Search Results (991)

To address the increased operational risk in distribution network caused by the grid integration of distributed wind power, a distribution network risk assessment method that combines a Dirichlet process mixture model (DPMM) with the cumulant method (CM) is proposed, to achieve effective quantification of operational risk. Firstly, a DPMM is employed to cluster wind power output data, and adaptive kernel density estimation is introduced to construct a probabilistic model of wind power output, thereby improving local fitting accuracy. Secondly, uncertainties arising from wind generation and load are considered, and a probabilistic power flow model for the distribution network is established based on the CM and the Gram–Charlier series expansion, in order to obtain the probability distributions of state variables and branch power flows. Then, distribution entropy theory is introduced to quantify the severity of limit violations for state variables such as voltage and power, so that operational risk assessment is enabled. Finally, simulations are conducted on a modified IEEE 34-bus distribution test system, and the results demonstrate the effectiveness of the proposed method. Full article

Pedestrian trajectory prediction is a fundamental task in autonomous driving and mobile robotics, where accurate forecasting requires modeling of both social interactions and scene-related constraints. However, existing methods typically rely on a fixed interaction modeling strategy, which may be insufficient under heterogeneous crowd densities. To address this limitation, we propose DAGH-Net, a density-adaptive gated hybrid network for pedestrian trajectory prediction. Built upon an SR-LSTM (State Refinement for LSTM) backbone, the proposed framework integrates two complementary reasoning pathways: a data-driven social interaction branch and a hybrid knowledge graph branch that encodes structured relational priors among pedestrians, obstacles, and walkable regions. A local-density-conditioned gating mechanism is further introduced to adaptively fuse these features according to the surrounding crowd condition of each pedestrian. This design helps suppress redundant interaction cues in sparse settings while strengthening socially compliant and scene-consistent reasoning in dense or conflict-prone environments. Experimental results on the ETH (Eidgenössische Technische Hochschule Zürich) and UCY (University of Cyprus) benchmarks, evaluated using Mean Average Displacement (MAD) and Final Average Displacement (FAD), show that DAGH-Net improves the average MAD and FAD by 1.6% and 4.2%, respectively, compared with SR-LSTM. Ablation studies further support the complementary contributions of the hybrid knowledge graph and the density-adaptive gating mechanism. We also discuss the limitations of the current density formulation and benchmark scale, which suggest several directions for future improvement. Full article

Background: Amorphous drug formulations are commonly used to improve the solubility and bioavailability of poorly soluble molecular pharmaceuticals, yet less is known about their molecular conformations and local bonding interactions than their crystalline phases. Methods: High-energy X-ray diffraction structure factor measurements have been made on liquid and glassy nifedipine (NIF), felodipine (FEL), NIF 1:3 polyvinylpyrrolidone (PVP), and FEL 1:3 PVP wt.% mixtures. The corresponding X-ray pair distribution functions have been interpreted using empirical potential structure refinement using different models and density functional theory conformer calculations. Results: In both NIF and FEL, the NH···O inter-molecular hydrogen bonds between the pyridyl nitrogen and ester carbonyls are found to be considerably weaker than those observed in the crystalline polymorphs. For nifedipine, it is proposed that either inter-molecular NH…ON nitro bonds are present and/or a fraction (<20%) of conformational changes, with the aryl ring flipped, occur in the liquid state. For felodipine, the models indicate significant disorder associated with the methyl and ethyl side chains in the liquid state, with the main peak intensity at 3.0 Å arising from intra-molecular Cl-Cl atom pairs. When nifedipine molecules are incorporated into PVP, our models show they possess stronger NH···O bonds to the PVP polymer than felodipine molecules, which have stronger affinity for bonding to the polymer than to other felodipine molecules. Conclusions: The amorphous forms of both NIF and FEL show much weaker hydrogen bonding than found in their crystalline phases. Liquid NIF also exhibits configurations which are not observed in the crystal phases. Full article

The DPF-DETR model has been designed to address the challenges encountered in object detection within drone imagery, particularly in scenarios involving significant target scale variations, dense targets, and complex backgrounds. To overcome the limitations of traditional object detection methods, the Density Sensing Mechanism (DSM) and Adaptive Density Map Loss (AdaptiveDM Loss) have been incorporated into the model to provide fine-grained supervision signals. The DSM optimizes the query selection mechanism by utilizing density maps, enabling the number of queries to be adaptively adjusted based on the distribution density of targets, thus improving detection accuracy in dense regions. Furthermore, the precision of the model in detecting dense targets is enhanced by AdaptiveDM Loss, which dynamically adjusts the weights for object localization and classification. Multi-scale feature fusion capabilities are also improved by the Multi-Scale Feature Fusion Network (MSFFN) and the Selective Feature Integration Module (SFIM). The MSFFN refines the fusion of features, which improves the detection of targets across various scales, particularly in complex scenes. Additionally, SFIM enhances the detection accuracy for small targets and complex backgrounds by integrating low-level spatial features with high-level semantic information. The Context-Sensitive Feature Interaction Module (CSFIM) further optimizes multi-scale feature fusion through context-guided interactions, bridging the semantic gap between features of different scales, thus improving the robustness of the model in dense scenarios. Experimental results have shown that DPF-DETR outperforms traditional models and state-of-the-art detection methods across multiple datasets, demonstrating superior robustness and accuracy, especially in dense target detection and complex background scenarios. Full article

Synthetic Aperture Radar (SAR) imagery is inherently degraded by multiplicative speckle noise, rendering traditional superpixel methods—which rely on hard assignment and uniform initialization—suboptimal for boundary preservation. This study proposes a complexity-aware superpixel generation framework featuring differentiable soft-assignment optimization. The approach employs an F-LGRP (Fusion of Local Gradient Pattern Representation) feature descriptor that fuses regional gradient statistics via Gaussian filtering to suppress speckle, coupled with a complexity-driven recursive quadtree initialization strategy yielding non-uniform seed density. A U-Net architecture predicts soft pixel–superpixel association maps within a 9-neighborhood constraint, supervised by a multi-objective loss integrating edge information reconstruction and boundary feature reconstruction. Comprehensive evaluations on simulated and real SAR images (WHU-OPT-SAR and Munich) demonstrate that the proposed method achieves state-of-the-art performance across Boundary Recall, Undersegmentation Error, Compactness, and Achievable Segmentation Accuracy compared to SLIC, SNIC, Mean-Shift, PILS, and SSN. Validation on downstream segmentation tasks further confirms superior accuracy and computational efficiency, establishing the framework as an effective solution for end-to-end SAR image analysis. Full article

Metal anodes offer substantially higher specific and volumetric capacities than conventional anode materials such as graphite in lithium-ion batteries or hard carbon in sodium-ion batteries. However, the integration of metal anodes into solid-state batteries poses significant challenges, particularly with respect to processing, interfacial stability, and cell assembly. Anode-free solid-state batteries (AFSSBs) address these challenges by eliminating the pre-installed metal anode, instead forming the metal in situ during the initial charging (formation) step. In anode-free solid-state batteries, the quality of the interfacial contact is particularly critical, as insufficient contact can lead to locally increased current densities. Consequently, the initial metal plating during the formation step plays a decisive role in determining the homogeneity and stability of the anode interface. Furthermore, conventional battery-grade copper foils (~10 µm) are considerably thicker than required for the targeted C-rates and are difficult to use as stand-alone anode-free current collectors, thereby hindering the industrial production of anode-free solid-state batteries. In this publication, we demonstrate the application of atmospheric plasma spraying (APS) to fabricate thin copper current collectors directly on the ceramic solid electrolytes LAGP (lithium aluminium germanium phosphate) and BASE (beta-alumina solid electrolyte) with superior interface contact. No mechanical damage or diffusion of copper into the solid electrolyte nor formation of secondary phases at the interfaces were observed in SEM or EDS despite the elevated process temperature. LAGP with a thickness as low as 300 µm was successfully coated and subsequently used for plating/stripping experiments. Finally, dense sodium metal was plated at the copper-substrate interface of a 1.4 mm thick BASE sample. Full article

Virtual vitality has become an important complementary dimension for describing urban vitality; however, the identification and formation mechanisms of its stable, high-vitality state during dynamic change remain insufficiently explored. Taking five urban districts of Harbin as the study area, this study uses TikTok short-video data from July to August 2024 (summer) and December 2024 to January 2025 (winter), together with Gaode Map POI data, as the core dataset. Kernel density differences between adjacent weeks are used to measure the dynamic changes in virtual vitality. Bivariate local spatial autocorrelation is applied to identify high-vitality stable zones, and a Random Forest model is employed to examine the nonlinear influence of physical vitality spatial structures. The results show the following: (1) Dynamic change patterns of virtual vitality differ significantly across seasons, and when online attention content points to specific physical spatial structures, a stable high-vitality state is more likely to be maintained. (2) Bivariate local spatial autocorrelation analysis indicates that high-vitality stable zones (HH zones) exhibit significant spatial clustering, with vitality-enhancing zones (LH zones) distributed around them and showing spillover effects, while vitality-declining zones (HL zones) are more scattered. (3) The Random Forest results show that the stable maintenance of high virtual vitality depends more on combinations of spatial structural characteristics with high recognizability, among which distance to activity center (tourism), functional composition dissimilarity (culture), and functional composition dissimilarity (shopping) have the strongest influence. These findings reveal a nonlinear relationship between the stable high-vitality state and the structure of physical vitality space, providing insights for guiding online attention to support physical spatial development. Full article

Background: Free-roaming cats in Australian cities contribute to wildlife impacts, community concerns, and high shelter intake. We used an observational pre–post evaluation study design of a targeted cat desexing program (“StrayCare Metro”) delivered with councils and community partners in four local government areas (LGAs) of Greater Sydney (2022–2024). Methods: Program records documented cat enrolments and services; council and state databases supplied annual shelter intake, euthanasia, and cat-related complaints; and transect drives in two LGAs (2021 and 2024) estimated cat encounter rates and population density. The analysis did not include control LGAs. Results: The program desexed 1225 cats; among enrolled cats not already microchipped, 72% received a microchip and 28% declined despite this being offered for free. Compared with pre-program baselines, annual council shelter intake decreased by 49–73% within LGAs (61% overall), with concurrent reductions in euthanasia. Cat-related complaints declined in three LGAs (47–64%) but increased in one. Transect drives indicated substantial declines in cat encounter rates in Blue Mountains (51%) and Campbelltown (35%) and lower density estimates in both surveyed LGAs. Conclusions: A collaborative targeted desexing approach was associated with large reductions in council pound intake, euthanasia, and, in most areas, nuisance complaints, alongside independent indications of reduced free-roaming cat density. Full article

This work develops a fuzzy piecewise fractional derivative (FPFD) model for cancer-immune-angiogenesis dynamics under uncertainty. Five fuzzy state variables track tumor cells, immune effectors, vessel density, oxygen, and drug concentration. We employ fuzzy triangular numbers with $\alpha$-cut interval arithmetic using constrained fuzzy arithmetic model parametric uncertainty, with numerical values. Oxygen-dependent carrying capacity follows a Hill-type function; hypoxia-induced angiogenesis follows a decreasing Michaelis–Menten function. The model transitions at $t_1=50$ days from memoryless fuzzy classical derivative to fuzzy ABC fractional derivative of order $\psi$. The transition time $t_1=50$ days is biologically justified based on experimental observations of the angiogenic switch in solid tumors, which typically occurs within 4–8 weeks post-inoculation. Positivity, boundedness, Lipschitz continuity, existence, and uniqueness of fuzzy solutions are proved via Banach fixed-point theorem in a weighted norm. A basic reproduction number interval ${\mathcal{R}}_0=[{\underline{\mathcal{R}}}_0,{\overline{\mathcal{R}}}_0]$ is derived; local and global stability conditions are established for disease-free and endemic equilibria using fuzzy differential inclusions. Global sensitivity analysis using latin hypercube sampling with $N=500$ samples explores the range of possible outcomes across the fuzzy parameter support. In the numerical implementation, we use a fourth-order fuzzy Runge–Kutta method (Phase I), and a fractional Adams–Bashforth–Moulton predictor-corrector method (Phase II), ensuring preservation of fuzzy number characteristics. Full article

Automated detection of suspicious human activities in complex and crowded environments remains a critical challenge in modern surveillance systems due to high false-positive rates, poor contrast and generalization across diverse scenes. We propose a GM_CNN3D Model for the classification of suspicious activity based on a Deep Fused Feature Block (DFFB) framework that integrates handcrafted spatial descriptors (PCA-HOG and Motion-HOG) with deep spatiotemporal features extracted from 3D Convolution Neural Network (3D-CNN). Motion regions are first localized using a Gaussian Mixture Model (GMM), after which handcrafted and deep features are concatenated in a dimensionality-normalized fusion stage, followed by a fully connected layer and softmax classification. The system is evaluated on five diverse and publicly available datasets: Violent Crowd, Hockey Fight, Kaggle Fight, Movies Fight, and Custom Annotated YouTube Clips, achieving up to 99.12% accuracy, 98.7% F1-score, and a ROC-AUC of 0.992, outperforming state-of-the-art CNN, LSTM, and SlowFast models. All datasets include real world scenarios with varying lighting, crowd density, and camera viewpoints, with annotations created manually where unavailable. The proposed method demonstrates robust cross-scene performance, enabling automated alarming and reduced false positives in real-time security operations. Full article

Individuals in the southeastern United States of America (USA) have an increasing risk of contracting a tick-borne disease. Land use changes, changing climate, and redistribution of both ticks and their hosts make systematic tick and tick-borne pathogen investigation crucial for public health protection. Prior to 2020, South Carolina had limited data on tick species distribution and tick infection rates. In this work, we describe establishment of a sustainable tick and tick-borne pathogen collaborative network for South Carolina. A major determinant of program success was sharing work effort between the University of South Carolina, the South Carolina Department of Public Health, and key partners including state park employees, local veterinarians, students, and volunteers. The program collected questing ticks from public lands and host-attached ticks from animal shelters. Amblyomma americanum was the most commonly collected tick, with highest density in South Carolina’s southern coastal region. A greater tick species diversity was seen in animal shelter collected versus questing ticks. Pathogen testing results yielded a high presence of Rickettsia amblyommatis among Am. americanum ticks with several other Rickettsia spp. detected including Rickettsia parkeri, Candidatus R. andeanae, R. montanensis, and R. asembonensis. Additional Rickettsiales detected included multiple Ehrlichia and Anaplasma species, with higher presence in the state’s northern region. Borrelia burgdorferi sensu stricto was detected in one questing Ixodes keiransi from the southern coastal region. The current report presents the initial steps for pathogen and tick species surveillance in South Carolina, providing successes and pitfalls as a model for other states and regions to establish similar efforts to improve national tick surveillance. Full article

In the era of information explosion, the effective configuration of labels on maps is crucial for the rapid comprehension of information. The point-feature label placement problem, particularly in large-scale and high-density scenarios with spatial mutual-exclusion constraints, is a classic NP-hard discrete optimization challenge. Existing metaheuristic algorithms (e.g., Simulated Annealing and Genetic Algorithm) often struggle to achieve high-quality global layouts due to their propensity to become trapped in local optima, inefficient random point-selection processes, and inadequate modeling of the spatial mutual-exclusion and blocking constraints between labels. To address these limitations, this paper proposes a Progressive Reinforcement Learning (PRL) algorithm specifically tailored for the point-feature label placement problem. The algorithm models the label placement process as a sequential decision-making problem within the Reinforcement Learning framework, optimized through agent–environment interaction. Its core design comprises the following: (1) a staircase-like policy learning mechanism that shifts from “broad exploration in the early stage to precise exploitation in the later stage” to balance global search and local optimization; (2) a data mining-based Intelligent Action Screening (IAS) mechanism, which dynamically identifies and prioritizes “high-value action points” critical for improving layout quality by constructing the “Contribution Decline Degree” and “Contribution Support Degree” metrics. Experiments on large-scale real-world POI datasets (10,000, 20,000, and 32,312 points) demonstrate that the proposed algorithm significantly outperforms 13 state-of-the-art comparative algorithms, including Simulated Annealing, Genetic Algorithm, Differential Evolution, POPMUSIC, and DBSCAN, in terms of both placement quality and the number of successfully placed labels. It exhibits remarkable adaptability and competitiveness in handling high-density and complex scenarios. Full article

To address the critical challenges of geological hazards, such as water and mud inrush, encountered during the construction of deep-buried tunnels in China, this study investigates the hydraulic properties of remolded mud-infill materials. A multi-scale approach, integrating indoor variable-head permeability tests with scanning electron microscopy (SEM), was employed to characterize the evolutionary patterns of the permeability coefficient (k). Specifically, the research evaluates the independent influences of moisture content, dry density, and confining pressure, alongside the synergistic coupling between dry density and hydration state. The results demonstrate the following: Under independent variable conditions, k exhibits a monotonic decline with increasing dry density and confining pressure while showing a positive correlation with moisture content, with the sensitivity varying significantly across different parameter regimes; under coupled effects, the permeability in both low- and high-moisture ranges manifests a distinct “increase–decrease–increase” fluctuation as dry density rises, reaching a local peak at 2.20 g/cm³. Notably, a relative minimum k (6.12 × 10⁻⁷ cm/s) is achieved at the optimum moisture content (5.8%); micro-mechanistic analysis reveals that low-moisture samples are characterized by randomized angular particles and well-developed interconnected macropore networks, facilitating higher k values. Conversely, high-moisture samples exhibit preferential plate-like stacking dominated by occluded micropores, resulting in a substantial reduction in hydraulic conductivity. This study elucidates the multi-factor coupling mechanism governing the seepage behavior of remolded mud, providing essential theoretical benchmarks for the prediction and mitigation of water–mud outburst disasters in deep underground engineering, thereby ensuring the structural stability and operational safety of tunnel projects. Full article

Thermal conductivity is a key input parameter in geotechnical and shallow geothermal engineering, directly influencing the design, efficiency, and long-term performance of ground heat exchangers, energy piles, and ground-source heat pump systems. Reliable parameterisation of this property in sandy soils remains challenging due to nonlinear interactions between water content, bulk density, and soil structure. This study develops a machine-learning-based workflow for robust parameterisation of thermal conductivity in quartz-rich sands using a large, internally consistent laboratory dataset comprising 1716 samples, including 1455 moist measurements used for modelling, obtained from nationwide site investigations. Air-dry specimens were identified as laboratory-induced drying states and excluded to restrict the analysis to hydro-mechanical conditions representative of typical shallow subsurface environments. Several regression algorithms representing different modelling strategies were evaluated within a unified and reproducible framework and benchmarked against selected classical empirical formulations. Model performance was assessed using standard accuracy metrics together with diagnostics describing the functional stability of predicted thermal-conductivity surfaces. The results reveal a systematic trade-off between predictive accuracy and functional consistency, indicating that models optimised for accuracy may produce functionally unstable and less suitable parameterisations for engineering applications. Accuracy-optimised models frequently produce locally irregular parameter fields, whereas more strongly regularised models yield smoother and physically more coherent response surfaces. The proposed workflow supports reliable thermal-property parameterisation for geotechnical design and shallow geothermal modelling. Full article

The microstructure of a high-manganese low-activation austenitic steel after aging for 500 and 1000 h at 700 °C was investigated using transmission and scanning electron microscopy. Two structural states were examined: cold rolling (CR) and high-temperature thermomechanical treatment (HTMT). After CR, aging leads to the precipitation of dispersed M₂₃C₆ carbides (M = Cr, W), primarily along grain and deformation twin boundaries. After HTMT, these particles are mainly localized at grain and low-angle boundaries. With increasing aging time, both the size and volume fraction of the particles increase. In both states, the microtwin and substructure are partially retained after aging. Local regions corresponding to the early stages of recrystallization were identified after both treatments. These regions were associated with intense decomposition of the supersaturated solid solution and the coarsening of carbide particles. The mechanical properties were evaluated by tensile testing at 20, 650, and 700 °C. Aging reduced average ductility after both treatments and at all test temperatures, with this trend persisting with increasing aging time. After CR and aging, a significant scatter in elongation to failure was observed, with minimum values of ≈2–3%. This behavior is attributed to the high density of plate-like M₂₃C₆ carbides at grain and microtwin boundaries. Microcrack formation and intercrystalline fracture features were observed, directly linked to the high density of boundary carbides. These effects were less pronounced in the HTMT condition after aging. In this paper, strategies for suppressing carbide precipitation in high-manganese low-activation austenitic steels via chemical composition and thermomechanical processing optimization are discussed. Full article