Search Results (4,677)

Managing vehicle speed is crucial for reducing crash risks and crash severity. South Korea’s ‘Safety Speed 5030’ policy introduced lower urban speed limits to enhance road safety, but speed limit reductions alone may not be sufficient to change driver behavior. This paper investigates how spatial and environmental factors beyond speed limits affect drivers’ speed choice. Using point-level speed data from Jeju Island’s C-ITS dataset combined with GIS information, spatial econometric techniques were employed to capture spatial dependencies in speeding degree. Results show that a spatial lag model (SLM) outperforms ordinary least squares (OLS) and spatial error models (SEMs), providing higher explanatory power and more consistent parameter estimates. Key factors influencing drivers’ speed choice include road geometry (e.g., curvature, number of lanes), node-level features (e.g., intersections, property change points), and the presence of enforcement measures. The findings suggest that the reduction in speed limits alone may not guarantee a corresponding decrease in vehicle speed. This underlines that sustainable traffic safety requires not only regulation but also careful consideration of spatial and environmental contexts. Full article

In modern livestock production, obtaining accurate body weight measurements for pigs is essential for feeding management and economic assessment, yet conventional weighing is laborious and can stress animals. To address these limitations, we developed a contactless image-based pipeline that first uses BiRefNet for high-precision background removal and YOLOv11-seg to extract the pig dorsal mask from top-view RGB images; from these masks we designed and extracted 17 representative phenotypic features (for example, dorsal area, convex hull area, major/minor axes, curvature metrics and Hu moments) and included camera height as a calibration input. We then compared eight machine-learning and deep-learning regressors to map features to body weight. The segmentation pipeline achieved mAP₅₀–₉₅ = 0.995 on the validation set, and the XGBoost regressor gave the best test performance (MAE = 3.9350 kg, RMSE = 5.2372 kg, R² = 0.9814). These results indicate the method provides accurate, low-cost and computationally efficient weight prediction from simple RGB images, supporting frequent, noninvasive monitoring and practical deployment in smart-farming settings. Full article

Embedding text on 3D triangular meshes is essential for conveying semantic information and supporting reliable identification and authentication. However, existing methods often fail to incorporate the geometric properties of the underlying mesh, resulting in shape inconsistencies and visual artifacts, particularly in regions with high curvature. To overcome these limitations, we present GeoText, a framework for generating 3D text directly on triangular meshes while faithfully preserving local surface geometry. In our approach, the control points of TrueType Font outlines are mapped onto the mesh along a user-specified placement curve and reconstructed using geodesic Bézier curves. We introduce two mapping strategies—one based on a local tangent frame and another based on straightest geodesics—that ensure natural alignment of font control points. The reconstructed outlines enable the generation of embossed, engraved, or independent 3D text meshes. Unlike Boolean-based methods, which combine text meshes through union or difference and therefore fail to lie exactly on the surface—breaking the symmetry between embossing and engraving—our offset-based approach ensures a symmetric relation: positive offsets yield embossing, whereas negative offsets produce engraving. Furthermore, our method achieves robust text generation without self-intersections or inter-character collisions. These capabilities make GeoTextwell suited for applications such as 3D watermarking, visual authentication, and digital content creation. Full article

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article

Recently, a link between gravitational tension (GT) and energy density via the Kretschmann scalar (KS) was proposed to construct regular black holes (RBHs) in pure Lovelock (PL) gravity. However, including a negative cosmological constant in PL gravity leads to a curvature singularity. Here, we choose the coupling constants such that the Lovelock equations admit an n-fold degenerate AdS vacuum (LnFDGS), allowing us to construct an RBH with $\Lambda <0$, where the energy density is analogous to the previously mentioned model. To achieve this, we propose alternative definitions for both the KS and GT. We find that, for mass parameter values greater than the extremal value $M_{\min }$, our RBH solution becomes indistinguishable from the AdS vacuum black hole from inside the event horizon out to infinity. At small scales, quantum effects modify the geometry and thermodynamics, removing the singularity. Furthermore, due to the lack of analytical relationships between the event horizon, photon sphere, and shadow in LnFDGS, we propose a numerical method to represent these quantities. Full article

In Kähler geometry, Calabi extremal metrics serves as a class of more available special metrics than Kähler metrics with constant scalar curvatures, as a generalization of Kähler Einstein metrics. In recent years, Maxwell–Einstein metrics (or conformally Kähler Einstein–Maxwell metrics) appeared as another alternative choice for Calabi extremal metrics. It turns out that some similar metrics defined by Futaki and Ono have similar roles in the Kähler geometry. In this paper, we prove that for some completions of certain line bundles, there is at least one k-generalized Maxwell–Einstein metric defined by Futaki and Ono conformally related to a metric in any given Kähler class for any integer $3\le k\le 13$. Full article

In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a decrease in accuracy and navigation performance. To address these problems, this paper proposes a coarse-to-fine registration framework. In the coarse registration stage, a Point Matching algorithm based on Curvature Feature Learning (CFL-PM) is proposed. Through CFL-PM and Farthest Point Sampling (FPS), the coarse registration of overlapping regions between the two point clouds is achieved. In the fine registration stage, the Iterative Closest Point (ICP) is used for further optimization. The proposed method effectively addresses the challenges of noise, initial pose and low overlapping ratio. In noise-free point cloud registration experiments, the average rotation and translation errors reached 0.34° and 0.27 mm. Under noisy conditions, the average rotation error of the coarse registration is 7.28°, and the average translation error is 9.08 mm. Experiments on pre- and intra-operative point cloud datasets demonstrate the proposed algorithm outperforms the compared algorithms in registration accuracy, speed, and robustness. Therefore, the proposed method can achieve the precise alignment of the surgical navigation-assisted pedicle screw fixation. Full article

The plasma membrane (PM) of eukaryotic cells plays a key role in the response to stress, acting as the first line of defense against environmental changes and protecting cells against intracellular perturbations. In this work, we explore how membrane-bound chaperones and membrane lipid domains work together to shape plasma membrane properties—a partnership we refer to as the “epichaperome–plasma membrane lipid axis.” This axis influences membrane fluidity, curvature, and domain organization, which in turn shapes the spatial and temporal modulation of signaling platforms and pathways essential for maintaining cellular integrity and homeostasis. Changes in PM fluidity can modulate the activity of ion channels, such as transient receptor potential (TRP) channels. These changes also affect processes such as endocytosis and mechanical signal transduction. The PM proteome undergoes rapid changes in response to membrane perturbations. Among these changes, the expression of heat shock proteins (HSPs) and their accumulation at the PM are essential mediators in regulating the physical state and functional properties of the membrane. Because of the pivotal role in stress adaptation, HSPs influence a wide range of cellular processes, which we grouped into three main categories: (i) mechanistic insights, differentiating in vitro (liposome, reconstituted membrane systems) and in vivo evidence for HSP-PM recruitment; (ii) functional outputs, spanning how ion channels are affected, changes in membrane fluidity, transcytosis, and the process of endocytosis and exosome release; and (iii) pathological effects, focusing on how rewired lipid–chaperone crosstalk in cancer drives resistance to drugs through altered membrane composition and signaling. Finally, we highlight Membrane Lipid Therapy (MLT) strategies, such as nanocarriers targeting specific PM compartments or small molecules that inhibit HSP recruitment, as promising approaches to modulate the functional stability of epichaperome assembly and membrane functionality, with profound implications for tumorigenesis. Full article

This study examines the effects of the extreme drought and heatwaves that occurred in Romania during the summer of 2024 on the microbiological and mycotoxicological quality of wheat (Triticum aestivum) stored until April 2025, as well as on the quality of wholemeal flour and bread derived from it. Comparative analyses were conducted against the contamination in wheat harvested in 2024. The hot and dry conditions significantly influenced the microbial and mycotoxicological contamination of both freshly harvested and stored wheat, as well as the derived flour and bread, due to their notably reduced moisture content and water activity. Although levels of total fungi, Fusarium-damaged kernels, and mycotoxins deoxynivalenol, aflatoxin B1, and ochratoxin A remained well below regulatory thresholds, higher contamination was observed in Transylvania and Moldavia—particularly in the Curvature Carpathians, likely due to their cooler and wetter microclimates. The observed quality changes were strongly associated with alterations in physico-chemical, rheological, and colorimetric parameters, posing potential economic challenges for the milling and baking industries. The study recommends implementing integrated regional strategies to enhance wheat resilience, optimize production systems, and improve contamination control in response to increasing climate stress across Southeastern Europe. Full article

This research is focused on driving stability issues, which can be caused by specifics of electric vehicle (EV) powertrains. Specific driving conditions, such as intensive road curvature and low grip, require precise control from the driver and very accurate and not delayed vehicle stabilization from its active safety systems. These systems, typically anti-lock braking systems (ABS) and electronic stability programs (ESP), perform their tasks sufficiently well, but new vehicle architectures are forcing a reassessment of their reliability, sometimes requiring additional safety subsystems. In the context of EV architecture and its propulsion systems, a possible lack of stability is anticipated when operating intensive regenerative braking in EVs with a rear–wheel–drive transmission. Experimental research conducted on two popular electric vehicles confirmed this hypothesis, as additional oversteering occurs even when ESP systems have intervened. Based on the experiment, a theoretical simulation model of an EV with regenerative braking on the rear axle was created and validated in MATLAB/Simulink (R2024a). The simulations showed how relevant this issue is and how limited stability systems are; therefore, new strategies were proposed and theoretically tested to ensure car safety. These dedicated regenerative braking control subsystems enable optimal use of regenerative braking and ensure more reliable stability in slippery corners. Full article

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

In this paper, we study Z-solitons and gradient Z-solitons on $\alpha$-cosymplectic manifolds. The soliton structure is defined by the generalized tensor $Z=S+\beta g$, where S denotes the Ricci tensor, g the metric tensor, and $\beta$ a smooth function. We investigate the geometric implications of Z-solitons under various curvature conditions, with a focus on the interplay between the Z-tensor and the Q-curvature tensor, as well as the case of Z-recurrent $\alpha$-cosymplectic manifolds. Our classification results establish that such manifolds can be Einstein, $\eta$-Einstein, or of constant curvature. Finally, we construct a concrete five-dimensional example of an $\alpha$-cosymplectic manifold that admits a Z-soliton structure, thereby illustrating the theoretical framework. Full article

We have proposed and presented a hybrid mini-two-path Mach–Zehnder interferometer (MTP-MZI) sensor based on four-mode fiber (FMF), where the reference path comprises of a section of a single-mode fiber (SMF), and the sensing path adopts a structure of SMF-FMF-SMF (SFS). Using arc discharge technology, the two paths are effectively fused and coupled, resulting in a robust MTP-MZI structure sensor. In the curvature detection, the maximum intensity sensitivity of curvature reaches 168.41 dB/m⁻¹ when the curvature ranges change from 0 m⁻¹ to 0.091 m⁻¹. To the best of our knowledge, it is the highest curvature sensitivity in the MZI fiber sensor with intensity modulation. Furthermore, we also conducted a temperature-sensing experiment. The experiment results show that the maximum temperature sensitivity is only 78 pm/°C with a temperature range of 30–65 °C. The diverse exhibition of sensing performance for curvature and temperature enables us to effectively mitigate cross-sensitivity challenges. These results provide the experimental basis for developing a high-sensitivity sensor by employing the mini-two-path structure combined with specialty fibers. Full article

This study presents a high-frequency heat-assisted incremental bending process for the high-efficiency, high-precision forming of medium-thickness (≥3 mm) double-curved metal plates, addressing the limitations of traditional stamping and line heating methods in aerospace and marine applications. A minimum energy loading path strategy is proposed to optimize the forming trajectory and reduce residual stress. A coupled thermomechanical finite element model was developed, incorporating high-frequency induction heating, temperature-dependent material properties, and Coulomb friction. The model was validated through experiments on Q235 steel plates. Results show that the proposed process reduces the peak forming force and decreases the number of forming points compared to conventional cold incremental bending. Springback is reduced, and the final shape accuracy reaches within 3 mm deviation from the target geometry. Double-curvature sail and saddle-shaped plates were successfully fabricated, demonstrating the feasibility and effectiveness of the method. This work provides a promising solution for low-cost, flexible manufacturing of complex medium-thickness components. Full article

Existing point cloud registration methods can effectively handle large-scale and partially overlapping point cloud pairs. However, registering unbalanced point cloud pairs with significant disparities in spatial extent and point density remains a challenging problem that has received limited research attention. This challenge primarily arises from the difficulty in achieving accurate local registration when the point clouds exhibit substantial scale variations and uneven density distributions. This paper presents a novel registration method for unbalanced point cloud pairs that utilizes the local point cluster structure feature for effective outlier rejection. The fundamental principle underlying our method is that the internal structure of a local cluster comprising a point and its K-nearest neighbors maintains rigidity-preserved invariance across different point clouds. The proposed pipeline operates through four sequential stages. First, keypoints are detected in both the source and target point clouds. Second, local feature descriptors are employed to establish initial one-to-many correspondences, which is a strategy that increases correspondences redundancy to enhance the pool of potential inliers. Third, the proposed Local Point Cluster Structure Feature is applied to filter outliers from the initial correspondences. Finally, the transformation hypothesis is generated and evaluated through the RANSAC method. To validate the efficacy of the proposed method, we construct a carefully designed benchmark named KITTI-UPP (KITTI-Unbalanced Point cloud Pairs) based on the KITTI odometry dataset. We further evaluate our method on the real-world TIESY Dataset which is a LiDAR-scanned dataset collected by the Third Railway Survey and Design Institute Group Co. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods in terms of both registration success rate and computational efficiency on the KITTI-UPP benchmark. Moreover, it achieves competitive results on the real-world TIESY dataset, confirming its applicability and generalizability across diverse real-world scenarios. Full article