Search Results (1,199)

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 concept of governance has gained increasing attention across various fields of study. However, its application within the specific context of educational policies, particularly within compulsory public education, remains fragmented and underexplored. To answer the questions “How is governance conceptualized in the context of the compulsory public education system?” and “What contributions to future research emerge from this review?”, 32 peer-reviewed articles published in open-access journals between 2019 and 2023 were extracted from the Web of Science, Scopus, and ERIC databases and selected following PRISMA guidelines. Results from this systematic literature review analysis suggest a sustained yet moderate interest in the field, as evidenced by the reviewed publications, different theoretical and conceptual approaches, and research themes that illustrate different aspects of educational systems. Research gaps include the lack of a consolidated and integrated theoretical–conceptual framework on educational governance; the under-representation of specific actors, contexts, and points of view about how educational policies intentions are interpreted and enacted; insufficient critical analyses of, among others, educational leadership, digital transformation, and non-state actors’ influence in educational governance; and limited discussion of governance’s effects on educational justice, equity and quality. The main limitations relate to geographic, linguistic, and cultural biases of the analyzed studies, the exclusion of non-open-access articles, and the predominance of qualitative methodological approaches, which restrict generalizability. To address these challenges, future research should follow the adoption of interdisciplinary approaches, longitudinal and context-sensitive studies, and the use of mixed methodologies. These findings could contribute to a more informed discussion, avoiding reductionist interpretations and more open and critical perspectives on how educational governance transcends organizational and technical structures by incorporating political, ethical, and contextual dimensions that challenge the quality of educational systems. Full article

Emergency diesel is prone to degradation during long-term storage, and experimental evaluations are costly and slow. Three-dimensional computational fluid dynamics (CFD) simulations were employed to model the diesel conditioning process. A physical model based on the actual dimensions of the storage tank was constructed. The volume of fraction (VOF) model tracked the gas–liquid interface, and the species transport model handled mixture transport. A UDF then recorded inlet and outlet flow rates and velocities in each cycle. The study focused on the effects of different baffle structures and numbers on conditioning efficiency. Results showed that increasing the baffle flow area significantly delays the mixing time but reduces the cycle time. Openings at the bottom of baffles effectively mitigate the accumulation of high-concentration conditioning oil caused by density differences. Increasing the number of baffles decreases the effective volume of the tank and amplifies density differences across the baffles, which shortens the mixing time. However, excessive baffle numbers diminish these benefits. These findings provide essential theoretical guidance for optimizing baffle design in practical diesel tanks, facilitating rapid achievement of emergency diesel quality standards while reducing costs and improving efficiency. Full article

Discrete manufacturing environments face increasing challenges in managing work-in-process (WIP) inventory due to growing product customization and demand volatility. While Value Stream Mapping (VSM) has been widely used for process improvement, traditional approaches lack the ability to dynamically control WIP levels while optimizing multiple performance dimensions simultaneously. This research addresses this gap by developing an integrated framework that synergizes Multi-Dimensional Value Stream Mapping (MD-VSM) with multi-objective optimization, functioning as a specialized digital twin for dynamic WIP control. The framework employs a four-layer architecture that connects real-time data collection, multi-dimensional modeling, dynamic WIP monitoring, and execution control through closed-loop feedback mechanisms. A mixed-integer optimization model is used to balance time, cost, and quality objectives. Validation using a high-fidelity simulation, parameterized with real-world industrial data, demonstrates that the proposed approach yielded up to a 31% reduction in inventory costs while maintaining production throughput and showed a 42% faster recovery from equipment failures compared to traditional methods. Furthermore, a comprehensive sensitivity analysis confirms the framework’s robustness. The system demonstrated stable performance even when key operational parameters, such as WIP upper limits and buffer capacity coefficients, were varied by up to ±30%, underscoring its reliability for real-world deployment. These findings provide manufacturers with a validated methodology for enhancing operational efficiency and production flexibility, advancing the integration of lean principles with data-driven, digital twin-based control systems. Full article

While supply chain competition increasingly occurs at the network level, most research remains limited to dyadic or triadic relationships, failing to capture the full complexity of multi-tier supply networks. This research investigates the influence of four distinct types of network ties—contractual, transactional, professional, and personal—on supply chain performance, evaluated across five dimensions: cost, quality, delivery, flexibility, and innovation. The analysis draws on data gathered from 153 component-level supply networks, encompassing a total of 1852 entities within South Korea’s automotive and electronics manufacturing sectors. We employed social network analysis with a directed-valued network approach to capture asymmetric relationships. Results reveal that network architecture affects performance dimensions differently: centralized professional knowledge sharing enhances delivery performance, while concentrated personal ties prove detrimental; for innovation, dense network connections and dominant transactional hubs unexpectedly hinder performance by fostering conformity; cost performance shows mixed effects, with transactional centralization impeding efficiency while professional and personal leadership facilitates cost reduction. The influence of the original equipment manufacturer on supplier selection moderates these relationships, particularly mitigating negative impacts of personal tie centralization. These findings challenge conventional assumptions about network density benefits and demonstrate that supply network competence—the ability to configure and leverage network architecture—requires careful consideration of multiple tie types and their distinct effects on different performance outcomes. Full article

Anthropogenic disturbances significantly impact plant biodiversity in subtropical forests. While prior research has primarily concentrated on taxonomic diversity, other dimensions of biodiversity, such as phylogenetic and functional diversities, remain insufficiently explored. This study simultaneously investigated these three facets of plant diversity in subtropical forests with two distinct disturbance histories in eastern China, aiming to elucidate the effects of intermediate anthropogenic disturbances on biodiversity. Disturbed deciduous broadleaf forests exhibited markedly lower Pielou evenness index values compared to their conserved counterparts (p < 0.05). Disturbed evergreen broadleaf forests demonstrated significantly higher species richness, Shannon–Wiener index scores, and phylogenetic diversity relative to those found in conserved forests (p < 0.05). Furthermore, both disturbed deciduous broadleaf and mixed evergreen–deciduous broadleaf forests displayed significantly reduced functional richness and quadratic entropy coefficient values when compared with their conserved equivalents (p < 0.05). Forest type exerted a significant influence on all three dimensions of biodiversity (p < 0.05). In conclusion, intermediate anthropogenic disturbances have the potential to enhance both plant taxonomic and phylogenetic diversities while concurrently diminishing functional diversity within the subtropical forests of eastern China. The mechanisms driving responses in plant diversity to intermediate anthropogenic disturbances vary according to forest types. Full article

Gotland has been designated by the Swedish government as a pilot region for the transition to a sustainable, fossil-free energy system by 2030. This transformation emphasizes local renewable energy production and system independence. Within this context, this study investigates the role of industrial waste heat as a resource to improve energy efficiency and support sector integration between electricity, heating, and industry. A mixed-methods approach was used, combining techno-economic energy system modeling, life cycle assessment, spatial GIS data, and stakeholder input. The study develops and analyzes future carbon-neutral energy scenarios for Gotland’s energy system. Industrial waste heat can significantly reduce primary energy demand, particularly in scenarios with expanded industry, carbon capture, and increased sector integration—such as through district heating. In such cases, up to 3000–4000 GWh/year of low-temperature industrial residual heat becomes available, offering substantial potential to improve overall energy efficiency. The scenarios highlight synergies and trade-offs across environmental, economic, and social dimensions, emphasizing the importance of coordinated planning. Scenarios with offshore wind enable energy exports and industrial growth but raise challenges related to emissions and public acceptance, while scenarios without cement production reduce environmental impact but weaken local economic resilience. Limitations of the study include the exclusion of global supply chain impacts and assumptions about future technological costs. The study underscores the need for integrated planning, regulatory innovation, and stakeholder collaboration to ensure a just and resilient transition for Gotland. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

Background: Street food vending provides vital employment and nutrition in low- and middle-income countries (LMICs), but poor health and safety compliance pose significant public health and business risks. Despite growing policy recognition, the link between hygiene practices and vendor performance remains underexplored. Objective: This integrative review examines the influence of health and safety practices on the business performance of informal street food vendors, with a particular focus on both global and South African contexts. Methods: A total of 76 studies published between 2015 and 2025 were retrieved between June 2024 and May 2025 and analyzed using an integrative review methodology. Sources were identified through five major academic databases and grey literature repositories. Thematic synthesis followed PRISMA logic and was guided by the Health Belief Model (HBM) and Balanced Scorecard (BSC) frameworks. Results: There was a marked increase in publications post-2019, peaking in 2023. Sub-Saharan Africa accounted for the majority of studies, with South Africa (28%) and Ghana (14%) most represented. Among the 76 included studies, the most common designs were quantitative (38%), followed by qualitative (20%), case studies (14%), and mixed-methods (11%), reflecting a predominantly empirical and field-based evidence base. Thematic analysis showed that 26% of studies focused on food safety knowledge and practices, 14% focused on infrastructure gaps, and 13% focused on policy and regulatory challenges. Of the 76 studies included, 73% reported a positive relationship between hygiene compliance and improved business performance (such as customer trust, revenue, and operational resilience), based on vote-counting across qualitatively synthesized results and business outcomes. The review identifies a conceptual synergy between the HBM’s cues to action and the BSC’s customer dimension, highlighting how hygiene compliance simultaneously influences vendor behaviour and consumer trust. Conceptual saturation was observed in themes related to hygiene protocols, consumer trust indicators, and regulatory barriers. Conclusions: Health and safety practices function not only as compliance imperatives but also as strategic assets in the informal food economy. However, widespread adoption is impeded by structural barriers including limited infrastructure, education gaps, and uneven regulatory enforcement. The findings call for context-sensitive policy interventions and public health models that align with vendor realities and support sustainable, safe, and competitive informal food systems. Full article

With the rapid growth of renewable energy integration, power systems are facing increasing uncertainty and variability in operation. The intermittent and uncontrollable nature of wind and solar generation requires operational decisions to anticipate future fluctuations, creating strong temporal coupling across days. This leads to large-scale mixed-integer linear programming (MILP) with a large number of binary variables, which is computationally intensive—especially in year-long simulations. As a result, there is a growing need for efficient modeling approaches that can reduce complexity while preserving key temporal features. This paper proposes a temporal–spatial acceleration framework for long-term power system operation simulation. In the temporal dimension, a monthly K-means clustering algorithm is applied to reconstruct typical scenario days from 8760 h time series, preserving the characteristics of seasonal and intraday variability. In the spatial dimension, thermal units with similar characteristics are aggregated, and binary decision variables are relaxed into continuous variables, transforming the MILP into a tractable LP model, and thereby reducing computational burden. Case studies are performed based on the six-bus and the IEEE RTS-79 systems to validate the framework, being able to provide a practical solution for renewable-integrated power system planning and dispatch applications. Full article

This research aims to develop a model for cultivating talents in sustainable food literacy education in Taiwan. The project adopts the professional and theoretical axes of the food industry, sustainable development, and food literacy. The research employs a mixed-method approach, combining qualitative and quantitative techniques, to construct sustainable food literacy assessment indicators for Taiwan. In the first year, through literature analysis and qualitative research, the core content of “sustainable food literacy” in Taiwan was extracted, resulting in four major dimensions with 24 indicator items. Then, using the Fuzzy Delphi method, the indicators were constructed, defining the core content and dimension indicators of sustainable food literacy, which include “sustainable agriculture and production”, “healthy diet and culture”, “green environmental protection and consumption”, and “food social responsibility and ethics”, encompassing a total of 20 indicators. In the second year, based on the dimensions identified in the first year, a sustainable food literacy curriculum was developed. A 10-week quasi-experimental teaching curriculum was conducted for students enrolled in the “Vegetable and Fruit Carving” elective course in two classes of the Department of Food and Beverage Management at Jingwen University of Science and Technology. By comparing the pre-test and post-test scores of students’ sustainable food literacy and their sustainable food works, as well as analyzing student learning portfolios and teacher reflections, it was shown that the curriculum developed in this research significantly enhanced students’ sustainable food literacy and their performance. The results of this two-year study can be used for the assessment of sustainable food literacy talents in Taiwan, contributing both academically and practically. Full article

Understanding the social drivers of pro-environmental behavior in urban forests and green spaces is critical for addressing sustainability challenges. Subjective norms serve as a key pathway through which social expectations influence individuals’ behavioral intentions. Despite mixed findings in the literature regarding the impact of subjective norms on individuals’ intentions, there is a research gap about the determinants of this construct. This study was conducted to explore how social expectations shape perceived subjective norms among visitors of urban forests. A theoretical model was developed with subjective norms at its center, incorporating their predictors including social identity, media influence, interpersonal influence, and institutional trust, personal norms as a mediator, and behavioral intention as the outcome variable. Using structural equation modeling, data was collected and analyzed from a sample of visitors of urban forests in Tehran, Iran. The results revealed that subjective norms play a central mediating role in linking external social factors to behavioral intention. Social identity emerged as the strongest predictor of subjective norms, followed by media and interpersonal influence, while institutional trust had no significant effect. Subjective norms significantly influenced both personal norms and intentions, and personal norms also directly predicted intention. The model explained 50.9% of the variance in subjective norms and 39.0% in behavioral intention, highlighting its relatively high explanatory power. These findings underscore the importance of social context and internalized norms in shaping sustainable behavior. Policy and managerial implications suggest that strategies should prioritize community-based identity reinforcement, media engagement, and peer influence over top-down institutional messaging. This study contributes to environmental psychology and the behavior change literature by offering an integrated, empirically validated model. It also provides practical guidance for designing interventions that target both social and moral dimensions of environmental action. Full article

As the world’s largest developing and carbon-emitting country, China is accelerating its greenhouse gas (GHG) emission reduction process, and it is of vital importance in achieving the goals set out in the Paris Agreement. This paper examines the historical development and current operation of China’s carbon emissions trading market (CETM). The current progress of research on the implementation of carbon emissions trading policy (CETP) is described in four dimensions: environment, economy, innovation, and society. The results show that CETP generates clear environmental and social benefits but exhibits mixed economic and innovation effects. Furthermore, this paper analyses the challenges of China’s carbon market, including the green paradox, the low carbon price, the imperfections in cap setting and allocation of allowances, the small scope of coverage, and the weakness of the legal supervision system. Ultimately, this paper proposes recommendations for fostering China’s CETM with the anticipation of offering a comprehensive outlook for future research. Full article

This study integrates the Theory of Planned Behavior and the Theory of Consumption Values through a mixed-methods approach (structured interview and structural equation model) to investigate cross-platform disposal behaviors for idle clothing on social media and second-hand platform ecosystems. The study reconstructs traditional theoretical variables: psychological motivation dimension (platform-enabled green attitude, social circle environmental demonstration, and cross-platform behavioral control) and perceived value dimension (functional integration value perception, socialized emotional empowerment, and community identity value). Key findings: Cross-platform behavioral control is the strongest predictor of behavioral intention. In the value dimension, emotional value has the strongest direct impact on disposal intentions, functional integration is key to enhancing behavioral control, and community identity value most significantly impacts the platform-enabled green attitude and the social circle environmental demonstration. Finally, proposing a governance framework of “technological empowerment–emotional resonance–identity motivation”, offering theoretical foundations for optimizing platform interoperability and formulating digital environmental policies. Full article

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article