Search Results (95,557)

Adverse weather and low illumination remain major challenges for autonomous driving perception, where semantic segmentation must stay reliable despite severe appearance degradation. In unsupervised domain adaptation without target annotations, self-training is widely used, but it is often limited by the inconsistent quality of teacher-generated pseudo labels across samples, regions, and training stages. This paper presents RaDA, a reliability-aware self-training framework that regulates pseudo supervision at three levels. First, a progressive exposure strategy determines which target images are admitted for training. Second, spatial reliability weighting suppresses gradients from degraded regions while retaining informative supervision. Third, adaptive teacher update scheduling stabilizes pseudo label generation over time. Experiments on real-world adverse driving benchmarks show that RaDA improves robustness, training stability, and cross-dataset generalization compared with strong baselines. Compared with the previous state-of-the-art method MIC, RaDA achieves mIoU gains of 10.6 percentage points on Foggy Zurich and 8.8 percentage points on the Foggy Driving benchmark. These results indicate that explicit reliability regulation can strengthen self-training domain adaptation for semantic segmentation in autonomous driving under challenging environmental conditions. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

To address the challenge of detecting weak targets with small radar cross-sections (RCS), this work explores an integrated framework that leverages cross-level multimodal fusion of radar echoes. This method considers the target’s motion properties via Doppler spectrum and phase sequences (direct physical level), and introduces the Gramian Angular Field (GAF) to map the echo amplitude sequence into two-dimensional (2D) structured images, thereby revealing the dynamic evolution characteristics of echo energy (abstract representation level). This approach integrates direct physical attributes and abstract system evolution features within a unified representation. To accommodate the structural differences among modalities, a heterogeneous branch processing network is designed: the Transformer is employed to capture long-range dependencies in one-dimensional (1D) sequences, while ResNet18 is used to extract spatial texture features from two-dimensional images. A self-attention mechanism is further introduced to achieve adaptive fusion of the multimodal data. Experimental results based on the IPIX dataset suggest that this cross-level strategy provides improved detection performance across various scenarios, as observed in complex marine environments. Full article

Instance segmentation of moso bamboo cells is a critical step in quantitative structural analysis of bamboo materials and plant phenomics research. Moso bamboo tissues are mainly composed of vascular bundles and parenchyma cells. Within vascular bundles, fiber cells exhibit thick cell walls and extremely dense arrangements, whereas vessel cells are characterized by large diameters and complex internal structures. These features frequently lead to blurred boundaries, structural complexity, and local overexposure in microscopic images, making it difficult for traditional segmentation algorithms to achieve stable and accurate results. Although the U-Net has demonstrated outstanding performance in biological microscopic image analysis, its feature extraction capability and boundary recognition stability remain insufficient when dealing with the composite structure of moso bamboo. To address these challenges, this study proposes an improved model based on a multi-scale attention mechanism, termed MBMSA-UNet (Moso Bamboo Multi-Scale Attention U-Net). Building upon the encoder–decoder architecture of U-Net, the proposed model introduces a multi-scale channel-spatial attention block, aiming to handle the pronounced morphological and scale differences among vessels, fibers, and parenchyma cells. By adaptively reweighting features at different scales, the model enhances cross-layer feature fusion and strengthens responses to key regions, thereby effectively suppressing local overexposure interference and emphasizing boundary features between different cell types. Experimental results demonstrate that, compared with the U-Net and several of its improved variants, MBMSA-UNet achieves higher segmentation accuracy and greater robustness on microscopic images of moso bamboo, providing a solid foundation for fine-grained quantitative analysis of complex bamboo tissues. Full article

Maritime ship target tracking plays an important role in applications such as maritime patrol and maritime surveillance. However, complex sea conditions, similar target appearances, and long-distance imaging often lead to target identity confusion and unstable trajectories. To address these issues, in this paper, a ship multi-object tracking algorithm, DeepSORT-OCR, that integrates hull number semantic features is proposed. Based on the YOLO detection framework and the DeepSORT tracking architecture, a CBAM-ResNet network is introduced to enhance the representation of ship appearance features. An Inner-SIoU metric is adopted to improve the geometric matching of slender ship targets, while an LSTM-Adaptive Kalman Filter is employed to model the nonlinear motion patterns of ships and improve trajectory prediction stability. In addition, a Hull Number Feature Extraction module is designed in order to recognize ship hull numbers using OCR and match them with a hull number database. The extracted hull number semantic features are dynamically fused with visual appearance features to strengthen identity constraints during target association. The experimental results show that the proposed method achieves an MOTA of 66.53% on the MOT16 dataset, representing an improvement of 5.13% over DeepSORT. On the self-constructed maritime ship dataset, the method achieves an MOTA of 70.89% and an MOTP of 80.84%. Furthermore, on the hull-number subset, the MOTA further increases to 77.18%, an improvement of 7.31% compared with DeepSORT, while the number of ID switches is significantly reduced. In addition, experiments conducted on pure real data, pure synthetic data, and cross-domain evaluation settings demonstrate the stability and strong generalization capability of the proposed algorithm under different data distributions. The proposed method effectively improves the stability and identity consistency of ship multi-object tracking in complex maritime environments. Full article

The current investigation, couched within variationist sociolinguistics, has the purpose of advancing knowledge of regional variation in mood use (the subjunctive and indicative contrast) in Spanish. Prior cross-dialectal research has reported that mood use in Spanish varies geographically. To contribute to the understanding of mood variation in Spanish, this study explored a range of sociolinguistic independent variables across three Spanish-speaking regions. The participant pool (N = 107) consisted of Spanish speakers residing in three metropolitan areas (Rosario, Argentina; Barcelona, Spain; and Seville, Spain). The analysis substantiated evidence of geographical variation in the frequency of use of verbal moods, the governors (e.g., preferir que ‘to prefer that’) that exhibited categorical and variable use, and the influence of time reference on mood use. These results provide additional insights into the presence of regional variation in mood use and reinforce the value of cross-dialectal analyses with the same type of data and mood-use contexts. Full article

With the widespread integration of smart buildings equipped with distributed photovoltaics (PV) and electric vehicles (EVs), distribution networks face significant challenges arising from source-load fluctuations. Conventional centralized dispatch approaches are constrained by communication bottlenecks and data privacy requirements. These limitations make it difficult to achieve global coordination while preserving the autonomy of individual entities. This paper proposes a hierarchical coordination framework for the coordinated operation of distribution networks and smart buildings. The distribution management system (DMS) and building energy management systems (BEMSs) perform independent optimization within their respective domains. Only aggregated boundary power information is exchanged to protect data privacy, enabling cross-entity coordination under information boundary constraints. Building-side models incorporating thermal dynamics, EV charging and discharging, and PV generation are developed, along with a distribution network power flow model. To solve the coordinated optimization problem, an Anderson-accelerated alternating direction method of multipliers (AA-ADMM) is introduced. A safeguarding mechanism based on combined residuals is incorporated to enhance convergence efficiency and stability. Case studies on the IEEE 33-bus test system demonstrate that compared with the uncoordinated baseline, the proposed method reduces network loss by 12.1% and lowers PV curtailment from 9.20% to 0.52%, while improving voltage profiles without significantly compromising occupant comfort or EV travel requirements. In addition, AA-ADMM achieves convergence with up to 66% fewer iterations than standard ADMM. Full article

Amid the accelerating waves of global digitalization and the deepening interplay of cultural diversity, urban creative spaces have become pivotal arenas for the digital creative industry—yet a systematic, cross-culturally robust tool for assessing their sustainability remains conspicuously absent. Here, we address this gap by constructing a multi-dimensional evaluation framework derived from a systematic literature review, comprising five primary dimensions—AIGC technology integration, cultural heritage preservation, the economic benefits of the digital cultural industry, ecological synergy and social inclusiveness, and governance and policy support—along with 20 secondary indicators. To enhance methodological rigor, we integrate the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) to determine indicator weights while mitigating the subjective biases inherent in traditional approaches and employ the TOPSIS method to quantitatively assess and rank the creative spaces of five representative cities: London, Shanghai, Los Angeles, Tokyo, and Berlin. Our findings reveal that London leads in comprehensive sustainability, followed closely by Shanghai, with sensitivity analysis confirming the high robustness of the rankings. The originality of this work lies in reconceptualizing AIGC not as a conventional digital instrument but as a core transformative driver embedded within the evaluation architecture, while the application of IFAHP substantially enhances the scientific validity and methodological reliability of the assessment. This research provides an operational diagnostic tool and actionable optimization pathways for advancing the sustainability of urban creative spaces worldwide, offering practical implications for fostering cultural innovation, bridging the digital divide, promoting social inclusiveness, and informing evidence-based urban governance policies. Full article

Background: Societal stigma toward eating disorders and obesity remains pervasive and is associated with psychological distress, maladaptive eating behaviors, reduced help-seeking, and barriers to care. Despite its documented impact, comprehensive and psychometrically robust instruments to assess stigma—particularly in Spanish-speaking populations—are scarce. This study aimed to develop and validate a multidimensional measure of societal stigma toward eating disorders and obesity in Spain, grounded in contemporary stigma frameworks. Methods: A cross-sectional observational study was conducted in a large community sample recruited online (N = 2121). An initial pool of stigma-related items was developed based on theoretical and empirical literature and refined through expert content validation. Psychometric evaluation included item screening, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), bifactor modeling, and reliability assessment. The sample was randomly split for EFA (n = 988) and CFA (n = 658). Associations between stigma scores and sociodemographic and experiential variables were examined. Results: The final 36-item instrument demonstrated excellent psychometric properties. Bifactor analyses supported an essentially unidimensional structure dominated by a strong general stigma factor, with secondary content-specific dimensions (e.g., legitimacy, personal responsibility, visibility, and treatment beliefs). The theory-driven bifactor model showed excellent fit (CFI = 0.991; TLI = 0.990; RMSEA = 0.024). The general factor exhibited high reliability (ωₕ = 0.87). Higher stigma was observed among men, older participants, and individuals without personal or familial experience of eating disorders or obesity. Conclusions: This study provides a reliable and theoretically grounded instrument for assessing societal stigma toward eating disorders and obesity in Spain. The scale enables systematic research on stigma and offers a valuable tool for public health surveillance, intervention development, and evaluation of anti-stigma initiatives aimed at promoting compassionate and equitable care. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (𝓛_align), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

The mixed lymphocyte reaction (MLR) is a classic functional assay that models in vitro interactions between responder T cells and allogeneic antigen-presenting cells (APCs). It quantifies the magnitude and quality of alloreactivity, integrating signals from allorecognition, co-stimulation, inflammatory context, and minor histocompatibility antigens that may not be captured by molecular matching alone. This review is narrative in nature and is intended as a practical, non-systematic synthesis of the field. To provide a modern, practice-oriented synthesis of MLR designs, readouts, and translational uses, highlighting how new technologies have expanded MLR from bulk proliferation into multidimensional immune profiling.We summarize why MLR remains valuable as a functional compatibility probe beyond HLA typing, including the high baseline frequency of alloreactive T cells that produces robust signals without priming. We then review key design options (one-way vs. two-way formats; stimulator inactivation; responder definition; APC source and maturation) and how these choices affect interpretation for rejection and graft-versus-host disease risk modeling, tolerance-focused studies, and immunomodulatory screening. Next, we outline major readouts—radiometric and flow cytometric proliferation (dye dilution, Ki-67), cytokine/chemokine profiling, cytotoxicity adaptations, and next-generation add-ons (e.g., scRNA-seq, TCR sequencing)—emphasizing complementary strengths and common pitfalls. Finally, we consolidate practical quality and reproducibility controls (donor variability, dynamic range, timing, batch effects, and acceptance criteria) to improve cross-study comparability and translational readiness. Modern MLR platforms combine controllable allogeneic stimulation with scalable, high-resolution readouts for mechanistic discovery, immune monitoring and translational immune profiling. Standardized modular design and rigorous quality control can improve reproducibility and support broader adoption across transplantation, immunotherapy, and immune-modulation research. Full article

Diabetes mellitus is a rapidly escalating worldwide health issue that involves intricate molecular, metabolic, and systemic dysregulation. In addition to hyperglycemia, disease pathogenesis involves β-cell dysfunction, insulin resistance, mitochondrial dysfunction, endoplasmic reticulum stress (ER stress), redox imbalance, lipotoxicity, chronic inflammation, and inappropriate epigenetic modifications. New evidence also emphasizes the participation of mechanotransduction, ion channel signaling, circadian regulation, and organ cross-talk among the pancreas, liver, adipose tissue, skeletal muscle, heart, brain, and gut in modulating disease heterogeneity and progression. This review highlights updates of molecular mechanisms in diabetes, focusing on the β-cell response to stress, the AMPK–Sirtuin 1 (or PGC-1α) signaling pathway, mitochondrial quality control, mechanosensitive ion channels, immunometabolic crosstalk, and epigenetic regulation. We consider the increasing importance of multi-omics methods for early identification of pathogenic signatures and integration of artificial intelligence to enable precision stratification and therapeutic tailoring. Finally, we highlight novel experimental and translational tools, such as iPSC-derived β-cells or organoids, CRISPR-based gene editing, sophisticated metabolic imaging, and electrophysiology. Taken together, this review shifts the paradigm of diabetes as a system-level network disease and emphasizes the importance of data-driven multi-target strategies for prevention and reduction in long-term complications. Full article

Foreign object intrusion in railway tracks is a major threat to train operation safety, yet current detection methods face challenges in identifying small distant targets and adapting to low-light conditions. Moreover, existing systems often lack the ability to assess intrusion risk levels, limiting real-time warning and graded response capabilities. To address these gaps, this paper proposes a novel method for intrusion detection and risk assessment based on keypoint spatial discrimination. First, an XS-BiSeNetV2-based track segmentation network is developed, incorporating cross-feature fusion and spatial feature recalibration to improve track extraction accuracy in complex scenes. Second, an enhanced STI-YOLO detection model is introduced, integrating a Shuffle attention mechanism for better feature interaction, a high-resolution Transformer detection head to improve small-target sensitivity, and the Inner-IoU loss function to refine bounding box regression. Detected targets’ bottom keypoints are then analyzed relative to track boundaries to determine intrusion direction. By combining lateral distance and motion state features, a multi-level risk classification system is established for quantitative threat assessment. Experiments on the RailSem19 and GN-rail-Object datasets show that the method achieves a track segmentation mIoU of 88.19% and a detection mAP of 82.6%. The risk assessment module effectively quantifies threats across scenarios and maintains stable performance under low-light and strong-glare conditions. This work offers a quantifiable risk assessment solution for intelligent railway safety systems. Full article

Anchored in the strategic framework of Vision 2030, the research departs from anecdotal or survey-based approaches by exclusively leveraging publicly available, auditable data from national ministries, international university rankings, and scholarly publication databases. An original Integrated Green Transformation Framework (IGTF) is operationalized through fixed-effects regression modeling, longitudinal policy document analysis, and cross-sectional benchmarking of sustainability performance indicators across twelve Saudi universities. The findings demonstrate a statistically significant and temporally coherent association between national green policy milestones, such as the Saudi Green Initiative and the National Renewable Energy Program 2018, and measurable improvements in university-level sustainability strategies, operational efficiency, and research output. The average share of renewable energy utilization across sampled institutions increased from 2.1 percent in 2016 to 18.7 percent in 2023, representing substantial progress yet remaining below the Vision 2030 national target of 50%, while per-student water consumption declined by 34 percent over the same period. Scholarly publications in green economy domains rose by 638 percent, with a strong positive correlation (r = 0.76, p < 0.001) between research intensity and curriculum integration of sustainability content. Despite these advances, persistent disparities exist in resource allocation and implementation depth, particularly between historically endowed universities and newer regional institutions, highlighting a “sustainability divide” that requires targeted policy intervention. Full article

Background: Oocyte cryopreservation has emerged as a viable fertility preservation method, gaining popularity among women delaying motherhood for non-medical reasons. This study examines the awareness, perceptions, and social factors influencing young women’s decisions regarding elective oocyte cryopreservation (EOC), intending to identify key demographic and psychosocial determinants. Methods: A cross-sectional study was conducted using an online survey distributed via digital platforms between November 2024 and February 2025. A structured questionnaire comprising 31 multiple-choice questions assessed participants’ sociodemographic characteristics, reproductive health history, lifestyle factors, and perceptions of fertility and EOC. Statistical analyses included Chi-square tests, t-tests, and binary logistic regression to identify factors associated with willingness to undergo EOC. Results: A total of 390 women (mean age 22.57 ± 1.41 years) participated. Awareness of oocyte cryopreservation was remarkably high (93.1%). Significant predictors for the intention to undergo EOC included higher educational attainment (Master’s level) (OR = 4.27, 95% CI: 1.10–16.48) and living in a student dormitory (OR = 15.39, 95% CI: 4.86–48.71). Conversely, living with a partner showed a non-significant downward trend in interest (OR = 0.07, 95% CI: 0.01–1.43). Psychological factors, specifically anxiety about future fertility (OR = 0.23, 95% CI: 0.08–0.62 for moderate vs. high anxiety) and a strong desire for future parenthood (OR = 21.75, 95% CI: 1.45–32.99), also emerged as primary drivers of women’s reproductive decisions. Conclusions: Despite high awareness, the willingness to undergo elective oocyte cryopreservation remains limited. Targeted fertility education and supportive policies are needed to address misconceptions, financial barriers, and psychological concerns influencing reproductive decision-making. Further research should explore longitudinal trends in women’s attitudes toward EOC. Full article