Search Results (10,812)

The inspection of minuscule insulator defects from high-resolution (HR) UAV imagery presents a significant algorithmic challenge. The severe scale mismatch between HR images and low-resolution model inputs often leads to feature distortion for sparsely distributed targets. To address these issues, this paper proposes an integrated data–model collaborative framework. At the data level, an offline label-guided optimal tiling (LGOT) strategy is introduced to alleviate scale mismatch by curating information-dense training tiles. At the model level, we design the semi-decoupled prior-driven detection head (SDPD-Head), which leverages evolutionary priors to stabilize the learning of microscopic spatial features. During inference, an online inference-time adaptive tiling (ITAT) strategy is used to match the spatial scale distribution between training and inference and to reduce feature loss caused by direct downscaling. Experiments on a real-world inspection dataset show that the proposed framework achieves an mAP@50 of 92.9% with 2.17 M parameters and 4.7 GFLOPs. Full article

High-temperature carburization (HTC, 950–1050 °C) has emerged as a pivotal low-carbon, energy-efficient manufacturing technology for gear steels, accelerating carbon diffusion for reducing processing cycles by over 60% while achieving significant energy savings and emission reductions. However, the inherent contradiction between HTC efficiency and microstructural stability, specifically austenite grain coarsening, severely degrades mechanical properties (e.g., strength, toughness, fatigue resistance) and limits widespread application. This review systematically synthesizes recent advances in austenite grain size regulation during HTC of gear steels, focusing on the core scientific framework of “grain coarsening mechanism—regulation strategy—performance enhancement”. It elaborates on thermodynamic and kinetic mechanisms of austenite grain growth, ripening behavior of microalloying precipitates (Nb(C,N), Ti(C,N), AlN, etc.), and their synergistic grain-refining effects. Comprehensive coverage of regulatory strategies (microalloying design, pretreatment technologies, process optimization, and integrated regulation) and characterization techniques is provided, along with a quantitative correlation between grain size, microstructure, and surface performance (wear resistance, corrosion resistance, and fatigue life). Numerical simulation and predictive models (empirical, theoretical, multiphysics coupling, machine learning-based) are critically analyzed, and current challenges (temperature-grain stability trade-off, multifactor synergy understanding, industrial scalability) and future research directions (advanced microalloying systems, intelligent process optimization, cross-scale modeling, green technology integration) are proposed. This review aims to provide theoretical guidance and technical support for optimizing the HTC performance of gear steels, catering to the demands of high-power-density transmission systems in automotive, aerospace, and heavy machinery industries. Full article

Limosilactobacillus fermentum KUB-D18 is a probiotic strain with significant potential in food fermentation and health promotion, yet the systems-level mechanisms underlying its physiological robustness remain elusive. To elucidate the metabolic remodeling strategies operating across growth phases, we developed an integrated framework combining genome-scale metabolic modeling (GSMM) with transcriptomics. A high-quality metabolic model for L. fermentum KUB-D18, designated iYH640 and comprising 640 genes, 1530 metabolites, and 1922 reactions, was constructed and validated against experimental growth data. Specifically, in vitro assays measuring biomass and glucose concentrations showed a maximum specific growth rate of 0.2696 h⁻¹ and a glucose uptake rate of 11.75 mmol gDCW⁻¹ h⁻¹, providing physiological constraints for the model. Using transcriptome-regulated flux balance analysis (TR-FBA), gene expression profiles from the logarithmic phase (L-phase) and stationary phase (S-phase) were integrated to quantify growth phase-specific metabolic flux distributions. These simulations revealed a distinct transcription-driven metabolic shift, in which the organism moves from a proliferation-oriented metabolic state with active central carbon metabolism and macromolecule synthesis to a maintenance-oriented state. This S-phase is characterized by reduced flux through anabolic pathways together with the selective preservation of redox balance and nucleotide homeostasis. Collectively, these results provide a quantitative explanation of how L. fermentum KUB-D18 balances growth and maintenance, offering a mechanistic basis for improving its stability and functional performance in industrial probiotic applications. Full article

In this study, a three-dimensional simulation of a walking-beam reheating furnace was developed to improve the steel slab reheating process and reduce surface oxidation kinetics using computational fluid dynamics (CFD). Combustion, heat transfer, fluid dynamics, and chemical reaction models were integrated into the numerical framework of this study. In addition, dynamic mesh remeshing was coupled through user-defined functions (UDFs), enabling the simultaneous simulation of slab movement and evolution of the involved transport phenomena. Turbulence was modeled with the realizable k-ε formulation, combustion with the Eddy Dissipation model, and radiation with the P-1 model coupled with WSGGM to include CO₂ and H₂O gas radiation. Scale formation was modeled using customized functions based on Arrhenius-type kinetics and Wagner’s oxidation model, evaluating its growth as a function of time, temperature, and furnace atmosphere. The predicted thermal evolution inside the furnace was validated using industrial data, yielding an average deviation of 5%. Furthermore, the proposed operating conditions led to an average slab temperature of 1289.77 °C at the exit of the homogenization zone, which was 16 °C higher than that under the current operation but still within the target range (1250 ± 50 °C). The reduction in combustion air decreased energy losses and improved product quality, lowering the molar oxygen content in the furnace atmosphere from 4.9 × 10² mol to 6.7 × 10¹ mol. Additionally, annual savings of 4,793,472 kg of natural gas and 13,884 tons of steel were estimated owing to reduced oxidation losses. The proposed air–fuel adjustment led to estimated annual energy savings (equivalent to 4,793,472 kg of natural gas) and a reduction in material loss due to oxidation from 4.5% to 3.75% (an absolute reduction of 0.75 percentage points; relative reduction ≈ 16.7%), which has a significant industrial impact on metal conservation and descaling cost reduction. Although there are CFD studies on plate overheating and scale growth separately, this work presents three main contributions: (1) the integration, within a single numerical framework, of combustion, radiation, species transport, oxidation kinetics, and actual plate movement using a dynamic mesh; (2) validation against continuous industrial records (16 thermocouples) and quantification of operational benefits such as fuel savings and reduced material loss; and (3) a comparative analysis between actual and optimized conditions, which standardize the air–methane ratio. Full article

To address the challenge of coarse aggregates hindering steel fiber dispersion and reducing toughening efficiency in ultra-high-performance concrete containing coarse aggregate (UHPC-CA), this study proposes a hybrid fiber design method based on reverse adaptation to the aggregate structure: a paradigm where fiber proportions are inversely designed to match the quantified void size distribution within the coarse aggregate skeleton. Industrial X-ray computed tomography (X-CT) was employed to capture the internal structure of UHPC-CA. Digital image processing techniques were used to quantitatively characterize the size distribution within the coarse aggregate skeleton gap. Based on this distribution, the blending proportions of multi-scale (3–16 mm) copper-plated steel fibers were systematically determined. Three fiber configurations were compared: mono-sized 13 mm fibers (Type A), an empirical model based on aggregate size (Type B), and a quantitatively designed blend based on skeleton gap distribution (Type C). At the same fiber volume fraction, the mechanical property test results show that the C type achieves approximately 18.6% higher flexural strength and 29.1% higher splitting tensile strength compared to the A type, while showing 5.3% and 6.7% improvements over the B type, and the compressive strength also increased slightly (about 3.0%). The microanalysis further confirms that the fiber distribution in the C-type design was more uniform, and the bridging effect and crack resistance were more sufficient. The proposed gap-adaptive fiber design paradigm offers an effective approach for optimizing reinforcement distribution in composites, providing theoretical and practical value for high-performance UHPC-CA applications. Full article

The rapid increase in point density and acquisition rate of UAV laser scanning (ULS) systems has shifted the primary bottleneck of LiDAR workflows from data acquisition to post-processing, particularly during direct georeferencing of massive LiDAR measurements. This study presents a systematic evaluation of parallel computing strategies for accelerating ULS direct georeferencing while preserving geodetic accuracy. Two georeferencing models are investigated: (1) a rigorous model that strictly follows the full geodetic transformation chain from sensor owned coordinates system (SOCS) to projected map coordinates, and (2) an approximate model that incorporates meridian convergence angle compensation and preprocessing of platform trajectories to reduce per-point computational complexity. For each model, a shared-memory multicore CPU implementation based on OpenMP and a heterogeneous GPU implementation based on CUDA are designed. Experiments were conducted on seven real-world ULS datasets, ranging from 2.9 × 10⁷ to 7.0 × 10⁸ points and covering diverse terrain types. Accuracy analysis shows that, in typical urban, plain, and industrial scenarios, the approximate model achieves millimeter-level mean errors and centimeter-level RMSEs relative to the rigorous model, satisfying the requirements of most engineering surveying applications. Performance evaluation demonstrates that parallelization yields substantial speedups: OpenMP-based method achieves 7–9 times acceleration, while GPU computing attains up to 24.6 times acceleration for the rigorous model and up to 16.7 times for the approximate model. The results highlight the complementary strengths of the two models and provide practical guidance for selecting accuracy-efficiency trade-offs in large-scale ULS production workflows. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

Background: Societal stigma toward eating disorders and obesity remains pervasive and is associated with psychological distress, maladaptive eating behaviors, reduced help-seeking, and barriers to care. Despite its documented impact, comprehensive and psychometrically robust instruments to assess stigma—particularly in Spanish-speaking populations—are scarce. This study aimed to develop and validate a multidimensional measure of societal stigma toward eating disorders and obesity in Spain, grounded in contemporary stigma frameworks. Methods: A cross-sectional observational study was conducted in a large community sample recruited online (N = 2121). An initial pool of stigma-related items was developed based on theoretical and empirical literature and refined through expert content validation. Psychometric evaluation included item screening, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), bifactor modeling, and reliability assessment. The sample was randomly split for EFA (n = 988) and CFA (n = 658). Associations between stigma scores and sociodemographic and experiential variables were examined. Results: The final 36-item instrument demonstrated excellent psychometric properties. Bifactor analyses supported an essentially unidimensional structure dominated by a strong general stigma factor, with secondary content-specific dimensions (e.g., legitimacy, personal responsibility, visibility, and treatment beliefs). The theory-driven bifactor model showed excellent fit (CFI = 0.991; TLI = 0.990; RMSEA = 0.024). The general factor exhibited high reliability (ωₕ = 0.87). Higher stigma was observed among men, older participants, and individuals without personal or familial experience of eating disorders or obesity. Conclusions: This study provides a reliable and theoretically grounded instrument for assessing societal stigma toward eating disorders and obesity in Spain. The scale enables systematic research on stigma and offers a valuable tool for public health surveillance, intervention development, and evaluation of anti-stigma initiatives aimed at promoting compassionate and equitable care. Full article

A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype electronic glove that translates Mexican Sign Language (LSM) signs into Spanish text using machine learning. Eight participants (four deaf and four hearing with LSM proficiency) completed four sessions involving 12 signs; three sessions (S1–S3) were used for model development and one session (T) was held out for evaluation. Models were trained on S1–S3 and tested on T using a session-level split without window mixing across sessions; therefore, results represent a speaker-dependent, inter-session pilot assessment rather than a speaker-independent generalization test. The glove integrates flex sensors and an inertial measurement unit IMU MPU6050 connected to an ESP32-C3 SuperMini microcontroller. These components were selected due to their low cost, availability, and ease of integration, making them suitable for the development of accessible wearable assistive technologies. Under this protocol, the system achieved a window-level overall test accuracy of 97.0% (95% CI computed at the window level: 96.00–97.00), with higher performance for the dynamic subset (98.0%) than for the static subset (95.0%), and an algorithmic decision delay of 1.2 s. Usability and acceptance were evaluated using the System Usability Scale (SUS) and a Technology Acceptance Model (TAM)-based questionnaire. The mean SUS score was 50.6 ± 1.8 (marginal usability), while participants reported positive perceptions across TAM constructs. Overall, findings demonstrate technical feasibility under controlled inter-session conditions and provide a foundation for iterative user-centered refinement, followed by strict speaker-independent validation and classroom deployment studies in future work. Full article

Traffic accidents are among the leading causes of injury worldwide, highlighting the urgent need to better understand the factors that contribute to accident occurrence and severity in order to improve road safety and reduce injuries and fatalities. This study analyzes the U.S. Accidents dataset, comprising data collected from 2016 to 2023, to identify the key determinants of accident severity and to evaluate feature-selection techniques for predictive modeling. To this end, several feature-selection methods are examined, including L1-regularized logistic regression, minimum redundancy maximum relevance (mRMR), conditional mutual information maximization (CMIM), ReliefF, and tree-based importance measures; these are compared with the Weighted Conditional Mutual Information (WCFR). The selected feature subsets are then evaluated using three machine learning models: logistic regression, random forest, and XGBoost. Experimental results show that WCFR consistently outperforms the competing methods, achieving higher validation accuracy (up to approximately 0.84) and Macro-F1 scores (up to approximately 0.55), while using fewer features and maintaining model interpretability. These results indicate that WCFR is particularly effective for accident severity modeling and highlight its potential as a robust feature selection strategy for large-scale transportation safety analytics and future severity prediction studies. Full article

This study introduces a novel risk detection and control system to enhance social stability in major construction projects. Utilizing a heterogeneous cellular automaton model, the system simulates complex interactions among project stakeholders to identify and mitigate Social Stability Risks (SSR). Integrating the Ignorant–Latent–Malcontent–Recovered (ILMR) framework, the model applies principles from epidemiology to predict and manage the spread of social stability risks. Simulation results demonstrate the model’s effectiveness in reducing the number of malcontent and ignorant individuals while increasing the recovered category, stabilizing the social environment around large projects. This approach helps manage immediate risks and improves long-term social acceptance and sustainability of engineering projects. By bridging risk management with advanced simulation techniques, this research contributes to major construction projects by providing a robust framework for managing complex social dynamics, thereby enhancing project success and stakeholder satisfaction. The findings underscore the potential of integrating innovative technological tools with traditional risk management strategies to address the socio-technical challenges of large-scale engineering projects. Full article

This scoping study assesses practical aspects of electrokinetic (EK) biocementation of clay soil underneath a railway embankment ahead of upscaled testing to include a reduced-scale field pilot as an intermediate step towards subsequent pilot embankment treatment. It considers suitable field setups and performs Life Cycle Analysis (LCA) of biocementation by biostimulation of carbonic anhydrase (CA)-producing bacteria compared to hydrated lime slurry, if both treatments were implemented electrokinetically. LCA analysis was conducted using SimaPro software (version 9.6.0.1) with Ecoinvent database and bench-scale laboratory testing data. Electroosmotic flow modelling was performed to instruct on suitable setups and for estimates of power consumption towards the field application of 30 m of railway embankment and foundation soil. LCA indicated a considerable reduction in global warming if CA biocementation is used (0.00823 kg CO₂ eq for biocement vs. 0.022136 kg CO₂ eq for lime), and resource usage (7.06 × 10⁻⁵ kg Cu eq compared to 8.47 × 10⁻⁵ kg Cu eq for lime). Biocementation was more water-consuming compared to lime, as it involved multiple chemical solutions. Terrestrial acidification, aquatic eutrophication, and ecotoxicity were slightly higher for biocement, possibly due to system boundaries and processes assumed for material production. Further sustainability improvements would be possible if waste materials (e.g., captured industrial CO₂) could be used. Field trials will be essential for validation, system optimisation, and advanced model calibration. Full article

When high-altitude aircraft operate in a low-density environment, the flow instability within their internal ducts poses a severe challenge to aerodynamic design and operational safety. Especially in the intake system of the tandem dual-fan configuration, the asymmetric flow caused by rotating machinery coupled with the low-density effect exacerbates flow distortion, momentum dissipation, and efficiency loss and may even trigger system instability risks such as rotational stall or surge. To address these challenges, this paper establishes a high-fidelity dynamic model of the internal flow field of the aircraft, based on the Reynolds-averaged Navier–Stokes equations and the SST k-ω turbulence model, combined with dynamic mesh technology. It reveals the unstable mechanism caused by angular momentum accumulation under co-rotation conditions and its intrinsic correlation with the degradation of aerodynamic performance. Inspired by the concept of micro-flow regulation, an active flow control strategy integrating discrete auxiliary injection and local geometric shape optimization is proposed. Numerical results show that by reasonably arranging auxiliary injection holes in the intake duct and optimizing local geometric fillets, the uniformity of intake flow can be effectively improved, and the formation of large-scale vortex structures can be suppressed. This method increases the system’s flow capacity by approximately 47.4%, significantly improves the total pressure recovery coefficient and fan aerodynamic efficiency, and reduces the amplitude of low-frequency pressure fluctuations by approximately 23.1%. Research shows that in high-altitude low-Reynolds-number conditions, micro-flow regulation combined with geometric reconstruction can effectively suppress flow instability induced by rotating machinery. This achievement provides a theoretical basis and feasible engineering path for aerodynamic stability design and optimization of key components, such as the aircraft intake and exhaust systems and thermal management systems, and is of significant value for improving the overall performance and reliability of high-altitude long-endurance aircraft. Full article

Global biomass estimates suggest that plants hold 81% of the Earth’s 550 GT C, yet carbon stocks in non-forested and dryland ecosystems remain the largest source of uncertainty in the global carbon budget. Small uncrewed aerial vehicle (UAV) platforms are increasingly used to estimate aboveground biomass at landscape scales. We conducted a systematic review of the remote sensing literature to determine: (1) which plant traits and related remote sensing indicators were used to develop aboveground biomass models; (2) statistical approaches; and (3) the key sources of uncertainty among these methods and models. We found that tundra, dryland, and savanna ecosystems were most underrepresented in the remote sensing literature. Within our systematic review process, we found no consistent UAV sensor combination, platform, or workflow that improved the accuracy and reduced the uncertainty in aboveground biomass estimates. Machine learning and regression models resulted in similar uncertainty levels in shrubland and savanna ecosystems. Expanding allometric equation development in shrublands and savanna ecosystems could reduce uncertainty and improve aboveground biomass estimation. Improved reporting on UAV logistics and workflows would further strengthen comparability. Standardized and validated UAV methods for estimating biomass, carbon stocks, and fuel loads will be essential for producing consistent datasets and enabling robust future meta-analyses. Full article

Background/Objectives: Cardiometabolic multimorbidity (CMM) significantly reduces healthy life expectancy in older adults. The specific role of adiposity indices derived from the triglyceride-glucose (TyG) index, body mass index (BMI), and waist-to-height ratio (WHtR) in predicting incident CMM has not been fully elucidated in longitudinal settings. We investigated these associations and the mediating role of the atherogenic index of plasma (AIP). Methods: We analyzed 304,586 community-dwelling adults aged ≥65 years from the prospective Guangzhou Older Longitudinal Dynamic Health (GOLD-Health) cohort (2018–2019), who were free of CMM at baseline. Multivariable Cox proportional hazards models evaluated the risk of incident CMM (coexistence of ≥2 cardiometabolic diseases) across quartiles of six TyG-derived indices. Mediation analysis quantified the contribution of atherogenic dyslipidemia via AIP. Results: Following a median observation time of 4.3 years, the study recorded 7816 participants who developed CMM. All six indices showed significant positive associations with CMM risk. TyG-WHtR demonstrated the strongest association (Hazard Ratio [HR] comparing highest vs. lowest quartile = 2.150; 95% Confidence Interval [CI] 1.998–2.314), closely followed by TyG-BMI (HR = 2.146). AIP significantly mediated the associations, explaining 7.5–33.0% of the effect, with the highest proportion observed for TyG using the Chinese visceral adiposity index (CVAI). Conclusions: TyG-derived adiposity indices, particularly TyG-WHtR and TyG-BMI, are robust independent risk markers for incident CMM in older adults. The substantial mediating role of AIP suggests that targeting atherogenic dyslipidemia may be a key strategy to interrupt the progression from insulin resistance to multimorbidity. These accessible metrics hold promise for large-scale risk stratification and early intervention in primary care settings. Full article