensive assessment of endoparasite occurrence, prevalence, and seasonality in brown bears in Greece, in relation to geographical location and the animal’s different physiological phases. A total of 918 faecal samples were collected over a three-year period from regions with brown bear presence in Greece. For each sample, the date of collection and the coordinates of the site were recorded. Samples were examined using sedimentation, flotation, and McMaster techniques, while the Baermann method was additionally applied to a subset of 195 samples. Spatial and temporal patterns in parasite occurrence and diversity were analysed using generalised additive models (GAMs). Ten parasitic taxa were identified, with Baylisascaris transfuga being the most prevalent (39.8%), followed by Crenosoma spp. (26%), Uncinaria spp. (18.09%), and Dicrocoelium dendriticum (14.38%). Less prevalent taxa included Eucoleus aerophilus, Sarcocystis spp., Toxascaris leonina, Eimeria spp., Linguatula serrata, and Taeniidae. Μixed infections, involving two or more parasites, were detected in 22% of the samples. The prevalence of B. transfuga was higher in late autumn, with high-risk infection areas identified in both late summer and autumn. In contrast, Uncinaria spp. and D. dendriticum showed no seasonal variation, while D. dendriticum exhibited spatial clustering patterns similar to B. transfuga but without clear seasonal trends. These findings highlight the widespread occurrence and complexity of parasitic infections in Greek brown bears. Continued long-term monitoring is essential to improve understanding of transmission dynamics and the ecological processes shaping parasite distribution in this animal species. Full article

Examining how artificial intelligence industry agglomeration (AIIA) affects carbon and pollution reduction is crucial for China’s agricultural sustainability. Existing research mainly examines the effect of artificial intelligence (AI) on the reduction of single pollutants while overlooking how industry agglomeration influences the marginal cost of coordinated abatement, a key issue for the agricultural resource–environment–economy system. Using panel data for 30 Chinese provinces from 2016 to 2024, this study constructs a marginal cost-based indicator of agricultural pollution–carbon reduction synergy (APCRS) and examines the effect of AIIA. The full-sample results reveal that AIIA has a U-shaped relationship with APCRS. Technological progress partially mediates this relationship. Agricultural socialized services and rural industrial integration buffer the initial negative association, whereas agricultural labor productivity strengthens the curvature of the estimated nonlinear pattern. The effect of AIIA also varies with external conditions and is more pronounced in regions with higher levels of marketization and industrialization while remaining significantly U-shaped across grain strategic zones. This dynamic process is more likely to emerge when public innovation investment and rural household income exceed critical thresholds. These findings provide new evidence for understanding how AI-driven agglomeration can support green agricultural transformation. Full article

The Engineering-Procurement-Construction (EPC) general contracting model has emerged as the dominant delivery method for large-scale infrastructure and industrial projects in China. However, contemporary EPC project cost control remains plagued by critical industry challenges, including fragmented cross-stage coordination, pervasive data silos, and the shallow integration of digital technologies into core management processes. This study considers three key stakeholders—government regulators, project owners, and EPC general contractors—and develops a tripartite evolutionary game model to analyze the strategic interactions underlying intelligent cost control in EPC projects. We examine the evolutionary stability of each stakeholder’s strategy selection, explore how various factors influence tripartite strategic choices, and further investigate the stability of equilibrium points in the game system. The key findings are summarized as follows: (1) Strengthening government incentives and penalties simultaneously promotes owners’ investment in intelligent cost control systems and general contractors’ active collaborative cost management. However, excessive incentive intensity undermines the government’s regulatory effectiveness. (2) Establishing a revenue-sharing mechanism for excess cost savings fully stimulates the spontaneous cooperation willingness of owners and general contractors, serving as the cornerstone for market-oriented operation of intelligent cost control. (3) Reducing owners’ intelligent construction investment costs and general contractors’ collaborative control costs effectively addresses practical implementation barriers and accelerates the digital upgrading of engineering cost management. Finally, numerical simulations are performed using MATLAB R2020b to validate theoretical findings. Full article

Railway transportation networks increasingly share constrained corridors with transmission lines, buried pipelines, and other linear infrastructure. Electromagnetic interference in these corridors is important for safe railway planning and operation, particularly when nearby high-voltage lines cross oil and gas pipelines. This paper investigates transmission-line-induced pipeline potential under crossing conditions in the Zhangbei region. The CDEGS moment-method framework is applied with locally refined segmentation in the crossing regions, and an electromagnetic coupling model for multiple-crossing transmission line-oil and gas pipeline systems is established. The qualitative effects of crossing angle and parallel length on pipeline potential were obtained under both normal operating conditions and single-phase ground fault transient conditions. The results show that induced voltage decreases nonlinearly as the crossing angle increases and rises markedly with crossing length. The contribution of ground potential rise during transient processes to pipeline potential is significantly greater than that during steady-state processes. Installing zinc ribbons as a drainage measure can reduce the pipeline-to-ground voltage. However, supplementary mitigation measures may still be required under severe interference conditions. These findings are relevant to railway transportation because railway corridors often coexist with transmission lines and buried pipelines, making coordinated electromagnetic compatibility assessment essential for infrastructure safety and operational reliability. The proposed framework supports corridor planning, risk assessment, and protective design for railway-related infrastructure in complex shared corridors. Full article

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

Tourism plays an increasingly important role in promoting economic growth and rural revitalization in ecologically fragile regions. However, tourism systems in Tibet–related areas of Sichuan, China, are highly vulnerable to natural disasters, ecological degradation, and regional development imbalances, posing challenges to sustainable tourism development. This study aims to evaluate tourism system resilience and identify its key influencing factors from a sustainability perspective. Based on the regional characteristics of Tibet-related areas in Sichuan, a comprehensive evaluation framework is constructed covering four subsystems: tourism infrastructure and scale, economy, society, and ecology. An integrated entropy weight–analytic hierarchy process (AHP) model, coupling coordination model, and obstacle degree model are employed to assess tourism system resilience and examine subsystem interactions using panel data from 2011 to 2020. The results indicate that: (1) the resilience levels of tourism subsystems show no clear spatial or temporal regularity across the study areas; (2) ecological resilience remains significantly lower than tourism, economic, and social resilience, representing the weakest component of the tourism system; (3) the coupling coordination among subsystems remains at a low level, suggesting insufficient synergy for sustainable regional development; and (4) ecological constraints are the primary limiting factors affecting overall tourism system resilience. This study contributes to sustainable tourism research by revealing the critical role of ecological governance and subsystem coordination in enhancing tourism resilience in ecologically sensitive regions. Policy implications include strengthening ecological protection, improving tourism infrastructure, promoting digital tourism marketing, and advancing rural revitalization to achieve long-term sustainable development. However, this study is limited by data availability and the spatial scope of the selected case-study areas, which may affect the generalizability of the findings. Full article

Learning agility has become an essential capability for employees working in technology-driven environments characterized by rapid change and uncertainty. Despite increasing attention on learning agility, limited empirical research has examined how different levels of cognitive abilities contribute to its development, particularly among Generation Z employees. This study investigates the cognitive determinants of learning agility by distinguishing between basic cognitive abilities and high-level cognitive abilities and examining their roles across established and start-up companies. A total of 270 Generation Z employees in Indonesia participated in the study, consisting of 135 employees from established companies and 135 from start-up companies. Cognitive abilities were assessed using objective psychometric instruments, where basic cognitive abilities (reasoning, memory, attention, coordination, and perception) were measured using CogniFit, while high-level cognitive abilities were assessed through the Divergent Association Task (DAT) for creativity, the Watson–Glaser Critical Thinking Appraisal for critical thinking, and the FourSight framework for problem-solving. Learning agility was measured using a multidimensional behavioral scale. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that higher-order cognitive abilities play a more prominent role in shaping learning agility than basic cognitive abilities. Creativity and problem solving consistently demonstrate significant positive relationships with learning agility across organizational contexts, while reasoning, critical thinking, and perception show context-dependent effects across organizational environments. These findings suggest that learning agility is primarily driven by generative and evaluative cognitive processes rather than by basic cognitive efficiency alone. The study contributes to a deeper understanding of the cognitive architecture of learning agility and provides insights for organizations seeking to develop adaptive talent in rapidly evolving technological environments. Full article

Sustainable water remediation requires catalytic strategies that remove contaminants efficiently while reducing chemical input, byproduct formation, and ecological disturbance. Conventional radical-dominated advanced oxidation processes can rapidly degrade pollutants, but their reliance on high oxidant dosages and freely diffusing reactive oxygen species often causes matrix quenching, non-selective oxidation, low oxidant utilization, and potential ecological risks. Mild interfacial catalysis provides a materials-chemistry strategy to regulate oxidative intensity and direct contaminant transformation under environmentally relevant conditions. In this review, mild catalysts are defined by pathway-selective, interfacially confined, and environmentally compatible oxidation rather than by low dosage alone. Representative non-radical or low-intensity pathways, including singlet oxygen generation, surface-mediated electron transfer, high-valent metal–oxo species, and direct oxidative transfer processes, are discussed in relation to active-site structure, oxidant utilization, matrix tolerance, and byproduct control. We further summarize how coordination environments, defect chemistry, heteroatom configurations, nanoconfinement, and immobilized interfaces regulate reactive-species formation and interfacial charge transfer. Key material platforms, including single-atom catalysts, heteroatom-doped carbons, defect-engineered oxides, catalytic membranes, hydrogels, and floating or immobilized composites, are evaluated from mechanistic and application-oriented perspectives. Finally, catalyst regeneration, cost, microbial community responses, algae–bacteria balance, ecotoxicity, and long-term safety are discussed to guide sustainable aquatic ecosystem restoration. Full article