Search Results (37,940)

High-resolution remote sensing images suffer from large intra-class variance, high inter-class similarity, and significant scale variations, leading to incomplete segmentation and imprecise boundaries. To address these challenges, Transformer-based methods, despite their strong global modeling capability, often suffer from feature confusion, weak detail representation, and high computational cost. Moreover, existing multi-scale fusion mechanisms are prone to semantic misalignment across levels, hindering effective information integration and reducing boundary clarity. To address these issues, a Category-Guided Transformer (CIGFormer) is proposed. Specifically, the Category-Information-Guided Transformer Module (CIGTM) integrates global and local branches: the global branch combines window-based self-attention (WSAM) and window adaptive pooling self-attention (WAPSAM), using class predictions to enhance global context modeling and reduce intra-class and inter-class confusion; the local branch extracts multi-scale structural features to refine semantic representation and boundaries. In addition, an Adaptive Wavelet Fusion Module (AWFM) is designed, which leverages wavelet decomposition and channel-spatial joint attention for dynamic multi-scale fusion while preserving structural details. Extensive experiments on the ISPRS Vaihingen and Potsdam datasets demonstrate that CIGFormer, with only 21.50 M parameters, achieves outstanding performance in small object recognition, boundary refinement, and complex scene parsing, showing strong potential for practical applications. Full article

Estimating the impacts of climate change on streamflow in the Xiaoxingkai Lake Basin is vital for ensuring sustainable water resource management and transboundary cooperation across the entire Xingkai Lake Basin, a transboundary lake system shared between China and Russia. In this study, 11 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to drive the Soil and Water Assessment Tool Plus (SWAT+) model. Streamflow projections were made for two future periods: the 2040s (2021–2060) and the 2080s (2061–2100). To correct for systematic biases in the GCM outputs, we applied the Delta Change method, which significantly reduced root mean square error (RMSE) in both precipitation and temperature by 3–35%, thereby improving the accuracy of SWAT+ simulations. To better capture inter-model variability and enhance the robustness of streamflow projections, we used the Bayesian Model Averaging (BMA) technique to generate a weighted ensemble, which outperformed the simple arithmetic mean by reducing uncertainty across models. Our results indicated that under SSP245, greater increases were projected in annual streamflow as well as in wet and normal-flow seasons (e.g., streamflow in normal-flow season in the 2080s increased by 13.0% under SSP245, compared to 7.0% under SSP585). However, SSP585 produced a much larger relative amplification in the dry season, with percentage changes relative to the historical baseline reaching up to +171.7% in the 2080s, although the corresponding absolute increases remained limited due to the low baseline flow. These findings quantify climate-driven hydrological changes in a cool temperate lake basin by integrating climate projections, hydrological modeling, and ensemble techniques, and highlight their implications for understanding hydrological sustainability under future climate scenarios, providing a critical scientific foundation for developing adaptive, cross-border water management strategies, and for further studies on water resource resilience in transboundary basins. Full article

Generative linguistic steganography aims to produce texts that remain both perceptually and statistically imperceptible. The existing embedding algorithms often suffer from imbalanced candidate selection, where high-probability words are overlooked and low-probability words dominate, leading to reduced coherence and fluency. We introduce a character-based diffusion embedding algorithm (CDEA) that uniquely leverages character-level statistics and a power-law-inspired grouping strategy to better balance candidate word selection. Unlike prior methods, the proposed CDEA explicitly prioritizes high-probability candidates, thereby improving both semantic consistency and text naturalness. When combined with XLNet, it effectively generates longer sensitive sequences while preserving quality. The experimental results showed that CDEA not only produces steganographic texts with higher imperceptibility and fluency but also achieves stronger resistance to steganalysis compared with the existing approaches. Future work will be to enhance statistical imperceptibility, integrate CDEA with larger language models such as GPT-5, and extend applications to cross-lingual, multimodal, and practical IoT or blockchain communication scenarios. Full article

Fuel injection and mixing remain a critical challenge in the development of supersonic propulsion systems. The efficiency of both mixing and combustion significantly influences the overall performance of these systems, underscoring the importance of optimizing fuel injection strategies. Injector arrays are extensively employed in such propulsion systems; however, conventional design methodologies predominantly focus on global mixing efficiency, neglecting injector-specific performance metrics. This research introduces a fuel trajectory tracing methodology, wherein hydrogen from each injector is treated as a distinct species, despite having identical physical and chemical properties. This approach enables the tracking of hydrogen transport and mixing within supersonic flows. The methodology has been demonstrated to accurately capture the mass fraction distribution of hydrogen from individual injectors without perturbing the flow field. Based on these distributions, injector-specific mixing and combustion efficiencies can be quantified, providing valuable insights for optimizing injector configurations and enhancing propulsion system performance. Full article

Under the typical scenario of high-speed mobility, channel disturbances at the physical layer may disturb the transmission of video base layers. Due to the close dependency of Scalable Video Coding (SVC) on base layers, such disturbances will result in retransmissions and handover delays. Meanwhile, ineffective enhancement layers continue to occupy resources, ultimately causing system performance collapse and further exacerbating physical-layer disturbances. To address this challenge, we propose an edge computing resource coordination optimization scheme for highly dynamic mobile terminals. The scheme first empowers the SVC layered transmission with the local caching capabilities, enabling rapid retransmission of base layer data by employing a Lyapunov optimization framework for transmission queue scheduling. Secondly, we design a strategy for dynamically releasing the enhancement layer (EL) cache. This can mitigate resource waste caused by invalid enhancement layers. Finally, Lyapunov drift optimization is implemented to ensure base layer transmission stability and accelerate system state convergence. Simulation and experimental results demonstrate that the proposed scheme significantly improves video transmission reliability and user experience in highly dynamic network environments. Full article

In complicated scenarios, due to the low precision of float solutions and poor reliability of fixed solutions, it is challenging to achieve a balance between accuracy and reliability of the integer least squares (ILS) estimation. To address this dilemma, the best integer equivariant (BIE) estimation, which makes a weighted sum of all possible candidates, has recently been attached great importance. The BIE solution approaches the float solution at a low ILS success rate, maintaining positioning reliability. As the success rate increases, it converges to the fixed solution, facilitating high-precision positioning. Furthermore, the posterior variance of BIE estimation provides the capability of reliability evaluation. However, in environments with a limited number or a deficient configuration of available satellites, there is a sharp decline in the strength of the GNSS precise positioning model. In this case, the exactness of weight allocation for integer candidates in BIE estimation will be severely compromised by unmodeled errors. When the ambiguity is incorrectly fixed, the wrongly determined optimal candidate is probably assigned an excessively high weight. Therefore, the BIE solution in a weak GNSS model always exhibits a significant positioning error consistent with the fixed solution. Moreover, the posterior variance of BIE estimation approximately resembles that of a fixed solution, losing error warning ability. Consequently, the BIE estimation may exhibit lower reliability compared to the ILS estimation employing a validation test with a loose acceptance threshold. To improve the positioning performance in weak GNSS models, a BIE ambiguity resolution (AR) method based on Tikhonov regularization is proposed in this paper. The method introduces Tikhonov regularization into the least squares (LS) estimation and the ILS ambiguity search, mitigating the serious impact of unmodeled errors on the BIE estimation under weak observation conditions. Meanwhile, the regularization factors are appropriately selected by utilizing an optimized approach established on the L-curve method. Simulation experiments and field tests have demonstrated that the method can significantly enhance the positioning accuracy and reliability in weak GNSS models. Compared to the traditional BIE estimation, the proposed method achieved accuracy improvements of 73.6% and 69.3% in the field tests with 10 km and 18 km baselines, respectively. Full article

This study investigates the aroma differences among various peach and nectarine varieties by sensory evaluation, electronic nose (E-nose) analysis, and metabolomics. Peach is a significant fruit crop in China, and identifying unique fragrances is essential for germplasm selection and cultivar improvement. Six peach and nectarine varieties were collected from the National Peach Germplasm Repository in Nanjing, China. Sensory evaluation revealed significant differences in aroma and taste, with ”Zi Jin Hong 3” and “Bai Mi Pan Tao” showing high scores for aroma, sweetness, and overall sensory quality, while “Tachibanawase” had the lowest overall impression score. E-nose analysis showed distinct response values among varieties, with sensors W1S, W1W, and W5S exhibiting the highest sensitivity. GC-MS identified 446 metabolites, including esters and terpenes. PCA and OPLS-DA differentiated metabolite profiles among varieties, revealing significant differences in metabolite expression. The integration of these techniques provides a comprehensive understanding of aroma differences, highlighting the potential for identifying unique germplasms for breeding high-quality cultivars with charming flavor, and offering a theoretical foundation for raw material selection and process optimization in the deep-processing industry of peach fruits in future research. Full article

Driven by the demand for cost-effective vehicle access, enhanced flexibility, and sustainable transportation practices, smart car-sharing has emerged as a prominent alternative to traditional vehicle rental systems. Leveraging the Internet of Vehicles (IoV) and wireless communication, these systems feature dynamic renter-vehicle mappings, enabling users to access any available vehicle rather than being restricted to a specific one pre-assigned by the service provider. However, many existing schemes in the IoV field conflate users and vehicles, complicating the identification and tracking of the vehicle’s actual driver. Moreover, most current authentication protocols rely on a strict, initial binding between a user and a vehicle, rendering them unsuitable for the dynamic nature of car-sharing environments. To address these challenges, we propose an enhanced certificateless signature scheme tailored for smart car-sharing. By employing a biometric fuzzy extractor and the Chinese Remainder Theorem, our scheme provides a fine-grained authentication mechanism that eliminates the need for local computations on the user’s side, meaning users do not require a smartphone or other digital device. Furthermore, our scheme introduces category identifiers to facilitate vehicle selection based on specific classes within car-sharing contexts. A formal security analysis demonstrates that our scheme is existentially unforgeable against adversaries under the random oracle model. Finally, a comprehensive evaluation shows that our proposed scheme achieves competitive performance in terms of computational and communication overhead while offering enhanced practical functionalities. Full article

Within the framework of a dynamic memory-event-triggered mechanism (DMETM), this paper proposes an estimate-based secure control algorithm for nonlinear networked control systems (NNCSs) that suffer from hybrid attacks. Firstly, a sampled-data observer is employed utilizing the output signals to estimate the states. Secondly, due to the limitation of data transmission capacity in NNCSs, a novel DMETM with auxiliary variable is proposed, which effectively leverages the benefits of historical sampled data. In the process of network data transmission, a hybrid attack model that simultaneously considers the impact of both deception and denial of service (DoS) attacks is introduced, which can undermine signal integrity and disrupt data transmission. Then, a memory-event-triggered controller is developed, and the mean square stability of the NNCSs can be ensured by selecting some appropriate values. Finally, a numerical simulation and a practical example are given to illustrate the meaning of the designed dynamic memory-event-triggered control (DMETC) algorithm. Full article

This study considers consumers’ risk perceptions and safety preferences as external shock factors in food safety incidents. These factors are incorporated into a general equilibrium model defined by the food safety hierarchy, and the computational experiment method is employed to examine the direction of spillover effects. According to the findings, the spillover direction and intensity of food safety incidents are jointly influenced by the characteristics of consumers, food and the market. When an incident raises consumers’ safety concerns, a negative effect occurs throughout all food sectors. When an incident has a specific impact on consumers’ risk perception, the direction of the spillover is contingent upon the safety level of the product in question. In the event that the food involved in an incident is extremely secure, it may have a detrimental effect on unrelated food goods; conversely, it may have a beneficial effect on unrelated food goods. The incident’s impact has increased in proportion to the market share of the affected food. When the market share remains constant, the impact intensity increases as the degree of food safety improves. Higher market-wide risk levels are associated with more pronounced and quicker effects. This study improves understanding of spillover patterns in food safety situations, which aids in the formulation of focused policy responses and initiatives. Full article

This study demonstrates that rearing duration (14 and 30 days) and environmental salinity (0, 4, 8, and 12 parts per thousand (ppt) of NaCl) synergistically modulate osmoregulation and muscle texture in adult freshwater drums (Aplodinotus grunniens). Salinity significantly reduced the hepatosomatic index at 30 days (p < 0.05). Furthermore, serum biochemical indices were markedly affected. Higher salinity and prolonged rearing time decreased triglycerides, total cholesterol, and low-density lipoprotein (LDL), while high-density lipoprotein (HDL) levels increased at 14 days (p < 0.05), indicating improved lipid metabolism efficiency. Crucially, osmotic pressure remained stable across salinities at 14 days but exhibited a dose-dependent increase at 30 days (p < 0.05), driven primarily by elevated Na⁺ and Cl⁻ concentrations. Salinity (8–12 ppt) markedly enhanced water-holding capacity, reducing cooking loss (~58%), centrifugal loss (~74%), drip loss (~83%), and thaw loss (~84%) versus 0 ppt controls (p < 0.05). Concurrently, key texture parameters also significantly improved, as reflected by hardness, chewiness, resilience, and gumminess. These enhancements might be attributed to hyperosmotic stress-induced cellular dehydration and ionic strength-mediated protein cross-linking. Full article

The disposal of waste slurry in engineering construction and water environment remediation has become increasingly prominent. The physicochemical composite method integrating flocculation, solidification, and precompression has emerged as an efficient treatment approach, yet the permeability characteristics of slurry reinforced by this method remain insufficiently understood. This paper takes the high-moisture-content sludge generated from lake dredging projects reinforced by the physicochemical composite method as the research objective. Through permeability tests, the permeability characteristics of the physicochemical composite-modified slurry under different factors are tested, and its permeability characteristics are quantified through fitting methods. The research results show that the permeability coefficient decreases with the extension of curing time, decreases with the increase in curing agent dosage, increases with the increase in initial moisture content, and decreases with the increase in pre-stress. Full article

With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. However, traditional CNN models still have deficiencies in the extraction of early weak fault features and the suppression of high noise. In response to these problems, this paper proposes a convolutional neural network (SAWCA-net) that integrates spectrum-guided dynamic variable-width convolutional kernels and dynamic interactive time-domain–channel attention mechanisms. In this model, the spectrum-adaptive wide convolution is introduced. Combined with the time-domain and frequency-domain statistical characteristics of the input signal, the receptive field of the convolution kernel is adaptively adjusted, and the sampling position is dynamically adjusted, thereby enhancing the model’s modeling ability for periodic weak faults in complex non-stationary vibration signals and improving its anti-noise performance. Meanwhile, the dynamic time–channel attention module was designed to achieve the collaborative modeling of the time-domain periodic structure and the feature dependency between channels, improve the feature utilization efficiency, and suppress redundant interference. The experimental results show that the fault diagnosis accuracy rates of SAWCA-Net on the bearing datasets of Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) reach 99.15% and 99.64%, respectively, which are superior to the comparison models and have strong generalization and robustness. The visualization results of t-distributed random neighbor embedding (t-SNE) further verified its good feature separability and classification ability. Full article

Air Traffic Flow Management (ATFM) delay refers to the difference between a flight’s Target Take-Off Time (TTOT) and its Calculated Take-Off Time (CTOT), reflecting congestion levels in the air traffic network. ATFM delays are assigned to balance demand and capacity at key points in the network. The traditional First-Come, First-Served (FCFS) approach allocates delays strictly in the order flights are ready to depart, which is simple but inflexible. This study proposes a dynamic priority-based aircraft sequencing method at critical waypoints under multi-resource scenarios, aiming to reduce ATFM delays. An improved Constrained Position Shifting (CPS) constraint is introduced into the optimization model to enhance the influence of flight priority during decision-making. Additionally, three different priority strategies are designed to compare their respective impacts on ATFM delay. Finally, a dynamic priority-based ATFM delay optimization model is developed to address the identified challenges. Experimental results demonstrate that, compared with the FCFS scheme, the three priority strategies achieve maximum ATFM delay reductions of 30.5%, 44.1%, and 19.9%, respectively. The proposed model effectively allocates shorter delays to critical flights, optimizing resource utilization and improving the operational efficiency of the air route network. The research provides a reference framework for air traffic managers in allocating spatiotemporal resources across multiple congestion hotspots. By aligning priorities with network-wide efficiency goals, it overcomes traditional model limitations, avoids local optima, and supports globally optimal ATFM policy and practice. Full article