Search Results (16,635)

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

Cone Penetration Tests with pore pressure measurements (CPTu) are widely used in geotechnical site investigations due to their high-resolution profiling capabilities. However, traditional interpretation methods—such as the Soil Behavior Type Index ($I_c$)—often fail to capture the internal heterogeneity typical of mining tailings deposits. This study presents a machine learning-based approach to enhance stratigraphic interpretation from CPTu data. Four unsupervised clustering algorithms—k-means, DBSCAN, MeanShift, and Affinity Propagation—were evaluated using a dataset of 12 CPTu soundings collected over a 19-year period from an iron tailings dam in Brazil. Clustering performance was assessed through visual inspection, stratigraphic consistency, and comparison with $I_c$-based profiles. k-means and MeanShift produced the most consistent stratigraphic segmentation, clearly delineating depositional layers, consolidated zones, and transitions linked to dam raising. In contrast, DBSCAN and Affinity Propagation either over-fragmented or failed to identify meaningful structures. The results demonstrate that clustering methods can reveal behavioral trends not detected by $I_c$ alone, offering a complementary perspective for understanding depositional and mechanical evolution in tailings. Integrating clustering outputs with conventional geotechnical indices improves the interpretability of CPTu profiles, supporting more informed geomechanical modeling, dam monitoring, and design. The approach provides a replicable methodology for data-rich environments with high spatial and temporal variability. Full article

This study investigates the subsurface rooting of the Merija anticline in the Missour Basin, Morocco, with a focus on copper mineralization exploration. A sequential geophysical workflow was implemented, combining gravity surveys, electrical resistivity (ER), and induced polarization (IP) methods. The gravity data, acquired along spaced profiles extending from outcropping areas to Quaternary-covered zones, clearly delineated the structural continuity of the anticline beneath the cover. The application of trend filtering in covered areas allowed the removal of regional effects, successfully isolating residual anomalies associated with the buried continuation of the anticline. Interpolated Bouguer anomaly maps highlighted a major regional fault, interpreted as controlling the deep rooting of the anticline. A resistivity profile was then deployed perpendicular to this fault, providing detailed imaging of the anticline’s geometry and lithological contrasts. Complementary IP profiles conducted near the mine site targeted the detection of chargeability anomalies associated with copper mineralization dominated by malachite, confirming the electrical signature of copper mineralization, particularly within the sandstone and conglomerate formations of the Lower Cretaceous. To validate the geophysical interpretations, a drilling campaign was conducted, which confirmed the presence of the identified lithological units and the anticline rooting, as revealed by geophysical data. This approach provides a robust framework for copper exploration in the Merija area and can be adapted to similar geological contexts elsewhere. Full article

This study investigates personal exposure to fine particulate matter (PM_2.5) during walking commutes in Gliwice, Poland—a city characterized by elevated levels of air pollution. Data from a low-cost air quality sensor were compared with a municipal monitoring station and the Silesian University of Technology laboratory. PM_2.5 concentrations recorded by the low-cost sensor (7.3 µg/m³) were lower than those reported by the stationary monitoring sites. The findings suggest that low-cost sensors may offer valuable insights into short-term peaks in PM_2.5 exposure to serve as a practical tool for increasing public awareness of personal exposure risks to protect respiratory health. Full article

Illegal logging is a major threat to tropical forests; however, control mechanisms and efforts to combat illegal logging have not effectively curbed fraud in the production chain, highlighting the need for effective methods to verify the geographic origin of timber. This study investigates the application of multi-elemental analysis combined with Principal Component Analysis (PCA) to discriminate the provenance of tropical timber in the Brazilian Legal Amazon. Wood samples of Hymenaea courbaril L. (Jatobá), Handroanthus sp. (Ipê), and Manilkara huberi (Ducke) A. Chevalier. (Maçaranduba) were taken from multiple sites. Elemental concentrations were determined via Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and CA was applied to evaluate geographic differentiation. Significant differences in elemental profiles were found among locations, particularly when using the intermediate disk portions (25% to 75%), and especially the average of all five sampled portions, which proved most effective in geographic discrimination of the trunk. Elements such as Ca, Sr, Cr, Cu, Zn, and B were especially important for spatial discrimination. These findings underscore the forensic potential of multi-elemental wood profiling as a tool to support law enforcement and environmental monitoring by providing scientifically grounded evidence of timber origin. Full article

Famatinite (Cu₃SbS₄) is an earth-abundant, nontoxic material with potential for thermoelectric energy generation applications. Herein, rapid, energy-efficient, and facile one-pot modified polyol synthesis was utilized to produce gram-scale quantities of phase-pure famatinite (Cu_2.7M_0.3SbS₄, M = Cu, Zn, Mn) nanoparticles (diameter 20–30 nm) with controllable and stoichiometric incorporation of transition metal dopants on the Cu-site. To produce pellets for thermoelectric characterization, the densification process by spark plasma sintering was optimized for individual samples based on thermal stability determined using differential scanning calorimetry and thermogravimetric analysis. Electronic transport properties of undoped and doped famatinite nanoparticles were studied from 225–575 K, and the thermoelectric power factor was calculated. This is the first time electronic transport properties of famatinite doped with Zn or Mn have been studied. All famatinite samples had similar resistivities (>0.8 mΩ·m) in the measured temperature range. However, the Mn-doped famatinite nanomaterials exhibited a thermoelectric power factor of 10.3 mW·m⁻¹·K⁻¹ at 575 K, which represented a significant increase relative to the undoped nanomaterials and Zn-doped nanomaterials engendered by an elevated Seebeck coefficient of ~220 µV·K⁻¹ at 575 K. Future investigations into optimizing the thermoelectric properties of Mn-doped famatinite nanomaterials are promising avenues of research for producing low-cost, environmentally friendly, high-performing thermoelectric materials. Full article

Protein enzymes are highly efficient catalysts that exhibit adaptability and selectivity under diverse biological conditions. In some organisms, such as bacteria, structurally similar enzymes, for instance, shikimate kinase (SK) and adenylate kinase (AK), coexist and act on chemically related ligands. This raises the question of whether these enzymes can accommodate and potentially react with each other’s ligands. In this study, we investigate the stability of non-cognate ligand binding in SK and explore whether external electric fields (EFs) can modulate this interaction, leading to cross-reactivity in SK. Using molecular dynamics simulations, we assess the structural integrity of SK and the binding behavior of ATP and AMP under EF-off and EF-on cases. Our results show that EFs enhance protein structure stability, stabilize non-cognate ligands in the binding pocket, and reduce local energetic frustration near the R116 residue located in the binding site. In addition to this, dimensionality reduction analyses reveal that EFs induce more coherent protein motions and reduce the number of metastable states. Together, these findings suggest that external EFs can reshape enzyme–ligand interactions and may serve as a tool to modulate enzymatic specificity and functional promiscuity. Thus, we provide computational evidence that supports the concept of using an EF as a tunable parameter in enzyme engineering and synthetic biology. However, further experimental investigation would be valuable to assess the reliability of our computational predictions. Full article

Traditional anti-inflammatory medications—such as corticosteroids, biological agents, and non-steroidal anti-inflammatory drugs—are commonly employed to mitigate inflammation, despite their potential for debilitating side effects. There is a growing need for alternative next-generation therapies for symptomatic, unchecked, and/or detrimental inflammation with more favorable adverse effect profiles. The long history of use of gold salts as anti-inflammatory agents and the more recent exploration of gold nanoparticle (AuNP) formulations for clinical indications suggest that the targeted delivery of nanoparticles to inflammatory sites may be a promising approach worth investigating. Coupled with peptides that specifically target immune cells, AuNPs could potently counteract inflammation. Here, we provide an overview of the selective infiltration of AuNPs into immune cells and summarize their interactions with and impact on these cells. Additionally, we provide a comprehensive mechanistic summary of how AuNPs exert their anti-inflammatory effects. Full article

The presence of ciprofloxacin antibiotic in water is a threat to humans and aquatic life since antibiotics are currently regarded as emerging contaminants of major concern. This work reported the use of Nexar^TM film, a sulfonated pentablock copolymer, to effectively remove ciprofloxacin antibiotic from water in a sustainable approach. The removal efficiency of Nexar film was evaluated in aqueous or salty (NaCl 0.5 M) ciprofloxacin solutions as a function of contact time and the initial ciprofloxacin concentration. In the investigated conditions, the polymeric film totally removed ciprofloxacin in MilliQ solution while its removal efficiency in salty solution was approximately 73%. This lower value is due to the presence of Na⁺ ions that compete with antibiotic molecules for adsorption on active surface sites of the polymeric film. No further release of adsorbed antibiotic molecules occurred. The kinetic studies, conducted for ciprofloxacin adsorption on Nexar film in both MilliQ and salty solutions, revealed that the overall sorption process is controlled by the rate of surface reaction between ciprofloxacin molecules and active sites on Nexar surface. Furthermore, at equilibrium conditions, the isotherm model that best fits experimental parameters was not linear. This indicates that the competition between the solute and the solvent for binding sites on the adsorbent should be considered to describe adsorption processes in both MilliQ and salty solutions. Full article

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

Improving construction site safety remains a critical challenge in Saudi Arabia’s rapidly growing construction sector, where high accident rates and diverse labor forces demand evidence-based managerial interventions. This study investigated the influence of Managers’ Safety Perceptions and Practices (MSP) on Workers’ Safety Behaviors (WSB) in the Saudi construction industry, emphasizing the mediating roles of Workers’ Safety Awareness (WSA), Safety Competency (WSC), and Safety Actions (SA). The conceptual framework integrates these three mediators to explain how managerial attitudes and practices translate into frontline safety outcomes. A quantitative, cross-sectional design was adopted using a structured questionnaire distributed among construction workers, supervisors, and project managers. A total of 352 from 384 valid responses were collected, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The findings revealed that MSP does not directly influence WSB but has significant indirect effects through WSA, WSC, and SA. Among these, WSC emerged as the most powerful mediator, followed by WSA and SA, indicating that competency is the most critical driver of safe worker behavior. These results provide robust empirical support for a multidimensional mediation model, highlighting the need for managers to enhance safety behaviors not merely through supervision but through fostering awareness and competency, providing technical training, and implementing proactive safety measures. Theoretically, this study contributes a novel and integrative framework to the occupational safety literature, particularly within underexplored Middle Eastern construction contexts. Practically, it offers actionable insights for safety managers, industry practitioners, and policymakers seeking to improve construction safety performance in alignment with Saudi Vision 2030. Full article

One hundred presumptive Pseudomonas isolates, recovered from 15 sites impacted by anthropogenic activity in the Foggia district (Italy), were screened for key adaptive and functional traits important for environmental applications. The isolates were phenotypically characterized for their ability to grow under combined pH (5.0–8.0) and temperature (15–37 °C) conditions, to produce proteolytic enzymes, pigments, and exopolysaccharides, and to tolerate SDS. Moreover, the resistance to six environmentally relevant heavy metals (Cd, Co, Cu, Ni, Zn, As) was qualitatively assessed. The results highlighted wide inter-strain variability, with distinct clusters of isolates showing unique combinations of stress tolerance, enzymatic potential, and resistance profile. PERMANOVA analysis revealed significant effects of both the isolation site and the metal type, as well as their interaction, on the observed resistance patterns. A subset of isolates showed co-tolerance to elevated temperatures and heavy metals. These findings offer an initial yet insightful overview of the adaptive diversity of soil-derived Pseudomonas, laying the groundwork for the rational selection of strains for bioaugmentation in contaminated soils. Full article

In this study, we present a comprehensive theoretical analysis of modifications in the physical and chemical properties of MoSe₂ upon the introduction of substitutional transition metal impurities, specifically, Ti, V, Cr, Fe, Co, Ni, Cu, W, Pd, and Pt. Wet systematically calculated the adsorption enthalpies for various representative analytes, including O₂, H₂, CO, CO₂, H₂O, NO₂, formaldehyde, and ethanol, and further evaluated their free energies across a range of temperatures. By employing the formula for probabilities, we accounted for the competition among molecules for active adsorption sites during simultaneous adsorption events. Our findings underscore the importance of integrating temperature effects and competitive adsorption dynamics to predict the performance of highly selective sensors accurately. Additionally, we investigated the influence of temperature and analyte concentration on sensor performance by analyzing the saturation of active sites for specific scenarios using Langmuir sorption theory. Building on our calculated adsorption energies, we screened the catalytic potential of doped MoSe₂ for CO₂-to-methanol conversion reactions. This paper also examines the correlations between the electronic structure of active sites and their associated sensing and catalytic capabilities, offering insights that can inform the design of advanced materials for sensors and catalytic applications. Full article

Introduction: Lichen sclerosus (LSc) is a chronic, progressive, inflammatory skin disease that primarily affects the anogenital region in both sexes and across all age groups. Aim: To investigate the relationship between quality of life (QoL) and disease severity, as measured by a newly developed Lichen Sclerosus Score (LSc score), with respect to anatomical site before and after 12 weeks of treatment. Methods: A total of 136 patients diagnosed with LSc (88 men, 48 women) were enrolled between March and September 2022. Patients were clinically evaluated using the LSc score and completed the Dermatology Life Quality Index (DLQI). Treatment was individualized based on clinical findings and history. At 12 weeks, both clinical assessment and DLQI were repeated. Results: LSc scores significantly decreased following treatment (p < 0.001), except in the female subgroup. In men, LSc scores were strongly correlated with DLQI scores both before (r = 0.709; p < 0.001) and after (r = 0.492; p < 0.001) treatment. Among women, a significant correlation was found only before treatment (r = 0.457; p < 0.001). Significant associations were identified between LSc score and DLQI items 1, 8, and 9 in men and the overall cohort. No statistically significant differences in LSc scores or DLQI were observed across anatomical sites after correction for multiple comparisons. Conclusions: Disease severity in genital LSc is closely associated with QoL impairment. This is, to our knowledge, the first study to examine the correlation between a clinical severity score and DLQI. While anatomical site did not significantly affect scores, certain sites may have a disproportionate impact, underscoring the complex ways in which LSc affects patients’ lives. Full article

The perianal skin is a unique “skin–gut” boundary that serves as a critical hotspot for the exchange and evolution of antibiotic resistance genes (ARGs). However, its role in the dissemination of antimicrobial resistance (AMR) has often been underestimated. To characterize the resistance patterns in the perianal skin environment of patients with perianal diseases and to investigate the drivers of AMR in this niche, a total of 51 bacterial isolates were selected from a historical strain bank containing isolates originally collected from patients with perianal diseases. All the isolates originated from the skin site and were subjected to antimicrobial susceptibility testing, whole-genome sequencing, and co-occurrence network analysis. The analysis revealed a highly structured resistance pattern, dominated by two distinct modules: one representing a classic Staphylococcal resistance platform centered around mecA and the bla operon, and a broad-spectrum multidrug resistance module in Gram-negative bacteria centered around tet(A) and predominantly carried by IncFIB and other IncF family plasmids. Further analysis pinpointed IncFIB-type plasmids as potent vehicles driving the efficient dissemination of the latter resistance module. Moreover, numerous unexplained resistance phenotypes were observed in a subset of isolates, indicating the potential presence of emerging and uncharacterized AMR threats. These findings establish the perianal skin as a complex reservoir of multidrug resistance genes and a hub for mobile genetic element exchange, highlighting the necessity of enhanced surveillance and targeted interventions in this clinically important ecological niche. Full article