Search Results (745)

Lead (Pb) in soil poses serious environmental and health risks, and its removal requires complex and costly treatment methods to meet strict regulatory standards. To effectively address this challenge, innovative and efficient techniques are essential. Sepiolite-supported MnFe₂O₄ (MnFe₂O₄/SEP) composites were synthesized via a chemical co-precipitation method. The effects of MnFe₂O₄/SEP on soil pH, cation exchange capacity (CEC), available Pb content, Pb²⁺ uptake, and the activities of antioxidant enzymes in Brassica chinensis (Pak Choi) were examined. MnFe₂O₄/SEP showed superior Pb²⁺ adsorption compared to SEP alone, fitting Langmuir models, Dubinin-Radushkevich (D-R) models, Temkin models and pseudo-second-order kinetics. The maximum adsorption capacities at 298, 308, and 318 K were 459, 500 and 549 mg·g⁻¹, respectively. XPS analysis indicated that chemisorption achieved through ion exchange between Pb²⁺ and H⁺ was the main mechanism. MnFe₂O₄/SEP increased the soil pH by 0.2–1.5 units and CEC by 18–47%, while reducing available Pb by 12–83%. After treatment with MnFe₂O₄/SEP, acid-extractable and reducible Pb in the soil decreased by 14% and 39%, while oxidizable and residual Pb increased by 26% and 21%, respectively. In Brassica chinensis, MnFe₂O₄/SEP reduced Pb²⁺ uptake by 76%, increased chlorophyll content by 36%, and decreased malondialdehyde (MDA) levels by 36%. The activities of antioxidant enzymes—superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT)—were decreased by 29%, 38% and 17%, respectively. These findings demonstrate that MnFe₂O₄/SEP is an efficient Pb²⁺ adsorbent that immobilizes Pb in soil mainly through ion exchange, thereby providing a highly effective strategy for remediating Pb-contaminated soils and improving plant health. Full article

In the context of the new power system, high-current switchgear is prone to various faults due to complex operation environments and severe load fluctuations. Among them, an abnormal temperature rise can lead to contact oxidation, insulation aging, and even equipment failure, posing a serious threat to the safety of the distribution system. The operation risk assessment of high-current switchgear has thus become a key to ensuring the safety of the distribution system. Ensemble learning, which integrates the advantages of multiple models, provides an effective approach for accurate and intelligent risk assessment. However, existing ensemble learning methods have shortcomings in feature extraction, time-series modeling, and generalization ability. Therefore, this paper first preprocesses and reduces the dimensionality of multi-source data, such as historical load and equipment operation status. Secondly, we propose an operation risk assessment method for high-current switchgear based on ensemble learning: in the first layer, an improved random forest (RF) is used to optimize feature extraction; in the second layer, an improved long short-term memory (LSTM) network with an attention mechanism is adopted to capture time-series dependent features; in the third layer, an adaptive back propagation neural network (ABPNN) model fused with an adaptive genetic algorithm is utilized to correct the previous results, improving the stability of the assessment. Simulation results show that in temperature rise prediction, the proposed algorithm significantly improves the goodness-of-fit indicator with increases of 15.4%, 4.9%, and 24.8% compared to three baseline algorithms, respectively. It can accurately assess the operation risk of switchgear, providing technical support for intelligent equipment operation and maintenance, and safe operation of the system. Full article

Salmonella presents serious risks to human health, causing about 150,000 deaths per year through the consumption of contaminated food, especially chicken eggs. Consequently, risk of salmonellosis from chicken eggs is of significant interest to the Saudi Food and Drug Authority (SFDA). Models that predict the risk of salmonellosis from chicken eggs are valuable tools for protecting public health. After a review of existing models, the SFDA selected the Poultry Food Assess Risk Model (PFARM) for the purpose of evaluating its ability to assess the risk and severity of salmonellosis for a small cohort of chicken egg consumers in Riyadh, Saudi Arabia, as a proof-of-concept and pilot study. The PFARM was selected because it uses novel methods to consider more risk factors for salmonellosis than other models, such as growth potential and zoonotic potential of Salmonella, buffering capacity of the meal, and consumer behavior, health, and immunity. The SFDA examined chicken eggs from retail stores in Riyadh for Salmonella contamination and surveyed 125 consumers to obtain data for simulating how they store, prepare, and consume eggs at home, and their resistance to salmonellosis. The prevalence of Salmonella in chicken eggs at retail was 7% (7/100). The isolated Salmonella serotypes were Cerro (n = 4), Enteritidis, Stanley, and Winston. Salmonella’s mean number (growth units) per contaminated egg was 1.58 log₁₀ (range: 0 to 3.08 log₁₀). The mean category for consumer survey results ranged from 1.1 (very low risk) for meal preparation time to 3.7 (high risk) for home storage time with 34.4% of consumers having low resistance to salmonellosis. Per 100,000 egg meals, the PFARM predicted 88 infections, two illnesses, and no hospitalizations or deaths. The consumers who became ill were exposed to Salmonella Enteritidis, had moderate resistance to salmonellosis but high-risk behaviors for egg storage (temperature abuse), meal preparation (poor hygiene), and consumption (undercooked eggs). These results showed that the studied chicken eggs posed a low risk and severity of salmonellosis for the surveyed consumer cohort in Riyadh, Saudi Arabia, and that the PFARM was fit-for-purpose. The next step is to improve the PFARM and apply it more broadly in Saudi Arabia to better define the problem and its control. Full article

Sludge is a common engineering byproduct that poses environmental and land-use challenges when disposed of directly. Converting sludge into high-quality subgrade filling material through solidification is therefore of both engineering and ecological significance. In this study, dynamic triaxial tests were conducted on sludge soils stabilized with mineral-based cementitious binders to investigate the effects of binder content, loading frequency, and curing age on the backbone curve, dynamic shear modulus, maximum shear modulus, ultimate stress amplitude, shear modulus ratio, and damping ratio. Scanning electron microscopy (SEM) was further employed to examine the microstructural evolution of the stabilized soils. The results indicate that increasing binder content and curing age significantly enhance the dynamic shear modulus while reducing the damping ratio, and the modulus exhibits a frequency-dependent behavior within the tested loading range. The modified Hardin-Drnevich constitutive model was successfully applied to fit the experimental data, accurately characterizing the dynamic response of stabilized sludge soils and enabling the development of a normalized model for the dynamic shear modulus ratio. SEM observations confirm that hydration reactions between the binder and soil produce gel products that fill interparticle pores, leading to a denser structure and explaining the observed macroscopic improvements in mechanical behavior. Overall, this work elucidates the dynamic response mechanisms of sludge stabilized with mineral-based cementitious materials and provides theoretical and experimental support for its resource utilization in road engineering applications. Full article

The Generalized Tempered Stable (GTS) distribution extends classical stable laws through exponential tempering, preserving the power-law behavior while ensuring finite moments. This makes it especially suitable for modeling heavy-tailed financial data. However, the lack of closed-form densities poses significant challenges for simulation. This study provides a comprehensive and systematic comparison of GTS simulation methods, including rejection-based algorithms, series representations, and an enhanced Fast Fractional Fourier Transform (FRFT)-based inversion method. Through extensive numerical experiments on major financial assets (Bitcoin, Ethereum, the S&P 500, and the SPY ETF), this study demonstrates that the FRFT method outperforms others in terms of accuracy and ability to capture tail behavior, as validated by goodness-of-fit tests. Our results provide practitioners with robust and efficient simulation tools for applications in risk management, derivative pricing, and statistical modeling. Full article

Developing classical three-dimensional consolidation theories considers the small-strain assumption. This small-strain assumption is inappropriate when studying the consolidation process of soft or very soft clay layers. Instead, this study derives a novel generalized mathematical model describing a three-dimensional finite-strain consolidation process and applies the deep Galerkin method to deduce its novel numerical solution. Developing this mathematical model uses the Reynolds transport theorem to describe mass and momentum balances for clay grain and pore water phases. The governing equation is the sum of the resulting mass and momentum balance equations. Next, the deep Galerkin method is applied to train a deep neural network to minimize the loss function defined by the governing equation and available initial and boundary conditions. The unknowns are the average velocity, effective stress, and pore water pressure. Predicting consolidation settlements is implemented by updating the problem domain using the resulting average velocity. Beneficial from the deep Galerkin method, two real-world examples demonstrate that the current mathematical model provides accurate predictions of consolidation settlements caused by the self-weight of two very soft clay layers. The deep Galerkin method helps resolve ill-posed problems by fitting a family of fields constrained by sampling/regularization rather than physics if the physics is under-determined. Full article

Karst aquifers are critical yet vulnerable water resources in semi-arid Mediterranean regions, where structural complexity, nonlinearity, and delayed hydrological responses pose significant modeling challenges under increasing climatic and anthropogenic pressures. This study examines the Jebel Zaghouan aquifer in northeastern Tunisia, aiming to simulate its natural discharge dynamics prior to intensive exploitation (1915–1944). Given the fragmented nature of historical datasets, meteorological inputs (rainfall, temperature, and pressure) were reconstructed using a data recovery process combining linear interpolation and statistical distribution fitting. The hyperparameters of the artificial neural network (ANN) model were optimized through a Bayesian search. Three deep learning architectures—Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were trained to model spring discharge. Model performance was evaluated using Kling–Gupta Efficiency (KGE′), Nash–Sutcliffe Efficiency (NSE), and R² metrics. Hydrodynamic characterization revealed moderate variability and delayed discharge response, while isotopic analyses (δ¹⁸O, δ²H, ³H, ¹⁴C) confirmed a dual recharge regime from both modern and older waters. LSTM outperformed other models at the weekly scale (KGE′ = 0.62; NSE = 0.48; R² = 0.68), effectively capturing memory effects. This study demonstrates the value of combining historical data rescue, ANN modeling, and hydrogeological insight to support sustainable groundwater management in data-scarce karst systems. Full article

Rice is one of the world’s most consumed foods, and the cereal that most efficiently uptakes and accumulates As, contributing to human health risk. Flooded rice fields alter Eh-pH conditions and, consequently, the proportion of As(III)/As(V), favoring their accumulation in the crop. The use of algae in paddy soils can improve fertility and C-stock and affect chemical conditions and As availability. This study aimed to evaluate the effect of algae application on: As adsorption capacity in paddy soils from Sado, Portugal, changes in pH-Eh conditions in the soil–water environment, and consequent As speciation. Batch-based As adsorption assays were performed with different solid–solution ratios and Chlorella minutissima algae application, and fitted to the Freundlich and Langmuir linear models. In semi-continuous column assays, simulating rice field conditions, the effect of algae on the pH-Eh of soil pore water was evaluated. The soil quality assessment showed pseudo-total contents of As and other elements higher than Portuguese agriculture limits (11 mg As kg⁻¹), but their availability was low, posing no environmental risk. The studied soils had great As adsorption, which increased with algae application (1.07 mg g⁻¹). Algae application favored oxygenation, increasing Eh values, and maintaining As(V) species. This indicated a potential approach to reducing As(III) mobility. Full article

Host plant limitation poses a major threat to the endangered specialist butterfly Luehdorfia chinensis Leech, 1893, whose larvae are oligophagous at the species level on Asarum spp., while local populations often appear monophagous, depending on the host plants (A. sieboldii Miq. or A. forbesii Maxim.) available in their habitat. To simulate natural starvation caused by host plant scarcity, third- to fifth-instar larvae were subjected to a three-day deprivation treatment, and the effects on individual fitness traits—including larval development, pupal duration, and adult fecundity—were assessed, along with population dynamics. Starvation significantly prolonged larval development, shortened the pupal stage, reduced female fecundity, and markedly decreased key population parameters, such as the intrinsic rate of increase (r_m) and the net reproductive rate (R₀). Population projections further indicated that repeated starvation stress could reduce population size by more than 83% within two years, potentially intensifying genetic drift, inbreeding depression, and demographic instability, ultimately increasing the risk of extinction. These findings provide direct evidence that host plant limitation drives population decline in L. chinensis, contribute to the broader understanding of global butterfly declines, and underscore the critical importance of conserving and restoring essential habitat resources. Moreover, they highlight the relevance of the resource-based habitat concept for the effective protection of specialist species. Full article

The proliferation of image sharing on social media poses significant privacy risks. Although some previous works have proposed to detect privacy attributes in image sharing, they suffer from the following shortcomings: (1) reliance only on legacy architectures, (2) failure to model the label correlations (i.e., semantic dependencies and co-occurrence patterns among privacy attributes) between privacy attributes, and (3) adoption of static, one-size-fits-all user preference models. To address these, we propose a comprehensive framework for visual privacy protection. First, we establish a new state-of-the-art (SOTA) architecture using modern vision backbones. Second, we introduce Graph Convolutional Networks (GCN) as a classifier head to counter the failure to model label correlations. Third, to replace static user models, we design a dynamic personalization module using Federated Learning (FL) for privacy preservation and Reinforcement Learning (RL) to continuously adapt to individual user preferences. Experiments on the VISPR dataset demonstrate that our approach can outperform the previous work by a substantial margin of 6% in mAP (52.88% vs. 46.88%) and improve the Overall F1-score by 10% (0.770 vs. 0.700). This provides more meaningful and personalized privacy recommendations, setting a new standard for user-centric privacy protection systems. Full article

Phosphonate-based antiscalants such as 1-hydroxyethane-1,1-diphosphonic acid (HEDP) are extensively employed in industrial water treatment but pose significant environmental challenges due to their persistence and phosphorus content. In this study, Mg-Al layered double hydroxide (Mg-Al LDH) was synthesized and evaluated for its capacity to adsorb and remove HEDP. Mg-Al LDH showed a pronounced adsorption affinity and an exceptionally high capacity of 276.0 mg g⁻¹ at pH 7.0. The adsorption process was remarkably fast, attaining 97% of equilibrium uptake within 45 min at 298 K. The adsorption data fit well to the Elovich kinetic model and the Langmuir isotherm, indicating that the adsorption process is dominated by chemisorption. Thermodynamic analysis further confirmed its spontaneous nature. Additionally, Mg-Al LDH demonstrated strong tolerance to environmental fluctuations. Characterization techniques, including XRD, FTIR, and zeta potential measurements, confirmed that HEDP adsorption onto Mg-Al LDH primarily occurs via surface complexation with metal sites and electrostatic attraction. These findings demonstrate that Mg-Al LDH is a highly effective adsorbent for removing persistent phosphonate pollutants from wastewater streams. Full article

Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low. Full article

With the growing adoption of CLO 3D in the fashion industry and educational settings, the need for accurate material representation and fit simulation in virtual environments is increasing. This study aimed to evaluate whether CLO 3D, without the aid of physical samples, can reliably simulate clothing pressure for compression wear made from different materials. Unlike previous CLO 3D studies that focused on design or pattern accuracy, this study critically examined material-specific simulation limitations and proposed technical enhancements. Two types of leggings with varying spandex content were tested across five yoga poses using the CLO 3D software(version 2024.2.214). The results showed that CLO 3D did not detect differences in clothing pressure caused by variations in spandex content. Furthermore, the pressure values remained constant across different poses for both fabrics, failing to reflect realistic mechanical differences. The highest total clothing pressure was recorded in the Lunge pose (277.02 kPa), and the lowest in the Plow pose (241.37 kPa). These findings suggest that the current simulation engine lacks sensitivity to fabric-specific mechanical properties and movement-based variation. To address these limitations, this study proposes five optimization functions for CLO 3D, including material property input, technical textile databases, environmental condition settings, AI-based comfort prediction, and data management tools. These proposals are expected to strengthen the scientific validity, functional realism, and user-centered applicability of CLO 3D in designing sportswear, medical compression garments, and customized apparel. Full article

In school settings, nomophobia—a newly identified form of problematic mobile phone use characterized by anxiety and discomfort experienced when an individual is unable to use or access their smartphone—poses significant challenges to students’ learning and daily life. Prior research on nomophobia has predominantly adopted a variable-centered perspective. However, if nomophobia is heterogeneous across subgroups, acknowledging this heterogeneity may inform the advancement of more tailored and productive therapeutic methods. Latent profile analysis (LPA) was conducted separately among high school students (N = 446) and college students (N = 667) to identify potential subgroup heterogeneity in nomophobia. To examine cross-group similarities in nomophobia profiles, a multi-group LPA was employed. Based on multiple model fit criteria, a three-profile solution—high nomophobia, moderate nomophobia, and low nomophobia—was identified for both groups. However, the multi-group LPA provided only partial support for the similarity of nomophobia profiles across educational stages, specifically in terms of configural and dispersion similarity. While similar nomophobia profiles emerged across groups, the partial equivalence suggests that intervention strategies for nomophobia may not be universally applicable across different educational levels. Additional studies should investigate the mechanisms underlying students’ nomophobia profiles and to inform differentiated interventions for educators, institutions, and policymakers. Full article

Background: Midfacial trauma involving the zygomatic-maxillary-orbital (ZMO) complex poses significant reconstructive challenges due to anatomical complexity and the necessity for high-precision alignment. Traditional manual reduction techniques often result in inconsistent outcomes, necessitating revisions. Methods: This feasibility study presents two clinical cases treated using a novel, fully digital workflow incorporating computer-aided design and manufacturing (CAD/CAM) of patient-specific osteosynthesis plates and surgical drill guides. Following virtual fracture reduction and implant design, drill guides and implants were fabricated using selective laser melting. Surgical procedures included intraoral and transconjunctival approaches with intraoperative 3D imaging (mobile C-arm CT) to verify implant positioning. Postoperative results were compared to the virtual plan through image fusion. Results: Both cases demonstrated precise fit and anatomical restoration. The “one-position-fits-only” orbital implant design enabled highly accurate orbital wall reconstruction. Key procedural refinements between cases included enhanced interdisciplinary collaboration and improved guide designs, resulting in decreased planning-to-surgery intervals (<7 days) and seamless intraoperative application. Image fusion confirmed near-identical congruence between planned and achieved outcomes. Conclusions: The presented method demonstrates that fully digital, CAD/CAM-based midface reconstruction is feasible in the primary trauma setting. The technique offers reproducible precision, reduced intraoperative time, and improved functional and aesthetic outcomes. It may represent a paradigm shift in trauma care, particularly for complex ZMO fractures. Broader clinical adoption appears viable as production speed and workflow integration continue to improve. Full article