Search Results (2,155)

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article

Background/Objectives: The global burden of spinal cord injury (SCI) is increasing due to aging populations and persistent regional disparities, highlighting an urgent need for updated epidemiological data. This study quantifies the global, regional, and national burden of SCI from 1990 to 2021 and projects its prevalence to 2050. Methods: Using data from the Global Burden of Disease (GBD) 2021 study, we estimated age-, sex-, and location-specific prevalence and years lived with disability (YLDs). Projections were developed using sociodemographic modeling, with analyses including Bayesian meta-regression (DisMod-MR 2.1) and Das Gupta decomposition. Results: In 2021, approximately 14.5 million people worldwide were living with SCI, including 7.30 million with neck-level and 7.22 million with below-neck-level injuries. The age-standardized prevalence per 100,000 people was 88 for neck-level SCI and 95 for below-neck-level SCI. Although age-standardized rates declined slightly from 1990 (−0.17% for neck-level and −0.18% for below-neck-level), the absolute burden increased substantially. This increase was particularly prominent in East Asia and low- and middle-income countries. The highest prevalence was observed in men aged 50–64 years. Projections indicate that global SCI cases will exceed 14.5 million by 2050. Conclusions: These findings underscore the growing absolute burden of SCI. Targeted prevention strategies, enhanced rehabilitation services, and equitable healthcare access are crucial to mitigate long-term disability and improve the quality of life for affected populations worldwide. Full article

Biologically treated swine farm wastewater still contains high levels of refractory organics, humic substances and antibiotic residues, posing environmental risks and limiting opportunities for water reuse. Wastewater treatment by ozonation alone suffers from low mass transfer efficiency and selective oxidation. To overcome these limitations, a catalytic ozonation process (O₃/Fe²⁺/H₂O₂) was applied and optimized using Response Surface Methodology (RSM) based on single-factor experiments and Central Composite Design (CCD) for advanced swine farm wastewater treatment. The optimal conditions ([O₃] = 25.0 mg/L, [Fe²⁺] = 25.9 mg/L, [H₂O₂] = 41.1 mg/L) achieved a COD removal of 44.3%, which was 86.8% higher than that of ozonation alone, and increased TOC removal to 29.5%, indicating effective mineralization. Biodegradability (BOD₅/COD) of swine farm wastewater effluent increased from 0.01 to 0.34 after the catalytic ozonation treatment. Humic-like and fulvic-like substances were removed by 93.7% and 95.4%, respectively, and antibiotic degradation was significantly accelerated and enhanced. The synergistic process improved ozone utilization efficiency by 33.1% and removed 53.95% of total phosphorus through Fe³⁺-mediated coprecipitation. These findings demonstrate that with catalytic ozone decomposition and production of hydroxyl radicals, the O₃/Fe²⁺/H₂O₂ system effectively integrates enhanced ozone utilization efficiency, radical synergy, and simultaneous pollutant removal, providing a cost-effective and technically feasible strategy for advanced swine farm wastewater treatment and safe reuse. Full article

With the proliferation of image-capturing and display-enabled IoT devices, ensuring the authenticity and integrity of visual data has become increasingly critical, especially in light of emerging cybersecurity threats and powerful generative AI tools. One of the major challenges in such sensor-based systems is the ability to protect privacy while maintaining data usability. Reversible data hiding has attracted growing attention due to its reversibility and ease of implementation, making it a viable solution for secure image communication in IoT environments. In this paper, we propose reversible data hiding techniques tailored to the content characteristics of images. Our approach leverages subsampling and quadtree partitioning, combined with multi-stage prediction schemes, to generate a predicted image aligned with the original. Secret information is embedded by analyzing the difference histogram between the original and predicted images, and enhanced through multi-round rotation techniques and a multi-level embedding strategy to boost capacity. By employing both subsampling and quadtree decomposition, the embedding strategy dynamically adapts to the inherent characteristics of the input image. Furthermore, we investigate the trade-off between embedding capacity and marked image quality. Experimental results demonstrate improved embedding performance, high visual fidelity, and low implementation complexity, highlighting the method’s suitability for resource-constrained IoT applications. Full article

Organic contaminants on optical components critically impair intense laser systems. Oxygen plasma cleaning is a promising non-contact method, yet the mechanism by which the initial kinetic energy of reactive oxygen species assists chemically driven removal remains unclear. This study employs ReaxFF molecular dynamics to elucidate how reactive oxygen species chemically decompose dibutyl phthalate and how kinetic energy assists chemical reactions by enhancing transport, penetration, and energy transfer. While the core removal mechanism is chemical, kinetic energy promotes plasma-contaminant encounters and facilitates access to otherwise sluggish pathways. The results show that kinetic energy is a key promoter that enhances chemical decomposition, with the contaminant decomposition rate enhanced by up to 1310% and residues reduced by 81.13% compared to pure chemical reactions. This study identifies and quantifies two dominant reaction pathways (butyl chain cleavage & benzene ring cleavage). The analysis of diffusion and energy transfer reveals that higher kinetic energy improves reactive oxygen species transport, enables deeper penetration, and selectively activates specific reaction pathways by overcoming energy barriers. Synergy with flux, dose, and temperature is also demonstrated. This work provides atomic-level insights into kinetic promotion mechanisms, supporting optimized plasma cleaning processes and contributing to the performance stability and operational longevity of intense laser systems. Full article

Given the inadequacies in the management of reactive power compensation equipment in distribution networks and insufficient power data mining, existing studies pay little attention to asymmetric reactive power compensation equipment and face pain points such as difficult quantification of nonlinear relationships and challenging evaluation of mechanical switches. First, this paper proposes a data mining-based diagnostic method for the operating status of asymmetric reactive power compensation equipment: it preprocesses data via singular value decomposition and matrix approximation. Second, it classifies load types with K-means clustering, defines “health degree” by introducing mutual information and a reliability coefficient, constructs dual switching criteria, and defines the switching qualification rate. Third, the TOPSIS method is employed for dual-index comprehensive evaluation, and equipment status levels are classified with statistical analysis. Finally, the case analysis demonstrates that the proposed method is accurate, applicable, and easy to implement, which can serve as a basis for equipment troubleshooting and maintenance, thereby filling the relevant research gap. Full article

Short-term passenger flow prediction provides critical data-driven support for optimizing resource allocation, guiding passenger mobility, and enhancing risk response capabilities in urban rail transit systems. To further improve prediction accuracy, this study proposes a hybrid SMA-VMD-Informer-BiLSTM prediction model. Addressing the challenge of error propagation caused by non-stationary components (e.g., noise and abrupt fluctuations) in conventional passenger flow signals, the Variational Mode Decomposition (VMD) method is introduced to decompose raw flow data into multiple intrinsic mode functions (IMFs). A Slime Mould Algorithm (SMA)-based optimization mechanism is designed to adaptively tune VMD parameters, effectively mitigating mode redundancy and information loss. Furthermore, to circumvent error accumulation inherent in serial modeling frameworks, a parallel prediction architecture is developed: the Informer branch captures long-term dependencies through its ProbSparse self-attention mechanism, while the Bidirectional Long Short-Term Memory (BiLSTM) network extracts localized short-term temporal patterns. The outputs of both branches are fused via a fully connected layer, balancing global trend adherence and local fluctuation characterization. Experimental validation using historical entry flow data from Weihouzhuang Station on Xi’an Metro demonstrated the superior performance of the SMA-VMD-Informer-BiLSTM model. Compared to benchmark models (CNN-BiLSTM, CNN-BiGRU, Transformer-LSTM, ARIMA-LSTM), the proposed model achieved reductions of 7.14–53.33% in $f_{mse}$, 3.81–31.14% in $f_{rmse}$, and 8.87–38.08% in $f_{mae}$, alongside a 4.11–5.48% improvement in $R^2$. Cross-station validation across multiple Xi’an Metro hubs further confirmed robust spatial generalizability, with prediction errors bounded within $f_{mse}$: 0.0009–0.01, $f_{rmse}$: 0.0303–0.1, $f_{mae}$: 0.0196–0.0697, and $R^2$: 0.9011–0.9971. Furthermore, the model demonstrated favorable predictive performance when applied to forecasting passenger inflows at multiple stations in Nanjing and Zhengzhou, showcasing its excellent spatial transferability. By integrating multi-level, multi-scale data processing and adaptive feature extraction mechanisms, the proposed model significantly mitigates error accumulation observed in traditional approaches. These findings collectively indicate its potential as a scientific foundation for refined operational decision-making in urban rail transit management, thereby significantly promoting the sustainable development and long-term stable operation of urban rail transit systems. Full article

Terrestrial cross-boundary inputs of leaf litter serve as a critical foundation for secondary productivity in freshwater ecosystems. The regulatory mechanisms of tannins in leaf litter on degradation rates under submerged conditions remain unclear. This study employed leaf litter from low-tannin plants Osmanthus fragrans (A) and Canna glauca (B) as decomposition substrates, with the high-tannin species Myriophyllum verticillatum (C) incorporated to adjust tannin levels. A 140-day hydroponic degradation experiment was conducted under controlled temperature and dark conditions, which included four mixed litter treatments with a gradient of tannin additions (AB as the control, 0 g; ABC1: 0.5 g; ABC2: 2.5 g; ABC3: 4.5 g) along with two single-species treatments (A and B). The following results were found: (1) Low tannin levels (ABC1) promoted degradation rates of A and B (increased by 1.33–12.70%), whereas high tannin (ABC3) inhibited decomposition (decreased by 6.21–6.82%). (2) Tannin–protein complexes reduce nitrogen bioavailability and inhibit nitrification, thereby disrupting the nitrogen cycle in aquatic systems. In ABC3, total nitrogen content in A and B litter increased by 17.69–26.46% compared to AB, with concurrent 59.29% elevation in water NH₄⁺-N concentration. (3) High tannin induced dominance of oligotrophic stress-resistant bacterial communities (e.g., Treponema) through nutrient limitation and toxicity stress; however, their low metabolic efficiency reduced overall decomposition efficiency. Research reveals that the ecological benefits of plant secondary metabolites outweigh their nutritional quality attributes. Full article

Generating non-standard fonts, such as running script (e.g., XingShu), poses significant challenges due to their high stroke continuity, structural flexibility, and stylistic diversity, which traditional component-based prior knowledge methods struggle to model effectively. While diffusion models excel at capturing continuous feature spaces and stroke variations through iterative denoising, they face critical limitations: (1) style leakage, where large stylistic differences lead to inconsistent outputs due to noise interference; (2) structural distortion, caused by the absence of explicit structural guidance, resulting in broken strokes or deformed glyphs; and (3) style confusion, where similar font styles are inadequately distinguished, producing ambiguous results. To address these issues, we propose a novel skeleton-guided diffusion model with three key innovations: (1) a skeleton-constrained style rendering module that enforces semantic alignment and balanced energy constraints to amplify critical skeletal features, mitigating style leakage and ensuring stylistic consistency; (2) a cross-scale skeleton preservation module that integrates multi-scale glyph skeleton information through cross-dimensional interactions, effectively modeling macro-level layouts and micro-level stroke details to prevent structural distortions; (3) a contrastive style refinement module that leverages skeleton decomposition and recombination strategies, coupled with contrastive learning on positive and negative samples, to establish robust style representations and disambiguate similar styles. Extensive experiments on diverse font datasets demonstrate that our approach significantly improves the generation quality, achieving superior style fidelity, structural integrity, and style differentiation compared to state-of-the-art diffusion-based font generation methods. Full article

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

Magnetocardiography (MCG) offers a noninvasive method for early screening and precise localization of cardiovascular diseases by measuring picotesla-level weak magnetic fields induced by cardiac electrical activity. However, in unshielded magnetic environments, geomagnetic disturbances, power-frequency electromagnetic interference, and physiological/motion artifacts can significantly overwhelm effective magnetocardiographic components. To address this challenge, this paper systematically constructs an integrated denoising framework, termed “AOA-VMD-WT”. In this approach, the Arithmetic Optimization Algorithm (AOA) adaptively optimizes the key parameters (decomposition level K and penalty factor α) of Variational Mode Decomposition (VMD). The decomposed components are then regularized based on their modal center frequencies: components with frequencies ≥50 Hz are directly suppressed; those with frequencies <50 Hz undergo wavelet threshold (WT) denoising; and those with frequencies <0.5 Hz undergo baseline correction. The purified signal is subsequently reconstructed. For quantitative evaluation, we designed performance indicators including QRS amplitude retention rate, high/low frequency suppression amount, and spectral entropy. Further comparisons are made with baseline methods such as FIR and wavelet soft/hard thresholds. Experimental results on multiple sets of measured MCG data demonstrate that the proposed method achieves an average improvement of approximately 8–15 dB in high-frequency suppression, 2–8 dB in low-frequency suppression, and a decrease in spectral entropy ranging from 0.1 to 0.6 without compromising QRS amplitude. Additionally, the parameter optimization exhibits high stability. These findings suggest that the proposed framework provides engineerable algorithmic support for stable MCG measurement in ordinary clinic scenarios. Full article

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area. Full article

In recent years, insects have emerged as a nutritious and eco-sustainable alternative food source, with the house cricket (Acheta domesticus, AD) recently authorized by the European Commission as a novel food. However, the presence of harmful substances in insects poses potential health risks. This study investigated the content of potentially toxic elements (PTEs) such as cadmium (Cd), arsenic (As), lead (Pb), mercury (Hg), nickel (Ni), chromium (Cr), and aluminium (Al) in Acheta domesticus fed diets enriched with graded levels of the red seaweed Palmaria palmata or the brown seaweed Ascophyllum nodosum in two feeding trials. Chemical analyses were carried out by graphite furnace atomic absorption spectrophotometry for all elements except Hg, which was analyzed by thermal decomposition amalgamation atomic absorption spectrometry. The results showed that PTE content in the diets was below the legal limits for feed. The PTEs in AD ranged (mg kg⁻¹ dry matter) as follows: Cd (0.069 ± 0.005–0.127 ± 0.002), As (0.08 ± 0.01–0.36 ± 0.03), Pb (0.05 ± 0.01–0.12 ± 0.01), Hg (0.0065 ± 0.0002–0.0141 ± 0.0010), Ni (0.64 ± 0.06–1.20 ± 0.10), Cr (0.16 ± 0.02–0.58 ± 0.01), and Al (17 ± 2–61 ± 1). AD bioaccumulated As and Hg; however, the PTE levels remained below European Union food safety limits. The absence of non-carcinogenic risk for consumers suggests that AD fed seaweed-enriched diets are a safe, healthy, and low-chemical risk food for humans. Full article

The study presents the results of investigations into various types of anhydrous pyrolysis aimed at determining the kinetic parameters of hydrocarbon generation processes from source rocks. Surface outcrop samples from the Silesian, Dukla, and Skole units, characterized by a low level of thermal maturity, were used as experimental material. The samples predominantly represented the Menilite Beds from the aforementioned three units, but also included Istebna, Lgota, Verovice, and Spas beds, which exhibit significantly lower parameters that describe generation properties. The anhydrous pyrolysis experiments provided information on the rate of organic matter decomposition (TG/DSC), the degree of conversion (Rock-Eval), the quality of the obtained products (Py/GC), and the isotopic composition of the gaseous products (Py/GC/IRMS). Chromatographic analyses confirmed the oil-prone nature of kerogen contained in the Menilites from the Dukla Unit (Tylawa area), the Silesian Unit (Iwonicz fold), and the Skole Unit, revealing an equal share of all hydrocarbon fractions: C₁–C₉, C₁₀–C₁₅, and C₁₅₊. Through the integration of pyrolytic studies conducted on potential source rocks in the polish Outer Carpathians, a new type of information was obtained regarding the rate of organic matter decomposition, as well as the fractional and isotopic composition of the pyrolysis products. The set of obtained results was used to estimate the activation energy and characterize the potential source levels. The innovative aspect of this approach involved the isotopic characterization of gaseous products generated during thermal degradation of the source rocks. These data were subsequently used to establish genetic correlations with natural gases accumulated in hydrocarbon reservoirs of the Carpathian region. It has been demonstrated that pyrolysis using PY-GC-IRMS can yield results comparable to those obtained through generation in natural geological conditions. Full article

Blow flies (Diptera: Calliphoridae) play a crucial role in the decomposition process and serve as important forensic indicators due to their predictable colonization patterns. This review focuses on the dynamics of maggot masses, highlighting their ecological roles, thermoregulation, and implications for forensics. We summarize data on the self-organizing behavior of maggot masses, which is influenced by chemical cues and environmental factors. These masses can generate internal temperatures that exceed ambient levels by 10–20 °C, accelerating larval growth and impacting competition among individuals. This localized heating complicates the estimation of the postmortem interval (PMI), as traditional models may not take these thermal influences into account. Furthermore, maggot masses contribute significantly to nutrient cycling and soil enrichment, while the behavior of the larvae includes both cooperation and competition, which is influenced by the species composition present. This review highlights challenges in PMI estimation due to heat production but also discusses advancements in molecular tools and thermal modeling that enhance accuracy. Ultimately, we identify knowledge gaps regarding species diversity, microbial interactions, and environmental variability that impact mass dynamics, suggesting future research avenues that could enhance ecological understanding and forensic applications. Full article