Search Results (7,046)

This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model—with conventional convolutional layers replacing the separable convolutional layers—can be adopted as a more precise alternative. Full article

Background/Objective: Staphylococcus aureus (S. aureus) is a clinically significant pathogenic bacterium. Daptomycin (DAP) is a cyclic lipopeptide antibiotic used to treat infections caused by multidrug-resistant Gram-positive bacteria, including S. aureus. However, DAP currently faces clinical limitations due to its short half-life, toxic side effects, and increasingly severe drug resistance issues. This study aimed to develop a biomimetic nano-drug delivery system to enhance targeting ability, prolong blood circulation, and mitigate resistance of DAP. Methods: DAP-loaded chitosan nanocomposite particles (DAP-CS) were prepared by electrostatic self-assembly. Macrophage membrane vesicles (MM) were prepared by fusion of M1-type macrophage membranes with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). A biomimetic nano-drug delivery system (DAP-CS@MM) was constructed by the coextrusion process of DAP-CS and MM. Key physicochemical parameters, including particle diameter, zeta potential, encapsulation efficiency, and membrane protein retention, were systematically characterized. In vitro immune escape studies and in vivo zebrafish infection models were employed to assess the ability of immune escape and antibacterial performance, respectively. Results: The particle size of DAP-CS@MM was 110.9 ± 13.72 nm, with zeta potential +11.90 ± 1.90 mV, and encapsulation efficiency 70.43 ± 1.29%. DAP-CS@MM retained macrophage membrane proteins, including functional TLR2 receptors. In vitro immune escape assays, DAP-CS@MM demonstrated significantly enhanced immune escape compared with DAP-CS (p < 0.05). In the zebrafish infection model, DAP-CS@MM showed superior antibacterial efficacy over both DAP and DAP-CS (p < 0.05). Conclusions: The DAP-CS@MM biomimetic nano-drug delivery system exhibits excellent immune evasion and antibacterial performance, offering a novel strategy to overcome the clinical limitations of DAP. Full article

Precipitation changes have significant impacts on biodiversity and ecosystem productivity. However, the effects of precipitation changes on species diversity have been the focus of most previous studies. Little is known about the contributions of different dimensions of biodiversity (species, functional, and phylogenetic diversity) in linking long-term precipitation changes to ecosystem functions. In this study, a randomized design was conducted in the desert steppes of Ningxia, which included three treatments: natural rainfall, precipitation reduced by 50%, and precipitation increased by 50%. After 4 years of treatment, the effects of precipitation changes on aboveground productivity and its underlying mechanisms were explored. The results showed that (1) reduced precipitation significantly decreased phylogenetic diversity and species diversity, but had no significant effect on functional diversity; (2) reduced precipitation significantly decreased aboveground productivity, while increased precipitation significantly enhanced aboveground productivity; and (3) changes in precipitation primarily regulated aboveground productivity by altering soil nitrogen availability and the size of dominant plant species. This study provides important theoretical and practical guidance for the protection and management of desert steppe vegetation under future climate change. Full article

Detecting power transformer winding degradations at an early stage is very important for the safe operation of nuclear power plants. Most transformer failures are caused by insulation breakdown; the winding turn-to-turn short circuit fault is frequently encountered. Experience has shown that routine testing techniques, e.g., winding resistance, leakage inductance, and sweep frequency response analysis (SFRA), are not sensitive enough to identify minor turn-to-turn short defects. The SFRA technique is effective only if the fault is in such a condition that the flux distribution in the core is prominently distorted. This paper proposes the frequency domain reflectometry (FDR) technique for detecting and locating transformer winding defects. FDR measures the wave impedance and its change along the measured windings. The wire over a plane model is selected as the transmission line model for the transformer winding. The effectiveness is verified through lab experiments on a twist pair cable simulating the transformer winding and field testing on a real transformer. The FDR technique successfully identified and located the turn-to-turn short fault that was not detected by other testing techniques. Using FDR as a complementary tool for winding condition assessment will be beneficial. Full article

Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale objects, while existing composite object detection methods are mostly part-based, limiting their ability to capture the structural and textural characteristics of composite targets like TPPs. Moreover, most of them rely on single-modality data, failing to fully exploit the rich information available in remote sensing imagery. To address these limitations, we propose a novel Cross-Modal Frequency-Responsive Attention Network (CFRANet). Specifically, the Modality-Aware Fusion Block (MAFB) facilitates the integration of multi-modal features, enhancing inter-modal interactions. Additionally, the Frequency-Responsive Attention (FRA) module leverages both spatial and localized dual-channel information and utilizes Fourier-based frequency decomposition to separately capture high- and low-frequency components, thereby improving the recognition of TPPs by learning both detailed textures and structural layouts. Experiments conducted on our newly proposed AIR-MTPP dataset demonstrate that CFRANet achieves state-of-the-art performance, with a ${\mathrm{mAP}}_{50}$ of 82.41%. Full article

The precise synthesis of non-precious metal single-atom electrocatalysts is crucial for enhancing the yield of highly active reactive oxygen species (ROSs). Conventional oxidation methods, such as Fenton or NaClO processes, suffer from poor efficiency, high energy demand, and secondary pollution. In contrast, heterogeneous electro-Fenton systems based on cascade oxygen reduction reactions (ORRs), which require low operational voltage and cause pollutant degradation through both direct electron transfer and ROS generation, have emerged as a promising alternative. Recent studies showed that carbon cathodes decorated with atomically dispersed transition metals can effectively integrate the excellent conductivity of carbon supports with the tunable surface chemistry of metal centers. However, the electronic structure of active sites intrinsically hinders the simultaneous achievement of high activity and selectivity in cascade ORRs. This review summarizes the advances, specifically from 2020 to 2025, in understanding the mechanism of cascade ORRs and the synthesis of transition metal-based single-atom catalysts in cathode electrocatalysis for efficient wastewater treatment, and discusses the key factors affecting treatment performance. While employing atomically engineered cathodes is a promising approach for energy-efficient wastewater treatment, future efforts should overcome the barriers in active site control and long-term stability of the catalysts to fully exploit their potential in addressing water pollution challenges. Full article

Cement–Soil Mixing (CSM) Pile is an important technology for soft ground reinforcement, and its as-formed compressive strength directly affects engineering design and construction quality. To address the significant discrepancy between laboratory-tested strength and field as-formed strength arising from differing environmental conditions, this study conducted modified laboratory experiments simulating key field formation characteristics. A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. Engineering validation demonstrated that the model achieved an RMSE of 0.138, an MAE of 0.112, and an R² of 0.961. It effectively addresses the issue of large prediction deviations caused by insufficient environmental simulation in traditional mix proportion tests. The research findings establish a quantitative relationship between as-formed strength and preparation parameters, providing an effective experimental improvement and strength prediction method for the engineering design of CSM Pile. Full article

This study aims to clarify the nonlinear pressure loss patterns of the pneumatic system in a pneumatic seeder under varying pipeline structures and airflow parameters, and to develop a rapid prediction equation for the main pipe’s pressure loss. The studied multi-branch pipeline system consists of a main pipe, a header, and ten branch pipes. The main pipe is vertically installed at the center of the header in a straight-line configuration. The ten branch pipes are symmetrically and evenly spaced along the axial direction of the header, distributed on both sides of the main pipe. The outlet directions of the branch pipes are arranged in a 180° orientation opposite to the inlet direction of the main pipe, forming a symmetric multi-branch configuration. Firstly, this study investigated the flow characteristics within the multi-branch pipeline of the pneumatic system and elaborated on the mechanism of flow division in the pipeline. The key geometric factors affecting airflow were identified. Secondly, from a microscopic perspective, CFD simulations were employed to analyze the fundamental causes of pressure loss in the multi-branch pipeline system. Finally, from a macroscopic perspective, a dimensional analysis method was used to establish an empirical equation describing the relationship between the pressure loss (P) and several influencing factors, including the air density (ρ), air’s dynamic viscosity (μ), closed-end length of the header (Δl), branch pipe 1’s flow rate (Q), main pipe’s inner diameter (D), header’s inner diameter (γ), branch pipe’s inner diameter (d), and the spacing of the branch pipe (δ). The results of the bench tests indicate that when 0.0018 m³·s⁻¹ ≤ Q ≤ 0.0045 m³·s⁻¹, 0.0272 m < d ≤ 0.036 m, 0.225 m < δ ≤ 0.26 m, 0.057 m ≤ γ ≤ 0.0814 m, and 0.0426 m ≤ D ≤ 0.0536 m, the prediction accuracy of the empirical equation can be controlled within 10%. Therefore, the equation provides a reference for the structural design and optimization of pneumatic seeders’ multi-branch pipelines. Full article

In road scenes, the collected 3D point cloud data is usually accompanied by a large amount of interference mainly composed of ground point clouds and the property of uneven density distribution, which will bring difficulties to subsequent recognition and prediction. To address these problems, this paper proposes a ground point cloud segmentation algorithm based on local density plane fitting. Firstly, for the uneven density distribution of 3D point clouds, density segmentation is used to obtain several regions with balanced density. Then, candidate sample selection and plane validity detection are carried out for each region. The modified classical DBSCAN clustering algorithm is used to obtain effective fitting planes and perform clustering according to the fitting planes. Finally, different planes are divided according to the clustering results, and abnormal inspection is performed on the obtained results to screen out the most reasonable result. This scheme can effectively improve the scalability of the algorithm, reduce training costs, and improve deployment efficiency and universality. Experimental results show that the algorithm used in this paper has advantages compared with advanced algorithms of the same category, and can greatly reduce ground interference. Full article

A monocular deflectometric system comprises a camera and a screen that collaboratively facilitate the reconstruction of a specular surface under test (SUT). This paper presents a methodology for solving the slope distribution of the SUT utilizing pose estimation derived from reflections, based on vision ray calibration (VRC). Initially recorded by the camera, an assisted flat mirror in different postures reflects the patterns displayed by a screen maintained in a constant posture. The system undergoes a calibration based on the VRC to ascertain the vision ray distribution of the camera and the spatial relationship between the camera and the screen. Subsequently, the camera records the reflected patterns by the SUT, which remains in a constant posture while the screen is adjusted to multiple postures. Utilizing the VRC, the vision ray distribution among several postures of the screen and the SUT is calibrated. Following this, an iterative integrated calibration is performed, employing the calibration results from the preceding separate calibrations as initial parameters. The integrated calibration amalgamates the cost functions from the separate calibrations with the intersection of lines in Plücker space. Ultimately, the results from the integrated calibration yield the slope distribution of the SUT, enabling an integral reconstruction. In both the numeric simulations and actual measurements, the integrated calibration significantly enhances the accuracy of the reconstructions when compared to the reconstructions with the separate calibrations. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

This paper addresses the trajectory tracking control problem of underactuated ships in ocean engineering, which faces the dual challenges of tracking error time–performance regulation and robustness design due to the system’s underactuated characteristics, model uncertainties, and external disturbances. Aiming to address the issues of traditional finite-time control (convergence time dependent on initial states) and fixed-time control (control chattering and parameter conservativeness), this paper proposes a predefined-time adaptive control framework that integrates an event-triggered mechanism and neural networks. By constructing a Lyapunov function with time-varying weights and designing non-periodic dynamically updated dual triggering conditions, the convergence process of tracking errors is strictly constrained within a user-prespecified time window without relying on initial states or introducing non-smooth terms. An adaptive approximator based on radial basis function neural networks (RBF-NNs) is employed to compensate for unknown nonlinear dynamics and external disturbances in real-time. Combined with the event-triggered mechanism, it dynamically adjusts the update instances of control inputs, ensuring prespecified tracking accuracy while significantly reducing computational resource consumption. Theoretical analysis shows that all signals in the closed-loop system are uniformly ultimately bounded, tracking errors converge to a neighborhood of the origin within the predefined-time, and the update frequency of control inputs exhibits a linear relationship with the predefined-time, avoiding Zeno behavior. Simulation results verify the effectiveness of the proposed method in complex marine environments. Compared with traditional control strategies, it achieves more accurate trajectory tracking, faster response, and a substantial reduction in control input update frequency, providing an efficient solution for the engineering implementation of embedded control systems in unmanned ships. Full article

The red plant phenotype of R1 cotton is a genetic marker produced by light-induced anthocyanin accumulation. GhPAP1D controls this trait. There are two 228 bp tandem repeats upstream of GhPAP1D in R1 cotton. In this study, GUS staining assays in transgenic Arabidopsis thaliana (L.) Heynh. demonstrated that tandem repeats in the GhPAP1D promoter-enhanced transcriptional activity. GhPAP1D is a homolog of A. thaliana AtPAP1. AtPAP1’s expression is regulated by photomorphogenesis-related transcription factors such as AtHY5 and AtBBXs. We identified the homologs of A. thaliana AtHY5, AtBBX21, and AtBBX24 in R1 cotton, designated as GhHY5, GhBBX21, and GhBBX24, respectively. Y1H assays confirmed that GhHY5, GhBBX21, and GhBBX24 each bound to the GhPAP1D promoter. Dual-luciferase reporter assays revealed that GhHY5 weakly activated the promoter activity of GhPAP1D. Heterologous expression assays in A. thaliana indicated that GhBBX21 promoted anthocyanin accumulation, whereas GhBBX24 had the opposite effect. Dual-luciferase assays showed GhBBX21 activated GhPAP1D transcription, while GhBBX24 repressed it. Further study indicated that GhHY5 did not enhance GhBBX21-mediated transcriptional activation of GhPAP1D but alleviates GhBBX24-induced repression. Together, our results demonstrate that GhBBX21 and GhBBX24 antagonistically regulate anthocyanin accumulation in R1 cotton under GhHY5 mediation, providing insights into light-responsive anthocyanin biosynthesis in cotton. Full article