Search Results (4,822)

Unmanned surface vehicles (USVs) have garnered significant interest due to their potential in various maritime applications, particularly target tracking. However, when USVs perform rotational motion around a target, their operations are often hindered by limited fields of view (FOVs) within formations. In this paper, we establish a constraint propagation model to integrate formation constraints, motion constraints, and FOV constraints. Then, we propose four strategies to enforce FOV constraints. The proposed strategies are divided into two categories: those that adjust formation radius and those that adjust rotational velocity. The advantages and disadvantages of each approach are systematically analyzed, highlighting their suitability for various operational scenarios. The effectiveness and robustness of these strategies are validated through simulations. Full article

Cross-view vehicle re-identification (ReID) between aerial and ground perspectives is challenging due to limited computational resources on edge devices and significant scale variations. We propose CVNet, a lightweight network with two key modules: the multi-scale localization (MSL) module and the deep–shallow filtrate collaboration (DFC) module. The MSL module employs multi-scale depthwise separable convolutions and a localization attention mechanism to extract multi-scale features and localize salient regions, addressing viewpoint variations. DFC employs a dual-branch design comprising deep and shallow branches, integrating a filtration module optimized via neural architecture search, a collaboration module, and lightweight convolutions. This design effectively captures both unique and shared cross-view features, ensuring efficient and robust feature representation. We also release a new CVPair v1.0 dataset, the first benchmark for cross-view ReID, containing 14,969 images of 894 vehicle identities, offering results of traditional and lightweight methods. CVNet achieves state-of-the-art performance on CVPair v1.0, VehicleID, and VeRi776, advancing cross-view vehicle ReID. The dataset will be released publicly. Full article

The loss rate of a bank’s portfolio traditionally measures what portion of the exposure is lost in the case of a default. To overcome the difficulties involved in its computation due to, e.g., the lack of private data, one can utilize an inferred loss rate (ILR). In the existing literature, it has been demonstrated that this indicator has sufficiently close properties to the actual loss rate to facilitate capital adequacy analysis. The current study provides complete mathematical proof of an earlier-stated conjecture, that ILR can be instrumental in identifying a conservative upper bound of the capital adequacy requirement of a bank credit portfolio, using the law of large numbers and other techniques from measure-theory-based probability. The assumptions required in this proof are less restrictive, reflecting a more realistic view. In the current study, additional empirical evidence of the usefulness of the indicator is provided, using publicly available data from the Bulgarian National Bank. Despite the definite conservativeness of the capital buffer implied from the analysis of ILR, the empirical analysis suggests that it is still within the regulatory limits. Analyzing ILR together with the Inferred Rate of Default, we conclude that the indicator provides signals about a bank portfolio’s credit risk that are relevant, timely, and adequately inexpensive. Full article

The main dimensional errors in stamped parts are caused by the springback phenomenon. Those errors usually lead to assembly difficulties and/or the malfunction of those parts. The objective of this contribution is to give a comprehensive and detailed view of the sheet metal-forming process of an actual industrial part, with the focus on the setup adjustment of the blank-holder force (BHF), using the springback as the determining factor of the manufacturing quality. The complete cycle of the simulation will be detailed from the experimental determination of the model parameters to the correlation with experimental results of the simulated values. Many studies use simple geometries with limited practical application, failing to provide a quantitative understanding of actual springback in industrial processes. This work aims to offer a realistic reference for springback in a real production part, combining numerical prediction during design using a well-established model and experimental measurements in the factory. The simulation, carried out using LS-DYNA, determines the influence of the BHF in the springback observed in the manufacturing process of a gas cooktop part made from non-stable austenitic 1.4301 steel. The material has been modeled using Barlat’s Yld2000, experimentally determining the strain rate-dependent hardening, yield locus and isotropic–kinematic hardening. To validate the model, an experimental campaign has been developed, testing the part with values of BHF within the range of 50 t to 200 t. The results show that the numerical model is able to represent the influence of the BHF on the springback, demonstrating the relation between them. Full article

The combination of dynamic vision sensors (DVSs) and polarization can overcome the limitation of DVSs whereby they can only detect dynamic scenes, and it also has the ability to detect artificial targets and camouflaged targets, and is thus expected to become a new means of remote sensing detection. Remote sensing detection often requires the field-of-view (FOV) and width to be large enough to improve detection efficiency, but when large FOV polarization imaging is performed, the polarization state in the edge FOV and the center FOV will not be consistent, which does not meet the paraxial approximation condition, and the inconsistency increases as the angle between the incident light and the optical axis increases. This affects the accuracy of target detection, so in this paper, based on the characteristics of polarization imaging using a DVS, factors such as the polarizer rotation step, incident light polarization state, and incident angle are considered to establish a theoretical model of large FOV polarization imaging using DVSs. And the influence of the detection ability is analyzed for three types of incident conditions, namely linearly polarized light, natural light, and partially polarized light. The results show that when the rotation step is 5°, the highest false alarm rate for natural light incident in the edge FOV will be nearly 53%, and the highest false alarm rate for linearly polarized light incident will be nearly 32%. Full article

The semantic segmentation of textured 3D meshes is a critical step in constructing city-scale realistic 3D models. Compared to colored point clouds, textured 3D meshes have the advantage of high-resolution texture image patches embedded on each mesh face. However, existing studies predominantly focus on their geometric structures, with limited utilization of these high-resolution textures. Inspired by the binocular perception of humans, this paper proposes a multimodal feature fusion network based on 3D geometric structures and 2D high-resolution texture images for the semantic segmentation of textured 3D meshes. Methodologically, the 3D feature extraction branch computes the centroid coordinates and face normals of mesh faces as initial 3D features, followed by a multi-scale Transformer network to extract high-level 3D features. The 2D feature extraction branch employs orthographic views of city scenes captured from a top-down perspective and uses a U-Net to extract high-level 2D features. To align features across 2D and 3D modalities, a Bridge view-based alignment algorithm is proposed, which visualizes the 3D mesh indices to establish pixel-level associations with orthographic views, achieving the precise alignment of multimodal features. Experimental results demonstrate that the proposed method achieves competitive performance in city-scale textured 3D mesh semantic segmentation, validating the effectiveness and potential of the cross-modal fusion strategy. Full article

In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or leaves the view field, it may result in tracking loss of the target. To address these limitations, this work proposes an improved CF tracking algorithm based on some existent ones. Firstly, as for the scale changing of tracking target, an adaptive scale box is proposed to adjustably change the scale of the target box. Secondly, to address boundary effects caused by fast maneuvering, a spatio-temporal search strategy is presented, utilizing spatial context from the target region in the current frame and temporal information from preceding frames. Thirdly, aiming at the problem of tracking loss due to occlusion or out-of-view situations, this work proposes a fusion strategy based on the YOLOv5s_MSES target detection algorithm. Finally, the experimental results show that, compared to the baseline algorithm on the UAV123 dataset, our DP and AUC increased by 14.07% and 14.39%, respectively, and the frames per second (FPS) amounts to $37.5$. Additionally, on the OTB100 dataset, the proposed algorithm demonstrates significant improvements in distance precision (DP) metrics across four challenging attributes compared to the baseline algorithm, showing a 12.85% increase for scale variation (SV), 16.45% for fast motion (FM), 18.66% for occlusion (OCC), and 17.09% for out-of-view (OV) scenarios. To sum up, the proposed algorithm not only achieves the ideal tracking effect, but also meets the real-time requirement with higher precision, which means that the comprehensive performance is superior to some existing methods. Full article

Accurate dietary assessment is essential for effective health management and disease prevention. However, conventional methods that rely on manual food logging and nutritional lookup are often time consuming and error prone. This study proposes an image-based nutritional advisory system that integrates multimodal deep learning to automate food classification, volume estimation, and dietary recommendation to address these limitations. The system employs a fine-tuned CLIP model for zero-shot food recognition, achieving high accuracy across diverse food categories, including unseen items. For volume measurement, a learning-based multi-view stereo (MVS) approach eliminates the need for specialized hardware, yielding reliable estimations with a mean absolute percentage error (MAPE) of 23.5% across standard food categories. Nutritional values are then calculated by referencing verified food composition databases. Furthermore, the system leverages a large language model (Llama 3) to generate personalized dietary advice tailored to individual health goals. The experimental results show that the system attains a top 1 classification accuracy of 91% on CNFOOD-241 and 80% on Food 101 and delivers high-quality recommendation texts with a BLEU-4 score of 45.13. These findings demonstrate the system’s potential as a practical and scalable tool for automated dietary management, offering improved precision, convenience, and user experience. Full article

Folk art includes objects that are items for everyday use and, at the same time, gracefully reflect the Greek artistic point of view, drawing its inspiration from life itself, the environment and its beauties, and local tradition. An 18th c. wood-carved and painted chest coming from the famous wood-carved centers of Epirus in Greece is presented in this study. As the number of studies and the general bibliographical references are limited for these kinds of items, prior to interventive conservation, a protocol of analysis was followed to identify the damages, the construction materials, and previous alterations. The main goal of this study is to identify the component materials using non-destructive techniques. The methodology followed for the documentation of the artifact includes the following: a. digital microscopy to identify damage from insects, different cracks and losses on the gesso and paint surface, corrosion products, etc.; b. 3D imaging using a polycam, with special attention given to the inside decoration of the cap; c. IR and UV photography to identify any previous alterations or signs of alterations in the varnish layers; d. and XRF analysis to identify the three (3) main colors of the chest, such as the blue used extensively as a background, red, and white. Nevertheless, the Greek folklore painting palette is limited, and for this reason, this study can be a foundation for research on similar artifacts. Full article

Objectives: This study explored the impact of physical activity (PA) participation on the lifestyle habits and school life of Korean elementary school students. Methods: We collected survey data from 28,514 elementary school students participating in the 2023 Student Health Examination conducted by the Korea Ministry of Education. PA participation was the independent variable, defined as whether elementary school students participate in exercise that makes them out of breath or sweat more than three times a week. The variables related to lifestyle habits included breakfast intake, amount of sleep, TV viewing, gaming/Internet use, thoughts about running away from home, perceived body image, and body mass index. The variables for school life included experiences of being bullied, the need for counseling regarding school life problems, and the need for counseling for distress. The collected data were analyzed using frequency analysis, chi-squared tests, and multivariate logistic regression analyses. Results: PA was significantly associated with regular breakfast intake, sufficient sleep, limited television viewing, reduced gaming/Internet use, and a positive perception of body image. Specifically, regarding breakfast intake, the average odds ratio (OR) was 1.160 for always having it. Regarding the amount of sleep, the average OR was 0.836 for less than 6 h, 0.692 for 6–7 h, and 0.767 for 7–8 h. Regarding TV viewing, the average OR was 0.831 for yes. For gaming, the average OR was 0.770 for yes. Regarding perceived body image, the average OR was 1.429 for slightly thin, 1.487 for normal, and 1.400 for slightly fat. Conclusions: These results suggest that children’s PA facilitates the formation of good lifestyle habits; therefore, it should be actively encouraged in children. Full article

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation. Full article

This review highlights a paradigm shift in our understanding of hypocalcemia during milk fever by introducing the concept of the Calci-Inflammatory Network. Traditionally viewed as a pathological deficiency necessitating rapid correction (e.g., through calcium borogluconate infusions or dietary adjustments like dietary cation-anion difference), periparturient hypocalcemia is reinterpreted here as an adaptive, protective response. Within this new framework, reduced circulating calcium levels may help temper systemic inflammation by limiting lipopolysaccharide (LPS) aggregation and curbing excessive macrophage activation. The review discusses how calcium signaling, the calcium-sensing receptor (CaSR), and immune cell functions adapt under hypocalcemic conditions to modulate inflammatory processes. This integrated perspective not only redefines the role of hypocalcemia but also proposes the Calci-Inflammatory Network as a novel concept through which we can understand how changes in calcium homeostasis mitigate inflammatory cascades—potentially lowering the incidence of periparturient diseases and enhance overall cow health and farm productivity. Future research should investigate the long-term effects of hypocalcemia, the environmental influences on this Calci-Inflammatory Network, and their collective impact on disease susceptibility and inflammation. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

A star tracker is widely used as a high-precision attitude measurement device for spacecraft. It calculates attitude by extracting the magnitude and the position of presumed detected stars by a CCD/CMOS sensor and matching them with stars in the star catalog. The traditional star identification methods typically require the selection of specific anchor stars, which may cause insufficient identification accuracy as the number of stars used in the rough search is limited. In this paper, we propose a star identification method based on spatial projection, which starts with preprocessing. Then, a method for online expansion and reconstruction of the star catalog is proposed, which provides more stored star data. After the rough recognition and coordinate system transformation, the final identification is realized in the polar coordinate system. All the star points in the star image are identified, and the attitude information is obtained at the same time. The performance of the identification method is verified by real night sky experiments. Stray light experiments are also carried out to prove good noise immunity capabilities. Compared with the traditional subgraph isomorphism method, the proposed method makes it easier to adjust the number of recognizable stars in the field of view and better recognition of specific areas. The method is of great significance for future tasks such as attitude measurement, celestial navigation, remote sensing measurement, and space target observation and tracking. Full article

While outside chief executive officers (CEOs) are often viewed as catalysts for strategic change compared to their inside counterparts, this study reveals their potential to undermine firms’ environmental responsibility. Integrating agency theory with social capital theory, we investigate whether and how board-level social capital can moderate the sustainability risks associated with outside CEO succession. Using a panel dataset of 989 pollution-intensive Chinese firms from 2010 to 2022, we apply propensity score matching (PSM) to reduce endogeneity in CEO succession decisions, followed by fixed-effects regressions. The empirical results show that outside CEOs, particularly during their early tenure, are more likely to prioritize short-term financial performance over environmental goals—due to limited firm-specific knowledge and heightened external pressure. However, external board social capital (e.g., ties to government and industry associations) enhances resource access and post-appointment accountability, while internal social capital (e.g., co-working experience among directors) establishes common norms that facilitate strategic continuity. This study positions board social capital as a relational governance mechanism that complements formal oversight. The findings contribute to succession and environmental research by linking executive origin to sustainability outcomes and provide practical guidance on leveraging board networks to support leadership transitions. Full article