Search Results (11,734)

Habitat fragmentation and human disturbance pose major challenges to bird movement and ecological connectivity, highlighting the need for effective ecological network construction in conservation planning. Although coastal ecological networks have received increasing attention, few studies have simultaneously examined seasonally explicit patterns, functional guild differences, and seasonally varying recreational disturbance. Using a coastal case study, we analyzed seasonal (spring, summer, autumn, winter) and guild-specific (wading birds, songbirds, raptors, and swimming birds) variations in bird ecological networks by integrating systematic field surveys (2023–2024) with citizen science records (2020–2025). Results indicated clear differences among guilds and seasons: swimming birds exhibited relatively complex and well-connected networks, whereas wading birds showed lower connectivity. Conservation priority areas varied markedly across seasons, being more extensive in spring (28.62%), autumn (23.69%), and winter (22.09%), but substantially reduced in summer (17.07%). Our findings provide a locally grounded reference for adaptive conservation planning in rapidly changing coastal landscapes, with particular attention to the protection and connectivity of coastal and estuarine wetlands for wading birds. Full article

Orbea is a morphologically diverse lineage within the subtribe Stapeliinae, yet plastome evolution in Arabian taxa remains insufficiently characterized. This study reports the first complete chloroplast genomes of Orbea sprengeri subsp. commutata and O. wissmannii var. eremastrum and investigates plastome structure, sequence variability, and phylogenetic relationships across tribe Ceropegieae. Chloroplast genomes were assembled, annotated, and compared with 13 published plastomes representing major Ceropegieae lineages. Both Arabian plastomes displayed the typical quadripartite structure and identical gene content of 114 unique genes, including 80 protein-coding genes, 30 transfer RNA genes, and four ribosomal RNA genes. However, O. wissmannii var. eremastrum exhibited pronounced structural divergence, possessing the largest plastome recorded for the tribe (170,054 bp), an 8.9 kb expansion of the inverted repeat regions, and an 8.4 kb inversion spanning the ndhG–ndhF region. Comparative analyses revealed conserved gene order across Ceropegieae but identified six highly variable loci (accD, clpP, ndhF, ycf1, psbM–trnD, and rpl32–trnL) as potential DNA barcodes. Selection pressure analyses indicated strong purifying selection across most genes, with localized adaptive signals in accD, ndhE, ycf1, and ycf2. Phylogenomic reconstruction consistently resolved the two Arabian Orbea taxa as a distinct clade separate from the African O. variegata. This study fills a gap in Ceropegieae plastid genomics and underscores the importance of sequencing additional Orbea species to capture the full extent of genomic variation within this diverse genus. Full article

In constrained waters, multi-USV cooperative encirclement of highly maneuverable targets is strongly affected by partial observability as well as obstacle and boundary constraints, posing substantial challenges to stable cooperative control. Existing deep reinforcement learning methods often suffer from low exploration efficiency, pronounced policy oscillations, and difficulties in maintaining the desired encirclement geometry in complex environments. To address these challenges, this paper proposes an adaptive difference-based multi-agent policy gradient method (MAADPG) under the centralized training and decentralized execution (CTDE) paradigm. MAADPG deeply integrates potential-field-inspired geometric guidance with a multi-agent deterministic policy gradient framework. Specifically, a guidance module generates geometrically interpretable candidate actions for each pursuer. Moreover, a difference-driven adaptive action adoption mechanism is introduced at the behavior policy execution level, where guided actions and policy actions are locally compared and the guided action is adopted only when it yields a significantly positive return difference. This design enables MAADPG to select higher-quality interaction actions, improve exploration efficiency, and enhance policy stability. Experimental results demonstrate that MAADPG consistently achieves fast convergence, stable coordination, and reliable encirclement formation across representative pursuit–encirclement scenarios, including obstacle-free, sparsely obstructed, and densely obstructed environments, thereby validating its applicability and stability for multi-USV encirclement tasks in constrained waters. Full article

Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. Full article

This study evaluated the fruit quality of 12 apple cultivars grafted onto the cold-resistant dwarfing interstock SH40 in the arid region of Lingwu, Ningxia, to identify well-adapted varieties for local production. A total of 21 indicators were measured, encompassing three major aspects: external quality (e.g., fruit size, shape index, peel color), internal flavor (e.g., soluble solids, soluble sugars, titratable acids, vitamin C content), and textural attributes (e.g., hardness, crispness, chewiness), and data were analyzed using principal component analysis and membership function methodology. The cultivars exhibited distinct quality profiles under identical management: ‘Red General’ performed well in fruit size, weight, and sugar–acid balance; ‘Yanfu 6’ showed the highest firmness and crispness; ‘Shengli’ had the greatest soluble solids content; and ‘Granny Smith’ was richest in vitamin C. Four principal components were extracted, explaining 80.06% of the total variance and simplifying the quality evaluation system. Based on the comprehensive membership function scores, ‘Red General’, ‘White Winter Pearmain’, and ‘Huashuo’ ranked highest in overall fruit quality. In conclusion, these three cultivars perform excellently on SH40 and are recommended for promotion, whereas ‘Red Delicious’ is not recommended due to poor performance. These findings offer a practical reference for selecting apple cultivars paired with SH40 in similar arid regions. Full article

A hybrid modeling framework for predicting the mechanical properties of Al-Mg-Si alloys, that blends physics-based and machine-learning models, is developed and tested. Motivated by a demand for post-consumer material (PCM) content in wrought aluminium applications, this work proposes, analyses, and discusses a parallel framework that applies an adaptive weighting coefficient derived from local observation density. Based on existing datasets from a range of Al-Mg-Si alloys, such a model is trained and tested in an iterative manner to study its robustness, by emulating a shift in observed alloy composition. The results indicate that the hybrid model is able to combine the interpolative strength of machine learning for cases similar to previous observations with the explorative strength of physics-based (Kampmann–Wagner Numerical) modeling for previously unobserved parameter combinations, as the hybrid model shows higher or similar accuracy than the best of its constituents across the majority of the sequence. The observed model characteristics are promising for predicting the effect of increased compositional variation inherent in PCM. Finally, possible future research is discussed. Full article

The flavivirus NS1 protein is a component of the viral replication complex and plays diverse, yet poorly understood, roles in the viral life cycle. To enable real-time visualization of the developing replication organelle and biochemical analysis of tagged NS1 and its interacting partners, we engineered a replication-competent yellow fever virus (YFV) replicon encoding a C-terminal fusion of NS1 with green fluorescent protein (NS1–GFP). The initial variant was non-viable in the absence of trans-complementation with wild-type NS1; however, viability was partially restored through the introduction of co-adaptive mutations in GFP (Q204R/A206V) and NS4A (M108L). Subsequent cell culture adaptation generated a 17-nucleotide frameshift within the NS1–GFP linker, resulting in a more flexible and less hydrophobic linker sequence. The optimized genome, in the form of a replicon, replicates in packaging cells that produce YFV structural proteins, as well as in naive BHK-21 cells. In the packaging cells, the adapted NS1–GFP replicon produces titers of infectious particles of approximately 10^6 FFU/mL and is genetically stable over five passages. The expressed NS1–GFP fusion protein localizes to the endoplasmic reticulum and co-fractionates with detergent-resistant heavy membranes, a hallmark of flavivirus replication organelles. This NS1–GFP replicon provides a novel platform for studying NS1 functions and can be further adapted for proximity-labeling strategies aimed at identifying the still-unknown protease responsible for NS1–NS2A cleavage. Full article

Traffic state estimation (TSE) is a core task in intelligent transportation systems (ITSs) that seeks to infer key operational parameters—such as speed, flow, and density—from limited observational data. Existing methods often face challenges in practical deployment, including limited estimation accuracy, insufficient physical consistency, and weak generalization capability. To address these issues, this paper proposes a hybrid estimation framework that integrates multi-agent reinforcement learning (MARL) with the Lighthill–Whitham–Richards (LWR) traffic flow model. In this framework, each roadside detector is modeled as an agent that adaptively learns fundamental diagram (FD) parameters—the free-flow speed and jam density—by fusing local detector measurements with global CAV trajectory sequences via an interactive attention mechanism. The learned parameters are then passed to an LWR solver to perform sequential (rolling) prediction of traffic states across the entire road segment. We design a reward function that jointly penalizes estimation error and violations of physical constraints, enabling the agents to learn accurate and physically consistent dynamic traffic state estimates through interaction with the physics-based LWR environment. Experiments on simulated and real-world datasets demonstrate that the proposed method outperforms existing models in estimation accuracy, real-time performance, and cross-scenario generalization. It faithfully reproduces dynamic traffic phenomena, such as shockwaves and queue waves, demonstrating robustness and practical potential for deployment in complex traffic environments. Full article

The current intrusion detection methods suffer from deficiencies in terms of cross-domain adaptability, privacy preservation, and limited effectiveness in detecting minority-class attacks. To address these issues, a novel intrusion detection model framework, TrMulS, is proposed that integrates federated learning, generative adversarial networks with multispace feature enhancement ability, and transformers with multi-source transfer ability. First, at each institution (source domain), local spatial features are extracted through a CNN, multiple subsets are constructed (to solve the feature singularity problem), and the multihead self-attention mechanism of the transformer is utilized to capture the correlation of features. Second, the synthetic samples of the target domain are generated on the basis of the improved Exchange-GAN, and the cross-domain transfer module is designed by combining the Maximum Mean Discrepancy (MMD) to minimize the feature distribution difference between the source domain and the target domain. Finally, the federated transfer learning strategy is adopted. The model parameters of each local institution are encrypted and uploaded to the target server and then aggregated to generate the global model. These steps iterate until convergence, yielding the globally optimal model. Experiments on the ISCX2012, KDD99 and NSL-KDD intrusion detection standard datasets show that the detection accuracy of this method is significantly improved in cross-domain scenarios. This paper presents a novel paradigm for cross-domain security intelligence analysis that considers efficiency, privacy and balance. Full article

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

The red fox (Vulpes vulpes) is a widely distributed and highly adaptive small carnivore known by its generalist diet, which includes small mammals, invertebrates, and fruits. Despite its ecological relevance, how habitat heterogeneity affects its diet across the Mediterranean, a biodiversity hotspot shaped by long-term human disturbance, remains insufficiently synthesized. In this review, we synthesized and analyzed published studies that reported habitat-specific data on the red fox diet in the Mediterranean. Only 12 studies met the selection criteria, and no study directly compared two different habitats. The studied areas covered three dominant habitats: forests, scrublands (garrigue), and agroecosystems, and diet items were grouped in 7 categories: birds, carcasses, fruits, invertebrates, lagomorphs, small mammals, and reptiles. Overall diet composition varied significantly, with invertebrates and fruits being the most frequent diet items. In turn, lagomorphs and reptiles were the least frequent. In turn, diet composition varied little across habitats, indicating that diet variation follows specific local resource abundance regardless of habitat type. Despite the analytical limitations associated with the limited availability of habitat-explicit studies. The results highlight the pronounced dietary plasticity of the red fox and its capacity to integrate resource availability across heterogeneous Mediterranean landscape mosaics. This trophic adaptability and top predator role support various ecosystem functions such as controlling invertebrate and small mammal populations, dispersing seeds, and cycling nutrients, reinforcing the potential of the red fox as functional bioindicator in the Mediterranean. Therefore, sustainable land management, especially in agricultural areas, and restoration efforts for degraded areas should consider the beneficial roles of generalist carnivores like the red fox. Full article

Time-varying formation tracking of networked unmanned aerial vehicle (UAV) systems plays a crucial role in cooperative missions such as encirclement, cooperative surveillance, and search-and-rescue operations, where human operators are often involved and system reliability is challenged by actuator faults and external disturbances. Motivated by these practical considerations, this paper investigates a human-in-the-loop time-varying formation tracking problem for networked UAV systems subject to compound actuator faults and external disturbances. To address this problem, a novel two-layer control architecture is developed, comprising a distributed observer and a fault-tolerant controller. The distributed observer enables each UAV to estimate the states of the human-in-the-loop leader using only local information exchange, while the fault-tolerant controller is designed to preserve formation tracking performance in the presence of compound actuator faults. By incorporating dynamic iteration regulation and adaptive laws, the proposed control scheme ensures that the formation tracking errors converge to a bounded neighborhood of the origin. Rigorous Lyapunov-based analysis is conducted to establish the stability, convergence, and robustness of the resulting closed-loop system. Numerical simulations further demonstrate the effectiveness of the proposed method in achieving practical time-varying formation tracking under complex fault scenarios. Full article

Defect inspection of Printed Circuit Board (PCB) is essential for maintaining the safety and reliability of electronic products. With the continuous trend toward smaller components and higher integration levels, identifying tiny imperfections on densely packed PCB structures has become increasingly difficult and remains a major challenge for current inspection systems. To tackle this problem, this study proposes the Multi-scale Edge-Aware Enhanced Detection Transformer (MEE-DETR), a deep learning-based object detection method. Building upon the RT-DETR framework, which is grounded in Transformer-based machine learning, the proposed approach systematically introduces enhancements at three levels: backbone feature extraction, feature interaction, and multi-scale feature fusion. First, the proposed Edge-Strengthened Backbone Network (ESBN) constructs multi-scale edge extraction and semantic fusion pathways, effectively strengthening the structural representation of shallow defect edges. Second, the Entanglement Transformer Block (ETB), synergistically integrates frequency self-attention, spatial self-attention, and a frequency–spatial entangled feed-forward network, enabling deep cross-domain information interaction and consistent feature representation. Finally, the proposed Adaptive Enhancement Feature Pyramid Network (AEFPN), incorporating the Adaptive Cross-scale Fusion Module (ACFM) for cross-scale adaptive weighting and the Enhanced Feature Extraction C3 Module (EFEC3) for local nonlinear enhancement, substantially improves detail preservation and semantic balance during feature fusion. Experiments conducted on the PKU-Market-PCB dataset reveal that MEE-DETR delivers notable performance gains. Specifically, Precision, Recall, and mAP_50–95 improve by 2.5%, 9.4%, and 4.2%, respectively. In addition, the model’s parameter size is reduced by 40.7%. These results collectively indicate that MEE-DETR achieves excellent detection performance with a lightweight network architecture. Full article

Power transformers are critical assets in power systems, and their reliable operation is essential for grid stability. Conventional fault diagnosis methods suffer from delayed response and limited adaptability, while existing artificial intelligence-based approaches face challenges related to data heterogeneity, limited interpretability, and weak integration of physical mechanisms. To address these issues, this paper proposes a physics-informed enhanced transformer-based framework for power transformer fault diagnosis. A unified temporal representation scheme is developed to integrate heterogeneous monitoring data using Dynamic Time Warping and physics-guided feature projection. Physical priors derived from thermodynamic laws and gas diffusion principles are embedded into the attention mechanism through multi-physics coupling constraints, improving physical consistency and interpretability. In addition, a multi-task diagnostic strategy is adopted to jointly perform fault classification, severity assessment, and fault localization. Experiments on 3000 samples from 76 power transformers demonstrate that the proposed method achieves high diagnostic accuracy and superior robustness under noise and interference, indicating its effectiveness for practical predictive maintenance applications. Full article

Hypoxia is a common feature of inflamed and ischemic tissues and represents an important regulatory signal for innate immune cells. The master regulator of this response is hypoxia-inducible factor-1α (HIF-1α), a transcription factor whose stabilization and activity are tightly regulated by the presence of oxygen, inflammatory signaling, and cellular metabolism. Monocytes, key players in innate immunity, rapidly sense oxygen deprivation and display specific responses during acute hypoxia, primarily aimed at adapting and maintaining cellular homeostasis. Unlike macrophages, in which HIF-1α activity is known, the mechanisms regulating HIF-1α stabilization, subcellular localization, and transcriptional activity in circulating monocytes remain incompletely elucidated. Recent studies indicate that acute hypoxia primarily triggers post-translational stabilization of HIF-1α, calcium- and PKC-dependent signaling, metabolic reprogramming, and early inflammatory responses, while transcriptional activation of HIF-1α may require additional inflammatory or stress-related signals. Furthermore, extensive crosstalk between HIF-1α and NF-κB integrates hypoxic and inflammatory signals, modulating cytokine production, cell migration, and survival. Epigenetic regulators can also modulate these responses and contribute to hypoxia-induced trained immunity. In this review, we summarize current knowledge of the mechanisms controlling the stabilization, localization, and function of HIF-1α in human monocytes and monocyte–macrophages during acute hypoxia, highlighting the key differences between these cell types and discussing their implications for inflammation, tissue homeostasis, and disease. Full article