Search Results (5,033)

Habitat utilization is a critical determinant of animal survival and reproductive success. Clarifying species-specific habitat preferences provides essential insights into ecological requirements and forms the basis for sound conservation planning. The Temminck’s Tragopan (Tragopan temminckii), a medium-sized, sexually dimorphic pheasant endemic to montane forests of central and southern China, is classified as a nationally protected Class II species. Nevertheless, its fine-scale habitat selection during the breeding season remains inadequately documented. In 2024, we conducted a field investigation in the Laojunshan National Nature Reserve, Sichuan Province, to examine microhabitat use during this critical period. Our analysis revealed a significant preference for sites characterized by greater tree and bamboo height, higher canopy and bamboo cover, increased litter coverage, and taller shrub layers. In contrast, the species consistently avoided locations dominated by dense, tall herbaceous vegetation. Principal Component Analysis identified six principal components, collectively explaining 71.78% of the total environmental variance. The first component was primarily associated with bamboo structural attributes, the second with tree-layer structure, and the third with proximity to forest edges and streams. These findings indicate that effective conservation of this pheasant requires targeted forest management practices that preserve this specific suite of habitat characteristics, which are essential for ensuring reproductive success and long-term population viability. Full article

The goal of the study presented in this work is to analyze all recent advances in the context of the computing continuum and meta-operating systems (meta-OSs). The term continuum includes a variety of diverse hardware and computing elements, as well as network protocols, ranging from lightweight Internet of Things (IoT) components to more complex edge or cloud servers. To this end, the rapid penetration of IoT technology in modern-era networks, along with associated applications, poses new challenges towards efficient application deployment over heterogeneous network infrastructures. These challenges involve, among others, the interconnection of a vast number of IoT devices and protocols, proper resource management, and threat protection and privacy preservation. Hence, unified access mechanisms, data management policies, and security protocols are required across the continuum to support the vision of seamless connectivity and diverse device integration. This task becomes even more important as discussions on sixth generation (6G) networks are already taking place, which they are envisaged to coexist with IoT applications. Therefore, in this work the most significant technological approaches to satisfy the aforementioned challenges and requirements are presented and analyzed. To this end, a proposed architectural approach is also presented and discussed, which takes into consideration all key players and components in the continuum. In the same context, indicative use cases and scenarios that are leveraged from a meta-OSs in the computing continuum are presented as well. Finally, open issues and related challenges are also discussed. Full article

Bacteriophages, or phages, are viruses that specifically infect and lyse bacterial cells. Since their discovery 110 years ago, they have held a unique place in microbiology, medicine, and agriculture as both scientific tools and potential therapeutic agents. The concept of employing phages to combat bacterial infections, known as phage therapy, predates the antibiotic era and has undergone cycles of enthusiasm, neglect, and revival. Initially explored in the early 20th century, phage therapy offered a targeted biological approach to bacterial disease control. However, the widespread adoption of antibiotics led to a significant reduction in phage research, which only regained momentum in recent decades owing to the global rise of antibiotic-resistant bacteria and increasing demand for environmentally sustainable disease management strategies. This review traces the complete timeline of this history, highlighting key milestones in phage discovery, molecular microbiology, the antibiotic era, and the resulting critical events that spurred the modern phage renaissance in plant disease management. Finally, the significance of cutting-edge integration of synthetic biology, advanced phage delivery systems, and artificial intelligence (AI), which could drive the development of next-generation biopesticides, is also discussed. Full article

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

Current electrical self-sensing methods for composite structural health monitoring face significant limitations. Firstly, they often require complicated electrode layouts. Secondly, accurately determining both the location and amplitude of external loads remains a significant challenge. In this study, a deep learning-based self-sensing method is developed to identify the location and amplitude of external mechanical loads in resin-based conductive composites with a simple electrode layout. First, conductive filler-filled resin composites are prepared, and three-dimensional conductive networks are constructed within them. Subsequently, four electrodes are installed at the edges of the composite plate, and boundary electrical resistance responses are collected when applying mechanical loads at various positions on the composite plate. Finally, a residual learning-based CNN model is proposed for the accurate localization and amplitude identification of the applied loads. Research results demonstrate that the trained CNN model can accurately and effectively determine both the load amplitude and position. The obtained localization error and amplitude error are 0.91 mm and 0.13 N, respectively, surpassing the reported error values in previous studies. The research presented here opens a new avenue for achieving highly accurate and efficient prediction of load location and amplitude, which can be widely applied in composite structural health monitoring. Full article

Deep reinforcement learning (DRL)-based algorithms for solving the Traveling Salesman Problem (TSP) have demonstrated competitive potential compared to traditional heuristic algorithms on small-scale TSP instances. However, as the problem size increases, the NP-hard nature of the TSP leads to exponential growth in the combinatorial search space, state–action space explosion, and sharply increased sample complexity, which together cause significant performance degradation for most existing DRL-based models when directly applied to large-scale instances. This research proposes a two-stage reinforcement learning framework, termed GCRL-TSP (Graph Convolutional Reinforcement Learning for the TSP), which consists of a heatmap generation stage based on a graph convolutional neural network, and a heatmap-assisted Proximal Policy Optimization (PPO) training stage, where the generated heatmaps are used as auxiliary guidance for policy optimization. First, we design a divide-and-conquer heatmap generation strategy: a graph convolutional network infers m-node sub-heatmaps, which are then merged into a global edge-probability heatmap. Second, we integrate the heatmap into PPO by augmenting the state representation and restricting the action space toward high-probability edges, improving training efficiency. On standard instances with 200/500/1000 nodes, GCRL-TSP achieves a Gap% of 4.81/4.36/13.20 (relative to Concorde) with runtimes of 36 s/1.12 min/4.65 min. Experimental results show that GCRL-TSP achieves more than twice the solving speed compared to other TSP solving algorithms, while obtaining solution quality comparable to other algorithms on TSPs ranging from 200 to 1000 nodes. Full article

Precise identification of early peanut leaf spot is strategically significant for safeguarding oilseed supplies and reducing pesticide reliance. However, general-purpose detectors face severe domain adaptation bottlenecks in unstructured field environments due to small feature dissipation, physical occlusion, and class imbalance. To address this, this study constructs a dataset spanning two phenological cycles and proposes POD-YOLO, a physics-aware and dynamics-optimized lightweight framework. Anchored on the YOLOv10n architecture and adhering to a “data-centric” philosophy, the framework optimizes the parameter convergence path via a synergistic “Augmentation-Loss-Optimization” mechanism: (1) Input Stage: A Physical Domain Reconstruction (PDR) module is introduced to simulate physical occlusion, blocking shortcut learning and constructing a robust feature space; (2) Loss Stage: A Loss Manifold Reshaping (LMR) mechanism is established utilizing dual-branch constraints to suppress background gradients and enhance small target localization; and (3) Optimization Stage: A Decoupled Dynamic Scheduling (DDS) strategy is implemented, integrating AdamW with cosine annealing to ensure smooth convergence on small-sample data. Experimental results demonstrate that POD-YOLO achieves a 9.7% precision gain over the baseline and 83.08% recall, all while maintaining a low computational cost of 8.4 GFLOPs. This study validates the feasibility of exploiting the potential of lightweight architectures through optimization dynamics, offering an efficient paradigm for edge-based intelligent plant protection. Full article

The homeostasis balance of copper, as an essential trace element for life activities, is crucial for maintaining the normal function of cells. Cuproptosis, discovered in recent years, is a novel type of programmed cell death triggered by the accumulation of excessive copper ions in mitochondria. The core mechanism lies in that copper ions, after being reduced by ferridoxin (FDX1), directly target and induce the oligomerization of the acylated tricarboxylic acid (TCA) cycle enzyme, thereby triggering fatal protein toxic stress. This distinctive mechanism operates independently of other recognized pathways of cell death, offering a novel perspective for elucidating the pathological processes underlying various diseases. A review of pertinent research conducted over the past four years reveals that cuproptosis is not only significantly implicated in the onset, progression, and treatment resistance of tumors but is also intricately associated with diverse pathological processes, including neurodegenerative diseases, cardiovascular diseases, metabolic disorders, and immune abnormalities. This article conducts a multi-level summary from molecular mechanisms to physiological and pathological significance; deeply explores the interaction between cuproptosis and various subcellular structures, as well as their complex signal regulatory network; and systematically expounds the cutting-edge strategies for treating cuproptosis, including traditional copper chelating agents, ion carriers, and copper-based nanomedicines, with a particular focus on the latest progress in the field of natural product research. This review has systematically summarized the therapeutic potential demonstrated by numerous natural active ingredients when precisely regulating the cuproptosis pathway to provide a theoretical reference for future research in this field. Full article

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analyzing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge-computing-based smart framework that captures data from sensors, including ultraviolet, electrochemical, and microfluidic, along with other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyzes the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments, and other treatment plans. A logic layer is designed at the server level to process the real-time data from the sensor clusters and identify the discharge of nutrients, metals, and emerging contaminants in the water that affect the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis, and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonize sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad hoc analyses even during critical conditions. A user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI-enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability. Full article

In order to achieve better accuracy, modern models have become increasingly large, leading to an exponential increase in computational load, making it challenging to apply them to edge computing. Binary neural networks (BNNs) are models that quantize the filter weights and activations to 1-bit. These models are highly suitable for small chips like advanced RISC machines (ARMs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs) and other edge computing devices. To design a model that is more friendly to edge computing devices, it is crucial to reduce the floating-point operations (FLOPs). Batch normalization (BN) is an essential tool for binary neural networks; however, when convolution layers are quantized to 1-bit, the floating-point computation cost of BN layers becomes significantly high. This paper aims to reduce the floating-point operations by removing the BN layers from the model and introducing the scaled weight standardization convolution (WS-Conv) method to avoid the significant accuracy drop caused by the absence of BN layers, and to enhance the model performance through a series of optimizations, adaptive gradient clipping (AGC) and knowledge distillation (KD). Specifically, our model maintains a competitive computational cost and accuracy, even without BN layers. Furthermore, by incorporating a series of training methods, the model’s accuracy on CIFAR-100 is 0.6% higher than the baseline model, fractional activation BNN (FracBNN), while the total computational load is only 46% of the baseline model. With unchanged binary operations (BOPs), the FLOPs are reduced to nearly zero, making it more suitable for embedded platforms like FPGAs or other edge computers. Full article

The present study systematically investigates, for the first time, the combined influences of detonator position (top-edge and bottom-edge initiations) and anvil material (steel and sand) on the interfacial microstructure and mechanical performance of explosively welded A1050/AZ31 dissimilar joints. When welding was conducted using a steel anvil with the detonator positioned at the top edge, significant cracking occurred both at the surface and along the weld interface. In contrast, placing the detonator at the bottom edge noticeably reduced these defects. Moreover, the use of a sand anvil nullified these defects by damping the reflecting shockwaves and minimizing vibrations. Hardness measurements revealed substantial increase at the interface under all the conditions, with the highest value observed with the steel anvil. Welds subjected to top-edge detonation showed higher hardness values compared to those of welds subjected to bottom-edge detonation. Overall, the results suggest that sand anvils with bottom-edge detonation provide the optimal weld quality. The rigid steel anvil reflects the shockwave, generating high pressure and velocity at the interface, whereas the sand anvil absorbs a part of the shock energy, suppressing high-magnitude reflections. The position of the detonator influences the propagation dynamics of the detonation wave and the resulting collision velocity, which in turn, affect the interfacial morphology and overall quality of the weld. Full article

This paper presents an extended investigation into deep learning-based cryptanalysis of block ciphers by introducing and evaluating a multi-server attack environment. Building upon our prior work in centralized settings, we explore the practicality and scalability of deploying such attacks across multiple distributed edge servers. We assess the vulnerability of five representative block ciphers—DES, SDES, AES-128, SAES, and SPECK32/64—under two neural attack models: Encryption Emulation (EE) and Plaintext Recovery (PR), using both fully connected neural networks and Recurrent Neural Networks (RNNs) based on bidirectional Long Short-Term Memory (BiLSTM). Our experimental results show that the proposed federated learning-based cryptanalysis framework achieves performance nearly identical to that of centralized attacks, particularly for ciphers with low round complexity. Even as the number of edge servers increases to 32, the attack models maintain high accuracy in reduced-round settings. We validate our security assessments through formal statistical significance testing using two-tailed binomial tests with 99% confidence intervals. Additionally, our scalability analysis demonstrates that aggregation times remain negligible (<0.01% of total training time), confirming the computational efficiency of the federated framework. Overall, this work provides both a scalable cryptanalysis framework and valuable insights into the design of cryptographic algorithms that are resilient to distributed, deep learning-based threats. Full article

This study evaluates the effectiveness of integrating a Residual Shifting (ResShift)-based deep learning super-resolution (SR) technique with the Robust Dense Feature Matching (RoMa) algorithm for high-precision inter-satellite image registration. The key findings of this research are as follows: (1) Enhancement of Structural Details: Quadrupling image resolution via the ResShift SR model significantly improved the distinctness of edges and corners, leading to superior feature matching performance compared to original resolution data. (2) Superiority of Dense Matching: The RoMa model consistently delivered overwhelming results, maintaining a minimum of 2300 correct matches (NCM) across all datasets, which substantially outperformed existing sparse matching models such as SuperPoint + LightGlue (SPLG) (minimum 177 NCM) and SuperPoint + SuperGlue (SPSG). (3) Seasonal Robustness: The proposed framework demonstrated exceptional stability, maintaining registration errors below 0.5 pixels even in challenging summer–winter image pairs affected by cloud cover and spectral variations. (4) Geospatial Reliability: Integration of SR-derived homography with RoMa achieved a significant reduction in geographic distance errors, confirming the robustness of the dense matching paradigm for multi-sensor and multi-temporal satellite data fusion. These findings validate that the synergy between diffusion-based SR and dense feature matching provides a robust technological foundation for autonomous, high-precision satellite image registration. Full article

To solve the problems of high resistance and blockage in stubble-breaking operations, it is necessary to reveal the interaction mechanism between disc coulters and crop root–soil composites. This study developed a discrete element method–multi-body dynamics (DEM-MBD) coupling model of the stubble-breaking operation and verified the accuracy of the model through soil bin tests (error < 20%) and field experiments (error < 32%). The model was used to investigate the effects of different design parameters (coulter type and disc radius) and operating parameters (tillage speed and depth) on the stubble-breaking operation. The results showed that due to the significant strengthening effect of roots on soil, the resistance of disc coulter stubble-breaking operation was high; the number of roots in contact with the blade edge and the amount of root deformation significantly affected the resistance of the disc coulter; irreversible deformation of roots and soil could easily lead to the holes and root hairpin effects in the seeding furrow; compared to plain disc coulters, the difference in the time of deformation and fracture of the roots made the resistance of the notched coulter lower. The wavy disc coulter with a longer edge curve made its resistance higher; the disc coulter with a greater radius, higher tillage speed, and deeper tillage depth significantly increased the tillage resistance. However, the disc coulter with a greater radius or a higher tillage speed was beneficial for improving stubble-breaking performance. This study revealed the interaction mechanism between disc coulters and maize root-soil composites, providing a theoretical basis for the optimization design of no-till stubble-breaking devices. Full article

Text-to-video retrieval offers an intelligent solution for Internet of Vehicles (IoV) users to access desired content on demand. However, the constrained communication channels in IoV, characterized by low signal-to-noise ratios (SNR), pose significant obstacles to retrieval performance. To tackle these issues, this study presents SemTVR, a semantic communication framework dedicated to achieving superior robustness in text-to-video retrieval tasks in low-SNR IoV environments. By integrating the semantic communication paradigm with edge–cloud collaboration, our architecture leverages roadside unit (RSU) features and cloud resources to enable collaborative retrieval. We introduce a multi-semantic interactive reliable transmission mechanism that utilizes historical search records to enhance semantic recovery accuracy under adverse channel conditions. Furthermore, we devise a cross-modal fine-grained matching strategy assigning differentiated weights to video content and query sentences. Experimental results conducted on authoritative datasets demonstrate that SemTVR significantly outperforms baseline methods in terms of search accuracy, particularly in low SNR scenarios, validating its effectiveness for future IoV applications. Full article