Search Results (779)

This study investigates the essential competencies for engineers in the context of digital transformation, with the aim of proposing a refined framework to guide professional development across career levels. A mixed-methods, sequential approach was adopted: (1) a systematic literature review, conducted between 2014 and 2024, which identified 46 competencies organized into seven dimensions; (2) a quantitative survey with 392 engineers who self-assessed their level of mastery for each competency; (3) semi-structured interviews with 20 company representatives, who validated and contextualized the essential competencies according to hierarchical levels (junior, mid-level, and senior); (4) data triangulation, resulting in a final competency model by career level. The findings reveal a widespread deficit in digital competencies, regardless of hierarchical level. In total, 33 competencies assessed by career level showed statistically significant differences in employer perceptions and were identified as progressive throughout the career trajectory. Analysis of self-assessments and interviews indicates that for early-career engineers, there is a strong emphasis on personal and basic cognitive competencies. For mid-level engineers, the data show a significant valuation of social competencies. Senior engineers are perceived as having accumulated experience across all seven mapped dimensions. This study offers a practical model that can be used by educational institutions, companies, and professionals to align education, market demands, and career planning. Full article

The World Health Organization (WHO) declared snakebite envenoming (SBE) as a neglected tropical disease in 2017. Antivenom is the gold standard of treatment, but many healthcare barriers exist, and hence, affected populations are often unable to access it. The challenge is further perpetuated by the lack of attention from national health authorities, poor regulatory systems and policies, and mismanagement of antivenom. This study aims to map the evidence regarding snake antivenom regulations globally and identify gaps in the literature to inform future research and policy. This review was conducted using the original Arksey and O’Malley framework by three independent reviewers, and the results were reported using the Preferred Reporting Items for Systematic reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR). A search strategy was developed with assistance from a librarian, and six databases were searched: PubMed, SCOPUS, ProQuest Central, Africa Wide Web, Academic Search Output, and Web of Science. Screening was conducted independently by the reviewers, using Rayyan, and conflicts were resolved with discussions. A total of 84 articles were included for data extraction. The major themes that emerged from the included studies were regarding antivenom availability, accessibility, manufacturing, and regulations. The study revealed massive gaps in terms of policies governing antivenom management, especially in Asia and Africa. The literature does not offer sufficient evidence on management guidelines for antivenom in the endemic regions, despite identifying the challenges in supply. However, significant information from Latin America revealed self-sufficient production, involvement of national health bodies in establishing efficient regulations, effective distribution nationally and regionally, and technology sharing to reduce SBE-related mortality. Full article

The aim of this study was to explore the use of neural networks as a decision-support tool for sustainable oilseed processing. The investigation focused on how different production profiles (crude vegetable oil, refined oil, hydrogenated oil and margarine) affect electricity and water use in selected Polish processing plants. The collected data were first grouped with cluster analysis to identify similar operational cases. The clusters were then visualized with a Self-Organizing Map (SOM), producing a two-dimensional topological feature map. This analysis indicated a subset of data for which it was appropriate to build predictive models of electricity and water consumption. Multi-layer perceptron (MLP) neural networks yielded highly accurate predictions of electricity (R² = 0.967 on the test set) and water (R² = 0.967 on the test set) use in oilseed processing. The resulting models can assist in selecting the most energy- and water-efficient processing configuration. Full article

With the widespread application of unmanned aerial vehicle technology in navigation mark inspection, path planning algorithm efficiency has become crucial to improve inspection effectiveness. The traditional self-organizing mapping (SOM) algorithm suffers from dual limitations in UAV inspection path optimization, including insufficient global exploration during early training stages and susceptibility to local optima entrapment in later stages, resulting in limited inspection efficiency and increased operational costs. For this reason, this study proposes two improved self-organizing mapping algorithms. First, the ORC_SOM algorithm incorporating a generalized competition mechanism and local infiltration strategy is developed. Second, the ORCTS_SOM hybrid optimization model is constructed by integrating the Tabu Search algorithm. Validation using two different scale navigation mark datasets shows that compared with traditional methods, the proposed improved methods achieve significantly enhanced path planning optimization. This study provides effective path planning methods for unmanned aerial vehicle navigation mark inspection, offering algorithmic support for intelligent maritime supervision system construction. Full article

A comprehensive exploration of the trade-offs/synergies and drivers of ecosystem services (ESs) is essential for formulating ecological plans. However, owing to the limited attention given to multiple scales, the relationship of ESs still needs to be further explored. Taking the Yangtze River Delta region of China as the study area, a multiscale data framework with a 1 km grid and 10 km grid and county was established, and six ESs were evaluated for 2000, 2010, and 2020. Then, the trade-offs and synergies between ESs were explored by Spearman’s correlation analysis and geographically weighted regression (GWR), and the ecosystem service bundles (ESBs) were identified by self-organizing maps (SOMs). Finally, the socioecological drivers of ESs were further analyzed via GeoDetector. The results showed that (1) the distribution of ESs exhibited spatial heterogeneity. (2) At the grid scale, there were very strong trade-off effects between crop production and the other ESs. The synergistic effects between ESs at the county level were further strengthened. (3) The ESBs identified at different temporal and spatial scales were different. (4) Land use had the strongest explanatory power for all the ESs. At the grid scale, climatic and biophysical factors had great impacts on ESs, whereas population density and night light remote sensing had significant impacts on crop production, carbon storage, and water yield at the county scale. Full article

Recent advancements in composite materials design have made glass fiber-reinforced polymer composites (GFRPC) a viable choice for a wide range of engineering and industrial applications. Although GFRPCs boast attractive characteristics such as low specific mass and high specific mechanical strength, identifying and characterizing damage mechanisms in these materials is challenging. Several scientific studies have examined the root causes of GFRPC failure using various methods, including non-destructive techniques and learning algorithms. Despite this, ongoing investigations aim to accurately detect mechanical defects in GFRPCs. This study explores the use of non-destructive testing (NDT) combined with unsupervised learning algorithms to identify and classify damage mechanisms in GFRPCs. The NDT method employed in this study is acoustic emission (AE), which identifies waveforms associated with various failure mechanisms during testing. These waveforms are categorized using unsupervised learning methods such as principal component analysis (PCA) and self-organizing maps. PCA selects the most appropriate AE descriptors for distinguishing between different damage mechanisms, while the self-organizing maps algorithm performs clustering analysis and classifies failure mechanisms. Scanning electron microscope images of the observed failures are provided to sup-port the findings derived from AE data. Full article

The hybrid inorganic–organic material concept plays a bold role in multifunctional materials, combining different features on one platform. Once varying properties coexist without cancelling each other on one matrix, a new type of supermaterial can be formed. This concept showed that silver nanoparticles can be embedded together with inorganic and organic surface coatings and silicon quantum dots for symbiotic antibacterial character and UV-excited visible light fluorescent features. Additionally, fluorosilane material can be coupled with this prepolymeric structure to add the hydrophobic feature, showing water contact angles around 120°, providing self-cleaning features. Optical properties of the components and the final material were investigated by UV-Vis spectroscopy and PL analysis. Atomic investigations and structural variations were detected by XPS, SEM, and EDX atomic mapping methods, correcting the atomic entities inside the coating. FT-IR tracked surface features, and statistical analysis of the quantum dots and nanoparticles was conducted. Multifunctional final materials showed antibacterial properties against E. coli and S. aureus, exhibiting self-cleaning features with high surface contact angles and visible light fluorescence due to the silicon quantum dot incorporation into the sol-gel-produced nanocomposite hybrid structure. Full article

The construction and maintenance of an ecological security pattern (ESP) are important for promoting the regional development of ecological civilizations, realizing sustainable and healthy development, and creating a harmonious and beautiful space for human beings and nature to thrive. Traditional construction methods have the limitations of a single dimension, a single method, and excessive human subjective intervention for source and corridor identification, without considering the multidimensional quality of the sources and the structural connectivity and resilience optimization of the corridors. Therefore, an ecological civilization demonstration area (Jiangxi Province) was used as the study area, a new research method for ESP was proposed, and an empirical study was conducted. To evaluate ecosystem service (ES) importance–disturbance–risk and extract sustainability sources through the deep embedded clustering–self-organizing map (DEC–SOM) deep unsupervised learning clustering algorithm, ecological networks (ENs) were constructed by applying the minimum cumulative resistance (MCR) gravity model and circuit theory. The ENs were then optimized to improve performance by combining the comparative advantages of the two approaches in terms of structural connectivity and resilience. A comparative analysis of EN performance was constructed among different functional control zones, and the ESP was constructed to include 42 ecological sources, 134 corridors, 210 restoration nodes, and 280 protection nodes. An ESP of ‘1 nucleus, 3 belts, 6 zones, and multiple corridors’ was constructed, and the key restoration components and protection functions were clarified. This study offers a valuable reference for ecological management, protection, and restoration and provides insights into the promotion of harmonious symbiosis between human beings and nature and sustainable regional development. Full article

The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below −0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

As organizations become increasingly projectified, safeguarding the resilience of project professionals and teams emerges as a critical organizational challenge. Adopting a systems lens, we investigate how agile mindsets and agile practices function as systemic antecedents of resilience at the individual and team levels. Eleven semi-structured interviews with experienced project managers, product owners, and team members from diverse industries were analyzed through inductive thematic coding and system mapping. The findings show that mindset supplies psychological resources—self-efficacy, openness and a learning orientation—while practices such as team autonomy, iterative delivery and transparent communication provide structural routines; together they trigger five interlocking mechanisms: empowerment, fast responsiveness, holistic team dynamics, stakeholder-ecosystem engagement and continuous learning. These mechanisms reinforce one another in feedback loops that boost a project system’s adaptive capacity under volatility. The synergy of mindset and practices is especially valuable in hybrid or traditionally governed projects, where cognitive agility offsets structural rigidity. This study offers the first multi-level, systems-based explanation of agile antecedents of resilience and delivers actionable levers for executives, transformation leaders, project professionals, and HR specialists aiming to sustain talent performance in turbulent contexts. Full article

Heavy metal contamination in groundwater has emerged as a significant environmental issue, driven by rapid industrialization and intensified human activities, particularly in southern China. Heavy metal pollution in groundwater often presents complex spatial patterns and multiple sources; understanding the spatial heterogeneity and controlling factors of heavy metals is crucial for pollution prevention and water resource management in industrial regions. This study applied spatial autocorrelation analysis and self-organizing maps (SOM) coupled with K-means clustering to investigate the spatial distribution and key influencing factors of nine heavy metals (Cr, Fe, Mn, Ni, Cu, Zn, As, Ba, and Pb) in a typical industrial area in southern China. Heavy metals show significant spatial heterogeneity in concentrations. Cr, Mn, Fe, and Cu form local hotspots near urban and peripheral zones; Ni and As present downstream enrichment along the river pathway with longitudinal increase trends; Zn, Ba, and Pb exhibit a fluctuating pattern from west to east in the piedmont region. Local Moran’s I analysis further revealed spatial clustering in the northwest, riverine zones, and coastal outlet areas, providing insight into potential source regions. SOM clustering identified three types of groundwater: Cluster 1 (characterized by Cr, Mn, Fe, and Ni) is primarily influenced by industrial pollution and present spatially scattered distribution; Cluster 2 (dominated by As, NO₃⁻, Ca²⁺, and K⁺) is associated with domestic sewage and distributes following river flow; Cluster 3 (enriched in Zn, Ba, Pb, and NO₃⁻) is shaped by agricultural activities and natural mineral dissolution, with a lateral distribution along the piedmont zone. The findings of this study provide a scientific foundation for groundwater pollution prevention and environmental management in industrialized areas. Full article

Microplastic (MP) contamination is a growing environmental concern with significant impacts on ecosystems, the economy, and potentially human health. However, accurately detecting and characterizing MPs in biological samples remains a challenge due to the complexity of biological matrices and analytical limitations. This study presents a novel, non-destructive visible near-infrared multispectral imaging (Vis-NIR-MSI) method combined with a supervised self-organizing map (SOM) to enable rapid qualitative and quantitative analysis of MPs in seafood. We specifically aimed to identify and differentiate four types of microplastics, namely PET, PE, PP, and PS, in the range 1–4 mm, present on the surface of minced shrimp and shrimp shell. For quantification, MPs were incorporated into minced shrimp surface at concentrations ranging from 0.04% to 1% w/w. The modified model achieved a high coefficient of determination (R² > 0.99) for PE and PP quantification. Unlike conventional techniques, this approach eliminates the need for pre-sorting or chemical processing, offering a cost-effective and efficient solution for large-scale monitoring of MPs in seafood, with potential applications in food safety and environmental protection. Full article

Pleurotus ostreatus, classified under the phylum Basidiomycota, order Agaricales, and family Pleurotaceae, is a prevalent gray edible fungus. Its physical damage not only compromises quality and appearance but also significantly diminishes market value. This study proposed an enhanced method for detecting Pleurotus ostreatus damage based on an improved YOLOv8n model, aiming to advance the accessibility of damage recognition technology, enhance automation in Pleurotus cultivation, and reduce labor dependency. This approach holds critical implications for agricultural modernization and serves as a pivotal step in advancing China’s agricultural modernization, while providing valuable references for subsequent research. Utilizing a self-collected, self-organized, and self-constructed dataset, we modified the feature extraction module of the original YOLOv8n by integrating a lightweight GhostHGNetv2 backbone network. During the feature fusion stage, the original YOLOv8 components were replaced with a lightweight SlimNeck network, and an Attentional Scale Sequence Fusion (ASF) mechanism was incorporated into the feature fusion architecture, resulting in the proposed OMB-YOLO model. This model achieves a remarkable balance between parameter efficiency and detection accuracy, attaining a parameter of 2.24 M and a mAP@0.5 of 90.11% on the test set. To further optimize model lightweighting, the DepGraph method was applied for pruning the OMB-YOLO model, yielding the OMB-YOLO-tiny variant. Experimental evaluations on the damaged Pleurotus dataset demonstrate that the OMB-YOLO-tiny model outperforms mainstream models in both accuracy and inference speed while reducing parameters by nearly half. With a parameter of 1.72 M and mAP@0.5 of 90.14%, the OMB-YOLO-tiny model emerges as an optimal solution for detecting Pleurotus ostreatus damage. These results validate its efficacy and practical applicability in agricultural quality control systems. Full article