Search Results (1,226)

Wildfires increasingly contribute to urban particulate matter (PM) exposure, particularly fine particles (PM_2.5), through atmospheric transport processes influenced by meteorological conditions and terrain complexity. This study investigated wildfire impacts on PM₁₀ and PM_2.5 concentrations in Split, Solin, and Kaštela (Croatia) using a terrain-aware wildfire transport framework combined with statistical and machine learning (ML) approaches. Daily PM observations (2016–2024) from three air quality monitoring stations were integrated with meteorological data from six stations, wildfire polygons, and a digital elevation model (DEM). A wildfire influence index accounting for fire size, transport distance, wind conditions, and terrain-modified airflow was evaluated using Ordinary Least Squares (OLSs) regression, Random Forest (RF) modelling, and SHAP (SHapley Additive exPlanations) analysis. Results showed stronger wildfire-related effects for PM_2.5 than for PM₁₀, while meteorological variables remained the dominant predictors of PM variability. RF models improved predictive performance relative to OLS, achieving $R^2$ = 0.474 for PM_2.5 and $R^2$ = 0.416 for PM₁₀. SHAP analysis identified precipitation, temperature, and lagged wildfire transport variables as important predictors. A total of 84 wildfire events were classified as effective wildfires, with most measurable impacts occurring within approximately 30–70 km of monitoring stations, indicating that wildfire impacts on urban air quality in Mediterranean coastal environments are strongly mediated by atmospheric transport and meteorological conditions. The proposed framework demonstrates the potential of explainable and geospatially informed ML for environmental monitoring and wildfire-related urban air quality risk assessment. Full article

Graphene and carbon nanotube (CNT) membranes are promising for filtration, desalination, and water treatment, yet their performance requires the joint interpretation of their architecture, nanoconfined transport, selectivity, fouling, swelling, defects, stability, and operating conditions. Here, GCMembrane-LLM was developed as an evidence-grounded domain-specific large language model. A curated 582-paper corpus generated 12,208 cleaned membrane-specific question–answer pairs for Low-Rank Adaptation (LoRA)-based supervised fine-tuning of Llama-3.1-8B-Instruct, and retrieval-augmented generation provided article-title and page-level traceability. GCMembraneBench included 100 application-oriented questions on graphene oxide (GO) membranes, CNT membranes, GO/CNT hybrids, and cross-material reasoning. Under direct answering without retrieval context, the anonymized and shuffled automatic evaluation showed that GCMembrane-LLM achieved a mean weighted score of 4.237/5.0, exceeding Llama-3.1-8B-Instruct and Doubao-1.5-lite. A stratified 30-question blinded manual assessment showed the same ranking. The application cases further yielded membrane science conclusions: CNT-assisted GO/CNT transport should be evaluated with dispersion, interfacial compatibility, defects, and stability; GO desalination depends on swelling control, interlayer spacing, and defect suppression; and CNT high flux requires joint examination of pore diameter, entrance chemistry, hydration barriers, ion rejection, and operating conditions. GCMembrane-LLM supports source-traceable evidence organization and preliminary hypothesis formulation before experimental validation. Full article

6082 aluminum alloy is widely applied in marine engineering, rail transportation and other industries owing to its excellent comprehensive performance. Welding heat source characteristics exert a decisive influence on the microstructure and mechanical properties of welded joints and become a major constraint for the application of medium-thick aluminum alloy welded structures. In this work, comparative tests of TIG and MIG welding were carried out on 16 mm thick 6082 aluminum alloy plates. Combining thermal simulation, metallographic observation and mechanical property tests, the temperature field distribution, microstructure, microhardness, tensile properties and bending properties of the two kinds of joints were systematically studied. The results show that TIG welding possesses high heat input, forming a broad temperature field with steep thermal gradients. Its weld microstructure is coarse and accompanied by severe coarsening of Mg₂Si precipitates, and the joint presents a highly fluctuating M-shaped microhardness distribution. The average tensile strength of TIG welded joints is 194 MPa, and all specimens fracture in the heat-affected zone. By contrast, MIG welding with low heat input produces a uniform temperature field, as well as a fine and homogeneous weld microstructure with dispersed precipitates. Its microhardness distribution is stable, and the average tensile strength reaches 256 MPa, 32% higher than that of TIG joints. Both welding methods deliver favorable bending performance. The difference in heat input and cooling behavior changes the grain evolution and precipitate characteristics and further dominates the final mechanical performance of joints. MIG welding is more suitable for multi-layer, multi-pass welding of 16 mm thick 6082 aluminum alloy. This work clarifies the correlation between heat input, microstructure and mechanical properties, and the optimized process can effectively improve the microstructural uniformity of the weld joint and enhance its mechanical properties. Full article

High Velocity Oxy-Fuel (HVOF) thermal spraying enables the deposition of dense coatings with low porosity, high hardness, and good fracture resistance. Tungsten carbide–cobalt (WC-Co) coatings are widely used in industrial and aerospace applications due to their excellent wear resistance; however, improving crack resistance and coating–substrate adhesion remains a key challenge. In this study, WC-Co+Ni composite coatings were deposited on ductile cast iron, with emphasis on the role of Ni addition in controlling microstructure development under HVOF conditions. Microstructural characterization was performed using optical, scanning, and transmission electron microscopy (OM, SEM, TEM), while phase composition and chemical analysis were determined by X-ray diffraction (XRD) and energy-dispersive spectroscopy (EDS). The coatings exhibited a dense, low-porosity microstructure composed of fine WC and W₂C carbides embedded in a Co–Ni binder, with locally nanocrystalline regions. XRD analysis confirmed WC and W₂C as the dominant phases, with weak reflections corresponding to the η-phase (Co₆W₆C), indicating local decarburization. The addition of Ni increases the fraction of the transient liquid phase during particle flight, enhancing carbide dissolution and mass transport in the binder, which accelerates decarburization kinetics and promotes η-phase formation. Simultaneously, Ni modifies the binder into a more ductile Co–Ni matrix, reducing the detrimental effect of brittle η-phase on coating integrity. Mechanical and tribological testing (instrumented indentation and scratch testing) demonstrated improved crack resistance, wear resistance, and adhesion. The results show that Ni addition enables process-driven microstructural tailoring of HVOF-sprayed WC-Co coatings, leading to enhanced performance despite the presence of η-phase. Full article

Increasing pressure on surface water resources in intensive agricultural regions has driven the search for sustainable alternatives for irrigation supply, especially in areas where water quality limits crop safety and export opportunities. In this context, riverbank filtration (RBF) systems offer a nature-based solution by utilizing physical, chemical, and biological processes associated with river–aquifer exchange. However, their implementation depends on suitable site selection supported by hydrogeological, geomorphological, and hydraulic criteria. This study developed an integrated methodology to identify zones with potential for implementing RBF systems in the Roldanillo–Unión–Toro irrigation district, located in northern Valle del Cauca, Colombia. This region requires irrigation water over 10,256 ha of agricultural land (mainly sugarcane, maize, grapes, and guava). We combined geophysical methods (vertical electrical soundings, 2D electrical resistivity tomography, and passive seismic), geotechnical methods (CPTu tests), and hydraulic characterization of the river reach to evaluate subsurface stratigraphy, preliminary hydrogeological suitability, inferred river–aquifer connectivity conditions, and channel stability. The evaluation covered four sectors along an approximately 21 km stretch of the Cauca River’s left-bank alluvial valley. The results revealed pronounced lateral and vertical heterogeneity of alluvial materials. However, the “El Palmar” sector was identified as the best-supported priority sector for future RBF validation, due to the presence of profile-scale evidence of potentially permeable sandy and gravelly units with intermediate resistivity values (52–61 Ω·m), favorable stratigraphic organization, and stable river-reach conditions during the field campaign. In contrast, the other three sectors (La Esperanza, Candelaria, and Cayetana) showed more fine-grained sediments with deeper permeable strata. River-flow measurements during the July 2025 field campaign indicated high discharge conditions at the evaluated reach, while river-channel observations showed active fine-sediment transport; these findings provide hydraulic and sedimentary context for the future evaluation of induced infiltration and potential clogging, but do not constitute direct evidence of river–aquifer exchange. This study highlights the value of integrated screening approaches for prioritizing candidate RBF sites in agricultural alluvial settings, while indicating that pumping tests, piezometric monitoring, hydraulic-gradient analysis, and water-quality validation remain necessary before engineering implementation. Full article

Urban healthcare systems are fundamentally constrained by the mismatch between static resource configurations and dynamically evolving patient demand. Under the tiered healthcare system, traditional static planning methods struggle to capture the complexity and randomness of patient flows. While recent reinforcement learning (RL) approaches enable adaptive decision-making, they suffer from dimensionality explosion and unstable convergence due to massive action spaces and delayed spatiotemporal credit assignment in city-scale environments. To address this gap, we propose Twin–Game: a digital twin-driven hierarchical reinforcement learning (HRL) framework that formulates adaptive healthcare resource optimization as a “Twin Game” between a simulation-based game environment (Strategic Sandbox) and a hierarchical decision policy. First, we construct the “first twin”—an offline digital twin that serves as the Strategic Sandbox parameterized with Wuhan’s observed facility, population, and transportation data, while patient arrivals and disease profiles are generated synthetically under documented assumptions because individual-level clinical flow data are not publicly available. This environment integrates a dynamic gravity model with a two-way referral mechanism to represent the nonlinear coupling between hospital attractiveness, crowding levels, and patient choice behaviors. Second, we build the “second twin”—an Option-based HRL policy. The Manager (Macro-level Strategic Layer) uses a Deep Q-Network (DQN) for discrete spatial attention allocation; the Worker (Micro-level Execution Layer) uses Proximal Policy Optimization (PPO) for continuous, fine-grained controls such as bed expansion ratios and personnel scheduling. The two twins interact in a closed-loop game, performing strategy search and game evolution under complex constraints to optimize allocation. Experimental results from the Wuhan case indicate that the Twin–Game framework outperforms static baselines and single-layer RL in reducing average travel times, enhancing resource utilization, and improving tiered diagnosis and treatment within the simulation setting. The results should be interpreted as simulation-based decision-support evidence rather than direct clinical validation. This study provides a data-driven, game-theoretic decision support tool for building resilient urban healthcare systems. Full article

Elevated concentrations of fine particulate matter ($P{M}_{2.5}$) are a critical threat to respiratory health worldwide. Therefore, there is an urgent need for precise urban forecasting systems for public health management. Technological advancements in the domains of continuous environmental monitoring and deep learning have enabled large-scale data acquisition, processing, and modeling. Existing predictive models typically depend on auxiliary meteorological inputs, which are frequently inaccessible within standard ground-level monitoring networks. Furthermore, conventional approaches often fail to adequately capture the complex spatio-temporal interactions of pollutants. To address these limitations, this study presents the Spatio-Temporal Endogenous Attention Network (STEA-Net), a forecasting framework designed to operate exclusively without weather variables. Validated on a comprehensive multi-year historical dataset (Jan 2015–Feb 2020) from diverse monitoring stations in India, STEA-Net employs a hybrid adjacency matrix that integrates physical geographical distances with functional clustering to accurately map pollutant transport pathways. Utilizing this structural map, a Graph Attention Network dynamically evaluates the spatial influence of neighboring nodes, while a Bidirectional LSTM processes the underlying temporal sequences. Experimental results demonstrate that STEA-Net substantially surpasses traditional machine learning algorithms and provides competitive performance against advanced deep learning baselines. The proposed model achieves a peak Coefficient of Determination ($R^2$) of 0.9294 (5-seed average: $0.9273\pm 0.0023$) and a peak RMSE of 14.38 µg/m³ (5-seed average: $14.59\pm 0.23$ µg/m³), effectively adapting to the dynamic volatility of urban pollution levels. The model exhibits architectural stability with a Monte Carlo dropout verified deviation of $\pm 2.22$ µg/m³. This research provides a forecasting architecture that retains competitive predictive performance under the strict operational constraint of meteorology-free deployment in resource-constrained urban monitoring environments. Full article

There is increasing interest in structured layered double hydroxide (LDH)-based catalysts because they combine tunable acid–base/redox chemistry with reactor architectures that can reduce diffusion lengths, improve heat management, and lower pressure-drop penalties. This review evaluates LDH, LDH-derived oxide (LDO/MMO), reduced metal/LDO, reconstructed hydroxide-rich, and mixed dynamic states integrated into honeycomb monoliths, open-cell foams, meshes/felts, thin films, washcoats, coated plates, microchannels, capillaries, and additively manufactured lattices. To move beyond descriptive comparison, the literature is assessed using unified evaluation dimensions: operative active state, support architecture, coating/integration route, active-phase loading, coating thickness and uniformity, reactor-volume-normalized productivity or STY, ΔP/L, axial/radial thermal gradients, time-on-stream, coating loss, regeneration recovery, and pilot-readiness. Representative benchmarks illustrate both the promise and reporting gaps of the field: NiFe-LDH-derived monoliths for CO₂ methanation have reached ~70% CO₂ conversion at 300 °C with >90% CH₄ selectivity and only 0.7% post-test mass loss; NiFe-LDH/iron-foam monoliths retained 85% ozone conversion after 168 h; high-entropy LDH-derived oxides showed T50/T90 values of 246/254 °C for toluene oxidation; and Au/LDH capillary films achieved 31.9% glycerol carbonate yield and 3.78 g h⁻¹ g⁻¹ productivity. The strongest current cases are pollution abatement and CO₂ methanation, whereas biomass upgrading, fine-chemical flow, high-entropy coatings, and photo/electrocatalytic films require deeper module-level validation. Overall, structured LDH catalysts should be treated as coupled chemistry–coating–reactor systems whose performance must be judged simultaneously by activity, accessible catalyst inventory, transport efficiency, pressure drop, thermal profile, durability, regeneration, and manufacturability. Full article

This study examined the chemical composition and quantitative source contributions of coarse (PM_10–2.5) and fine (PM_2.5) particles in ship-based PM₁₀ and PM_2.5 filter samples from 2020 to 2024 across the Yellow Sea. The observations were primarily conducted during the spring season, when the influence of continental air masses from East Asia is pronounced, and detailed analyses of water-soluble ions and elemental species were performed. In coarse particles, sea salt components (e.g., Na⁺ and Cl⁻) and soil-derived species (e.g., nss-Ca²⁺ and CO₃²⁻) were predominant, whereas fine particles were dominated by secondary inorganic species such as nss-SO₄²⁻, NO₃₋, and NH₄⁺. Source contributions were estimated using Dispersion Normalized Positive Matrix Factorization (DN-PMF), and eight common factors were identified, including sea salt, soil, secondary nitrate, secondary sulfate, oil combustion, biomass burning, marine biogenic emissions, and plant growth. Additionally, an industry factor was uniquely resolved in coarse particles, whereas a mobile source factor was identified in fine particles. In coarse particles, sea salt (30.9%) and soil (15.1%) were the major contributing sources, whereas fine particles were dominated by secondary nitrate (48.6%) and secondary sulfate (15.6%). Potential Source Contribution Function (PSCF) analysis indicated that the sea salt and oil combustion factors in coarse particles were associated with coastal regions of the Yellow Sea and the East China Sea, while the soil factor corresponded spatially with inland regions of northern China. In contrast, the secondary nitrate, secondary sulfate, and biomass burning factors in fine particles showed strong associations with inland regions of eastern China. Using size-resolved DN-PMF and five years of repeated observations over the same marine region, this study provides the first quantitative source apportionment analysis of interannual atmospheric composition variability and long-range transport affecting air quality over the Yellow Sea. Full article

Demand-responsive transit (DRT) is increasingly promoted as a means to enhance the resilience and inclusiveness of sustainable urban mobility. However, how users form early-stage adoption intentions toward such interface-mediated services remains insufficiently understood. While prior research has focused on conventional transit or mature mobility-on-demand platforms, the role of fine-grained human–computer interaction (HCI) characteristics in shaping initial adoption intentions toward DRT received limited empirical attention. This study proposes an integrated framework linking five HCI characteristics—interaction responsiveness, real-time interaction, controllability of interactivity, personalization of interactivity, and playfulness—to behavioral intention through the mediating mechanisms of perceived service quality and platform trust. The framework was tested by applying partial least-squares structural equation modeling to cross-sectional survey data (N = 147) collected from existing early users of an early-stage DRT pilot in Wuxi, China. Platform trust emerged as the strongest direct predictor of behavioral intention, while real-time interaction and interaction responsiveness contributed mainly through trust- and service-quality-based pathways. Controllability and personalization showed no statistically significant association with the mediators in this early-stage sample, and playfulness exhibited a significant but modest effect on platform trust. By integrating HCI design, service-quality perceptions, and platform trust into a single nomological framework, this study offers context-sensitive guidance for designing interface-mediated shared mobility services that may support more resilient and sustainable urban transport. Full article

This study evaluates zero-shot domain transfer for multilingual sentiment analysis in sustainable urban mobility using XLM-RoBERTa, a transformer pre-trained on social media data and applied to transport reviews without task- or domain-specific fine-tuning. Starting from a manually annotated English corpus of 375 transport-related user reviews, we created sentence-aligned translations in Spanish, French, German, and Italian, yielding a multilingual evaluation dataset of 1875 instances. Results show that the model assigns consistently high confidence to polarized content (mean: 0.76–0.85) and lower confidence to neutral or ambiguous expressions (0.58–0.65), with visible but preliminary cross-lingual variations that require further linguistic validation. Confidence scores are treated as diagnostic indicators of model certainty, not as evidence of correctness or calibration. A qualitative analysis of 113 categorized low-confidence predictions identifies six recurring linguistic patterns associated with model uncertainty (led by translation drift, mixed sentiment, and idiomatic expressions) with substantial inter-annotator agreement (κ = 0.664). By releasing the annotated multilingual dataset and code publicly, this work provides a reproducible exploratory evaluation framework for annotation-scarce, domain-specific multilingual NLP. Full article

Background: An increasing number of commercially available drug substances and bioactive ingredients are characterized by poor flowability. Inadequate flow properties may lead to material blockage during transport within production lines, as well as the formation of air voids within the bulk. Such phenomena can disrupt the technological process and may even result in batches that fail to meet quality requirements. Therefore, ensuring adequate powder flow is of utmost importance in the manufacture of health-related products. Methods: Binary mixtures were prepared using one of four model substances (ibuprofen, metamizole sodium, mefenamic acid, or sunflower lecithin) combined with a glidant (colloidal silica, precipitated silica, or tricalcium phosphate). The glidant content ranged from 0.5 to 10.0% w/w depending on the model substance, and mixing was carried out for 5–30 min. The resulting binary mixtures were evaluated for flow properties using the angle of repose method, and in selected cases, bulk density was also determined. Results/Conclusions: The study demonstrated that powder flow improvement depended not only on the glidant but primarily on the properties of the host material (particle size, shape, and bulk density). Coarser powders such as ibuprofen responded well to low glidant levels, although excessive silicon dioxide caused oversilication. Metamizole sodium showed progressively better flow with increasing particle size and density, and tribasic calcium phosphate further improved performance, particularly with longer mixing times. Very fine or cohesive powders, such as mefenamic acid and sunflower lecithin, showed limited response to silica-based glidants, whereas tribasic calcium phosphate proved more effective and additionally increased bulk density. Overall, no universal glidant strategy was identified; effective flow enhancement requires a tailored approach based on specific powder characteristics. Full article

The presence of residual moisture in tetrahydrofuran (THF) greatly limits its suitability for moisture-sensitive processes, including polymerization, Grignard chemistry, and fine-chemical production, where the allowable water concentration is generally lower than 10 mg/kg. Here, a hierarchical microporous/mesoporous composite adsorbent was prepared via extrusion molding, combining an LTA-type zeolite microporous framework with an amorphous mesoporous matrix. Characterization by XRD, FTIR, SEM, and pore analysis confirmed that the LTA crystal structure was retained while mesopores provided channels for mass transport. Static dehydration tests showed that the composite reduced THF water content from 70 mg/kg to 8.3 mg/kg, compared to 23.4 mg/kg for commercial 3A molecular sieves. The enhanced performance arises from micropores supplying uniform adsorption sites for deep dehydration and mesopores accelerating diffusion. Water vapor adsorption, kinetic and isotherm analyzes, regeneration, and competitive adsorption experiments indicated improved water accessibility and high selectivity, with kinetics described by a double-exponential model. The adsorbent remained stable over six adsorption–regeneration cycles. These results demonstrate that hierarchical microporous/mesoporous structures effectively achieve deep THF dehydration. Full article

Oxygen transfer enhancement in aquaculture was investigated using a low-cost fine-bubble spray system operated under controlled hydrodynamic conditions. Experiments were conducted under oxygen-depleted conditions (initial DO = 2.4 mg L⁻¹), and oxygen transfer kinetics were evaluated using the dynamic method. The dissolved oxygen concentration increased to 6.2 mg L⁻¹ within 1 h, corresponding to a net oxygen transfer of 9.55 ± 0.46 g. The volumetric mass transfer coefficient (kLa) was determined to be 1.44 h⁻¹ (R² = 0.97), while the specific oxygen transfer efficiency (SOTE) reached 76.4 ± 7.8 gO₂ kWh⁻¹. Dimensionless analysis (Re ≈ 2 × 10⁴, Sc ≈ 500, Sh ≈ 682) indicates a turbulent, convection-dominated transport regime. Biological observations showed a 43% increase in fish growth under spray-assisted conditions, indicating improved oxygen availability. The observed oxygen transfer enhancement was primarily associated with hydrodynamic interfacial area generation rather than diffusion-limited transport. The low-power configuration and simplified system design suggest potential applicability for decentralized aquaculture operations. The proposed approach also provides an engineering framework for evaluating low-cost aeration technologies under aquaculture operating conditions. Full article

Transboundary fine particulate matter (PM2.5) in Southeast Asia is commonly assessed using static source–receptor frameworks or descriptive associations that may not resolve how directional dependence changes through time under shifting meteorological conditions. This study examines regional PM2.5 as a time-varying, meteorology-adjusted directional coupling system using monthly data for 2003–2024 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) meteorological covariates, climate controls, and administrative aggregation. Using a rolling-window directed network framework based on Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery, we inferred lagged conditional-dependence networks from covariate-adjusted PM2.5 anomalies and summarized their structure at national and first-order administrative levels. The inferred network structure varies over time but retains measurable continuity across rolling windows. At the country level, cross-border links consistently account for a large share of the directed structure, indicating that PM2.5 variability within the study domain is strongly shaped by transboundary coupling rather than by country-contained dynamics alone. A recurrent backbone of country-level directional coupling corridors emerges, including persistent links among China, Indonesia, Myanmar, and Thailand. At the first administrative level, stable gateways and receptor basins become more evident, especially the bidirectional coupling corridor between Yunnan Province, China, and Shan State, Myanmar, which appears throughout the full window sequence. These results show that subnational structure can reveal transport-relevant coupling patterns that national summaries may conceal. The framework provides an interpretable basis for corridor-oriented monitoring and regime-aware early warning, while the inferred links should be interpreted as directional statistical dependence rather than direct emissions attribution or resolved physical transport pathways. Full article