Search Results (812)

This study investigates the impact of non-uniform carbon sphere diameter distributions on the structural and electrochemical performance of catalyst layers (CLs) in proton exchange membrane fuel cells (PEMFCs), utilizing the lattice Boltzmann method (LBM) for detailed simulations. The impact of carbon sphere diameter range and gradient distribution on oxygen transport, electrochemical reactivity, and catalyst layer morphology was investigated. The results show that gradient designs of carbon sphere diameters effectively modulate pore size distribution, electrochemically active surface area, and oxygen diffusion pathways within the CL. Specifically, placing larger carbon spheres near the gas diffusion layer improves pore connectivity and oxygen transport, while smaller spheres near the membrane enhance the availability of reaction sites. The three-layered gradient design, particularly the L-M-S configuration, demonstrated superior oxygen distribution, reduced concentration gradients, and increased current density by 15.4%. These findings underline the importance of optimizing carbon sphere diameter distributions for enhancing CL performance. This study offers a novel framework for designing catalyst layers with improved mass transport and electrochemical efficiency, providing significant insights for the future development of high-performance PEMFCs. Full article

Background: Reconstruction of limbs with extensive bone loss often requires complex surgical procedures, which can be technically demanding, time-consuming, and physically and psychologically burdensome for patients. Historically, the lack of alternatives for large bone defects often led to primary amputation. Modern musculoskeletal practice allows for reconstruction using autologous or allogeneic bone grafts, or through more complex procedures such as the Masquelet technique or distraction osteogenesis. However, these methods share a common challenge: the need for a docking site procedure in cases of insufficient bony fusion of the transport segment. The aim of this study was to identify predictive factors for the need for a docking site procedure. Methods: A retrospective analysis was conducted on 93 patients treated for lower extremity bone defects between January 2013 and June 2023. Of these, 39 patients (41.9%) underwent segmental bone transport and formed the study cohort for the predictive model analysis. Patients of all ages and both genders were included, regardless of the etiology and size of the defect. The need for a docking site procedure was analyzed using logistic regression, ROC analysis, and ANOVA. Results: The study included 93 patients (73 male, 19 female) aged 7 to 83 years. The mean defect size was 76.46 mm (range: 12.1 to 225.1 mm). The mean transport duration was 149.97 days, with a mean transport speed of 0.61 mm/day. Among the 39 segmental transport patients, a docking site procedure was performed in 64.1% (n = 25). Logistic regression and ROC analysis were performed on this subgroup (n = 39, with 25 events). Significant predictors for the need for a docking site procedure were age (p = 0.024), vascular injury (p = 0.009), transport duration (p = 0.001), and transport speed (p < 0.001). ROC analysis demonstrated that transport speed (AUC = 0.931) and transport duration (AUC = 0.911) showed strong discriminative ability for predicting docking site procedure necessity, suggesting potential utility as clinical decision-support parameters. Conclusions: The study identified transport duration and speed as potentially valuable predictive factors in this retrospective cohort for the need of a docking site procedure, though prospective validation is required. A transport duration exceeding 290.5 days significantly increased the likelihood of requiring a docking site procedure. These findings can help optimize treatment planning and improve long-term limb preservation. Full article

In recent years, the increasing frequency of extreme weather events, the large-scale integration of distributed generation into distribution networks, and the widespread application of new power electronic devices have posed severe challenges to the security of power supply in distribution networks. To enhance the power supply reliability of the distribution network while considering its economic efficiency, this paper proposes a collaborative planning method for a flexible distribution network focused on power supply restoration and resilience enhancement In this method, a planning model for flexible distribution networks is established by optimally determining the siting and sizing of soft open point (SOP), with the objective of minimizing the annual comprehensive cost of the distribution network under multiple operational and planning constraints. Second-order cone programming (SOCP) relaxation and polyhedral approximation-based linearization techniques are employed to reformulate and solve the model, thereby obtaining the optimal siting and sizing Case for SOPs. Finally, simulations are conducted on a modified IEEE 33-bus test system to verify the effectiveness of the proposed method. The results show that, through appropriate siting and sizing of SOPs, outage loss costs can be significantly reduced, nodal voltage profiles can be improved, and load support can be provided to de-energized areas, leading to a reduction of more than 70% in the annual comprehensive cost of the distribution network and an improvement in the system reliability index from 99% to 99.999%, thus effectively enhancing both the economic efficiency and reliability of the distribution system. Full article

Automated detection of impact craters is essential for planetary surface studies, yet it remains a challenging task due to variable morphology, degraded rims, complex geological settings, and inconsistent illumination conditions. This study presents a novel crater detection methodology designed for large-scale analysis of Lunar Reconnaissance Orbiter Wide-Angle Camera (WAC) imagery. The framework integrates several key components: automatic region-of-interest (ROI) selection to constrain the search space, Canny edge detection to enhance crater rims while suppressing background noise, and a modified Hough transform that efficiently localizes elliptical features by restricting votes to edge points validated through local fitting. Candidate ellipses are then refined through a two-stage adjustment, beginning with L1-norm fitting to suppress the influence of outliers and fragmented edges, followed by least-squares optimization to improve geometric accuracy and stability. The methodology was tested on four representative Wide-Angle Camera (WAC) sites selected to cover a range of crater sizes (between ~1 km and 50 km), shapes, and geological contexts. The results showed detection rates between 82% and 91% of manually identified craters, with an overall mean of 87%. Covariance analysis confirmed significant reductions in parameter uncertainties after refinement, with standard deviations for center coordinates, shape parameters, and orientation consistently decreasing from the L1 to the L2 stage. These findings highlight the effectiveness and computational efficiency of the proposed approach, providing a reliable tool for automated crater detection, lunar morphology studies, and future applications to other planetary datasets. Full article

Accurate characterization of seafloor sediment properties is critical for marine engineering design, resource assessment, and environmental management. Sidescan sonar offers efficient wide-area mapping capabilities, yet establishing robust quantitative relationships between acoustic backscatter intensity and sediment texture remains challenging, particularly in heterogeneous coastal environments. This study investigates the correlation between sidescan sonar backscatter intensity and sediment grain size parameters in waters southwest of Hainan Island, China. High-resolution acoustic data (450 kHz) were acquired alongside surface sediment samples from 18 stations spanning diverse sediment types. Backscatter intensity, represented by grayscale values, was systematically compared with grain size distributions and individual size fractions. Results reveal that mean grain size shows no meaningful correlation with backscatter intensity; however, fine sand fraction content (0.075–0.25 mm) exhibits a strong negative linear relationship (R² = 0.87 under optimal conditions). Distribution-level analysis demonstrates that backscatter variability mirrors sediment textural complexity, with coarse sediments producing broad, elevated intensity distributions and fine sediments yielding narrow, suppressed distributions. Inter-survey variability highlights the sensitivity of absolute intensity values to environmental conditions during acquisition. Spatial distribution analysis reveals that sediment grain size follows a systematic NE-SW gradient controlled by hydrodynamic energy, with notable local anomalies controlled by reef structures (producing coarse bioclastic sediment) and topographic sheltering (maintaining fine-grained deposits in shallow areas). These findings provide a quantitative basis for fraction-specific acoustic classification approaches while emphasizing the importance of multi-scale analysis incorporating both regional hydrodynamic trends and local morphological controls. The established relationship between fine sand abundance and acoustic response enables semi-quantitative sediment prediction from remotely sensed data, supporting improved seafloor mapping protocols for offshore infrastructure siting, aggregate resource evaluation, and coastal zone management in morphologically complex environments. Full article

Background/Objective: Leadless pacemakers (LPs, Micra™, Medtronic) offer a safe alternative to traditional transvenous systems. However, optimal implantation site within the right ventricular septum (RVS) and its effect on long-term pacing threshold stability remains under debate. The aim was to evaluate the relationship between pacing site within the RVS and pacing threshold stability following leadless pacemaker implantation. Methods: We retrospectively analyzed 36 patients who underwent LP implantation at two centers between 2022 and 2023. Patients were classified into two groups based on final device position by fluoroscopy: Group A (mid or upper RVS, n = 8) and Group B (low or apical RVS, n = 28). Pacing threshold, QRS duration, and left ventricular ejection fraction (LVEF) were assessed over 6 months. Results: At the 6-month follow-up, Group A demonstrated significantly lower and more stable pacing thresholds compared to Group B (0.57 ± 0.09 mV vs. 1.55 ± 0.97 mV, p < 0.001). No significant differences were observed in QRS duration or LVEF changes between groups. Echocardiography did not reveal new-onset tricuspid regurgitation. Conclusions: Given the small sample size, particularly in the mid/high septal group, these findings should be interpreted as hypothesis-generating and require confirmation in larger prospective studies. These findings highlight the importance of careful anatomical targeting during LP implantation. Prospective studies incorporating advanced imaging are warranted to confirm these results and evaluate long-term clinical outcomes. Full article

The sustained growth of electricity demand and the global transition toward low-carbon energy systems have intensified the need for efficient, flexible, and reliable operation of electrical distribution networks. In this context, the coordinated integration of distributed renewable energy resources and demand-side flexibility has emerged as a key strategy to improve technical performance and economic efficiency. This work proposes an integrated optimization framework for active power supply in a radial, distribution-like network through the optimal siting and sizing of photovoltaic (PV) units and wind turbines (WTs), combined with a real-time pricing (RTP)-based demand-side response (DSR) program. The problem is formulated using the branch-flow (DistFlow) model, which explicitly represents voltage drops, branch power flows, and thermal limits in radial feeders. A multiobjective function is defined to jointly minimize annual operating costs, active power losses, and voltage deviations, subject to network operating constraints and inverter capability limits. Uncertainty associated with solar irradiance, wind speed, ambient temperature, load demand, and electricity prices is captured through probabilistic modeling and scenario-based analysis. To solve the resulting nonlinear and constrained optimization problem, an Improved Whale Optimization Algorithm (I-WaOA) is employed. The proposed algorithm enhances the classical Whale Optimization Algorithm by incorporating diversification and feasibility-oriented mechanisms, including Cauchy mutation, Fitness–Distance Balance (FDB), quasi-oppositional-based learning (QOBL), and quadratic penalty functions for constraint handling. These features promote robust convergence toward admissible solutions under stochastic operating conditions. The methodology is validated on a large-scale radialized network derived from the IEEE 118-bus benchmark, enabling a DistFlow-consistent assessment of technical and economic performance under realistic operating scenarios. The results demonstrate that the coordinated integration of PV, WT, and RTP-driven demand response leads to a reduction in feeder losses, an improvement in voltage profiles, and an enhanced voltage stability margin, as quantified through standard voltage deviation and fast voltage stability indices. Overall, the proposed framework provides a practical and scalable tool for supporting planning and operational decisions in modern power distribution networks with high renewable penetration and demand flexibility. Full article

To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model’s lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots. Full article

To address the challenges of insufficient uncertainty characterization and inadequate flexible resource coordination in active distribution network (ADN) planning under high-penetration distributed renewable energy integration, this paper proposes a WGAN-GP-based coordinated source–network–storage expansion planning method for ADNs. First, an improved Wasserstein Generative Adversarial Network (WGAN-GP) model is employed to learn the statistical patterns of wind and photovoltaic (PV) power outputs, generating representative scenarios that accurately capture the uncertainty and correlation of renewable generation. Then, an ADN expansion planning model considering the E-SOP (Energy Storage-integrated Soft Open Point) is developed with the objective of minimizing the annual comprehensive cost, jointly optimizing the siting and sizing of substations, lines, distributed generators, and flexible resources. By integrating the energy storage system on the DC side of the SOP, E-SOP achieves coordinated spatial power flow regulation and temporal energy balancing, significantly enhancing system flexibility and renewable energy accommodation capability. Finally, a Successive Convex Cone Relaxation (SCCR) algorithm is adopted to solve the resulting non-convex optimization problem, enabling fast convergence to a high-precision feasible solution with few iterations. Simulation results on a 54-bus ADN demonstrate that the proposed method effectively reduces annual comprehensive costs and eliminates renewable curtailment while ensuring high renewable penetration, verifying the feasibility and superiority of the proposed model and algorithm. Full article

The large-scale integration of distributed photovoltaics (DPV) and their inherent uncertainties have significantly increased the operational risks of distribution networks. Moreover, frequent outages caused by extreme events further impose substantial losses on these networks, highlighting the urgent need to enhance their disaster resilience and load-supply capabilities. To address these challenges, this paper proposes an energy storage allocation method that simultaneously considers economic performance and comprehensive vulnerability. First, a vulnerability assessment framework for distribution networks is established from both pre-disaster and post-disaster perspectives. In the pre-disaster stage, an improved electrical betweenness index, voltage deviation index, and network-balance index are employed to identify weak lines and nodes. In the post-disaster stage, based on the identified weak components, two types of scenarios, namely random line failures and worst-case failures, are constructed to emulate extreme events, and an enhanced network supply efficiency index is developed to quantitatively evaluate the network’s recovery capability. Subsequently, a multi-objective optimal allocation model for energy storage is formulated with economic cost and comprehensive vulnerability as objective functions, and an Enhanced Beluga Whale Optimization algorithm is adopted to obtain the optimal siting and sizing of energy storage systems. Case studies on an improved IEEE 33-bus distribution system show that, compared with the no-ESS scheme, the proposed plan yields about a 66.4% reduction in network loss cost, around 22% improvement in average voltage deviation, and a roughly 10% reduction in the comprehensive vulnerability index under normal operation. Under random and targeted line outage scenarios, the proposed scheme also achieves the highest area under curve and average network effectiveness indices and the lowest performance volatility among the benchmark strategies. These results demonstrate that, for the tested IEEE 33-bus system, the vulnerability-driven ESS planning framework can markedly enhance both economic efficiency and resilience to extreme events. Full article

Frequent extreme-weather events pose severe challenges to the secure and economical operation of power systems with high renewable energy penetration. To strengthen grid resilience against such low-probability, high-impact events while maintaining good performance under normal conditions, this paper proposes an optimal energy storage allocation method for power systems with high-wind-power penetration. We first identify two representative extreme wind power events and develop a risk assessment model that jointly quantifies load-shedding volume and transmission-line security margins. On this basis, a multi-scenario joint siting-and-sizing optimization model is formulated over typical-day and extreme-day scenarios to minimize total system cost, including annualized investment cost, operating cost, and risk cost. To solve the model efficiently, a two-stage hierarchical solution strategy is designed: the first stage determines an investment upper bound from typical-day scenarios, and the second stage optimizes storage allocation under superimposed extreme-day scenarios within this bound, thereby balancing operating economy and extreme-weather resilience. Simulation results show that the proposed method reduces loss-of-load under extreme-weather scenarios by 32.46% while increasing storage investment cost by only 0.18%, significantly enhancing system resilience and transmission-line security margins at a moderate additional cost. Full article

Platinum is known as the most efficient catalyst for oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER). However, Pt catalysts still encounter high loading demands, poor atom utilization, and uncontrolled nanoparticle aggregation, which severely restrict their practical use. To address these issues, we designed a Pt-W_1.33C hybrid catalyst with strong interfacial coupling between Pt nanoparticles and the vacancy-rich i-MXene, W_1.33C matrix. This robust Pt-W_1.33C interaction effectively restricts Pt overgrowth, producing uniformly dispersed nanoparticles with an average physical size of 3.1 nm. The results show that the modulated electronic structure facilitates electron transfer from W_1.33C to neighboring Pt sites, which reduces the energy barriers of chemical reactions and enhances the intrinsic electrochemical catalytic activity of the hybridized catalysts. As a result, the Pt-W_1.33C catalyst with low Pt loading achieves an ORR overpotential of 320 mV at 0.1 mA cm⁻², an HER overpotential of 36 mV at 10 mA cm⁻², and Tafel slopes of 66 and 27.8 mV dec⁻¹ for ORR and HER, respectively. The enhanced ORR and HER performance of Pt-W_1.33C can be attributed to the synergistic interplay between Pt and W_1.33C, including the disordered stacking of W_1.33C, high conductivity of W_1.33C, high catalytic activity of Pt, and strong Pt-W_1.33C interfacial coupling, which, together, optimize electronic interaction and active-site accessibility in the hybrid catalyst. Full article

Acellular dermal matrices (ADMs) are widely used in soft-tissue reconstruction, yet the optimal timing for split-thickness skin grafting (STSG) remains unsettled. We conducted a single-center retrospective cohort study (January 2023–August 2025) of adults undergoing ADM-based reconstruction with Integra^® Double Layer (IDL), Integra^® Single Layer (ISL), or Nevelia^®. Primary endpoints included length of stay (LOS), STSG requirement and timing, and in-hospital complications; secondary endpoints included spontaneous epithelialization. Prespecified adjusted analyses (linear/logistic models) controlled for age, sex, etiology, anatomical site, diabetes/PAOD, smoking, wound size (when available), wound contamination, and matrix type. Histology and immunohistochemistry (H&E, Masson trichrome, CD105, D2-40) assessed matrix integration and vascular/lymphatic maturation. Seventy-five patients were included (IDL n = 40; ISL n = 20; Nevelia n = 15). On multivariable analysis, matrix type was not an independent predictor of LOS (ISL vs. IDL β = +2.84 days, 95% CI −17.34 to +23.02; Nevelia vs. IDL β = −4.49 days, 95% CI −16.24 to +7.26). Complications were infrequent (6/75, 8.0%) and comparable across matrices; spontaneous epithelialization occurred in 3/75 patients (4.0%). A day-14 grafting strategy, applied only after documented clinical integration, was feasible in 30/75 (40.0%) patients without excess complications. Histology/IHC at 3–4 weeks demonstrated CD105-positive, perfused capillary networks with abundant collagen; at 4–6 weeks, D2-40-positive lymphatic structures confirmed progressive neo-dermis maturation, supporting the biological plausibility of earlier grafting once integration criteria are met. In this cohort, outcomes were broadly similar across matrices after adjustment. A criteria-based early STSG approach (~day 14) appears safe and operationally advantageous when integration is confirmed, while a minority of defects may heal without grafting. Prospective multicenter studies with standardized scar/functional measures and cost analyses are needed to refine patient selection and graft timing strategies. Full article

This study investigates the influence of solidification conditions on the high-cycle fatigue (HCF) behavior of a second-generation DD6 single-crystal superalloy. Single-crystal bars with a [001] orientation were prepared using the high-rate solidification (HRS) and liquid-metal cooling (LMC) techniques under various pouring temperatures. The HCF performance of the heat-treated alloy was subsequently evaluated at 800 °C using rotary bending fatigue tests. The results demonstrate that increasing the pouring temperature effectively reduced the content and size of microporosity in the HRS alloys. At an identical pouring temperature, the LMC alloy exhibited a significant reduction in microporosity, with its content and maximum pore size being only 44.4% and 45.8% of those in the HRS alloy, respectively. Consequently, the HCF performance was enhanced with increasing pouring temperature for the HRS alloys. The LMC alloy outperformed its HRS counterpart processed at the same temperature, showing a 9.4% increase in the conditional fatigue limit (at 10⁷ cycles). Microporosity was identified as the dominant site for HCF crack initiation at 800 °C. The role of γ/γ′ eutectic in crack initiation diminished or even vanished as the solidification conditions were optimized. Fractographic analysis revealed that the HCF fracture mechanism was quasi-cleavage, independent of the solidification conditions. Under a typical stress amplitude of 550 MPa, the deformation mechanism was characterized by the slip of a/2<011> dislocations within the γ matrix channels, which was also unaffected by the solidification conditions. In conclusion, optimizing solidification conditions, such as by increasing the pouring temperature or employing the LMC process, enhances the HCF performance of the DD6 alloy primarily by refining microporosity, which in turn prolongs the fatigue crack initiation life. Full article

Anomaly Detection (AD) in telecommunication networks is critical for maintaining service reliability and performance. However, operational networks present significant challenges: high-dimensional Key Performance Indicator (KPI) data collected from thousands of network elements must be processed in near real time to enable timely responses. This paper presents an unsupervised approach leveraging Mahalanobis Distance (MD) to identify network anomalies. The MD model offers a scalable solution that capitalizes on multivariate relationships among KPIs without requiring labeled data. Our methodology incorporates preprocessing steps to adjust KPI ratios, normalize feature distributions, and account for contextual factors like sample size. Aggregated anomaly scores are calculated across hierarchical network levels—cells, sectors, and sites—to localize issues effectively. Through experimental evaluations, the MD approach demonstrates consistent performance across datasets of varying sizes, achieving competitive Area Under the Receiver Operating Characteristic Curve (AUC) values while significantly reducing computational overhead compared to baseline AD methods: Isolation Forest (IF), Local Outlier Factor (LOF) and One-Class Support Vector Machines (SVM). Case studies illustrate the model’s practical application, pinpointing the Random Access Channel (RACH) success rate as a key anomaly contributor. The analysis highlights the importance of dimensionality reduction and tailored KPI adjustments in enhancing detection accuracy. This unsupervised framework empowers telecom operators to proactively identify and address network issues, optimizing their troubleshooting workflows. By focusing on interpretable metrics and efficient computation, the proposed approach bridges the gap between AD and actionable insights, offering a practical tool for improving network reliability and user experience. Full article