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Under the combined pressures of dual carbon policy constraints, the integration of intelligent construction technologies, and intensifying market competition, the development of a scientific and robust performance evaluation system has become essential for building construction enterprises seeking to enhance their core competitiveness. Traditional evaluation methods, however, often suffer from incomplete indicator systems and limited capability in addressing high-dimensional and nonlinear problems, rendering them inadequate for the evolving demands of the industry. To address these challenges, this study proposes a performance evaluation model for building construction enterprises based on the least squares support vector machine (LSSVM), optimized by an improved Pied Kingfisher Optimizer (IPKO). Drawing on environment–behavior theory, the model incorporates three environmental and ten behavioral factors. To overcome the limitations of the original PKO algorithm—namely, insufficient exploration capability and weak local search—the exploration phase of PKO is integrated with that of the Marine Predators Algorithm. Empirical results demonstrate that: (1) the proposed IPKO outperforms Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), Ospery Optimization Algorithm (OOA), and the original PKO in most benchmark functions; (2) the ReliefF feature selection algorithm improves the model’s test set accuracy by approximately 2.18%; and (3) the IPKO-LSSVM model achieves 6.53%, 4.16%, and 6.74% higher prediction accuracy than Backpropagation Neural Networks (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), respectively. These findings highlight the model’s effectiveness in addressing small-sample, high-dimensional, and nonlinear problems, offering a scientifically sound and practical tool for performance evaluation in building construction enterprises. Full article

Background: Gastrointestinal (GI) cancers remain a major global health concern, where timely and accurate interpretation of endoscopic findings plays a decisive role in patient outcomes. In recent years, deep learning–based decision support systems have shown considerable potential in assisting GI diagnosis; however, their broader adoption is often limited by patient privacy regulations, uneven data availability, and the fragmented nature of clinical data across institutions. Federated learning (FL) offers a practical solution by enabling collaborative model training while keeping patient data local to each hospital. Methods: Vision Transformers (ViTs) are particularly well suited for endoscopic image analysis due to their ability to capture long-range contextual information. Nevertheless, their high computational and communication costs pose a significant challenge in federated settings, especially when data distributions vary across clients. To address this issue, we propose a privacy-preserving federated framework that combines ViTs with a Clinical-Entropy Guided Quantum Evolutionary Algorithm (CEQEA) for adaptive token pruning. The CEQEA leverages the diagnostic diversity of each client’s local dataset to guide population initialization, evolutionary updates, and mutation strength, allowing the pruning strategy to adapt naturally to different clinical profiles. Results: The proposed framework was evaluated on curated upper- and lower-GI tract subsets of the HyperKVASIR dataset under realistic non-IID federated conditions. On the final test sets, the model achieved a mean micro-averaged accuracy of 92.33% for lower-GI classification and 90.19% for upper-GI classification, while maintaining high specificity across all diagnostic classes. At the same time, the adaptive pruning strategy reduced the number of tokens processed by approximately 40% and decreased the number of required federated communication rounds by 33% compared to ViT-based federated baselines. Conclusions: Overall, these results indicate that entropy-aware, quantum-inspired evolutionary optimization can effectively balance diagnostic performance and efficiency, making transformer-based models more practical for privacy-preserving, multi-institutional gastrointestinal endoscopy. Full article

The transition to net-zero energy systems requires operationally grounded decision-making frameworks that integrate technology performance, infrastructure readiness, and policy constraints at local scale. Food transportation represents a high-emission and operationally critical component of regional energy and supply chain systems, particularly in food-producing regions. This study proposes a preliminary Local Area Energy Planning (LAEP) framework to support operational decision-making for the decarbonisation of food transportation, using Lincolnshire, UK, as a case study. The framework evaluates alternative freight transport technologies—battery electric vehicles (BEVs), hydrogen fuel cell electric vehicles (HFCEVs), battery electric road systems (BERS), and conventional internal combustion engine vehicles—across energy efficiency, CO₂ emissions, infrastructure requirements, and cost implications. Secondary data from national statistics, regional planning documents, and peer-reviewed literature are analysed using comparative quantitative and qualitative assessment methods. Results indicate that BEVs currently offer the most energy-efficient and cost-effective solution for short-haul and last-mile food logistics, achieving overall efficiencies of approximately 77–82% with zero tailpipe emissions. HFCEVs and BERS present potential long-term operational advantages for heavy-duty and long-haul freight, but remain constrained by high infrastructure investment, energy conversion losses, and system-level costs. The findings highlight the importance of phased technology adoption, renewable energy integration, and infrastructure prioritisation to enable sustainable energy operations in freight transport systems. By embedding technology comparison within a place-based planning framework, this study contributes actionable insights for local authorities, logistics operators, and policymakers seeking to support operational decision-making in sustainable energy systems. The proposed LAEP framework is transferable to other food-producing regions aiming to decarbonise freight transportation while maintaining operational efficiency. Full article

Objective: The aim of this case series was to investigate the safety and efficacy of vancomycin–gentamicin embedded PMMA beads (VGPB) in the setting of acute pyogenic soft tissue infections (STIs) of the extremities. Materials and Methods: A retrospective study of 19 cases diagnosed with pyogenic STIs of the lower or upper extremity in two academic institutions was conducted between January 2017 and December 2023. All patients underwent surgical debridement, systemic antibiotics and intrawound deposition of vancomycin and gentamicin embedded cement beads (2 g of vancomycin plus 1 g of gentamicin diluted in 40 g of PMMA). Upon second look (4th–7th day post-index surgery) the cement beads were removed, serum samples from the surgical site of infection and from peripheral blood were obtained and the concentration of eluted vancomycin and gentamicin was measured. Furthermore, the white blood cell count (WBC), C reactive protein serum levels (CRP) and erythrocyte sedimentation rate (ESR) were measured before the surgical debridement and after the end of the bead therapy. All patients were reevaluated after discharge with a mean follow-up of 4.4 years (range, 1 to 7.6). Results: Wound vancomycin and gentamicin levels were significantly higher than those measured in the serum (34.01 ± 4.47 μg/mL versus 11.96 ± 2.79 μg/mL, p < 0.001 and 5.75 ± 1.22 μg/mL versus 0.51 ± 0.14 μg/mL, p < 0.001 respectively). Serum vancomycin and gentamicin concentrations were below the level of toxicity and no adverse events related to antibiotic-embedded bead treatment were documented. Serum WBC, ESR and CRP levels before debridement (13,446 ± 935.7 c/μL, 42.3 ± 18.7 mm/h and 113.9 ± 20.26 mg/L respectively) were significantly higher than those after the end of treatment (7889 ± 1203.6 c/μL, p < 0.001; 30.3 ± 9.14 mm/h, p = 0.017; and 22.7 ± 6.68 mg/L, p < 0.001 respectively). Two cases (10.5%) had a local recurrence of their STIs. Both of them relapsed within 4 months after their treatment and both had Gram-negative pathogens. Conclusions: Vancomycin–gentamicin PMMA bead pouch therapy appears to be a safe and effective adjuvant treatment for pyogenic soft tissue infections, offering high local antibiotic availability without systemic adverse effects. Full article

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km² area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article

Traditionally, repairing coated substrates requires completely removing damaged, wear-resistant layers before recoating. This process leads to high costs, extended downtime, and material waste. Flexible brazing tapes, which are composed of alloy powder and an organic binder, offer an alternative to full coating removal for targeted repairs. Despite this, the process of vacuum brazing these tapes may lead to the formation of defects, including pores caused by trapped gases or residual binder, which compromise coating durability and corrosion resistance. This study focuses on the utilization of laser remelting as a method for post-processing nickel- and iron-based mixed alloy brazing tapes, with the aim of improving the integrity of the coating. Surface quality was assessed via microscopy and microhardness testing by systematically varying laser power, scanning speed, and hatch distance. Among the parameters studied, the most suitable laser parameter combination was found to be 350 W laser power, 250 mm/s scanning speed, and a hatch distance of 0.02 mm. These parameters yielded crack- and pore-free coatings with a remelting depth of 160.3 ± 17.2 µm and a microhardness of 701 ± 23 HV1, which is an 85% increase over as-brazed samples. Wear testing revealed a reduced coefficient of friction, and electrochemical corrosion tests showed lower corrosion current density and enhanced repassivation behavior in remelted coatings. These improvements demonstrate that laser remelting significantly enhances the microstructure, hardness, wear resistance, and corrosion performance of brazed coatings, providing an effective method for localized repair while minimizing material consumption and processing duration. Full article

Heavy metals in lake sediments represent typical persistent contaminants characterized by recalcitrance, bioaccumulation potential, and delayed toxic effects, thereby exerting sustained adverse impacts on lacustrine ecosystem stability and human health. Liuye Lake is a representative small-to-medium urban lake impacted by ambient domestic sewage discharge and agricultural non-point source pollution, with documented nitrogen and phosphorus enrichment. However, the contamination profile of heavy metals in its surface sediments has not been systematically investigated to date. In this work, surface sediment samples were collected from Liuye Lake, and nine heavy metal elements (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn) were determined. An integrated approach incorporating Monte Carlo simulation, the geo-accumulation index (I_geo), and the enrichment factor (EF) method was employed to assess the ecological risk and human health risk imposed by these metals. The results revealed the following: (1) Average concentrations of eight heavy metals exceeded the background values of the Dongting Lake water system, with the exception of As, and Hg displayed potential localized anomalies. (2) Surface sediments were collectively categorized as slightly contaminated, with Hg identified as the primary pollutant, followed by minor contamination of Mn, Cr, and Ni; Monte Carlo simulation further suggested a probable risk that Mn contamination could progress to moderate levels. (3) All heavy metals posed low potential ecological risk, with an overall potential ecological risk index (RI) of 62.71, where Cd, Hg, and As were the dominant contributors. (4) Both non-carcinogenic and carcinogenic risks were generally within acceptable limits, whereas children exhibited higher non-carcinogenic susceptibility relative to adults; As and Mn were the leading contributors to non-carcinogenic risk, while Cr and As dominated carcinogenic risk. This study offers a scientific foundation for the prevention and control of heavy metal pollution and the ecological management of urban lakes. Full article

Long-span bridges are critical components of transportation infrastructure because they promote efficient connectivity between agricultural production centers, tourist destinations, and major urban areas. To construct these structures, the balanced cantilever method is widely used; however, the lack of rigid longitudinal connections between the pylons and the deck often allows for large displacement demands during seismic activities. Fluid viscous dampers (FVDs) are employed to mitigate these effects. This study investigates the impact of using FVDs at the abutments of the Hisgaura cable-stayed bridge located on the Curos-Malaga corridor in the department of Santander, Colombia. A nonlinear response history analysis was conducted using seismic records from crustal sources, scaled to the local seismic hazard, and performed in SAP2000©. The results indicate that the presence of FVDs does not adversely affect the axial forces in the stay cables under the Extreme Event Limit State I. Furthermore, demand reductions were observed at the pylon closest to the abutment (Pylon 4). Under critical seismic records, reductions of up to 81.95% in relative deck-pylon displacement, 62.17% in bending moment, and 58.46% in base shear were achieved. These findings demonstrate an improved global structural behavior under severe seismic loading conditions. Full article

Generating high-precision normalized digital surface models (nDSMs) from a single remote sensing image remains a challenging and ill-posed problem due to the absence of reliable geometric constraints. In this work, we show that monocular depth provides structurally stable cues of local geometry but lacks the global scale and vertical reference required for absolute height recovery. This intrinsic mismatch limits direct depth-to-height regression, particularly when transferring across heterogeneous terrains, land-cover compositions, and imaging conditions. Building on this idea, we propose the Relative Depth–Absolute Height Prediction Network (RDAH-Net), a framework that exploits relative depth as a geometry-aware prior while learning terrain-dependent height mappings from image appearance to absolute height. As the backbone, we employ a lightweight MobileNetV2 enhanced with a Convolutional Block Attention Module (CBAM), and further incorporate a cross-modal bidirectional attention fusion scheme with positional encoding to achieve a deep and effective fusion of image appearance and depth prior cues. Finally, a PixelShuffle-based upsampling strategy is used to sharpen prediction details and mitigate typical upsampling artifacts. Extensive experiments across diverse regions demonstrate that RDAH-Net achieves robust and generalizable height estimation, providing a practical alternative for large-scale mapping and rapid update scenarios. Full article

This paper presents an improved hybrid Eulerian–Lagrangian framework, which has been augmented with an adaptive mesh refinement technique, for simulating passive scalar transport on deforming interfaces. We capture interface deformation using an Eulerian level-set method while solving the interfacial transport equation with a single-layer smoothed particle hydrodynamics method. As a result, the proposed hybrid approach combines the high efficiency of the Eulerian formulation with the strict mass conservation property of smoothed particle hydrodynamics method. To further accelerate the simulations, we employ adaptive mesh refinement for the Eulerian solver and restrict particles to the finest refinement level. To mitigate Lagrangian particle clustering, we adopt a remeshing procedure that generates particle distributions adapted to the local interface geometry on the finest mesh. This remeshing also enables accurate, mass-conservative reconstruction of the interfacial concentration field. Moreover, by incorporating an adaptive remeshing strategy, we tune the remeshing frequency to balance computational cost and accuracy. The accuracy and robustness of the proposed method are demonstrated through a suite of benchmark test cases. Additionally, we evaluate the effectiveness of adaptive mesh refinement through benchmark test cases, verifying its compatibility with the interfacial smoothed particle hydrodynamics method and quantifying the resulting speedup. Full article

Autonomous ground vehicles operating in structured and semi-structured environments—such as urban roads, parking lots, and logistics warehouses—require fast, reliable, and collision-free path planning on occupancy grid maps. Existing metaheuristic planners often suffer from premature convergence, insufficient population diversity, and poor feasibility maintenance, limiting their deployment in safety-critical vehicular navigation. This paper proposes a multi-strategy enhanced Crested Porcupine Optimizer (MSCPO) that systematically addresses these limitations through four coordinated enhancements: chaos-opposition initialization with feasibility repair to ensure high-quality and diverse initial routes; a diversity-coupled adaptive mechanism for dynamic strategy scheduling throughout the search; elite-guided differential Lévy perturbation to escape local optima and accelerate convergence; and a two-stage safety-aware objective with elite local refinement to sharpen final solution precision. Experiments on four representative grid maps with varying obstacle densities, conducted over 30 independent runs per algorithm, demonstrate that MSCPO consistently outperforms state-of-the-art metaheuristic planners and deterministic baselines in path length, smoothness, and convergence speed. Statistical analysis via Wilcoxon rank-sum and Friedman tests confirms the significance of the improvements. An ablation study quantifies the individual contribution of each enhancement module, confirming the practical effectiveness of MSCPO for autonomous vehicle navigation tasks. Full article

Technological development and thermal management are closely related, as chip-based units demand efficient cooling. Microchannel cooling is a key solution. This study, for the first time, experimentally and numerically investigates fin distributions with decreasing numbers, with/without staggered configurations, and the effect of dimples on single-phase flow in micro-pin-finned heat sinks. The database covers mass fluxes from 500 to 750 kg m⁻² s⁻¹ (in 50 increments) and four heat sinks (coded as MH-0, MH-1, MH-2, MH-3), with Reynolds numbers ranging from 234 to 327. Complementary numerical simulations were also employed to visualize flow structures and local Nusselt distributions to elucidate the experimental observations. It was concluded that low-velocity eddies occur in the dimples and between the successive pin-fins. The best thermal performance was obtained for MH-3, while the lowest pressure drop was measured for MH-1. Therefore, if heat transfer is the primary aim, MH-3 is preferred. MH-3 increases average Nusselt Number (Nu_avg) by between 11.45% and 14.38% compared to MH-0. However, the pumping power results underline the importance of MH-1. Compared to MH-0, the pumping power decreases by up to 18.4% for MH-1, 16.6% for MH-2, and 13.8% for MH-3. Full article

RGBT tracking holds irreplaceable value in unmanned aerial vehicle (UAV) ground observation missions, effectively supporting scenarios such as nighttime monitoring and low-altitude reconnaissance. However, existing frameworks based on CNNs or Transformers face inherent trade-offs between interaction capabilities and computational efficiency. Furthermore, current methods perform poorly in challenging scenarios involving target scale variations and rapid motion from UAV perspectives. To address these issues, this paper proposes a novel multimodal interaction and fusion Mamba network (MIFMNet), which achieves fundamental innovations relative to existing RGB-T fusion trackers and recent Mamba-based tracking methods. Different from existing RGB-T trackers that rely on CNN’s local convolution or Transformer’s quadratic-complexity self-attention for cross-modal fusion, MIFMNet departs from these architectures and designs modality-adaptive interaction mechanisms based on Mamba, fully leveraging the complementary information while resolving the efficiency-accuracy trade-off. Specifically, this paper designs the scale differential enhanced Mamba (SDEM), which expands the receptive field through multiscale parallel convolutions while amplifying complementary information via differential strategies to enhance feature responses to scale-varying objects. Furthermore, we propose flow-guided multilayer interaction Mamba (FMIM), which integrates inter-frame motion information into scanning prediction. This enables the network to adaptively adjust interaction priorities between shallow texture and high-level semantic features based on motion intensity, mitigating early information forgetting and enhancing robustness in dynamic scenes. Extensive experiments on four major benchmarks demonstrate that MIFMNet achieves state-of-the-art performance on precision and success rate, particularly excelling in UAV scenarios involving occlusion, scale variations, and rapid motion. Simultaneously, it achieves an inference speed of 35.3 FPS, enabling efficient deployment on resource-constrained platforms, thereby providing robust support for UAV applications of RGBT tracking. Full article

An all-silica-based sensor comprising a section of capillary fiber spliced between two singlemode fibers (SMFs) is proposed for the simultaneous measurement of strain and temperature. By intentionally introducing a controlled transversal offset at one of the fusion splice points, core and cladding modes are simultaneously excited in the capillary, enabling the coexistence of two distinct guiding mechanisms within the sensor. The resulting spectral response exhibits two superimposed modulations associated with antiresonance (AR) guidance and a Mach–Zehnder interferometer (MZI). A comprehensive numerical model is developed to describe the interaction between the two mechanisms as a function of the offset. The model is experimentally validated through characterization of the spectral response for increasing offsets, confirming the coexistence and evolution of the AR and MZI components through free spectral range and visibility analysis. The two interference components allow for independent tracking of their wavelength shifts, enabling simultaneous strain and temperature measurements with estimated resolutions of 11.9 με and 0.45 °C, respectively. Owing to the single-element, one-step fabrication process, and the entirely silica-based configuration, the proposed sensor offers a compact and cost-effective solution for localized multiparameter monitoring. Full article

Background/Objectives: Artificial intelligence (AI)-assisted endoscopy has shown high sensitivity for early gastric cancer detection; however, false-positive diagnoses remain a clinical challenge. This study aimed to evaluate the real-world diagnostic performance of a commercially available AI system and to identify factors associated with false-positive diagnoses, focusing on repeated AI evaluations and confidence stratification. Methods: This single-center retrospective study included 47 patients with 89 localized gastric lesions evaluated between March 2024 and March 2025. Endoscopic examinations were performed under white-light, non-magnified observation with repeated AI assessments of each lesion. The rates of “Consider biopsy” (B) judgments were calculated. Lesions with a B judgment rate of ≥50% were defined as AI-positive and classified into four AI confidence categories. Diagnostic performance was assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Factors associated with false-positive diagnoses were analyzed using penalized logistic regression. Results: The AI system demonstrated a sensitivity of 97.6% and an NPV of 95.7%, with a specificity of 45.8%. Pathology-positive rates decreased stepwise across the four AI confidence categories (p < 0.001). Among AI-positive lesions, low regional reproducibility, lesion size ≥ 30 mm, scar, and erosion were independently associated with false-positive diagnoses. In analyses restricted to non-neoplastic lesions, lesion size ≥ 30 mm remained significantly associated with false-positive diagnosis. Conclusions: In real-world clinical practice, a commercially available AI system provides high sensitivity for early gastric cancer detection. Incorporating confidence stratification and regional reproducibility into clinical decision-making may enhance the effective use of AI-assisted endoscopic diagnosis beyond binary interpretations. Full article