Search Results (105,774)

Background and Objectives: Trauma is a frequent cause of emergency healthcare requests that requires timely intervention and transport to definitive care, ideally within the first 60 min. On-scene time is considered a modifiable component of the prehospital phase and holds the greatest potential for optimizing prehospital operational time. However, factors contributing to on-scene delays are emerging across different trauma systems, highlighting the need to optimize operational efforts. Data on these factors vary across settings and have rarely been explored in Europe. Romania is an Eastern European country with a distinct demographic distribution and a unique emergency healthcare system. This study aims to evaluate compliance with the golden hour of prehospital trauma care and factors associated with on-scene and total prehospital operational time in Iasi County, Romania. Materials and Methods: A retrospective analysis of data from the electronic dispatch registry from Iasi County was performed for cases dating from January 2025 to February 2026. Data regarding demographics, trauma characteristics, and operational data were retrieved. Generalized linear modeling was used to investigate associations of total prehospital time or on-scene time with demographic and case-specific factors. Multivariable logistic regression was used to evaluate factors associated with total prehospital time exceeding 60 min and on-scene time of 20 min or more. Further sensitivity analyses were performed to account for scene complexity. Age association with operational time was assessed for non-linearity using restricted cubic splines. Results: Of the 8553 included trauma cases, 33.1% presented with on-scene delays, and 59.5% presented with total prehospital time above 60 min. Factors associated with on-scene time were age, rural environment, nature of trauma and severity, and scene-related complexity factors such as number of performed interventions and multiple victim incidents, while for total prehospital time the moment of intervention was also a relevant factor. Age displayed a non-linear association with on-scene time (p < 0.001), with a more pronounced increase after approximately 60 years of age, while for total prehospital time no evidence of non-linearity was observed (time ratio: 1.0024, 95% CI:1.0019–1.0029, p < 0.001). Rural environment was also a factor associated with both operational measurements (time ratio:1.17, 95%CI:1.15–1.2, p < 0.001 for on-scene time and time ratio:1.42, 95% CI:1.38–1.45, p < 0.001 for total prehospital time) and was associated with higher odds of on-scene time ≥ 20 min (OR:1.74, 95% CI:1.58–1.92, p < 0.001) and total prehospital time >60 min (OR:4.91, 95% CI:4.45–5.43, p < 0.001). Conclusions: The large proportion of cases identified with delayed operational times supports the need for further exploration of modifiable factors. The study highlights that, beyond case-specific factors and scene complexity, age and rural environment are relevant to prehospital operational time in Romanian trauma care settings. Further studies are needed to determine the underlying mechanisms of these associations and find targeted strategies to improve operational efficiency. Full article

The interface failure modes between fibers and asphalt mastic exhibit complex and diverse morphologies under the influence of multiple factors, making cluster analysis difficult. To address this issue, this study proposes an improved growth curve mixture model (IGCMM) for the unsupervised clustering of interface failure modes. Through single-fiber pull-out tests on 90 specimens under three temperatures (−10 °C, 25 °C, and 60 °C) and three fiber types (basalt, glass, and polyester), load–displacement curves were obtained. The multivariate power exponential (MPE) distribution was used to characterize the peak and heavy-tailed features of residuals. Due to the high dimensionality caused by the joint analysis of multiple datasets, principal component analysis (PCA) was employed to compress the 200-dimensional curve data into three principal components, with all model parameters estimated in the low-dimensional space. The Expectation–Maximization (EM) algorithm combined with the extended Bayesian Information Criterion (eBIC) determined the optimal eight failure mode clusters. The results exhibit a high posterior assignment confidence: 96.7% of samples had posterior probabilities exceeding 0.999. The eight statistical patterns were successfully mapped to four theoretical failure modes—fiber pull-out, medium-temperature matrix failure, high-temperature matrix failure, and mixed failure—revealing the coupled regulatory mechanism of temperature and fiber type on interface failure. The framework established in this study—“data-driven clustering–physical parameter anchoring–failure mechanism interpretation”—provides new theoretical tools and methodological support for the mesoscale interface failure diagnosis and crack resistance optimization design of fiber-reinforced asphalt pavement materials. Full article

In the current manuscript, the hot deformation behavior and the thermal processing map of 6082-T6 rolled aluminum alloy sheet were studied. A series of compression tests were conducted using the Gleeble-3500 thermal simulation machine under the conditions of 200–350 °C and 0.001–1 s⁻¹. In order to tackle the stress errors caused by friction and plastic deformation temperature rise, the friction correction model and the adiabatic temperature rise interpolation method were used, respectively, to correct the flow stress curve. Based on the corrected data, a strain-compensated Arrhenius constitutive equation was constructed. Through the 4th-order polynomial fitting of material parameters and strain, the measured and predicted stresses were compared, with the average relative error reaching 10.50%. The thermal processing map was drawn based on the dynamic material model, and the material instability region was concentrated in the low-temperature high-strain rate zone. Within the investigated temperature and strain rate range, the optimal processing window was 320–350 °C and 0.001–0.031 s⁻¹. Combined with microscopic characterization by Optical microscope and transmission electron microscope, it was found that deformation at low temperature and high strain rate was mainly dynamic recovery, and dynamic recrystallization could fully occur at high-temperature low-strain rate. The research results can provide theoretical support for the optimization of the hot forging and hot stamping processes of this alloy. Full article

This study proposes the Turtle Optimization Algorithm (TOA), a bio-inspired metaheuristic motivated by the long-distance navigation behavior of marine turtles. TOA integrates four main mechanisms: geomagnetic orientation modeled through sinusoidal modulation, ocean current drift, stamina-aware adaptive reference selection, and an Environmental Coordination Strategy (ECS). These mechanisms are jointly designed to balance exploration and exploitation while reducing premature convergence. The TOA was evaluated on 29 benchmark functions, including classical and CEC2019 benchmarks, and compared with established and recent metaheuristic algorithms. Friedman analysis revealed statistically significant differences among the compared methods (p < 0.05). The TOA achieved first place average ranks of 1.43 and 1.33 in two classical benchmark comparison groups. On CEC2019, TOA obtained average ranks of 1.60, 3.20, and 2.70, corresponding to first, second, and first place, respectively. The practical applicability of the TOA was further evaluated on three constrained engineering design problems––welded beam, speed reducer, and clutch brake design––where competitive solutions were obtained. Overall, the results demonstrate that the TOA provides a competitive and robust optimization framework across diverse benchmark and engineering problems. Full article

With immune checkpoint inhibitors becoming the mainstay of systemic therapy in both metastatic and early-stage settings across many cancer types, the management of immune-related adverse events has emerged as a central priority of modern oncological supportive care. These toxicities can substantially impair the quality of life of cancer patients, including those who achieve long-term survival. Consequently, there is a pressing need to develop reliable predictive biomarkers to better tailor immune checkpoint inhibitor treatment and optimize patient selection. This review summarizes several of the most promising predictive biomarkers currently under investigation, including genetic factors; peripheral blood parameters and their ratios; autoantibodies; cytokines and chemokines; cytomegalovirus serostatus; gut microbiome characteristics; body composition metrics; molecular imaging features; and tumour- and patient-related factors such as cancer type, gender, and physical activity. Because single biomarkers have limited predictive value, multi-omics prediction models and composite immune-cell scores are increasingly demonstrating greater potential. However, none of these candidate biomarkers have yet undergone sufficient validation to support their incorporation into routine clinical practice. Full article

Under varying demand, pump states, load allocation, speed settings, and supply pressure are strongly coupled in parallel pumping systems. This study develops a steady-state energy-efficiency optimization framework for homogeneous and heterogeneous three-branch systems by integrating continuous pump-performance modeling, hard hydraulic-feasibility handling, mixed-variable optimization, and demand-dependent pressure regulation. An improved snake optimizer (ISO) is constructed through targeted strategies for population initialization, global exploration, and local exploitation. Flow–head and flow–power models established from LVR5-5 and CDL3-50 experimental data achieved a total-power MAPE of 2.36% and R² = 0.9972 over 40 implemented steady-state schemes. Deterministic constrained solutions for all effective pump-state combinations were used as references for the pumping-system optimization. Under the same budget of 21,000 function evaluations, ISO, SO, PSO, DE, and GA completed 1500 independent runs; ISO achieved the best overall search performance, with all 300 runs reaching the 0.1% neighborhood of the reference solution and the lowest mean NAUC and final gaps. Using ISO for both pressure-control modes, variable-pressure operation reduced mean power consumption by 10.92% and 7.51% for the homogeneous and heterogeneous systems, respectively, relative to constant-pressure operation under the same minimum service-pressure requirement. The results demonstrate that combining high-quality mixed-variable optimization with demand-dependent pressure regulation can effectively improve the steady-state energy efficiency of parallel pumping systems. Full article

The building sector faces an urgent challenge in balancing carbon neutrality goals with natural resource conservation. This study constructs a three-dimensional dynamic coupling model integrating Life Cycle Assessment, carbon footprint, and emergy analysis to evaluate the sustainability of a hydrogen–solar–geothermal hybrid energy system for an ecological office building in China’s hot summer and cold winter climate zone over a twenty-year horizon. The model incorporates dynamic factors including grid decarbonization, equipment efficiency degradation, and replacement cycles to overcome the systematic bias inherent in static LCA. Results reveal a significant trade-off: the hybrid system achieves a 29.8% reduction in global warming potential with a seven-year carbon payback period, yet non-renewable resource consumption doubles and resource scarcity damage increases by 173%. The carbon payback trajectory exhibits non-monotonic fluctuation, with electrolyzer replacement in year ten generating 360 tonnes of additional emissions that nearly reset the cumulative net value to zero. Multi-objective optimization identifies photovoltaic capacity as the system baseline (170–210 kW) and electrolyzer capacity as the primary regulating variable (35–62 kW), with the TOPSIS-recommended compromise solution of 200 kW photovoltaic, 50 kW electrolyzer, 30 kW fuel cell, and 32 m³ hydrogen storage achieving annual carbon emissions of 280 tonnes and a 33.3% reduction. Carbon pricing exhibits a nonlinear leverage effect with an incentive threshold of 200 RMB per tonne, substantially above China’s current 60–80 RMB per tonne level. This study concludes that while hydrogen–solar–geothermal hybrid systems offer substantial climate benefits, their comprehensive sustainability depends on proactive management of material scarcity costs, precise planning of equipment replacement cycles, and coordinated multi-level policy instruments. The findings provide methodological foundations for transitioning building carbon neutrality assessment from static LCA to dynamic coupling frameworks and from single carbon metrics to integrated carbon-resource-cost evaluations. All quantitative results presented herein are derived from this specific case study under the stated assumptions and parameter values; generalization to other building types or climate zones requires recalibration. Full article

Leaf water content (LWC) is an important indicator of plant water status, and its rapid estimation is essential for water diagnosis and cultivation management in fig production. Although hyperspectral sensing provides an effective means of estimating LWC, spectral redundancy and noise may hinder the extraction of water-sensitive information. Wavelet analysis can extract localized spectral information; however, single-scale wavelet features may not simultaneously preserve fine spectral details and suppress noise, and thus cannot fully characterize the complementary LWC-related responses across different scales. This study therefore developed multiscale double wavelet indices (MSDWIs) and multiscale triple wavelet indices (MSTWIs) to improve the hyperspectral estimation of fig LWC. Savitzky–Golay (SG) filtering and multiplicative scatter correction (MSC) were compared, and random forest (RF) and support vector regression (SVR) were used to evaluate the estimation performance of traditional vegetation indices (VIs), MSDWIs, MSTWIs, and their fused feature sets. The results showed that multiscale wavelet indices generally achieved higher estimation accuracy than traditional VIs, while multi-feature fusion further improved model performance. The SVR model based on the SG-preprocessed VIs+MSDWI+MSTWI feature set achieved the best validation performance (R² = 0.760, RMSE = 0.0232, and MAE = 0.0152). SHAP analysis of the optimal RF and SVR models showed that MSTWI was the dominant feature category, accounting for 57.8% and 57.0% of the total SHAP importance, respectively. These findings demonstrate that integrating multiscale wavelet indices with traditional VIs can enhance the representation of LWC-related spectral information and provide an effective approach for estimating fig LWC. Full article

Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions. Full article

Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N = 50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60 ± 0.18 s and a path success rate of 95.8 ± 1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections. Full article

In the present work, an alternative methodology was developed for the rapid prediction of pressure distributions over wings of low-speed aircraft. A hybrid neural network architecture, named “MARTHA” (Model for Airloads Reconstruction using a Trained Hybrid Architecture), was presented, which is composed of a Multilayer Perceptron and the decoder of an Autoencoder. Three compact representation models—Principal Component Analysis (PCA), Autoencoder (AE), and Variational Autoencoder (VAE)—were systematically evaluated to determine the optimal dimensionality reduction architecture; the AE was selected based on its superior reconstruction accuracy and training stability. The main feature of MARTHA is that it provides predictions of the differential pressure coefficient field in the form of monochrome images, where the pixel intensity directly represents the normalized pressure value. One of the main objectives of developing MARTHA was to create a rapid surrogate model that can approximate vortex lattice method (VLM) simulations in preliminary design and optimization tasks, particularly when thousands of wing configurations need to be evaluated. The key feature of the proposed model is its ability to predict the pressure distribution for trapezoidal wings of various geometries 10¹–10⁴ times faster than numerical models, while maintaining accuracy (R² = 0.9998). The data obtained are presented in a convenient format for their further use in CAE systems of strength analysis. To assess the practical utility of the proposed model, implementation cases were carried out using the finite element software ANSYS 18.2 for three wing configurations not present in the training dataset. The pressure fields predicted by MARTHA were mapped onto the wing meshes, and linear static structural analyses were performed. The obtained Von Mises stress distributions showed good agreement with the corresponding distributions obtained using numerical models. Full article

Robotic multi-seam welding path planning for box-type thin-walled structures faces significant challenges due to multiple constraints, multiple objectives, and its high-dimensional discrete combinatorial nature. To address these issues, a deformation-informed multi-objective path planning method for multi-seam welding (DMOPP) is proposed, comprising a multi-objective formulation and an optimization algorithm. At the modeling level, welding path length and maximum structural deformation are defined as the optimization objectives. Collision-free path planning is used to calculate the path length, while an XGBoost-based surrogate model is developed to establish the mapping between welding path variables and maximum deformation, enabling rapid deformation prediction without computationally expensive finite element simulations. At the optimization level, a modified discrete artificial lemming algorithm (MODALA) is proposed to improve global search capability and convergence stability. Experimental results show that MODALA outperforms the comparison algorithms on benchmark functions and discrete optimization problems, demonstrating its superior optimization performance. The XGBoost surrogate model achieves an R² of 0.8447 and an RMSE of 0.0841 mm, indicating good predictive accuracy. In welding path planning simulations, the proposed method effectively reduces the path length and welding deformation while achieving a favorable trade-off between path efficiency and structural quality. Robotic welding experiments further validate its practical effectiveness. Full article

Ground source heat pump (GSHP) systems contribute to sustainable building decarbonization while confronting two intertwined challenges: long-term ground thermal imbalance in shallow borefields and the requirement for coordinated operation between thermal systems and electrical grid dynamics. Hybrid shallow–deep borefield configurations have been proposed to mitigate thermal imbalance for sustainable geothermal resource exploitation, yet their grid-interactive demand–response potential remains unexplored. Here, we develop a coupled thermal–electrical model for a shallow–deep mixed GSHP (SDBHE) system equipped with water-tank thermal storage, validated against scaled sand-tank experiments (3.5–8.3% error), and assess its year-round performance under time-of-use electricity tariffs for a 200,000 m² residential district in cold-climate conditions. The SDBHE system reduces the required shallow borehole count by 28% and total drilling length by 22% compared with a shallow-only baseline, saving 11% on operational electricity costs over 10 years. Integrating water-tank thermal storage with a 50% load-shifting strategy yields an additional 10.9–11% cost reduction without degrading the system’s coefficient of performance. Under higher load-shifting ratios, the combined capital and operational savings reach 19–29%, with the optimal allocation assigning the incremental high-price-period load preferentially to deep boreholes (COP 6.29 versus 5.25 for shallow). These results demonstrate that integrating shallow and deep geothermal tiers with thermal storage enables both capital-efficient borefield design and economically viable demand-side grid participation. The findings are bound by the cold-climate residential context and the rule-based control scheme; field-scale validation and lifecycle cost analysis are needed to generalize the conclusions. Full article

Building construction sites are dynamic systems in which safety decisions interact with time, cost, productivity, equipment movement, and spatial constraints. This systematic review examines how building-construction-stage safety is represented in optimization-based and optimization-linked decision studies published between 1 January 2016 and 30 June 2026. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-informed workflow used searches of the Web of Science Core Collection and IEEE Xplore, supplemented by Google Scholar and backward-citation checks. Seventy-nine studies met the core inclusion criterion, which required safety to appear as a quantified objective, constraint, evaluation metric, decision criterion, or prediction target. Studies were classified by problem type, safety role, method family, digital integration, and validation evidence. The synthesis identifies a problem-type-dependent formulation pattern: site-layout and scheduling studies mainly optimize safety or exposure objectives; crane/lifting studies distribute safety across constraints, objectives, and decision criteria; risk-decision studies use criteria or metrics; and prediction studies tune models whose targets are safety or risk outcomes. The core corpus is concentrated in site-layout and crane/lifting studies, whereas temporary works, monitoring-to-intervention, and construction-stage emergency response are less often formulated as optimization problems. Strict real-site/field evidence was identified in 7 of 79 studies, with an upper sensitivity bound of 11. Future research should prioritize transparent metrics, benchmarks, field validation, and closed-loop workflows. Full article

With the rapid evolution of Large Language Models (LLMs) in natural language understanding and generation, Relation Extraction (RE) has achieved substantial milestones in low-resource and open-domain scenarios. However, prevailing LLM-based RE methodologies predominantly rely on one-shot prompting or static workflows, which lack autonomous evaluation and iterative optimization mechanisms. Consequently, these approaches are prone to issues such as missing relations, type confusion, and factual hallucinations when navigating complex relational contexts. To address these limitations, this paper proposes DyFIRER (Dynamic Feedback Iterative Relation Extraction Framework), a multi-agent framework characterized by dynamic feedback. By constructing three functionally complementary agents—Extraction, Verification, and Optimization—the framework models the RE task as a closed-loop iterative process consisting of “extraction-verification-feedback-optimization,” thereby enabling dynamic adjustment and continuous refinement of extraction strategies. Experimental results on the DuIE 2.0 open relation extraction extension subset demonstrate that DyFIRER achieves an F1-score of 80.5%, modestly but statistically significantly outperforms GPT-4 (p = 0.014), a result that holds on both the augmented and non-augmented test sets and mainstream static methods (yielding a 10.3% improvement over Qwen-7B). Ablation studies further substantiate the critical role of the dynamic feedback iterative mechanism and the strategy retrieval module in mitigating complex relation omissions and factual hallucinations. The framework requires no additional annotated data or fine-tuning, suggesting potential applicability to low-resource settings, though this was not directly evaluated in the current study. Full article