Search Results (61,193)

Dynamic umbilicals, as critical components connecting offshore platforms to subsea production systems, can effectively decouple platform motions through a lazy-wave configuration, thereby reducing top tension and fatigue damage. To address the engineering challenges of numerous configuration design variables and time-consuming dynamic analyses for dynamic umbilicals, an efficient design optimization framework based on surrogate modeling and multi-objective optimization is proposed. An integrated finite-element model of a lazy-wave dynamic umbilical–offshore platform system is developed in OrcaFlex, incorporating environmental loads, material properties, and geometric parameters. The arrangement parameters of clump weights and buoyancy modules are selected as design variables, and the dynamic responses and parameter sensitivities of multiple configurations are investigated. Using simulation data, surrogate models for predicting tension and curvature are constructed via random forest regression, achieving coefficients of determination (R²) of 0.9948 and 0.9121 on the test set, respectively. Based on the surrogate predictors, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to solve a multi-objective optimization problem that minimizes the maximum tension and curvature, yielding a set of Pareto-optimal solutions. The proposed approach effectively improves the stability and reliability of the dynamic umbilical system under complex sea states. Full article

This study systematically examines the robustness of the Akaike Information Criterion (AIC) in determining the optimal order (p) of an autoregressive (AR) model applied to the RR interval time series of the PhysioNet healthy subject database. The AR approach is widely used to estimate the power spectral density (PSD) of heart rate variability (HRV), and accurate order selection is essential for model stability and reliable spectral estimation. Although the AIC is designed to balance model fit and complexity, it suffers from the problem of arbitrary model selection. This study provides a quantitative robustness analysis of information-criterion-based AR order selection under controlled expansion of the search space. Specifically, we investigated the behavior of the AIC using the PhysioNet database (N = 1257) under conditions where the maximum search order was set to an excessively high value (p = 50), far exceeding the commonly recommended range. Our analysis suggested that the AR model began to capture subtle noise and nonstationary components rather than the intrinsic HRV structure, leading to overfitting and excessive order selection, resulting in false peaks in the PSD and reduced robustness. In conclusion, order decisions based solely on information criteria such as the AIC become unstable when the search range is too large. To ensure robustness, it is recommended to complement the AIC with more stringent criteria such as the Bayesian Information Criterion (BIC) or Final Prediction Error (FPE), in addition to the traditional maximum order restriction. Full article

The development of rapid and sensitive point-of-care nucleic acid tests benefits from robust synthetic recognition elements. Here, a biotin-specific molecularly imprinted polymer (MIP) was synthesized using an optimized protocol and integrated as a biomimetic test zone into two paper-based formats: nucleic acid vertical flow (NAVF) and nucleic acid lateral flow (NALF). Both platforms were evaluated for the detection of double-tagged PCR amplicons from Escherichia coli. NAVF enabled a 3 min visual readout with an LOD of 1.00 × 10⁻² ng mL⁻¹. NALF provided a total assay time of <15 min and achieved a visual LOD of 3.17 × 10⁻² ng mL⁻¹. Overall, the results demonstrate the versatility of biotin-MIPs as stable synthetic receptors for rapid, low-cost paper-based nucleic acid assays, with NAVF prioritizing speed and design flexibility and NALF prioritizing higher analytical sensitivity. Full article

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

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing cross-line counting accuracy. Additionally, edge devices deployed in practical scenarios frequently process multiple video streams simultaneously, resulting in computational resource constraints. To address these challenges, this paper proposes a lightweight, enhanced multi-object pedestrian tracking and counting method tailored for indoor scenarios by optimizing deep learning models. Firstly, modular optimizations are applied to the YOLOv8n model to construct a more lightweight detector, RL_YOLOv8, reducing computational overhead while maintaining accuracy. Secondly, correlated pedestrian auxiliary prediction and pedestrian position change constraints are employed to mitigate ID switching, tracking interruptions, and trajectory jumps in dense scenes. Finally, a buffer zone auxiliary counting strategy is designed to further reduce missed detections of pedestrians crossing lines. Experimental results demonstrate that compared to the original detection-and-tracking-based line-crossing counting method, the improved approach effectively enhances counting accuracy and real-time performance, better meeting the requirements of practical intelligent security and crowd monitoring systems. Full article

Lightweight concrete incorporating expanded polystyrene (EPS) remains an active area of research due to its potential to produce more sustainable resource-efficient construction materials. However, identifying the optimal mix design for EPS-infused concrete typically requires extensive experimental trials, resulting in significant time, cost, and material consumption. To address this challenge, this study proposes an artificial neural network (ANN) predictive model with 5-fold cross-validation to estimate compressive strength performance and to develop mix design recommendations based on actual and predicted results. A total of 55 experimental samples were prepared and grouped into 11 batches, with the EPS volume replacement levels ranging from 0% to 50% at 5% increments. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R²), and scatter index (SI), with graphical representations like predicted vs. actual plots, response plots, and residual plots, and the results were benchmarked against a multiple linear regression (MLR) model. Among the tested configurations, the 4-5-1 ANN model demonstrated the highest predictive accuracy. Furthermore, a Shapley (SHAP) analysis was conducted to interpret the model behavior and determine the relative importance of the input variables. The findings reveal that EPS content had the greatest influence on compressive strength prediction, followed by slump value, then gravel content, and finally concrete density. Full article

To address the topological mismatch between signal space and physical space caused by uneven signal feature distribution in indoor non-line-of-sight and complex topological environments, this paper proposes an indoor positioning algorithm based on Energy-domain Fingerprint Manifold Graph Total Variation (EFM-GTV). To mitigate neighborhood distortion caused by uneven high-dimensional signal feature distribution, a UMAP manifold topology graph construction method based on fuzzy simplicial sets is designed to establish a graph basis consistent with physical space topology. To reduce false matching risks in global search, a physical topology pruning strategy combining Jaccard similarity is proposed, effectively eliminating pseudo-connections. Building upon this foundation, we introduced an optimization model based on graph total variation, reformulating the positioning problem as a graph signal recovery task. This approach effectively overcomes signal fluctuation interference in complex topologies like U-shaped corridors, achieving robust position estimation. Experiments demonstrate that this algorithm effectively leverages manifold structure constraints to correct NLOS errors. On real-world field test datasets, compared to traditional weighted algorithms, the average positioning accuracy improves to 1.4267 m, with maximum positioning error reduced by over 50%, achieving high-precision robust positioning. Full article

Field breeding trial-plot harvesting is one of the key processes in crop breeding, as any mixing between varieties during harvest directly leads to the invalidation of breeding data. Therefore, achieving zero-residue self-cleaning inside the machine during harvesting is essential. Existing studies have largely relied on simulations to optimize cleaning parameters. However, research specifically targeting the synergistic design of the mechanical and pneumatic components of the cleaning device to achieve efficient and thorough self-cleaning in complex real-world conditions remains lacking. To address this issue, this paper presents a novel cleaning system specifically designed for efficient self-cleaning and optimizes its key parameters. Key structural parameters of the straw walker, vibrating sieve, and cleaning fan were analyzed, establishing preliminary ranges for crank speed, sieve-airflow angle, and fan speed. A test bench was developed, and single-factor experiments were conducted to investigate the effects of these parameters on core self-cleaning indicators, including the self-cleaning rate and self-cleaning time. The optimal parameter combination was obtained using the Box–Behnken design (BBD) response surface methodology: a crank speed of 390.80 r/min, a sieve-airflow angle of 29.88°, and a fan speed of 1995 r/min. Bench tests validated that the system achieved excellent cleaning performance while ensuring a self-cleaning rate of 100% and a reduced self-cleaning time of 20 s. The system’s effectiveness was further validated through field experiments using a 4LX1 prototype harvester on three wheat varieties. Results demonstrated zero grain mixing between plots, with self-cleaning times of 9–12 s. Both bench and field test results exceeded the relevant standards, effectively resolving the long-standing issue of grain residue in trial plot harvesting. Through dual validation, this study provides a referential solution for addressing grain residue and establishes a theoretical foundation for the synergistic design of efficient and precision breeding harvest technologies. Full article

For deep-draft cylindrical platforms with a large annular column boot, the influence of the column boot diameter-to-height ratio (d/h) on motion performance remains unclear. This study investigates the effect of d/h on platform hydrodynamics while keeping the main body geometry, displacement, and draft unchanged. A hybrid numerical model validated against tests is adopted: STAR-CCM+ free-decay simulations identify equivalent linear damping, and ANSYS AQWA predicts hydrodynamic coefficients, response amplitude operators, and coupled time-domain responses under a 100-year survival sea state in the western South China Sea. Increasing d/h substantially increases heave added mass and added pitch moment of inertia, leading to longer natural periods and higher damping in heave and pitch. However, its effect on motion responses is non-monotonic and strongly response-dependent. As d/h increases, the responses are initially reduced markedly. The minimum surge and heave responses occur at d/h = 2.39 and 4.67, with reductions of about 34.0% and 87.2%, respectively, while the pitch response is already reduced by about 67.3% at d/h = 7.22. Further increases in d/h may weaken surge and heave mitigation while providing limited additional benefit for pitch. These findings provide qualitative understanding and quantitative guidance for response-oriented column boot design and optimization of similar platforms. 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

This study investigates the thermal performance of a flat-plate phase-change thermal energy storage system, focusing on two structural innovations: a cascaded arrangement of multiple phase-change materials (PCMs) with varying melting points, and the implementation of series/parallel flow configurations. A combined numerical and experimental approach is employed to analyze dynamic charging/discharging behavior. Quantitative results indicate that the cascaded configuration (three PCMs) reduces phase-change completion time by 13% and increases cooling energy storage power from 2.00 kW to 2.43 kW during charging compared to single-PCM systems. Flow configuration significantly impacts thermal response: the parallel layout delivers more stable cooling output, while the series layout achieves faster initial cooling (reaching 6.24 °C within 1200 s, 31% faster than the parallel layout). Experimental results reveal that inlet water temperature is the most critical operating parameter, with each 2 °C increase significantly prolonging charging time. This work offers practical guidance for the design and optimization of efficient cascaded PCM thermal storage systems. Full article

To address the current absence of targeted gradation design for porous asphalt pavements both domestically and internationally, this study employs the Coarse Aggregate Void Filling (CAVF) method to design the gradation of porous asphalt mixtures. Marshall stability tests, rutting tests, and scattering tests were conducted to investigate the relationship between coarse aggregate proportions and the structural stability of the mixture skeleton. An orthogonal experimental design was further utilized to examine the influence of three levels of fine aggregate gradation on the acoustic absorption characteristics of the mixture, and to analyze the effects of aggregate gradation on the primary pore diameter, connected pore diameter, and connected pore length. The results indicate that the coarse aggregate gradation predominantly governs the skeleton strength and overall pavement performance of the mixture, whereas the fine aggregate gradation exhibits significant effects on the interconnected void ratio, pore structure, and sound absorption performance. The optimal roughness range of coarse aggregates in porous asphalt mixtures is determined to be 0.46–0.52. The proportion of 0.6–1.18 mm aggregates has a pronounced influence on the primary pore diameter, connected pore diameter, and connected pore length. By integrating the design considerations for both coarse and fine aggregate gradations, a recommended gradation range for porous asphalt mixtures is proposed that achieves a balance between pavement performance and sound absorption/noise-reduction effectiveness. Full article

Background: Congested tournament schedules impose substantial physiological stress in team sports; however, the integrated endocrine and inflammatory responses to real competitive match load in female handball players remain insufficiently characterized. Objective: This study aimed to characterize the acute biochemical responses, including hormonal, inflammatory, muscle damage, and bone metabolism markers, elicited by competitive tournament load in female handball players and to provide practical insights for optimizing recovery strategies and load management during short-term competitive periods. Methods: In a pre–post study design, venous blood samples were collected from competitive female athletes (n = 8; age 20.83 ± 2.93 years) before the first match and after the fourth consecutive match of an official university qualification tournament. Biochemical analyses included cortisol, insulin, IL-6, creatine kinase (CK), IGF-1, irisin, lactate dehydrogenase (LDH), osteocalcin, and testosterone. Pre-to-post changes were assessed using paired t-tests and effect sizes. Results: Tournament load induced substantial multisystem physiological perturbations. Significant increases were observed in cortisol (p < 0.001), insulin (p = 0.044), IL-6 (p < 0.001), CK (p < 0.001), and osteocalcin (p = 0.005), indicating activation of the hypothalamic–pituitary–adrenal axis, systemic inflammation, muscle membrane disruption, and enhanced bone turnover. Conversely, IGF-1 (p < 0.001) and testosterone (p = 0.004) significantly decreased, reflecting suppression of anabolic signaling and a shift toward a catabolic hormonal environment under cumulative match stress. LDH significantly decreased (p = 0.002), while irisin showed no significant change (p > 0.05). Conclusions: These findings demonstrate that congested tournament schedules provoke an integrated endocrine–inflammatory stress response in female handball players. Importantly, the observed anabolic–catabolic imbalance highlights the need for individualized recovery strategies, optimized load management, and adequate recovery periods to mitigate maladaptation and reduce injury risk during short-term competitive tournaments. 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

Produced-water (PW) management in the Permian Basin faces tightening injection constraints, induced seismicity concerns, and volatile saltwater disposal (SWD) costs. At the same time, chemistry-rich PW contains dissolved constituents (e.g., Li, B, and Sr) that may be valorized if SWD recovery performance and market conditions support favorable techno-economics. Here, we develop an integrated decision-support framework that couples (i) chemistry-informed surrogate models for unit process performance (recovery, effluent quality, and energy/chemical intensity) with (ii) a network-based allocation model that routes PW from sources through pretreatment, optional treatment and mineral-recovery modules (e.g., desalination and direct lithium extraction), and end-use nodes (beneficial reuse, hydraulic fracturing reuse, mineral recovery/valorization, or Class II disposal). This is a screening-level demonstration using publicly available chemistry percentiles and representative pilot-reported performance windows; it is not a site-specific facility design or a bankable TEA for a particular operator. The optimization is posed as a tri-objective problem—to maximize expected net present value, minimize SWD, and minimize an injection-risk indicator R—subject to mass balance, capacity, quality, and regulatory constraints. Uncertainty in commodity prices, recovery fractions, and operating costs is propagated via Monte Carlo scenario sampling, yielding PARETO-efficient portfolios that quantify trade-offs between profitability and risk mitigation. Using the PW chemistry percentiles reported by the Texas Produced Water Consortium for the Delaware and Midland Basins, we derive screening-level break-even lithium concentrations and illustrate how lithium-carbonate-equivalent price and recovery govern the extent to which mineral revenue can offset SWD expenditures. Comparative brine benchmarks (Smackover Formation and Salton Sea geothermal systems) contextualize the Permian’s generally lower-Li PW and highlight transferability of the workflow across brine types. The proposed framework provides a transparent, extensible basis for design matrix planning under evolving injection limits, enabling risk-aware PW management strategies that reduce disposal dependence while improving water resilience. Full article