搜索结果：Environmental Engineering

The development of stable, environmentally benign, and high-performance perovskite solar cells (PSCs) has increasingly focused on innovative inorganic absorber materials. In this study, we conduct a detailed evaluation of the optoelectronic and mechanical properties of Ca3AsBr3, a promising non-toxic halide perovskite, using density functional theory (DFT) alongside SCAPS-1D simulations. The DFT results indicate that Ca3AsBr3 possesses a direct bandgap of 1.66 eV, along with good mechanical stability and strong optical absorption, making it well-suited for photovoltaic applications. To further investigate device performance, four electron transport layers (ETLs)-WS2, SnS2, CdS, and TiO2 were incorporated into HTL-free FTO/ETL/Ca3AsBr3/Au architecture, allowing analysis of energy band alignment, defect tolerance, and overall efficiency. Among these configurations, the WS₂-based device demonstrated superior performance, achieving a power conversion efficiency (PCE) of 20.50%, with an open-circuit voltage (Voc) of 1.165 V, a short-circuit current density (Jsc) of 20.55 mA/cm², and a fill factor (FF) of 85.64%. Further simulation results highlight that an optimal absorber thickness of 1200 nm, along with reduced bulk and interface defect densities (≤ 10¹⁵ cm⁻³ and ≤ 10¹³ cm⁻²), plays a crucial role in minimizing non-radiative recombination losses and improving charge carrier collection. Overall, this work identifies Ca3AsBr3 as a viable eco-friendly absorber material and emphasizes the importance of ETL optimization in achieving efficient, stable, and scalable PSC devices.

Although recent studies have suggested an association between air pollution and Kawasaki disease (KD), evidence regarding prenatal exposure and subsequent KD risk in children remains limited. This study aimed to evaluate the association between prenatal air pollution exposure and KD incidence in children. We used the Big CHildren's ENvironmental health Study covering mother-child pairs based on the National Health Information Database. We defined KD onset using the tenth revision of the International Classification of Diseases (M30.3) and immunoglobulin prescriptions. We used a multivariate Cox proportional hazards model to evaluate the association between prenatal air pollution exposure (fine particulate matter [PM2.5], particulate matter [PM10], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]) and KD in children. The model was adjusted for maternal age, child sex, income level, maternal occupation, birth season, birth year, and region. Hazard ratios (HRs) and 95% confidence intervals (CIs) were evaluated per interquartile range increase in exposure to PM2.5, PM10, SO2, NO2, and O3. We analyzed 1624,230 mother-child pairs and identified KD onset in 13,126 children (0.8%). Prenatal exposure to PM10, SO2, and NO2 during the second and third trimesters, as well as across the entire pregnancy, was associated with an increased risk of KD, with the strongest associations observed during the third trimester (PM2.5: 1.046, 95% CI: 1.007-1.087; PM10: 1.104, 95% CI: 1.061-1.149; SO2: 1.052, 95% CI:1.117-1.153; NO2: 1.117, 95% CI: 1.082-1.153). We found that exposure to air pollution during pregnancy was positively associated with KD risk in children.

Paddy soils derived from basalt weathering contain high levels of Fe-Mn oxides, along with elevated nickel (Ni) and chromium (Cr), posing threats to rice safety. Unlike Fe oxides, Mn oxides exhibit both adsorption and oxidation capabilities, creating complex regulatory mechanisms for Ni and Cr. The environmental impacts of these oxides depend on their spatial distribution, though the mechanisms remain unclear. This study investigates the synergistic regulation of δ-MnO2 on the speciation transformation and bioavailability of Ni and Cr. Pot experiments were setup using δ-MnO2 distributed either in the rhizosphere or sub-root layers, combined with continuous or intermittent flooding water management. Results show that δ-MnO2 spatial distribution critically influences the distinct environmental behaviors of Ni and Cr. For Ni, δ-MnO2 exhibits adsorption and immobilization effect, but these effects are strongly dependent on the position: distribution in the rhizosphere reduces the concentration of available forms and decreases Ni accumulation in rice grains, while distribution in the sub-root layer hinders downward Ni migration and increases grain Ni accumulation. For Cr, δ-MnO2 primarily converts inert Cr(III) into highly reactive Cr(VI) through oxidation, resulting in increased Cr accumulation in grains. Water management and the spatial distribution of δ-MnO2 show significant synergistic effects: continuous flooding promotes Ni release and Cr(VI) reduction, while intermittent flooding favors Ni adsorption and immobilization. This study challenges the conventional understanding that "metal oxides universally exhibit immobilization effects on heavy metals", clarifying the differential regulatory roles of Mn oxide spatial distribution in paddy soil profiles on the environmental behaviors of Ni and Cr. It reveals the "double-edged sword effect" of Mn oxides in adsorbing/immobilizing Ni while oxidizing/activating Cr, and elucidates the core principle that neglecting their vertical distribution would lead to counterproductive heavy metal control measures. The findings not only provide new insights into the mechanisms by which Mn oxides regulate Ni and Cr accumulation in rice within basalt weathering zones, but also offer scientific and theoretical support for precise management of rice safety production in geologically high-background regions based on the differential properties of heavy metals.

The co-incineration of municipal solid waste (MSW) with sewage sludge (SS) is pivotal for urban waste-to-energy strategies, yet its operational instability poses significant challenges for environmental management, leading to incomplete combustion and elevated pollutant emissions. This study investigates how to minimize its environmental footprint by optimizing key operational parameters. A validated three-dimensional full-scale unsteady-state model of a 500 t/d mechanical grate incinerator was developed to simulate the real incineration process. It systematically quantified the impact of fuel heterogeneity on combustion stability and pollutant generation under different sewage sludge blending ratios, sewage sludge moisture content, and primary air distribution ratio. Results demonstrate that exceeding a sewage sludge blending ratios of 7 % induces calorific value dilution and stratified combustion, shifting the drying zone outward by 0.2-0.6 m and increasing the risk of incomplete combustion. Similarly, sewage sludge moisture content above 40 % extends the main combustion zone by 0.8-2.5 m, substantially raising CO emissions. Critically, this study proposes and validates an optimized primary air distribution ratio scheme (1.2:1.5:2.5:2.5:1.2:1.1) as a process intensification strategy. This management lever effectively enhances fuel drying and reactor environment, achieving a carbon burnout rate of 99.4 % and reducing CO emissions to 0.006 % in the MSW/SS co-incineration process. This work translates complex combustion mechanisms into actionable operational thresholds and control strategies, providing a robust simulation-driven framework for plant managers and policymakers to optimize co-incineration performance, minimize environmental footprint, and advance sustainable waste management.

Marine microalgae are frequently promoted as sustainable biofuel feedstocks because of their halotolerance, high photosynthetic efficiency, and limited land requirements, yet commercial deployment remains elusive. This gap is primarily systemic rather than biological, reflecting the fragmented development of strain engineering, harvesting, conversion, and sustainability assessment. This review reframes marine microalgae as circular biofactories and advances a system-centric paradigm for integrated biorefineries. We synthesise recent advances in metabolic and genetic engineering, low-energy harvesting, and thermochemical and biochemical conversion, highlighting how cross-stage interdependencies dominate overall performance. We further discuss how artificial intelligence, digital twins, nutrient recycling, carbon utilisation, and high-value coproducts enable predictive optimisation and techno-economic viability. This perspective provides a road map for translating marine microalgal biofuels from laboratory promise to industrial relevance.

Radioactive cesium-rich microparticles (CsMPs) released from the Fukushima Daiichi Nuclear Power Plant (FDNPP) in 2011 pose a persistent environmental concern, yet their initial atmospheric dispersion has remained poorly constrained. Here we quantify CsMP abundance and radioactive fraction (RF) in 100 surface soil samples collected across Fukushima Prefecture in July 2011 and integrate the results with WSPEEDI atmospheric simulations. CsMP abundance ranged from 0 to 52.3 particles g⁻¹ (dry weight), with RF values of 0-61.85%. The combined analysis identifies a major CsMP formation and release event at ∼03:00 JST on 15th March 2011, producing a plume strongly enriched in CsMPs. Plumes released after 00:00 JST on 16th March contained no detectable CsMPs, indicating that particle formation had ceased by that time. The widespread distribution of CsMPs across Fukushima is therefore attributed primarily to this single plume. Directional variations in CsMP abundance reflect temporal changes in plume composition, with peak concentrations of ∼2070 particles m⁻³ toward the southwest and ∼4700 particles m⁻³ toward the northwest. These findings constrain CsMP formation mechanisms and improve reconstruction of radiological dispersion relevant to the long-term environmental risk assessment of nuclear power plants.

The formation of Small Colony Variants (SCVs) by environmental biological hazards represents a formidable challenge to hazard detection and mitigation in engineered environments. In the present study, the physiological characteristics and formation mechanisms of Listeria monocytogenes SCVs induced by peroxyacetic acid (PAA) were investigated. PAA exposure resulted in the emergence of transient, miniaturized SCVs characterized by metabolic dormancy, indicated by an extended lag phase, reduced enzymatic activity, and ATP depletion. Transcriptional analysis revealed upregulation of stress response (sigB) and efflux (mdrL) genes, with concurrent downregulation of virulence (hly, inlA) and metabolic (betL, ftsZ) genes. Despite flaA upregulation, SCVs exhibited impaired motility but enhanced biofilm formation. Physiologically, SCVs displayed membrane hyperpolarization, elevated intracellular ROS, and cross-protection against acid, thermal, and osmotic stresses. Crucially, inhibition of ATP synthesis using CCCP shifted the population from culturable SCVs to a non-culturable state, confirming that SCV formation is an active, energy-dependent adaptation rather than a passive injury. Furthermore, while invasion capability was compromised, cell surface hydrophobicity and adhesion were significantly increased. These findings demonstrate that PAA drives L. monocytogenes into a defensive, dormant state that prioritizes persistence over pathogenesis, providing new insights into the toxicological responses and persistence strategies of this biological hazard under environmental oxidative stress.

Rheumatoid Arthritis (RA) greatly affects patient's life. Systematic reviews of recent epidemic trend and the pathogenesis of RA are inadequate. Although multiple health benefits of lactic acid bacteria (LAB) were reported, comprehensive reviews addressing the mechanisms by which LAB alleviate RA remain limited. This review systematically examines the epidemiology and pathogenesis of RA, emphasizing the potential modulatory role of LAB in maintaining intestinal homeostasis. Drawing on both animal and clinical evidence, the review critically evaluates the molecular mechanisms by which LAB may alleviate RA, thereby offering a theoretical foundation for microbiota-based therapeutic interventions. Meanwhile, it highlighted the challenges and opportunities of LAB for RA. Genetic predisposition, environmental factors, and immune system dysfunction play very important roles in causing RA. LAB provided numerous advantages and had great potential for improving RA as its ability to regulate intestinal barrier, modulate related enzyme activity, inhibit oxidative damage, restore unbalanced gut microbiota, produce bioactive metabolites, and regulate gut-joint immune axis. In addition, this review advice to screen effective LAB by cell models and metabolites, to determined the optimal intake dose of LAB through dose-effect relationship studies, to promote the understanding of LAB by investigating the mechanism, and to improve the design of the clinical study to improve the lives of RA patients. This will contribute to understanding the epidemiological characteristics, pathogenesis, and treatment of RA, and promote the development of targeted therapeutic RA products such as LAB.

Catalysis was an effective method for uranium recovery and environmental remediation. However, the weak flexoelectric response, despite being universal in dielectric materials, greatly limited its appeal for research and catalytic applications. Here, we proposed a strategy to enhance the flexoelectric response by bridging inorganic chains with metal-organic chains within the structure. The resulting hybrid material, Co[C4H4N2]V2O6, demonstrated excellent uranyl removal performance, surpassing that of state-of-the-art piezocatalysts. Co[C4H4N2]V2O6 showed flexocatalytic uranyl activity across a broad pH range and under high-salinity conditions. Under a dynamic experimental setup, Co[C4H4N2]V2O6 showed strong potential for practical flexocatalytic applications. Co[C4H4N2]V2O6 could efficiently separate uranyl in contaminated potable water, reducing the uranium concentration (~2.0 ppm) to below the drinking water standard (30 ppb). It could also lower the uranium concentration (~5.6 ppm) in mining wastewater to below the discharge limit (300 ppb). Its intrinsic anisotropic mechanical properties and cantilever-like morphology endowed high deformability, which, together with a large dielectric constant, enhanced its flexoelectric polarization. The revealed flexocatalytic mechanism confirmed that uranyl was converted into insoluble (UO2)O2·2H2O by active species generated through dynamic polarization. This work provided a promising avenue for the design of advanced flexocatalysts and offered an effective strategy for uranium recovery and environmental remediation.

Rapid bacterial detection remains a critical need in clinical diagnostics, environmental monitoring, and food safety, yet conventional approaches are often limited by the small size and physiological variability of bacteria. To address this, we have developed a label-free impedance cytometry platform that combines a planar double-differential electrode configuration with an upper sheath fluid-assisted vertical compression. This design actively positions bacteria toward the bottom of the microchannel, where the electric field is strongest, thereby substantially improving signal stability and detection sensitivity. Through systematic optimization, a sheath-to-sample flow rate ratio of 2:1 was established as the optimal operating condition, providing an approximately 37% enhancement in impedance amplitude for bacteria while sustaining chip stability. The platform enables comprehensive evaluation of bacterial species, thermal viability, and antimicrobial susceptibility through real-time, dual-frequency (1.5 and 9 MHz) electrical profiling at the single-cell level. It effectively discriminates Escherichia coli (E. coli) from Bacillus subtilis (B. subtilis) based on their distinct amplitude and phase opacity profiles, assesses thermal viability in E. coli by detecting an approximately 11% increase in diameter after heat treatment, and performs rapid antimicrobial susceptibility testing-capturing an approximately 15% increase in average diameter (from 1.326 μm to 1.532 μm) within 20 min of polymyxin B (PMB) exposure. These results confirm the platform's ability to deliver multi-functional and high-precision bacterial characterization, offering a versatile tool for rapid microbiological analysis in clinical diagnostics and research.

Federated learning (FL) has become a highly promising paradigm for privacy-preserving distributed model training by enabling edge devices to train without sharing raw data. But in practice, edge environments are both non-stationary and asymmetric, with varying data distributions due to shifts in user behaviour, sensing conditions, and overall environmental dynamics. This causes concept drift (sudden, gradual, and recurrent), leading to poor model performance, slower convergence, and predictive bias. Current approaches to FL are not combined to tackle problems of drift adaptation, differential privacy (DP) and resource efficiency (FedAvg, DP-FedAvg). To address these constraints, we present FedDriftGuard. This Federated learning layer unifies client-level drift detection, drift-adaptive aggregation, and adaptable differential privacy into a single, FLE architecture-compatible system. The proposed DP-DriftNet model implements attention-based time encoding to capture changing data patterns and drift-directed feature weighting to allow greater flexibility in the presence of distributional changes. A drift-optimal privacy scheduler allocates noise probabilistically, subject to a limited privacy budget, thereby enforcing an appropriate privacy-utility trade-off without cancelling formal DP guarantees. Also, update sparsification, compression and periodic transmission techniques are used to reduce communication overhead. Decades of experimentation on real-world and synthetic drift datasets have shown that FedDriftGuard outperforms baseline FL techniques, achieving accuracy and F1-score gains of 9-14% and 11-17%, respectively, with adaptation latency 28% shorter and communication cost 20-35% lower. Such findings are statistically significant and confirm the soundness of the suggested method. FedDriftGuard offers effective, scalable privacy-preserving learning in adaptable, edge-drifting environments.

The practical application of Fe-N-C catalysts in proton exchange membrane fuel cells is fundamentally constrained by the inherent activity-stability trade-off. Here, we propose a "repair-and-upgrade" engineering strategy that not only repairs pyrolysis-induced defects through carbon and nitrogen supplementation but also evolves conventional FeN4 moieties into stabilized FeN5 configurations via an in situ constructed carbon bilayer. The axial nitrogen modulates the electronic structure of Fe center to enhance catalytic activity, while the adaptive interlayer spacing of the N-linked carbon bilayer compensates for fluctuations in the axial Fe─N bond length during catalysis, therefore anchoring the Fe active sites. When integrated into membrane electrode assemblies, the catalyst delivers a high peak power density of 1221 mW cm-2 and exhibits exceptional durability, retaining over 85% of its initial power density after 10,000 cycles in H2-O2 and showing negligible decay over 45 h at 0.6 V in H2-air tests. This work presents a novel design strategy for stable single-atom catalysts, centered on creating an adaptive local environment that ensures exceptional electrocatalytic stability.

The timeliness of treatment for out-of-hospital cardiac arrest (OHCA) is critical for patient survival. Automated External Defibrillators (AEDs) are a proven effective intervention, yet China's rapidly developing Public Access Defibrillation (PAD) program may be accompanied by significant spatial inequities in AED distribution. This study developed a comprehensive multi-dimensional evaluation model to assess the spatial equity of AED allocation in four first-tier Chinese cities: Beijing, Shanghai, Guangzhou, and Shenzhen. The model integrated four dimensions: resource allocation (supply-demand ratio), spatial coverage (service coverage index), opportunity accessibility (accessibility index via an enhanced Gaussian two-step floating catchment area method), and spatial distribution (Gini coefficient). These dimensions were aggregated into a Comprehensive Equity Index (CEI) using the Entropy Weight Method (EWM). Leveraging high-resolution gridded population data and precise AED locations, our analysis captures fine-scale spatial variations often obscured in aggregate statistics. Furthermore, to uncover the spatially heterogeneous drivers of equity, we employed an integrated Principal Component Analysis and Geographically Weighted Regression (PCA-GWR) framework to analyze socioeconomic and urban environmental factors. The results indicate that: (1) Overall comprehensive equity was low across all cities (mean CEI&#x2009;<&#x2009;0.3). Shenzhen exhibited the highest equity (mean CEI: 0.252), followed by Beijing (0.207), with Shanghai and Guangzhou lagging. (2) A significant "core-periphery" disparity was observed in all cities, with core districts showing markedly higher equity than suburban districts, a gap particularly pronounced in Beijing and Shanghai. (3) The PCA-GWR analysis revealed pronounced spatial heterogeneity in the associations between external factors and AED equity. Degree of urbanization showed a generally positive association, which was consistently weaker in urban cores. Public service facility provision exhibited inconsistent (often negative) associations, while the wealth-population density trade-off demonstrated marked city-specific variation. This study provides a systematic, multidimensional assessment of AED allocation equity in major Chinese cities. By employing a spatially nuanced PCA-GWR framework, it reveals that equity is shaped by complex, location-specific interactions of urban development, service provision, and socioeconomic structure. The findings underscore the necessity for spatially differentiated policy interventions within China's PAD program to achieve more equitable and efficient deployment of these lifesaving resources.

Direct recovery of phosphorus from Sargassum spp. in the form of magnesium-whitlockite (Ca9.5MgO28P7) was proposed for the first time. The effects of the acid digestion and calcination sequence on the naturally adsorbed minerals in Sargassum spp. were investigated through two treatment approaches (I and II). Treatment I (producing Ash-1) consisted of acid digestion followed by calcination, while Treatment II (producing Ash-2) involved initial calcination, subsequent acid digestion, and a final calcination step. It was found that Treatment I is effective in obtaining ashes containing magnesium-whitlockite (Mg-WH) together with diatoms frustules. Whereas Ash-2 showed an almost complete absence of diatom frustules and phosphate-based components. It was revealed that in Treatment I, a significant fraction of phosphorus, particularly organic ones, were less susceptible to acid digestion and therefore remained intact. During subsequent calcination, together with calcium- and magnesium-containing species, they can be thermally decomposed or oxidized, and Mg-WH will be the preferred final product. In contrast, in Treatment II, the initial calcination predominantly transformed organic phosphorus into acid-soluble inorganic forms, resulting in almost no phosphorus remaining after final calcination. Conclusively, this work unravels the importance of acid digestion and calcination sequence in phosphorus recovery and determining the yield and properties of the minerals. This study can shed light on the requirements and limitations of this efficient treatment in promoting the valorization of marine biomass and supporting sustainable strategies for mitigating the environmental impacts of excessive sargassum proliferation in coastal regions.

Prenatal polycyclic aromatic hydrocarbon (PAH) exposure may contribute to neural tube defects (NTD), but combined effects and underlying mechanisms remain unclear. We conducted a matched case-control study (128 pairs) in six high-risk counties in Shanxi Province, China (2004-2016). At delivery or pregnancy termination, placenta samples were collected. Sixteen placental PAHs and 11 hydroxylated metabolites were quantified by GC-MS/MS as biomarkers of fetal intrauterine exposure, alongside 16 DNA adducts measured by UPLC-MS/MS. Complementary ICR mouse experiments with benzo[a]pyrene (BaP, 100&#x202f;mg/kg) validated mechanistic findings. Statistical analyses used conditional logistic regression, WQS and BKMR (for parent PAHs and hydroxylated metabolites, respectively), followed by mediation analysis. Results showed that increased fresh vegetable intake and kitchen-living separation significantly reduced placental PAH concentrations. A dose-dependent increase in NTD risk was observed across tertiles of measured placental PAHs. The highest versus lowest tertile was associated with increased NTD risk for total PAHs (OR=5.04, 95%CI:1.96-12.96), phenanthrene (OR=4.96, 95%CI:2.05-11.97), and low-molecular-weight PAHs (OR=4.47, 95%CI:1.87-10.67) after adjustment for confounders. In subtype analyses, higher phenanthrene, low-molecular-weight PAH and total PAH related to anencephaly, whereas benzo[b]fluoranthene, indeno[1,2,3-cd]pyrene, and 9-hydroxyfluorene were associated with spina bifida. Mixture models confirmed significant joint effects of multiple PAHs. Mediation analyses showed 5-HmdC changes explained 10.7% (phenanthrene) and 12.4% (total PAHs) of NTD risk. In mice, BaP increased NTD incidence (5.7% vs. 1.8%), stillbirths (1.8% vs. 0%), and fetal resorptions (3.6% vs. 1.8%) versus controls. BaP-exposed groups showed reduced 5-HmdC in fetal tissues, consistent with human findings. This study provides evidence that individual and mixed PAH exposures are associated with an increased risk of NTD. DNA damage, particularly 5-HmdC alterations, may partially contribute to this association. These findings further support preventive strategies, including dietary and household environmental modifications.

Bone microtissue grafts mimicking skeletal features and organogenesis are an emerging strategy, different from the traditional tissue engineered bone grafts using the 3D cell encapsulation and the top cell seeding, and remain challenging in regenerative medicine and drug discovery, because the existing scaffold-free and microcarrier-based microtissue systems are difficult to manipulate the microtissue morphologies towards native bone microstructures limited by their mechanical weakness and the absence of interconnected inner cavities. Herein, we synthesized a supramolecular cryogel by an ice-templated freezing-polymerization process, acquiring a promising microcarrier resembling native bone trabecular morphology for 3D culture of trabecular bone microtissue grafts. In our strategy, a macromolecular chitosan monomer and two supramolecular monomers including glycinamide and phytic acid constituted the supramolecular cryogel, the modification using glycinamide and phytic acid components enables the cryogel microcarrier with porous cavities and compression-resistant abilities like the native trabecular bone tissues. Moreover, the mineralization of the cryogel microcarriers was also improved by the modification of phytic acid monomers, consequently strengthening osteogenic differentiation of bone-marrow-derived mesenchymal stem cells (BMSCs) and in vitro microtissue biomineralization. The in vivo results also revealed that a trabeculae-like bone microtissue forms on the cryogel microcarriers, with vessel invasion into inner cavities of the trabecular bone microtissues. After in situ implantation of the prepared trabecular bone microtissues, the bone regeneration characterized by raising bone mineral density and remodeling bone trabecular microstructures was observed on a rat femur condyle defect model. Last but not least, we also discovered the intestinal bacterial communities and compositions are closely related to the bone regeneration after implantation of the engineered bone microtissue grafts, which is the first evidence focusing on the intestinal microbiota response to the bone injury and bone regeneration events representing a feasible approach to bone regeneration examination. In brief, the supramolecular cryogels we developed in this study have been proved to be a promising microcarrier for the construction and 3D culture of trabecular bone microtissues, and this work offers a novelty insight into microtissue engineering and bone regeneration.

The growing worldwide population has increased the use of electric arc furnaces (EAF), resulting in a surge of EAF slag and a huge environmental concern. EAF slag's complex physical qualities have a considerable impact on concrete's mechanical performance, mainly its compressive strength (CS). This study introduces a novel framework for forecasting the CS of EAF slag concrete that uses advanced machine learning models such as Extreme Gradient Boosting (XGB), AdaBoost (ADB), Random Forest (RF), Hybrid XGB-RF, and Hybrid XGB-ADB. A full dataset of 730 samples was meticulously created, containing essential input parameters such as binders, aggregates, and other necessary variables, with CS as the desired outcome. Based on the findings, the XGB model showed highest accuracy, with an test R2 of 0.951, MAPE of 1.128, and RMSE of 1.393&#xa0;MPa, indicating its potential for dependable performance forecasting. In addition, the hybrid XGB-RF model also demonstrated strong predictive accuracy, achieving an R2 value of 0.947 during the testing phase. Furthermore, explainability tools such as SHapley Additive ExPlanations (SHAP) and partial dependence curves (PDCs) identified the curing period and content of cement as the most influential factors to predict the CS of EAF slag concrete. The methodologies and outcomes of this study will help to reduce reliance on resource-intensive experimental methods, pave the way for efficient, precise, and ecologically conscientious concrete design.

Accurate detection and segmentation of moving objects constitute a fundamental challenge in computer vision, particularly for intelligent video surveillance systems operating under variable illumination, dynamic backgrounds, and environmental noise. This paper presents a fully unsupervised dual-phase motion analysis framework that effectively combines statistical independence modeling and geometric contour evolution to achieve high-precision motion detection and segmentation. In the first phase, an enhanced Fast Independent Component Analysis (Fast-ICA) algorithm is employed to perform statistical decomposition of video sequences, exploiting temporal independence to distinguish moving foregrounds from static backgrounds. This process generates an initial motion mask with strong robustness to illumination variation and noise artifacts. In the second phase, a hybrid level set segmentation model integrating the global Chan-Vese formulation and a locally adaptive Yezzi-based energy function refines object boundaries through an adaptive energy minimization process. A stabilization term and a self-regulating convergence criterion are further incorporated to ensure contour smoothness, numerical stability, and resilience to topological changes. Comprehensive experiments conducted on the CDNet-2014 benchmark dataset demonstrate that the proposed method achieves an average recall of 0.9613, precision of 0.9089, and F-measure of 0.9310, outperforming several state-of-the-art supervised, semi-supervised and unsupervised background subtraction algorithms. The proposed Fast-ICA-Level Set fusion framework thus provides a robust, adaptive, and computationally efficient solution for real-world intelligent surveillance and autonomous visual monitoring applications.

Air pollution is a major environmental and public health challenge in Greater Cairo due to rapid urbanization, intense traffic activity, and recurrent regional dust intrusions. This study presents a data driven analytical framework for identifying and interpreting recurring air pollution regimes in the city. The framework combines unsupervised K-means clustering with supervised Decision Tree (DT) and Random Forest (RF) models using atmospheric reanalysis data derived from the Copernicus Atmosphere Monitoring Service (CAMS) for the period 2023-2024. The optimized K-means model identified four distinct pollution regimes. Low pollution conditions accounted for approximately 75.1% of the analyzed period, whereas higher pollution regimes were mainly associated with traffic related emissions and episodic dust events. To assess the separability of the identified regimes, DT and RF classifiers were trained to predict cluster membership. The optimized Decision Tree achieved an accuracy of 93.10%, while the Random Forest model showed better classification performance, reaching a maximum accuracy of 97.49%, with a practical optimum of 97.43% obtained using 300 trees. Feature importance analysis showed that NO&#x2082; was the dominant variable for distinguishing traffic related pollution regimes, whereas PM&#x2081;&#x2080; played a key role in identifying dust related events. Overall, the findings indicate that integrating clustering with tree based classification provides an interpretable and effective approach for characterizing urban air pollution patterns. The resulting framework may support regime based air quality interpretation and targeted management strategies in Greater Cairo.