Search Results (3,456)

Ammonia (NH₃) emissions from concentrated animal feeding operations (CAFOs) are recognized contributors to secondary fine particulate matter (PM_2.5) formation, yet empirically derived secondary PM_2.5 emission factors applicable to livestock operations remain limited. This study investigated NH₃-derived secondary PM_2.5 formation under controlled laboratory conditions using a PTFE flow reactor in which NH₃ was reacted with sulfur dioxide (SO₂) across ammonia-rich NH₃:SO₂ ratios, with and without zero air. The resulting aerosols were characterized using gravimetric analysis, elemental analysis, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), and particle size distribution (PSD) measurements. The recovered particles were dominated by inorganic ammonium–sulfur species, with FTIR and elemental trends indicating sulfite-related intermediates under no-zero-air conditions and more oxidized ammonium–sulfur products under oxygenated conditions. Accounting for both filter-collected and wall-deposited particles, unit particulate emission factors normalized to ammonia input were derived. Size-based apportionment using PSD data indicated that approximately 76.6% of the recovered particulate mass was within the PM_2.5 size range. Scaling the experimentally derived unit emission factors using literature-based ammonia emission rates yielded an estimated secondary PM_2.5 emission factor of 0.351 ± 0.084 g PM_2.5 per animal head per day for cattle feedlots, corresponding to approximately 3–4% of reported total PM_2.5 emissions. Because the experimental system isolates NH₃–SO₂ interactions under idealized conditions and does not represent full atmospheric chemistry, the derived values should be interpreted as screening-level estimates of NH₃-derived secondary PM_2.5 formation potential intended to support comparative air quality assessments of CAFOs rather than direct predictions of ambient PM_2.5 concentrations. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

This study presents an assessment of ambient air quality in Chichiri and Malawi University of Business and Applied Sciences (MUBAS) locations, Blantyre City, Southern Malawi. The study aimed at assessing temporal trends, identifying exceedance of thresholds, investigating relationships between pollutants and meteorological factors, and exploring the predictability of air quality index (AQI). Five pollutants: ${\mathrm{P}\mathrm{M}}_{2.5}$, ${\mathrm{P}\mathrm{M}}_{10}$, ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$, ${\mathrm{C}\mathrm{O}}_2$ and TVOC were assessed over a two-month period using fixed low-cost sensors. Daily and hourly temporal analysis showed that pollutants peak during morning and evening hours. A significant number of exceedances for ${\mathrm{P}\mathrm{M}}_{2.5}$ and ${\mathrm{P}\mathrm{M}}_{10}$ were observed when compared to indicative thresholds. Chichiri exhibited more frequent AQI classifications in the “unhealthy” range. A strong positive relationship between ${\mathrm{P}\mathrm{M}}_{2.5}$ and ${\mathrm{P}\mathrm{M}}_{10}$ (r $=$ 0.84) and positive correlations between ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$ and ${\mathrm{C}\mathrm{O}}_2$ were observed. A multiple linear regression model achieved a high coefficient of determination (${\mathrm{R}}^2$ $=$ 0.938), identifying ${\mathrm{P}\mathrm{M}}_{10}$ and ${\mathrm{N}\mathrm{O}}_{\mathrm{x}}$ as dominant predictors of AQI variability. Temperature and humidity showed modest inverse relationship with AQI, suggesting dispersion effects. A comparison with African cities showed that the study areas’ pollution levels were within regional norms, but that there is a need for targeted mitigation. These findings underscore the importance of continuous monitoring, data-driven policy making and regional collaboration to address urban air quality challenges. Full article

To investigate the multi-factor aging mechanisms of silicone rubber used in the outer sheath of composite bushings, this study focused on HTV silicone rubber employed in the sheath layer of 1100 kV high-voltage bushings. The samples were subjected to temperature–humidity–corona coupled aging in a multi-factor aging platform. The aged samples were characterized by scanning electron microscopy, energy-dispersive spectroscopy, Fourier-transform infrared spectroscopy, hydrophobicity measurements, hardness tests, and dielectric constant measurements. The results indicate that different aging factors affect the material differently. Corona aging primarily affects the sample surface, leading to substantial methyl group detachment, surface oxidation, and a decrease in hydrophobicity, with the local static contact angle decreasing by up to 70%. In contrast, wet heat aging affects the bulk material; under high-temperature and high-humidity conditions, the internal small-molecule chains accelerate silicon-oxide crosslinking, leading to a marked increase in hardness and a relative dielectric constant that initially decreases and then increases. Considering the complex field environment, surface performance measurements are easily influenced by external factors. Therefore, hardness and relative dielectric constant are proposed as key indicators for evaluating the aging degree of silicone rubber sheaths in service. The findings provide a valuable reference for the service-life evaluation of composite bushings. Full article

Stock price sequences are characterized by pronounced nonlinearity, non-stationarity, and multi-scale volatility. They are further influenced by complex, multi-source factors, such as macroeconomic conditions and market behavior, making high-precision forecasting highly challenging. Existing approaches are limited by noise and multi-dimensional market features, as well as difficulties in balancing prediction accuracy with model complexity. To address these challenges, we propose Wavelet Entropy and Cross-Attention Network (WECA-Net), which combines wavelet decomposition with a multimodal cross-attention mechanism. From an information-theoretic perspective, stock price dynamics reflect the time-varying uncertainty and informational complexity of the market. We employ wavelet entropy to quantify the dispersion and uncertainty of energy distribution across frequency bands, and multiscale entropy to measure the scale-dependent complexity and regularity of the time series. These entropy-derived descriptors provide an interpretable prior of “information content” for cross-modal attention fusion, thereby improving robustness and generalization under non-stationary market conditions. Experiments on Chinese stock indices, A-Share, and CSI 300 component stock datasets demonstrate that WECA-Net consistently outperforms mainstream models in Mean Absolute Error (MAE) and R² across all datasets. Notably, on the CSI 300 dataset, WECA-Net achieves an R² of 0.9895, underscoring its strong predictive accuracy and practical applicability. This framework is also well aligned with sensor data fusion and intelligent perception paradigms, offering a robust solution for financial signal processing and real-time market state awareness. Full article

Conventional therapies for rheumatoid arthritis (RA) are limited by poor selectivity, insufficient modulation of the oxidative inflammatory microenvironment, and systemic side effects. Oxidative stress and macrophage-driven immune dysregulation represent critical therapeutic targets. Cinnamaldehyde (CA) and arginine (Arg) possess antioxidant, anti-inflammatory, and anti-osteoclastogenic activities, but their poor solubility, instability, and lack of targeting restrict clinical application. Here, we report a pH-responsive cinnamaldehyde–arginine nanoprodrug (Arg-CA NPs), synthesized via Schiff base reaction, that spontaneously self-assembles into uniform nanoparticles capable of acid-triggered dual-drug release. Arg-CA NPs enhanced the solubility and stability of CA, exhibited excellent dispersibility and circulatory stability, and demonstrated intrinsic antioxidant and anti-inflammatory properties. Mechanistically, Arg-CA NPs attenuated intracellular ROS accumulation, preserved mitochondrial function, and reprogrammed macrophages toward an anti-inflammatory M2 phenotype by suppressing hypoxia-inducible factor-1α (HIF-1α), cyclooxygenase-2 (COX-2), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling. In an adjuvant-induced arthritis (AIA) rat model, Arg-CA NPs selectively accumulated in inflamed joints and significantly alleviated joint swelling, synovial inflammation, cartilage erosion, and bone destruction. These findings identify Arg-CA NPs as a promising redox-active nanoplatform for RA therapy by targeting oxidative stress and immune dysregulation. Full article

Temperature and nutrient availability are pivotal drivers of coastal phytoplankton dynamics; however, how they regulate the interplay between community assembly and ecological network stability remains less explored. In this study, we integrated 18S rRNA high-throughput sequencing with molecular ecological network analysis and the iCAMP model to investigate the seasonal succession and driving mechanisms of phytoplankton in a coastal region (Qiongdong) of the South China Sea. Our results suggest that water temperature is a key factor influencing community succession. However, rather than following a linear response to temperature rise, the molecular ecological network exhibited a significant network contraction in spring, characterized by minimized complexity and peak vulnerability. This structural shift coincided with a transition in nutrient limitation (from phosphorus to nitrogen) induced by spring upwelling. Assembly process analysis revealed that while stochastic processes dominated overall community construction, a notable increase in dispersal limitation occurred in spring. The intensification of dispersal limitation driven by changes in the nutritional structure may be the main cause of network simplification and reduced stability. In conclusion, our findings highlight that while temperature affects the seasonal replacement of phytoplankton species, nutrient-induced shifts in assembly mechanisms degrade ecological network integrity in coastal environments. Full article

Reliable underwater acoustic (UWA) communication is fundamental to marine sensing applications, including environmental monitoring, underwater sensor networks, and autonomous platforms, yet remains severely challenged by multipath propagation, Doppler effects, and limited bandwidth. This paper investigates a memory-based multicarrier modulation framework in which controlled phase continuity is introduced at the symbol-mapping stage to enhance robustness against channel-induced distortions. Unlike conventional memoryless multicarrier schemes, the proposed approach embeds intentional phase memory at the transmitter and exploits it at the receiver, improving reliability in highly dispersive underwater environments. A comprehensive bit-error-rate (BER) evaluation is conducted using extensive simulations over realistic shallow-water acoustic channel models. The analysis examines rational modulation indices, pulse-shaping filters, roll-off factors, transmitter–receiver separation distances, and receiver structures. Both matched-filter and zero-forcing receivers are considered to assess trade-offs between interference mitigation and noise amplification. Results demonstrate consistent and significant BER improvements compared with conventional memoryless multicarrier systems. A modulation index of 7/16 achieves the minimum BER with matched-filter detection, while 3/10 yields optimal performance with zero-forcing detection. The Dirichlet pulse provides the most robust performance across operating conditions. These findings establish phase-memory-aware multicarrier design as a practical strategy for reliable underwater sensing and communication systems. Full article

Oil spills along the northeast coast of Brazil have the potential to cause catastrophic contamination of coastal environments and their associated biota. Beyond the direct contamination processes occurring on beaches, oil can also be transported inland by tides through estuaries. In addition, wind-driven transport of oil was observed in nearly all sections studied along the coast. Therefore, this study evaluated the potential of wind to transport oil fragments inland using both direct and indirect methods, including field observations and GIS-based mapping tools. The results identified and quantified oil fragmentation processes and wind-driven transport over relatively large distances (hundreds of meters). The presence of exhumed beachrock, combined with the absence or low elevation of foredunes and the high potential for wind transport, plays a crucial role in trapping oil on the beach surface. These factors further facilitate the fragmentation and inland dispersal of oil particles, allowing them to penetrate deeper into the coastal environment. The findings underscore the importance of assessing the contamination risks posed by oil fragments as they become incorporated into aeolian and other interconnected inland systems. Full article

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated and human-designed posters exhibits measurable structural divergence under thematically matched conditions. A dataset of jazz festival posters was analyzed using interpretable geometric and information-theoretic descriptors, including spatial density (padding ratio), edge density, chromatic dispersion, and entropy-based measures. Instead of relying on deep neural detection architectures, we employed a transparent machine-learning framework to examine intrinsic structural separability within feature space. Results demonstrated highly stable group separation (ROC-AUC = 0.99; 95% CI: 0.978–0.999) under cross-validated evaluation. Distributional analysis further revealed a pronounced divergence in spatial density allocation (Kolmogorov–Smirnov statistic = 0.76, p < 10⁻²⁸), accompanied by a very large effect size (Cohen’s d = 1.365). While padding ratio emerged as the dominant discriminative factor, additional entropy- and chromatic-based descriptors contributed to group separation even when spatial density was excluded (AUC = 0.903). These findings indicate that AI-generated and human-designed posters can diverge in negative space allocation and chromatic organization under controlled thematic and platform-specific conditions. The study contributes to the explainable analysis of generative visual systems by reframing human–AI differentiation as a structural divergence problem grounded in interpretable image statistics rather than as a model-specific artifact detection task. Full article

With the growing trend toward insulator hybridization, higher requirements are imposed on the synergistic improvement of interfacial durability and pollution flashover performance. Machining annular grooves at the green-body stage and embedding silicone rubber enables the construction of an embedded structure with improved durability, forming hydrophilic/hydrophobic alternating surfaces. However, the outdoor insulation characteristics of such hybrid surfaces remain insufficiently investigated, and their engineering feasibility requires further validation. In this study, a series of hydrophilic/hydrophobic alternating surfaces were fabricated, and artificial pollution tests were conducted. The results show that the AC pollution flashover voltage exhibits a saturated increasing trend as the hydrophobic interfaces become more dispersed. When twenty 4 mm wide hydrophobic interfaces were distributed along a 16 cm creepage distance, the flashover voltage was 12.4% higher than that of a fully hydrophobic surface. These results indicate that appropriate design of hydrophobic interface distribution can achieve excellent pollution flashover performance even at relatively low hydrophobic coverage (≤50%). High-speed imaging combined with infrared thermography reveals the discharge mechanism governed by hydrophobic interface distribution from an electro–thermal coupling perspective. The coexistence of multiple dry bands induced by discrete hydrophobic interfaces is identified as the key factor enhancing flashover withstand capability. A static pollution flashover model was established to quantitatively estimate the AC flashover voltage, confirming the external insulation feasibility of the embedded hybrid concept. Full article

With the continuous expansion of deepwater oil and gas development, annular pressure buildup in gas wells has become an increasingly critical safety concern. Rupture discs, as passive pressure relief devices, have attracted attention for potential application in annular pressure management in deepwater wells. However, their performance under complex downhole environments characterized by high temperature, dynamic loading, gas flow, and corrosion remains insufficiently understood. In this study, a laboratory-scale rupture disc burst-pressure experimental system with independently controllable temperature, pressure, and gas flow rate was developed. By simulating the coupled loading process caused by thermal expansion and controlled gas pressurization in a sealed annulus, a series of systematic experiments considering multiple operating factors were conducted to investigate rupture disc activation behaviour under representative deepwater well conditions. The experimental programme examined the effects of temperature, annular pressure ramp rate, gas flow rate, and acidic corrosion degradation. The results show that increasing temperature, higher annular pressure ramp rates, and elevated gas flow rates significantly reduce the rupture disc burst pressure and increase its statistical dispersion, indicating a transition of the loading state from quasi-static to dynamically coupled conditions. Under high flow rates and rapid pressurization, transient stress redistribution and amplification of local defects become dominant, shifting the failure mechanism from strength-controlled to defect-controlled behaviour. In contrast, corrosion degradation exhibits a stage-dependent influence: although burst pressure decreases with increasing corrosion time, the reduction rate gradually stabilizes, and the variability of burst pressure decreases as corrosion severity increases. These findings provide experimental insights into rupture disc behaviour under coupled environmental and operational factors and offer useful guidance for rupture disc selection and safety margin design in annular pressure control systems for deepwater gas wells. Full article

Heavy metal contamination of freshwater systems represents a persistent environmental challenge due to metal toxicity, non-biodegradability, and bioaccumulation potential. This study compared the phytoremediation performance of Eichhornia crassipes, Pistia stratiotes, and Chrysopogon zizanioides for the removal of chromium (Cr), copper (Cu), cadmium (Cd), and lead (Pb) from contaminated water under controlled conditions. Plants were exposed to aqueous solutions containing 5 mg L⁻¹ of the four metals for 45 days. Metal concentrations in roots and shoots were determined by wavelength-dispersive X-ray fluorescence, translocation factor (TF), bioconcentration factor (BCF), and removal efficiency (RE) were calculated. TF values (0.02–2.90) varied across species, metals, and experimental conditions, indicating a general tendency for metal retention in roots, although translocation to shoots occurred in several cases. BCF values (0.04–87.55) were significantly influenced by species, exposure time, and treatment (p < 0.05), with P. stratiotes showing higher accumulation under specific conditions (Cu = 87.55; Pb = 44.56). In contrast, RE showed high variability (−616.21 to 72.72%) and no significant differences among experimental factors. Overall, the results demonstrate context-dependent variation in metal uptake and translocation, highlighting the potential of aquatic macrophytes as low-cost alternatives for the treatment of metal-contaminated wastewater systems. Full article

Developing regions rich in ethnic cultures face structural tensions between cultural heritage preservation, ecological conservation, and economic development. Yet existing research analyzes village types in isolation, overlooks non-additive factor interactions, and lacks frameworks connecting spatial heterogeneity to differentiated sustainability pathways. This study addresses these three gaps through integrated spatial analysis of 4083 characteristic villages across five nationally designated types in Southwest China, a region harboring over 40% of China’s Traditional Villages and high densities of Forest Villages, Key Tourism Villages, Ethnic Minority Characteristic Villages, and Historic and Cultural Villages. Kernel Density Estimation, Average Nearest Neighbor analysis, Standard Deviational Ellipse, and Geographical Detector methods are employed in a three-stage analytical progression. Spatial characterization reveals pronounced heterogeneity with “large-scale dispersion, small-scale agglomeration” patterns and systematic cross-type spatial co-location in high-heritage, high-vulnerability zones. Mechanism quantification shows that intangible cultural heritage (q-values 0.66–0.78) and GDP per capita (q-values 0.68–0.82) are dominant drivers whose pairwise interactions exceed individual effects by 40–60%. Sustainability classification translates q-value-weighted composite indices into four context zones across 506 counties, Culture-Ecology Tension Zones (22.7%), Economic Isolation Nodes (17.0%), Tourism-Driven Development Corridors (19.6%), and Balanced Development Potentials (40.7%), each exhibiting a distinct configuration of cultural, ecological, and economic conditions that necessitates differentiated pathways. The “culture-ecology-economy” tripartite framework advances sustainability science in three ways: it empirically identifies non-additive spatial interactions as generative mechanisms of heterogeneity, achieves a methodological progression from pattern description to sustainability diagnosis, and reconceptualizes cultural heritage from a development constraint into a measurable sustainability asset. The framework is transferable to analogous mountain regions globally where heritage-rich communities confront coupled ecological and economic vulnerabilities. Full article

Granular deposition and grading strongly influence pore-space topology and hence hydraulic conductivity in natural and engineered porous media, yet quantitative links between deposition sequence, particle-scale morphology, pore-network descriptors, and permeability anisotropy remain incomplete. Here, we develop a process-based digital porous-media framework that couples discrete element method (DEM) deposition with pore-network characterization and Darcy-scale permeability evaluation. Two deposition sequences—normal grading (coarse-to-fine) and reverse grading (fine-to-coarse)—are simulated using bi-disperse particle sets with controlled size ratios. To further isolate the role of particle morphology, particle irregularity is parameterized by a Perlin-noise-based shape perturbation factor and incorporated into the DEM-generated packings. For each packing, pore networks are extracted and quantified in terms of pore/throat size distributions and connectivity, while pore-space complexity is measured via box-counting fractal dimension. Single-phase flow is solved under imposed pressure gradient, and intrinsic permeability is computed along three orthogonal directions to evaluate anisotropy. Results show that increasing size contrast reduces porosity, shifts pore and throat distributions toward smaller characteristic radii, increases pore-space fractal dimension, and yields a monotonic permeability reduction. For identical size ratios, reverse grading consistently yields higher permeability than normal grading, suggesting that deposition sequence exerts a strong control on the continuity and efficiency of effective flow pathways at the sample scale. Increasing particle irregularity decreases permeability and systematically modifies permeability anisotropy, transitioning from weak horizontal anisotropy toward near-isotropy and, at strong irregularity, toward preferential vertical permeability. The proposed framework provides a reproducible route to relate depositional history and particle morphology to pore-network structure and directional permeability, offering implications for filtration, packed-bed design, and sedimentary reservoir characterization. Full article