Search Results (7,670)

Logistics alliance and integrated passenger-freight transit are two widely adopted collaborative logistics modes in rural areas. With the rapid development of agricultural e-commerce, rural “first-mile” logistics has become critical for agricultural products' upward circulation, but remains constrained by high costs and insufficient service provision. Existing studies mainly focus on a single transportation mode and pay limited attention to logistics service providers’ strategic choice among alternative modes under government intervention. Using a Stackelberg game framework, this study models the interaction among the government, a logistics service provider, and a rural bus company, and analyzes transportation mode choice and subsidy effectiveness. The results show that government subsidies improve rural “first-mile” logistics service levels and stimulate demand for cargo collection services. Transportation mode choice is jointly influenced by market share, service cost coefficient, and subsidy intensity. Large-scale logistics service providers tend to adopt the integrated passenger-freight transit mode when subsidies are insufficient, and prefer the logistics alliance mode when subsidy support becomes adequate. These findings suggest that subsidy policies should consider fiscal capacity and regional operating costs: the integrated passenger-freight transit is more suitable under limited budgets, while the logistics alliance becomes preferable for promoting regional logistics development when sufficient subsidies can be sustained. Full article

Unmanned Aircraft Systems (UASs) offer a promising future for aviation operations, even though it suffers larger cyber-related challenges. As such, cyber-resilience becomes a core property for drones’ operations. This paper presents a systematic meta-review of the scientific literature on Unmanned Aircraft Systems cyber-resilience, starting from 28 literature reviews and surveys in the field. This study examines three areas: the typologies of cyber threats being investigated, the cyber-resilience aspects and functions, and how proposed mitigation strategies align with and support these resilience functions. Overall, 69 cyber threats were identified, where Global Positioning System (GPS) spoofing and jamming were the most frequent ones, underscoring the vulnerability of GPS-based navigation systems in UAS. In terms of cyber-resilience functions, the largest focus remains on the identification, protection, and detection of cyber threats, while limited attention emerges to incident handling and post-event recovery. This is confirmed by the higher frequency of preventive, rather than recovery-oriented, mitigation strategies. Overall, the findings point towards a still limited cyber-resilience implementation for Unmanned Aircraft Systems, witnessing the need for more systemic efforts to guarantee truly resilient UAS operations. Full article

Navigation line extraction is essential for visual navigation in agricultural machinery, yet existing methods often perform poorly in complex environments due to challenges such as weed interference, broken crop rows, and leaf adhesion. To enhance the accuracy and robustness of crop row centerline identification, this study proposes an improved segmentation model based on SegNeXt with integrated adaptive region of interest (ROI) extraction for multi-growth-stage maize row perception. Improvements include constructing a Local module via pooling layers to refine contour features of seedling rows and enhance complementary information across feature maps. A multi-scale fusion attention (MFA) is also designed for adaptive weighted fusion during decoding, improving detail representation and generalization. Additionally, Focal Loss is introduced to mitigate background dominance and strengthen learning from sparse positive samples. An adaptive ROI extraction method was also developed to dynamically focus on navigable regions, thereby improving efficiency and localization accuracy. The outcomes revealed that the proposed model achieves a segmentation accuracy of 95.13% and an IoU of 93.86%. The experimental results show that the proposed algorithm achieves a processing speed of 27 frames per second (fps) on GPU and 16.8 fps on an embedded Jetson TX2 platform. This performance meets the real-time requirements for agricultural machinery operations. This study offers an efficient and reliable perception solution for vision-based navigation in maize fields. Full article

Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article

Image deraining is a crucial preprocessing task for enhancing the robustness of high-level vision systems under adverse weather conditions. However, most of the existing methods are limited to a single RGB color space, and it is difficult to effectively separate high-frequency rain streaks from low-frequency backgrounds, resulting in color distortion and detail loss in the restored image. Therefore, a rain removal network that combines dual-color space and frequency domain priors is proposed. Specifically, the devised network employs a dual-branch Transformer architecture to extract color and structural features from the RGB and YCbCr color spaces, respectively. Meanwhile, a Hybrid Attention Feedforward Block (HAFB) is constructed. HAFB achieves feature enhancement and regional focus through a progressive perception selection mechanism and a multi-scale feature extraction architecture, thereby effectively separating rain streaks from the background. Furthermore, a Wavelet-Gated Cross-Attention module is designed, including a Wavelet-Enhanced Attention Block (WEAB) and a Dual Cross-Attention module (DCA). This design enhances the complementary fusion of structural information and color features through frequency-domain guidance and bidirectional semantic interaction. Finally, experimental results on multiple datasets (i.e., Rain100L, Rain100H, Rain800, Rain12, and SPA-Data) demonstrate that the proposed method outperforms other approaches. Full article

Using the panel data of Chinese A-share firms from 2012 to 2023, we find that executives’ focus on digitalization is significantly and positively associated with corporate sustainability performance. The finding holds firm after a suite of endogeneity and robustness tests. Heterogeneity tests indicate that such a favorable impact is more salient for large enterprises, industry players with superior competitiveness, and entities situated in eastern China. The mechanism tests reveal that executives’ digital attention enhances corporate sustainable development by improving resource structuring capability, resource bundling capability, and resource leveraging capability. Additionally, financing constraints weaken, while media attention will enhance this promoting effect. Additional dimension-focused analyses demonstrate that the direct promotional impact of executives’ digital attention on corporate financial performance remains statistically insignificant, whereas it exerts a markedly positive catalytic effect on corporate environmental performance. This research offers novel theoretical interpretations and practical implications regarding the role of executive cognitive orientation in advancing corporate sustainable development against the backdrop of digital transformation. Full article

The liner temperature distribution of an aero-engine combustor is a critical parameter for evaluating its cooling effectiveness. It provides essential guidance for designing the combustor cooling flow field, assessing combustion performance, identifying critical regions, and predicting service life. However, current research on surface temperature field measurements in real or model aero-engine combustors remains limited. Existing studies focus primarily on the liner temperature measurement under near-steady-state conditions, with less attention to its dynamic changes. This study employs Laser-Induced Phosphorescence (LIP) thermometry to measure the effusion-cooled liner temperature field of an aero-engine model combustor under various CH₄/Air swirling premixed flame conditions and varying blowing ratios. Based on the geometric characteristics of the effusion-cooled liner, an optimization method for matching phosphorescence images of different wavelengths is proposed. This enhances the applicability of phosphorescence intensity ratio-based LIP thermometry in high-vibration environments. The study specifically focuses on the dynamic response of LIP thermometry for monitoring combustor liner temperature. The instantaneous effects of blowing ratio variations on liner temperature rise rates were investigated. Additionally, the influence mechanisms of a broad range of combustion conditions and the blowing ratios on the combustor liner temperature distribution and cooling effectiveness were examined. These findings provide theoretical and technical support for cooling design and dynamic liner temperature field measurement in real aero-engine combustors. Full article

Remote sensing image scene classification holds significant application value and has long been a research hotspot in remote sensing. However, remote sensing images contain diverse objects and complex backgrounds. Reducing background interference while focusing on key target regions in the images remains a challenge, which limits the potential improvement of classification accuracy. In this paper, a local image generation module (LIGM) is proposed to generate weights for the original images. The resulting local images, generated by weighting the original images, effectively focus on key target regions while suppressing background regions. Based on the LIGM, a two-stream architecture with self-distillation and local streams (SDLS) is proposed. The self-distillation stream extracts features from the original images using a convolutional neural network (CNN) and two MobileNetV2 networks. Furthermore, a multiplex-guided attention (MGA) module is introduced into this stream to facilitate cross-network attention-guided learning between the CNN and MobileNetV2 features. In the local stream, a MobileNetV2 network is employed to extract features from the local images. The classification logits produced by the two streams are fused, resulting in the final SDLS classification score. Experimental results demonstrate that SDLS achieves competitive performance on multiple datasets. Full article

Religious festivals are increasingly recognized as significant cultural and tourism phenomena, yet their study from a tourism perspective remains underexplored. This systematic literature review examines the thematic focus, stakeholder involvement, research methods, and theoretical frameworks employed in the study of religious festivals. Using the PRISMA framework and the Covidence data management tool, 24 studies were selected from an initial pool of 493. The findings reveal that research on religious festivals has primarily focused on visitor experiences, motivations, perceptions, and impacts, with limited attention to stakeholder integration and theoretical diversity. Notably, religious leaders and ministers, key actors in festival organization, are underrepresented in the literature. This review identifies critical gaps, including the need for sustainability-focused research, broader stakeholder engagement, and the application of diverse theoretical frameworks. By synthesizing existing knowledge, this study provides a roadmap for advancing research on religious festivals and their intersections with tourism. Full article

Landslides in mountainous regions threaten infrastructure and human safety, making high-accuracy landslide inventories crucial for disaster management. However, fine-grained identification using high-resolution remote sensing imagery is hindered by low small-landslide detection accuracy and bare soil spectral interference. The aim of this study is to propose a lightweight UCTransNet with Triplet Attention and Pyramid Kernel Interaction (UCTransNet-TPKI) deep learning model for accurate multi-scale landslide extraction. The study area is located in Wushan County, Chongqing. GF-2 imagery from 2022 was collected, along with field sampling data and Mengdong dataset as validation data. The model proposed in this study, named UCTransNet-TPKI, is based on an improved UCTransNet architecture. Its key innovations include the introduction of two critical modules: the Pyramid Kernel Interaction module and the Triplet Attention mechanism. The PKI module captures multi-scale local contextual information in parallel under different receptive fields, significantly enhancing the network’s ability to extract landslide features. Concurrently, the Triplet Attention mechanism effectively refines feature representations by capturing the interaction dependencies across the three dimensions of a feature map. This enables the model to focus more precisely on key areas, such as the main body and edges of a landslide, while simultaneously suppressing interference from background noise. The experimental results show that UCTransNet-TPKI achieved the highest F1-score of 0.9008 and an IoU of 0.8252, outperforming MFFENet, TransLandSeg, and Segformer++. Ablation studies confirmed the contributions of each component, with the PKI module improving IoU by 0.72%, the Triplet Attention mechanism increasing IoU by 0.9%, and their combination yielding a clear synergistic enhancement of overall performance. Furthermore, UCTransNet-TPKI demonstrated strong generalization on the Mengdong dataset, achieving an F1-score of 0.9230 and an IoU of 0.8560. These results demonstrate that UCTransNet-TPKI provides an accurate automated landslide mapping solution, offering significant value for post-disaster emergency response and geological hazard management. Full article

Heavy precipitation events are characterized by sudden onset, limited spatiotemporal scales, rapid evolution, and high disaster potential, posing long-standing challenges in weather forecasting. With the development of deep learning, an increasing number of researchers have leveraged its powerful feature representation and non-linear modeling capabilities to address the challenge of precipitation nowcasting. Despite recent advances in deep learning for precipitation nowcasting, most existing methods do not explicitly separate precipitation from non-precipitation regions. This often leads to the extraction of redundant or irrelevant features, thereby causing models to learn misleading patterns and ultimately reducing their predictive capability for heavy precipitation events. To address this issue, we propose a novel dynamic region attention (DRA) mechanism, and an improved model TPDTC-Net-DRA, based on our previously introduced TPDTC-Net. The proposed TPDTC-Net-DRA applies the DRA mechanism and incorporates its two key components: a dynamic region module and a weight control module. The dynamic region module generates a mask matrix that is applied to the feature maps, guiding the attention mechanism to focus only on precipitation areas. Meanwhile, the weight control module produces a location-sensitive weight matrix to direct the model’s attention toward regions with intense precipitation. Extensive experiments demonstrate that TPDTC-Net-DRA achieves superior performance for heavy precipitation, outperforming current state-of-the-art methods, and indicate that the proposed DRA mechanism exhibits strong generalization ability across diverse model architectures. Full article

There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments. Full article

Background. The rapid expansion of esports within higher education, accelerated by the COVID-19 pandemic, has raised important questions regarding their impact on students’ physical and psychological development. While traditional sports are well known for their benefits on motor and physical skills, esports primarily engage cognitive processes through sustained interaction with digital environments. This study compares motor skills and cognitive performance among higher education male students participating in esports and traditional sports in a post-pandemic context. Methods. The present study employs a quantitative, comparative, cross-sectional design to examine differences in motor skills (using standardized physical tests) and cognitive performance (focused attention, short-term memory, and information processing speed) between higher education male students engaged in esports and those participating in traditional sports. Results. Male students engaged in traditional sports demonstrated superior motor outcomes, particularly in muscle strength and postural control. Cognitive performance was comparable between groups, with a slight advantage for traditional sports participants in focused attention and processing speed. Conclusions. Although esports may support certain aspects of cognitive performance to a degree comparable with traditional sports, they do not provide equivalent benefits in terms of motor and postural development. These results highlight the importance of maintaining physical activity within university settings and suggest that esports should complement rather than replace traditional sports in higher education programs. Full article

Cerebrovascular diseases, encompassing a spectrum of conditions affecting the blood vessels supplying the brain, represent a significant global health burden. Among the diverse mechanisms implicated in cerebrovascular pathology, emerging evidence highlights the role of ferroptosis, a regulated form of cell death characterized by iron-dependent lipid peroxidation. The review also elucidates the molecular mechanisms underlying ferroptosis, emphasizing the pivotal role of iron, the intracellular antioxidant system, and lipid metabolism. Subsequently, it explores the growing body of literature implicating ferroptosis in the pathogenesis of various cerebrovascular diseases, including atherosclerosis, ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage. Special attention is given to the interplay between ferroptosis and other established mechanisms, such as oxidative stress, and inflammation. Moreover, pharmacological interventions and therapeutic strategies aimed at modulating key players in the ferroptosis cascade are explored, with a focus on their translational potential for clinical application. Finally, the review addresses current gaps in knowledge and proposes future research directions, emphasizing the need for a deeper understanding of the specific roles of ferroptosis in the pathogenesis of cerebrovascular diseases. The elucidation of these aspects holds promise for advancing our comprehension of cerebrovascular pathology and opening new avenues for therapeutic intervention in these debilitating conditions. Full article

Structural health monitoring of steel buildings requires accurate detection and localization of bolt loosening, a critical yet challenging task due to complex joint geometries and varying environmental conditions. We propose an intelligent framework that integrates an improved YOLOv9 model with multi-view image fusion to address this problem. The method constructs a comprehensive dataset with multi-angle images under diverse lighting, occlusion, and loosening conditions, annotated with multi-task labels for precise training. The YOLOv9 backbone is enhanced with attention mechanisms to focus on key bolt features, while an angle-aware detection head regresses both bounding boxes and rotation angles, enabling loosening state determination through a threshold-based criterion. Furthermore, the framework unifies camera coordinate systems and employs epipolar geometry to fuse 2D detections from multiple views, reconstructing 3D bolt positions and orientations for precise localization. The proposed method achieves robust performance in detecting loosening angles and spatially localizing bolts, offering a practical solution for real-world structural inspections. Its significance lies in the integration of advanced deep learning with multi-view geometry, providing a scalable and automated approach to enhance safety and maintenance efficiency in steel structures. Full article