Search Results (3,693)

FPGA-based reconfigurable systems play a vital role in many critical domains by virtue of their unique advantages. They can effectively adapt to dynamically changing application scenarios, while featuring high parallelism and low power consumption. As a result, they have been widely adopted in key sectors such as aerospace, nuclear industry, and weapon equipment, where high performance and stability are of utmost importance. However, these systems face significant challenges. The continuous and drastic reduction in chip process size has led to increasingly complex and delicate internal circuit structures and physical characteristics. Meanwhile, the operating environments are often harsh and unpredictable. Additionally, the adoption of untrusted third-party foundries to reduce development costs further compounds these issues. Collectively, these factors make such systems highly susceptible to reliability threats, including environmental radiation, aging degradation, and malicious hardware attacks. These problems severely impact the stable operation and functionality of the systems. Therefore, ensuring the highly reliable operation of reconfigurable systems has become a critical issue that urgently needs to be addressed. There is a pressing need to summarize their technical characteristics, research status, and development trends comprehensively and in depth. In response, this paper conducts relevant research. By systematically reviewing 183 domestic and international research papers published between 2012 and 2024, it first provides a detailed analysis of the root causes of reliability issues in reconfigurable systems, thoroughly exploring their underlying mechanisms. Second, it focuses on the key technologies for achieving high reliability, encompassing four types of fault-tolerant design technologies, three types of aging mitigation technologies, and two types of hardware attack defense technologies. The paper comprehensively summarizes relevant research findings and the latest advancements in this field, offering a wealth of references for related research. Finally, it conducts a detailed comparative analysis and summary of the research hotspots in the field of high-reliability reconfigurable systems. It objectively evaluates the achievements and shortcomings of current research efforts and delves into the development trends of key technologies for high-reliability reconfigurable systems, providing clear directions for future research and practical applications. Full article

Nature-based Solutions (NbS) such as rolled cover crops are increasingly adopted in rainfed vineyards to reduce soil degradation and drought risk, but their effectiveness depends on local soil physical conditions. We compared spontaneous inter-row vegetation managed by mowing (Control) with a cereal-based rolled cover crop (C-R) in two vineyards of the Oltrepò Pavese (Northern Italy) with contrasting texture, structure, and slope: Canevino (CNV) and Santa Maria della Versa (SMV). From 2021 to 2025, continuous soil moisture monitoring was combined with field measurements of saturated hydraulic conductivity (K_s) and bulk density, interpreted using temporal indicators (MRD, ITS) and a drought index (SWDI) calibrated to field moisture thresholds. During wet phases, average saturation at 50 cm was consistently higher at SMV (about 78 to 84 percent) than at CNV (about 68 to 75 percent). Under water-limited conditions, management contrasts were most evident at SMV: at 50 cm during the post-termination dry phase, saturation remained around 70 percent under C-R versus about 64 percent under the Control, and K_s was higher under C-R (8.32 × 10⁻⁶ m/s in topsoil) than under the Control (7.39 × 10⁻⁶ m/s). At CNV, SWDI at 50 cm indicated a moderate improvement in one agronomic year (median −1.2 under C-R versus −5.3 under the Control in 2021 to 2022), while a full tillage operation in 2024 defined a disturbed phase that was interpreted separately. SWDI occasionally suggested severe drought levels not fully matching field evidence, highlighting the need for site-calibrated reference thresholds in structured fine-textured soils. Overall, soil physical properties set the hydrological envelope, while rolled cover management can enhance buffering and preserve conductive pathways during dry phases; therefore, NbS performance should be evaluated with site-adapted monitoring and cautious inference from temporally autocorrelated time series. Full article

Existing 3D point cloud enhancement methods typically rely on artificially designed geometric transformations or local blending strategies, which are prone to introducing illogical deformations, struggle to preserve global structure, and exhibit insufficient adaptability to diverse degradation patterns. To address these limitations, this paper proposes SFD-ADNet—an adaptive deformation framework based on a dual spatial–frequency domain. It achieves 3D point cloud augmentation by explicitly learning deformation parameters rather than applying predefined perturbations. By jointly modeling spatial structural dependencies and spectral features, SFD-ADNet generates augmented samples that are both structurally aware and task-relevant. In the spatial domain, a hierarchical sequence encoder coupled with a bidirectional Mamba-based deformation predictor captures long-range geometric dependencies and local structural variations, enabling adaptive position-aware deformation control. In the frequency domain, a multi-scale dual-channel mechanism based on adaptive Chebyshev polynomials separates low-frequency structural components from high-frequency details, allowing the model to suppress noise-sensitive distortions while preserving the global geometric skeleton. The two deformation predictions dynamically fuse to balance structural fidelity and sample diversity. Extensive experiments conducted on ModelNet40-C and ScanObjectNN-C involved synthetic CAD models and real-world scanned point clouds under diverse perturbation conditions. SFD-ADNet, as a universal augmentation module, reduces the mCE metrics of PointNet++ and different backbone networks by over 20%. Experiments demonstrate that SFD-ADNet achieves state-of-the-art robustness while preserving critical geometric structures. Furthermore, models enhanced by SFD-ADNet demonstrate consistently improved robustness against diverse point cloud attacks, validating the efficacy of adaptive space-frequency deformation in robust point cloud learning. Full article

The textile industry generates significant volumes of waste, making the development of reliable methods to evaluate biodegradability a pressing need. While standardised protocols exist for plastics, no specific methodologies have been established for textiles, and the quantification of non-degraded residues is commonly based on mass loss: a measurement that is prone to recovery errors. This study investigated the biodegradation of cotton, polyester, and cotton/polyester blend fabrics in soil under thermophilic conditions using a combined methodological approach. Carbon mineralisation was quantified through a respirometric assay that was specifically adapted for textile substrates, while residual solid fractions were assessed in situ by X-ray microtomography (micro-CT), thus avoiding artefacts associated with sample recovery. Complementary analyses were performed using SEM and FTIR to characterise morphological and chemical changes. Results showed substantial biodegradation of cotton, negligible degradation of polyester, and intermediate behaviour for the cotton/polyester blend. Micro-CT enabled the visualisation of fibre fragmentation and the quantification of the residual. The integration of respirometric, imaging, and spectroscopic techniques provided a comprehensive assessment of textile biodegradability. This study highlights the potential of micro-CT as a non-destructive tool to improve the accuracy and robustness of textile biodegradability assessment by enabling direct quantification of the residual solid fraction that can support future LCA studies and the development of standardised protocols for textile biodegradability. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article

Seawater electrolysis offers a promising route for sustainable hydrogen production in coastal areas, leveraging solar energy while reducing freshwater consumption. Yet, chloride-induced corrosion severely limits conventional electrodes such as titanium, which depend on passive titanium dioxide films and display minimal hydrogen evolution reaction activity (|i₀,H₂| ≈ 0.001–0.01 A/m²). Here, we report for the first time the use of copper-based coordination compounds—a triazole-derived polymer (CC_Cu) and a Prussian Blue Analogue (CuHCF)—as dual-function electrodes combining corrosion resistance with electrocatalytic activity. Structural integrity was verified by FTIR, TGA, XRD, and SEM/EDS analyses. Electrochemical tests in 0.5 M NaCl, interpreted using mixed potential theory, revealed corrosion potentials (E_corr) of −40 mV versus Standard Hydrogen Electrode (CuHCF) and −23 mV versus Standard Hydrogen Electrode (CC_Cu), and corrosion current densities of 0.259 and 0.379 A/m², respectively. Both exhibited hydrogen evolution reaction exchange current densities significantly higher than titanium (0.019 A/m² for CuHCF and 0.062 A/m² for CC_Cu). CuHCF achieved a Tafel slope of 222 mV/dec, comparable to NiMoP alloys and carbon steel. Complementary density functional theory calculations elucidated how metal–ligand interactions and electronic redistribution govern both catalytic performance and degradation. These findings introduce a new concept of semi-electrocatalysts, where copper coordination compounds act as structurally adaptive, low-cost materials bridging corrosion resistance and hydrogen evolution in seawater systems. Full article

Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the $(x,t)$ domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to $h(x,t)$ and $u(x,t)$, thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative $L^2$ errors of about 6–$7\%$ for flow depth and 10–$15\%$ for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately $36\times$ with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments. 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

Traditional ultrasonic bolt stress measurement is hindered by high power consumption. Lowering excitation voltage reduces power but degrades signal-to-noise ratio (SNR), compromising accuracy. This paper proposes a synergistic algorithm combining Empirical Mode Decomposition (EMD) with Adaptive Threshold Wavelet Denoising (ATWD). The method preserves transient features by reconstructing high-frequency components via EMD, then suppresses noise by precisely processing low-frequency components using ATWD. Finally, cross-correlation estimates ultrasonic delay. Evaluated at excitation voltages from 12 V to 0.5 V, the EMD-ATWD method maintains measurement errors below 10% even at 0.5 V, improving accuracy by over 48% compared to conventional Finite Impulse Response (FIR) and Threshold Wavelet Denoising (WTD) methods, while enhancing key echo waveform fidelity by over 35%. This method provides a reliable low-power bolt stress monitoring idea for engineering applications. Full article

Cleisthenes herzensteini is a commercially important demersal fish in the Northwest Pacific. However, the resource stock of this species has undergone a drastic decline due to overfishing and habitat degradation. As a representative taxon for benthic adaptation in the order Pleuronectiformes, the molecular mechanisms underlying its specialized phenotypic traits remain poorly elucidated. Furthermore, population-level studies focusing on the mitochondrial genome of Cleisthenes herzensteini are currently scarce. Given that the mitochondrial genome serves as an ideal genetic tool for deciphering species evolution and population genetics, sequencing of its mitogenome will help fill critical gaps in genetic resources and provide essential support for species conservation and phylogenetic research. In this study, we sequenced, assembled, and annotated its complete mitochondrial genome. The circular mitogenome is 17,171 bp in length and exhibits a typical A + T bias (54.04%). Repeat sequence analysis identified 35 dispersed repeats. Codon usage analysis revealed that leucine was the most frequently encoded amino acid, with CUU being the preferred codon. Several protein-coding genes possessed incomplete stop codons (T--/TA-), and a nucleotide preference for A and C was observed at the third codon position. Phylogenetic reconstruction based on mitogenomes from 23 species supported the monophyly of the order Pleuronectiformes. C. herzensteini showed the closest relationship with Dexistes rikuzenius, forming a distinct clade alongside Hippoglossoides dubius and Limanda aspera. These results provide essential genetic resources for understanding the evolution and population genetics of C. herzensteini and related flatfishes. According to the investigation, this study represents the first report on the sequencing and analysis of the complete mitochondrial genome of the Cleisthenes herzensteini. This not only fills the gap in mitochondrial genetic information for this species but also provides a reference for subsequent investigations into the phylogenetic relationships and evolutionary processes within the family Pleuronectidae. Full article

The continuous detection of emerging pollutants (EPs) in water poses potential threats to aquatic environmental safety and human health, and their efficient removal is a frontier in environmental engineering research. This review systematically summarizes research progress from 2005 to 2025 on the application of activated carbon (AC) and its composites for removing EPs from water and analyzes the development trends in this field using bibliometric methods. The results indicate that research has evolved from the traditional use of AC for adsorption to the design of novel materials through physical and chemical modifications, as well as composites with metal oxides, carbon-based nanomaterials, and other functional components, achieving high adsorption capacity, selective recognition, and catalytic degradation capabilities. Although AC-based materials demonstrate considerable potential, their large-scale application still faces challenges such as cost control, adaptability to complex water matrices, material regeneration, and potential environmental risks. Future research should focus on precise material design, process integration, and comprehensive life-cycle sustainability assessment to advance this technology toward highly efficient, economical, and safe solutions, thereby providing practical strategies for safeguarding water resources. Full article

Given the high cost of acquiring large-scale annotated datasets, few-shot object detection (FSOD) has emerged as an increasingly important research direction. However, existing FSOD methods face two critical challenges in remote sensing images (RSIs): (1) features of small targets within remote sensing images are incompletely represented due to extremely small-scale and cluttered backgrounds, which weakens discriminability and leads to significant detection degradation; (2) unified classification boundaries fail to handle the distinct confidence distributions between well-sampled base classes and sparsely sampled novel classes, leading to ineffective knowledge transfer. To address these issues, we propose TS-FSOD, a Transfer-Stable FSOD framework with two key innovations. First, the proposed detector integrates a Feature Enhancement Module (FEM) leveraging hierarchical attention mechanisms to alleviate small target feature attenuation, and an Adaptive Fusion Unit (AFU) utilizing spatial-channel selection to strengthen target feature representations while mitigating background interference. Second, Dynamic Temperature-scaling Learnable Classifier (DTLC) employs separate learnable temperature parameters for base and novel classes, combined with difficulty-aware weighting and dynamic adjustment, to adaptively calibrate decision boundaries for stable knowledge transfer. Experiments on DIOR and NWPU VHR-10 datasets show that TS-FSOD achieves competitive or superior performance compared to state-of-the-art methods, with improvements up to 4.30% mAP, particularly excelling in 3-shot and 5-shot scenarios. Full article

Lignin is a complex aromatic polymer that constitutes a major fraction of plant biomass and represents a valuable renewable carbon resource. Naturally decaying wood serves as an environmental reservoir of microorganisms capable of degrading lignin. In this study, we isolated and characterized sixteen bacterial strains from decaying Tilia cordata wood using an enrichment culture technique with lignin as the sole carbon source. Taxonomic identification via 16S rRNA gene sequencing revealed microbial diversity spanning the genera Bacillus, Pseudomonas, Stenotrophomonas, and several members of the Enterobacteriaceae family, including Raoultella terrigena isolates. Metagenomic sequencing of the wood substrate revealed an exceptionally rich and balanced bacterial community (Shannon index H′ = 5.07), dominated by Streptomyces, Bradyrhizobium, Bacillus, and Pseudomonas, likely reflecting a specialized consortium adapted to lignin rich late-stage decay. Functional phenotyping demonstrated that all isolates possess ligninolytic potential, evidenced by peroxidase/laccase-type activity through methylene blue decolorization. Dynamic Light Scattering (DLS) and HPLC analyses showed that some isolates, such as Raoultella terrigena MGMM806, effectively depolymerized lignosulfonate into low molecular weight fragments (1.23 nm), while others accumulated intermediate metabolites or completely mineralized the substrate. Growth profiling on monolignol substrates revealed a broad spectrum of catabolic specialization in lignin monomer degradation. The results demonstrate a complex system of metabolic partitioning within a natural bacterial consortium. This collection represents a foundational genetic resource for developing engineered biocatalysts and synthetic microbial communities aimed at the efficient conversion of lignin into valuable aromatic compounds. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

Non-stationary, multi-component vibration signals in rotating machinery are easily contaminated by strong background noise, which masks weak fault features and degrades diagnostic reliability. This paper proposes a joint denoising method that combines an improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and an improved wavelet thresholding scheme. ICFO, enhanced by Chebyshev chaotic initialization, a longitudinal–transverse crossover fusion mutation operator, and a thinking innovation strategy, is used to adaptively optimize the SVMD penalty factor and number of modes. The optimized SVMD decomposes the noisy signal into intrinsic mode functions, which are classified into effective and noise-dominated components via the Pearson correlation coefficient. An improved wavelet threshold function, whose threshold is modulated by the sub-band signal-to-noise ratio, is then applied to the effective components, and the denoised signal is reconstructed. Simulation experiments on nonlinear, non-stationary signals with different noise levels (SNR = 1–20 dB) show that the proposed method consistently achieves the highest SNR and lowest RMSE compared to VMD, SVMD, VMD–WTD, CFO–SVMD, and WTD. Tests on CWRU bearing data and gearbox vibration signals with added −2 dB Gaussian white noise further confirm that the method yields the lowest residual variance ratio and highest signal energy ratio while preserving key fault characteristic frequencies. Full article