Search Results (25,844)

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

Wind power has gained attention as a vital renewable energy source capable of reducing emissions and serving as an effective alternative to fossil fuels. Floating wind farms could significantly enhance the energy capacities of Mediterranean countries. However, location selection for offshore wind farms (OWFs) is a challenge for renewable energy policy and marine spatial planning (MSP). To address these issues, this study considers the marine space of Greece to propose a GIS-based multi-criteria decision-making (MCDM) framework employing the Analytic Hierarchy Process (AHP) to identify suitable sites for OWFs. The approach assesses 19 exclusion criteria encompassing legislative, environmental, safety, and technical constraints to determine the eligible areas. Subsequently, 10 evaluation criteria are weighted to determine the selected areas’ level of suitability. The study considers baseline conditions (1981–2010) and future climate scenarios based on RCP 4.5 and RCP 8.5 for two horizons (2011–2040 and 2041–2070), integrating projected wind velocities and sea level rise to evaluate potential shifts in suitable areas. Results indicate the central and southeastern Aegean Sea as the most suitable areas for OWF deployment. Climate projections indicate a modest increase in suitable areas. The findings serve as input for climate-resilient MSP seeking to promote sustainable energy development. 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

Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities. Full article

The detection of seabed boulders is a critical step in mitigating geological hazards during the planning and construction of offshore wind energy infrastructure, as well as in supporting benthic ecological and palaeoglaciological studies. Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. The method was tested on a 0.24 km² site in Long Island Sound using 0.5 m resolution data, achieving 83% recall, 73% precision, and an F1-score of 77—slightly outperforming the average of expert manual picks while offering a substantial improvement in time-efficiency. The workflow was validated against a consensus-based master dataset and applied across a 79 km² study area, identifying over 75,000 contacts and delineating 89 contact clusters. The method enables objective, reproducible, and scalable boulder detection using only MBES data. Its ability to reduce reliance on SSS surveys while maintaining high accuracy and offering workflow customization makes it valuable for geohazard assessment, benthic habitat mapping, and offshore infrastructure planning. 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

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

In order to improve the durability performance of sticky rice–lime paste in ancient masonry restoration materials, the effect of graphene oxide–nanosilica hybrids (GO–NS) on its basic physical properties and durability performance was investigated. The surface morphology, physical phase characteristics and infrared spectra of GO–NS and its sticky rice–lime paste were analysed by SEM, FE-TEM, XRD and FTIR. It was shown that NS successfully attached to the GO surface and improved the interlayer structure of GO. GO–NS reduces the fluidity and shrinkage of sticky rice–lime paste, prolongs the initial setting, shortens the final setting and significantly improves the compressive strength, water resistance and freeze resistance. As NS improves the interlayer structure of GO, it provides nucleation sites for the hardening of the sticky rice–lime paste, improves the quantity and structural distribution of the hardening products and reduces the pores. The NS undergoes a hydration reaction with Ca(OH)₂ in the lime to produce calcium silicate hydrate (C–S–H), which further refines the internal pore structure of the sticky rice–lime paste. As a result, the GO–NS-modified sticky rice–lime paste has a denser interior and better macroscopic properties. Full article

The topic of environmental pollution by petroleum products is highly relevant due to rapid urbanisation, including industrial development, road infrastructure and fuel distribution. Potential threat areas include refineries, fuel stations, pipelines, warehouses and transshipment bases, as well as sites affected by accidents or fuel spills. This study aimed to determine whether organic and mineral materials could mitigate the effects of diesel oil pollution on the soil’s trace element content. The used materials were compost, bentonite and calcium oxide. Diesel oil pollution had the most pronounced effect on the levels of Cd, Ni, Fe and Co. The levels of the first three elements increased, while the level of Co decreased by 53%. Lower doses of diesel oil (2.5 and 5 cm³ per kg of soil) induced an increase in the levels of the other trace elements, while higher doses caused a reduction, especially in Cr. All materials applied to the soil (compost, bentonite and calcium oxide) reduced the content of Ni, Cr and Fe. Compost and calcium oxide also increased Co accumulation in the soil. Bentonite had the strongest reducing effect on the Ni and Cr contents of the soil, reducing them by 42% and 53%, respectively. Meanwhile, calcium oxide had the strongest reducing effect on Fe and Co accumulation, reducing it by 12% and 31%, respectively. Inverse relationships were recorded for Cd (mainly bentonite), Pb (especially compost), Cu (mainly compost), Mn (mainly bentonite) and Zn (only compost) content in the soil. At the most contaminated site, the application of bentonite reduced the accumulation of Pb, Zn and Mn in the soil, while the application of compost reduced the accumulation of Cd. Applying various materials, particularly bentonite and compost, limits the content of certain trace elements in the soil. This has a positive impact on reducing the effect of minor diesel oil pollution on soil properties and can promote the proper growth of plant biomass. Full article

Blast mitigation of structures is an important research topic due to increasing intentional and accidental human-induced threats and hazards. This research area is essential to building capabilities in sustaining structural protection, site planning, protective design efficiency, occupant safety, and response and recovery plans. This paper investigates experimental tests and finite element analysis (FEM) of thin A36 steel sheets subjected to blast. Six field blast tests were performed at standoff distances of 300 mm and 500 mm. The explosive charges comprised 334 g of bare Composition B, and the steel sheets were 2 mm thick. The experimental results, derived from the analysis of high-speed camera recordings of the blast events, were compared with FEM simulations conducted using Abaqus^®/Explicit version 6.10. Three constitutive material models were considered in these simulations. First, the FEM simulation results were compared with experimental results. It was shown that the FEM analysis provided reliable results and was proven to be robust and cost-effective. Second, an extensive set of 460 additional numerical simulations was carried out as a parametric study involving varying standoff distances and steel sheet thicknesses. The results and methodologies presented in this paper offer valuable and original insights for engineers and researchers aiming to predict damage to steel structures during real detonation events and to design blast-resistant structures. Full article

One major challenge in cellular neuroscience is to elucidate how the accurate alignment of presynaptic release sites with postsynaptic densely clustered ligand-gated ion channels at chemical synapses is achieved upon synapse assembly. The clustering of neurotransmitter receptors at postsynaptic sites is a key moment of synaptogenesis and determinant for effective synaptic transmission. The number of the ionotropic neurotransmitter receptors at these postsynaptic sites of both excitatory and inhibitory synapses is variable and is regulated by different mechanisms, thus allowing the modulation of synaptic strength, which is essential to tune neuronal network activity. Several well-regulated processes seem to be involved, including lateral diffusion within the plasma membrane and local anchoring as well as receptor endocytosis and recycling. The molecular mechanisms implicated are numerous and were reviewed recently in great detail. The role of pre-synaptically released neurotransmitters within the complex regulatory apparatus organizing the postsynaptic site underneath presynaptic terminals is not completely understood, even less for inhibitory synapses. In this mini review article, we focus on this aspect of synapse formation, summarizing and contrasting findings on the functional role of the neurotransmitters glycine and γ-aminobutyric acid (GABA) for initiation of postsynaptic receptor clustering and regulation of Cl⁻ channel receptor numbers at inhibitory synapses gathered over the last two decades. Full article

Alpha2-plasmin inhibitor (α2PI) has a heterogeneous structure due to proteolytic cleavages in the circulation. The C-terminally cleaved form loses the plasminogen binding site and is, therefore, a slow plasmin inhibitor (NPB-α2PI). As FXIII primarily crosslinks the plasminogen-binding intact form (PB-α2PI) to fibrin, the effect of NPB-α2PI on fibrinolysis has been less studied. Herein, we investigated the effect of C-terminal truncation. Total-, PB-, and NPB-α2PI antigen levels and α2PI incorporation were measured by ELISAs from samples of 80 healthy individuals. Clot lysis parameters of the same subjects were investigated using an in vitro clot lysis assay. α2PI incorporation into the clot was demonstrated by Western blotting. Clot lysis and clot structure were also analyzed using an α2PI-deficient plasma substituted with recombinant PB- and NPB-α2PI. Both plasma and clot-bound levels of total- and NPB-α2PI showed a significant positive correlation with clot lysis parameters. NPB-α2PI was detected in the clot due to non-covalent binding. Regardless of the type of binding, both forms affected the clot structure by increasing the thickness of the fibrin fibers and reducing the pore size. In conclusion, we found that NPB-α2PI can bind non-covalently to fibrin, and this binding contributes to changes in clot structure and inhibition of fibrinolysis. Full article

Ujimqin sheep, known for its distinctive multi-vertebrae phenotypes (T13L7, T14L6, and T14L7) and economic value, has garnered significant attention. However, conventional phenotypic detection methods suffer from low efficiency and high costs. In this study, based on a key SNP locus (ABCD4 gene, Chr7:89393414, C > T) identified through a genome-wide association study (GWAS), a TaqMan-MGB (minor groove binder) genotyping system was developed. the objective was to establish a high-throughput and efficient molecular marker-assisted selection (MAS) tool. Specific primers and dual fluorescent probes were designed to optimize the reaction system. Standard plasmids were adopted to validate genotyping accuracy. A total of 152 Ujimqin sheep were subjected to TaqMan-MGB genotyping, digital radiography (DR) imaging, and Sanger sequencing. the results showed complete concordance between TaqMan-MGB and Sanger sequencing, with an overall agreement rate of 83.6% with DR imaging. For individuals with T/T genotypes (127/139), the detection accuracy reached 91.4%. This method demonstrated high specificity, simplicity, and cost-efficiency, significantly reducing the time and financial burden associated with traditional imaging-based approaches. the findings indicate that the TaqMan-MGB technique can accurately identify the T/T genotype at the SNP site and its strong association with the multi-vertebrae phenotypes, offering an effective and reliable tool for molecular breeding of Ujimqin sheep. Full article

Vertically oriented morphologies of the semiconducting metal oxide (SMO) surface provide a simple and effective means of enhancing gas sensor performance. We successfully synthesized explicitly aligned ZnO nanorods using a simple automated SILAR technique to improve acetone detection. In this work, we found that vertically oriented morphologies, such as well-aligned ZnO nanorods, can significantly enhance the sensor response due to an increase in specific active area and electron mobility, allowing a faster response to changes in the gas environment. The optimal operating temperature for our ZnO nanorod-based sensors in detecting acetone gas is 260 °C. At this temperature, the sensors exhibit a 96% response with a rapid response time of just 3 s. The improved sensing performance is attributed to both electronic and chemical sensitization mechanisms, which enhance the formation of active sites and shorten electron diffusion paths. Full article

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote sustainable farming practices. An integrated geospatial approach was adopted, combining Remote Sensing (RS), Geographic Information Systems (GIS), and Multi-Criteria Decision Analysis (MCDA). Key thematic layers, including soil type, land use/land cover, slope, and drainage density were processed in a GIS environment to model runoff potential. The Soil Conservation Service Curve Number (SCS-CN) method was used to estimate surface runoff. Criteria were weighted using the Analytical Hierarchy Process (AHP), enabling a structured and consistent evaluation of site suitability. The resulting suitability map classifies the region into four categories: very high suitability (10.2%), high (26.6%), moderate (40.4%), and low (22.8%). The integration of RS, GIS, AHP, and MCDA proved effective for strategic RWH site selection, supporting cost-efficient, sustainable, and data-driven agricultural planning in water-stressed environments. Full article