Search Results (88,617)

Forest fires occur frequently in China; however, the complex terrain and incomplete road networks severely constrain ground rescue efficiency. Accurate forest road information is essential for the optimization of emergency response and rescue force deployment. Existing road extraction algorithms are primarily designed for urban environments and exhibit limited efficacy in forest scenarios due to dense canopy, complex background interference and specific forest road features. To address this gap, this study proposes a forest road extraction method based on an enhanced DeepLabv3+ model using multi-temporal, high-resolution satellite imagery. Specifically, a Multi-Scale Channel Attention (MCSA) mechanism is embedded in skip connections to suppress background interference, while strip pooling is integrated into the Atrous Spatial Pyramid Pooling (ASPP) module to better capture slender road features. A composite Focal-Dice loss function is also constructed to mitigate sample imbalance. Finally, by applying the model in multi-temporal remote sensing images, a fusion strategy is introduced to integrate multi-seasonal road masks to enhance overall accuracy and topological integrity. Experimental results show that the proposed method achieves a precision of 54.1%, an F1-Score of 59.3%, and an IoU of 41.8%, effectively enhancing road continuity and providing robust technical support for fire-rescue decision-making. Full article

Infrared small target detection is a critical task in remote sensing. However, it remains highly challenging due to low contrast, heavy background clutter, and large variations in target scale. Traditional convolutional networks are inadequate for joint modeling, as they cannot effectively capture both fine structural details and global contextual dependencies. To address these issues, we propose FSGPNet, a frequency–spatial domain jointly guided perceptual network that explicitly exploits complementary representations in both the frequency and spatial domains. Specifically, a Frequency–Spatial Enhancement Module (FSEM) is introduced to strengthen target details while suppressing background interference through high-frequency enhancement and Perona–Malik diffusion. To enhance global context modeling, we propose a Multi-Scale Global Perception (MSGP) module that integrates non-local attention with multi-scale dilated convolutions, enabling robust background modeling. Furthermore, a Gabor Transformer Attention Module (GTAM) is designed to achieve selective frequency–spatial feature aggregation via self-attention over multi-directional and multi-scale Gabor responses, effectively highlighting discriminative structures of various small targets. Extensive experiments are conducted on two benchmark datasets (IRSTD-1K and NUDT-SIRST) that cover typical remote sensing infrared scenarios. Quantitative and qualitative results demonstrate that FSGPNet consistently outperforms state-of-the-art methods across multiple evaluation metrics. These findings validate the effectiveness and robustness of the proposed FSGPNet for detecting small infrared targets in remote sensing applications. Full article

Background: Adolescence is a critical developmental period during which dietary habits are formed and may influence long-term health outcomes. Monitoring changes in adolescents’ eating behaviors and nutrition-related knowledge over time is important for developing effective health promotion strategies. The aim of this study was to compare adolescents’ (Lower Silesia, Poland) dietary habits and nutritional knowledge between two study periods (2011 and 2023) using comparable survey methods. Methods: A repeated cross-sectional comparison of two independent cohorts was conducted using an identical questionnaire in both study periods. The 2023 cohort included 14-year-old primary school students (n = 100; 48 girls and 52 boys), while the comparison group consisted of adolescents aged 13–15 years assessed in 2011 (n = 377; 202 girls and 175 boys). Anthropometric measurements and self-reported data on dietary habits and nutritional knowledge were analyzed using descriptive statistics and group comparison tests. Results: The findings indicate changes in selected dietary behaviors and levels of nutritional knowledge among adolescents over the studied period. A higher percentage of students in 2023 reported eating four meals per day and obtaining information about healthy eating from the Internet rather than from television. Students in 2023 were also more likely to recognize the relationship between diet and attention, identify the harmful effects of energy drinks and excessive fast-food consumption, and provide correct answers regarding proper nutrition. Nutritional knowledge improved over time, with a mean percentage of correct responses of 71.9% in 2023 compared with 63.7% in 2011. Although nutritional awareness improved in several areas, certain unhealthy eating habits remained prevalent, including irregular breakfast consumption and frequent intake of sweets. Changes in the distribution of body weight categories were also observed, with gender-specific differences between cohorts. Conclusions: The results suggest that improvements in nutritional knowledge alone may not be sufficient to ensure positive changes in dietary behavior among adolescents. Continued monitoring of adolescent nutrition and the development of comprehensive health promotion strategies addressing both knowledge and environmental influences remain necessary. Full article

E-commerce platforms are increasingly adopting cross-platform targeted advertising strategies, and the design of such strategies warrants attention. Focusing on cooperation between e-commerce and social media platforms, this study considers targeting precision, advertising intensity, privacy concerns and social utility on the effectiveness of targeted advertising. Using a game-theoretic model, we examine the decision between single- and cross-platform for e-commerce platforms in fully and partially overlapping user groups. The main findings indicate that (1) the social utility of social media platforms is a key factor in implementing cross-platform targeted advertising; (2) cross-platform targeted advertising is not always the optimal choice for e-commerce platforms; and (3) low-precision cross-platform strategy achieves three-party optimum in fully and partially overlapping user groups. The implications of the main findings include: (1) e-commerce platforms should prudently use social media platforms instead of relying excessively on their traffic; (2) e-commerce platforms should not regard cross-platform cooperation as the default option but as a differentiated, situation-specific decision; and (3) e-commerce platforms should promote co-creation of value and proprietary data accumulation when cooperating with social media platforms. The findings can help e-commerce platforms to choose proper targeted advertising strategy in practice. This study also provides a theoretical supplement for cross-platform targeted advertising research. Full article

The extensive cultivation scale of sugar beet seedlings has resulted in the necessity for accurate identification and monitoring of the seedling count, a task which has become crucial and highly challenging in the sugar industry. However, sugar beet seedlings in UAV aerial photography scenarios are mostly small targets with complex backgrounds. Existing general detection models not only have insufficient detection accuracy, but also struggle to balance computational efficiency and resource consumption. To meet the practical needs of field monitoring, this paper proposes the LDH-RTDETR, a sugar beet seedling detection model that balances high accuracy and light weight. This model uses LSNet for feature extraction to reduce size, adds a deformable attention (DAttention) module to capture fine-grained seedling features, and adopts HS-FPN to improve multi-scale feature fusion in the neck network. Experimental results show that the improved model significantly outperforms the original RT-DETR model, with a 3.6% increase in accuracy, a 2.1% increase in mAP50, a recall rate of 86.0%, and a final model size of only 43.3 MB, thus achieving an effective balance between accuracy and model size. This study’s improved model offers an efficient solution for large-area identification and counting of sugar beet seedlings, and is highly significant for advancing the automation of sugar crop field management and agricultural digital transformation. Full article

In abnormal goat lung sound recognition, high inter-class similarity and large intra-class variability pose significant challenges. To address this issue and improve recognition performance, we propose a deep learning model, AAF-SwinT, based on an improved Swin Transformer. The model replaces the original Swin Transformer self-attention module with Axial Decomposed Attention (ADA), modeling the temporal and frequency axes separately and integrating attention weights to mitigate inter-class feature similarity. Adaptive Spatial Aggregation for Patch Merging (ASAP) is designed to emphasize key time-frequency regions, and a Frequency-Aware Multi-Layer Perceptron (FAM) is introduced to model features across different frequency bands, further enhancing the discriminative ability for abnormal lung sounds. Experiments on a self-constructed goat lung sound dataset demonstrate that AAF-SwinT achieves an accuracy of 88.21%, outperforming existing mainstream Transformer-based models by 2.68–5.98%. Ablation studies further confirm the effectiveness of each proposed module, improving the accuracy of baseline Swin Transformer model from 85.53% to 88.21%. These results indicate that the proposed approach exhibits strong robustness and practical potential for abnormal lung sound recognition in goats, providing technical support for early diagnosis and management of respiratory diseases in large-scale goat farming. Full article

Building height is a key indicator of vertical urbanization and urban morphological complexity, yet accurately mapping building height at fine spatial resolution and large spatial scales remains challenging. This study proposes an attention-based deep learning framework (ABHNet) for building height estimation at a 10 m spatial resolution by integrating multi-source remote sensing data and socioeconomic information. The model jointly exploits Sentinel-1 synthetic aperture radar data, Sentinel-2 multispectral imagery, and point of interest (POI) data. The proposed framework is evaluated in Shanghai, a megacity with dense and vertically complex urban structures, using Baidu Maps-derived building height data as reference information. The results demonstrate that the proposed method achieves accurate building height estimation, with a root mean squared error (RMSE) of 3.81 m and a mean absolute error (MAE) of 0.96 m for 2023, and an RMSE of 3.30 m and an MAE of 0.78 m for 2019, indicating robust performance across different time periods. Also, this model is applied in two other cities (Changzhou and Guiyang) and the results indicate good performance. In addition, the expandability of the framework is examined by incorporating higher-resolution ZY-3 imagery, for which the spatial resolution was increased to 2.5 m, highlighting the potential extension of the model to heterogeneous data sources. Overall, this study demonstrates the effectiveness of attention-based deep learning and multimodal data fusion for large-scale and fine-resolution building height estimation using open-source data. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Neurodegenerative diseases of the central nervous system, such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, represent a growing health challenge in ageing populations. Among the mechanisms underlying these disorders, increasing attention has been directed toward the role of cellular senescence. This process, triggered by chronic cellular and oxidative stress as well as DNA damage, leads to irreversible cell-cycle arrest and the development of the senescence-associated secretory phenotype (SASP). Within the central nervous system, the accumulation of senescent cells induces chronic inflammation, blood–brain barrier disruption, and progression of neurodegenerative processes. In this review, we present current evidence regarding the mechanisms of cellular senescence in the central nervous system, with particular emphasis on the role of SASP in neuroinflammation, vascular dysfunction, and neural tissue damage. Experimental and clinical data supporting the involvement of cellular senescence in the pathogenesis of Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis are discussed. The review also covers methods for identifying senescent cells in the brain, including molecular marker-based approaches and machine learning-based tools. Importantly, we discuss the methodological limitations of commonly used senescence markers, such as their limited specificity and the risk of false-positive detection, particularly in the heterogeneous cellular environment of the central nervous system. Strategies to improve detection reliability discussed in this review include the use of multimarker signatures, analysis of SASP components using qRT-PCR and ELISA, as well as transcriptomic approaches such as RNA sequencing and single-cell RNA sequencing. Furthermore, we analyze therapeutic strategies targeting senescent cells—senolytics, senomorphics, and SASP modulation—together with their limitations and associated clinical challenges. The collected evidence indicates that precise characterization of senescent cell populations in the brain is essential for the development of disease-modifying therapies for neurodegenerative disorders. Full article

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article

Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system scheduling. An uncertainty-aware temporal transformer framework for wind power forecasting is presented, integrating probabilistic modeling with deep temporal representation learning to jointly optimize prediction accuracy and uncertainty characterization. Crucially, rather than treating uncertainty quantification merely as a post-processing step, the central conceptual contribution lies in modularizing uncertainty directly within the attention mechanism. A probability-driven temporal attention mechanism is incorporated at the encoding stage to emphasize high-variability and high-risk time slices during feature aggregation, while a multi-quantile output and interval modeling strategy is adopted at the prediction stage to directly learn the conditional distribution of wind power, enabling simultaneous point and interval forecasts with statistical confidence. Extensive experiments on multiple public wind power datasets demonstrate that the proposed method consistently outperforms traditional statistical models, deep temporal models, and deterministic transformers, as validated by formal statistical significance testing. Specifically, the method achieves an MAE of $0.089$, an RMSE of $0.132$, and a MAPE of $10.84\%$ on the test set, corresponding to reductions of approximately $8\%$–$10\%$ relative to the deterministic transformer. In uncertainty evaluation, a PICP of $0.91$ is attained while compressing the MPIW to $0.221$ and reducing the CWC to $0.241$, indicating a favorable balance between coverage reliability and interval compactness. Compared with mainstream probabilistic forecasting methods, the model further reduces RMSE while maintaining coverage levels close to the 90% target, effectively mitigating excessive interval conservatism. Moreover, by adaptively generating heteroscedastic intervals that widen during high-volatility events and narrow under stable conditions, the model achieves a highly focused and effective capture of critical uncertainty information. Full article

Background. Smart warehousing increasingly relies on digital twin technologies to enhance operational efficiency, real-time visibility, and decision-making in logistics systems. However, existing research primarily focuses on technological capabilities while paying limited attention to the organizational practices that shape successful implementation. Methods. This study aims to identify and prioritize the critical success factors (CSFs) for integrating digital twins into smart warehousing using the Practice-Based View (PBV) as the theoretical lens. Based on insights from prior research and expert validation, nine CSFs were identified and evaluated using the Best–Worst Method (BWM). Empirical input was obtained from six industry experts with experience in digital transformation, warehousing, and supply chain management. Results. The results indicate that collaborative learning, contextual training, and gamification elements emerge as the most influential critical success factors, highlighting the importance of organizational practices in supporting digital twin adoption in smart warehousing. Conclusions. By linking technological capabilities with organizational routines, the proposed framework provides both theoretical insights and practical guidance for implementing digital twins in smart warehouse environments. Full article

This study investigates how porous structure formation influences the properties and safety characteristics of composite rocket propellants. Particular attention was given to approaches that may support more sustainable propellant formulations and processing methods. The work compares the efficiency of different sample-structuring and foaming methods, including a chemical foaming strategy based on two ammonium salts. Additionally, it evaluates the feasibility of generating porosity in propellants containing glycidyl azide polymer through the retention of a low-boiling solvent, remaining from synthesis. This approach is expected to reduce the number of processing steps and simplify them, translating into lessened environmental impact. Propellants incorporating this polymer were found to exhibit consistent low-level porosity and improved performance compared to other ammonium nitrate-based propellants, constituting a potential sustainable alternative to perchlorate-based propellants. The investigation encompassed decomposition kinetics (including decomposition activation energy), combustion product analysis, and exploratory nitrogen porosimetry. From a sustainability perspective, the investigated approach addresses key limitations of perchlorate-based propellants by eliminating chlorine-containing oxidising agents and reducing the need for auxiliary chemicals. In particular, the physical foaming strategy enables pore formation using residual solvent, which is already present in the system, supporting waste minimisation and inherently safer processing. These aspects are discussed in the context of selected principles of Green Chemistry and fundamental properties–sustainability trade-offs. Overall, the results highlight how foaming method selection affects not only propellant behaviour but also opportunities for more resource-efficient and environmentally conscious manufacturing routes. Full article

Anti-unmanned aerial vehicle (Anti-UAV) detection is critical for airspace security, yet existing single-modality approaches suffer from severe performance degradation under adverse illumination, thermal crossover, and extreme scale variation. In this paper, we propose CSFADet, a dual-modal detection framework that jointly exploits visible and infrared imagery through four tightly integrated modules. First, a Cross-Spectral Feature Alignment (CSFA) module performs early-stage spectral calibration by computing cross-modal query–value attention maps, generating modality-aware channel descriptors that re-weight and concatenate the two spectral streams. Second, a Dual-path Texture Enhancement Module (DTEM) enriches fine-grained spatial details via cascaded convolutions with residual connections. Third, a Dual-path Cross-Attention Module (DCAM) introduces a feature-shrinking token generation strategy followed by symmetric cross-attention branches with learnable scaling factors, Squeeze-and-Excitation recalibration, and a $1\times 1$ convolution fusion head, enabling deep bidirectional interaction between modalities. Fourth, a Dual-path Information Refinement Module (DIRM) embeds Adaptive Residual Groups (ARGs) that cascade Multi-modal Spatial Attention Blocks (MSABs) with channel and dynamic spatial attention, culminating in a Multi-scale Scale-aware Fusion Refinement (MSFR) unit that employs three parallel multi-head attention branches with a Scale Reasoning Gate and Channel Fusion Layer to produce scale-discriminative enhanced features. Experiments on the public Anti-UAV300 benchmark show that CSFADet achieves 91.4% mAP@0.5 and 58.7% mAP@0.5:0.95, surpassing fifteen representative detectors spanning single-stage, two-stage, YOLO-family, and Transformer-based categories. Ablation studies confirm the complementary contributions of each module, and heatmap visualizations verify the model’s capacity to focus on small, distant UAV targets under challenging conditions. Full article

Metabolic diseases, including type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated fatty liver disease (MAFLD), are chronic disorders characterized by dysregulated glucose and lipid homeostasis and represent major contributors to insulin resistance, cardiovascular complications, and liver injury. Despite considerable progress in elucidating their pathogenesis, effective preventive and therapeutic strategies remain limited. N6-methyladenosine (m⁶A) RNA demethylase AlkB homolog 5 (ALKBH5), a nuclear epitranscriptomic “eraser,” broadly regulates post-transcriptional gene expression by modulating RNA splicing, nuclear export, stability, and translation. Dysregulation of ALKBH5 has been implicated in tumorigenesis, immune dysfunction, and stress responses, underscoring its wide-ranging biological significance. Emerging evidence further indicates that ALKBH5 plays a pivotal role in maintaining metabolic homeostasis. However, most existing reviews have focused primarily on its roles in cancer, leaving its functions in metabolic diseases relatively unexplored. In this context, this review summarizes the structural characteristics and molecular mechanisms of ALKBH5 and discusses its emerging roles across a spectrum of metabolic diseases, including MAFLD, metabolic complications such as diabetic retinopathy (DR), diabetes-associated cognitive impairment (DACI), atherosclerosis (AS), and diabetic cardiomyopathy (DCM), as well as metabolism-related inflammatory diseases represented by rheumatoid arthritis (RA). Furthermore, recent pharmacological strategies targeting ALKBH5 are discussed, with attention to the challenges posed by its context-dependent, tissue-specific, and disease stage-specific activities. Overall, ALKBH5 emerges as a key epitranscriptomic regulator in metabolic diseases, and advancing therapeutic strategies that account for molecular context and tissue specificity will be critical for achieving safe and effective clinical interventions. Full article