This paper addresses the issues of incomplete safety management systems and the challenge of optimizing multiple safety objectives concurrently in wind power project construction. An approach for solving Multi-objective Optimization Problem (MOP) based on the Non-Dominated Sorting Genetic Algorithm (NSGA) is proposed. First, key safety risk factors in the construction process of wind power projects are systematically analyzed and identified. A multi-dimensional evaluation index system, including personnel safety, equipment safety, environmental safety, and management safety, is established. Next, a mathematical model is developed with safety, cost, and construction period as the optimization objectives. The NSGA-II and NSGA-III algorithms are applied to solve the model. Case study results show that: (1) the proposed MOP model effectively balances the multiple objectives in wind power project construction; (2) compared with traditional methods, the NSGA demonstrates significant advantages in solution efficiency and diversity; (3) the obtained Pareto optimal solution set provides multiple feasible options for engineering decision-making. The research results provide theoretical foundations and practical guidance for safety management in wind power project construction.
The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts.
This study investigates the relationship between financial risk management, corporate social responsibility (CSR), and sustainable development within the petrochemical industry. The research aims to explore the impact of financial risk management practices on CSR initiatives and to assess how these factors collectively contribute to the long-term sustainability of petrochemical companies. A key focus of the study is the role that CSR plays in advancing sustainable development, particularly in sectors facing significant financial and operational risks. The research is applied in nature, offering practical insights for improving risk management strategies in petrochemical corporations. The study sample consisted of 130 experienced managers from the petrochemical industry, selected based on the number of items in the survey questionnaire. The measurement tool used was a researcher-developed questionnaire, which was designed following an extensive review of relevant literature and consultations with subject matter experts. To ensure the validity of the instrument, content validity was assessed, and reliability was confirmed through the calculation of Cronbach's alpha coefficient. Data were analyzed using Partial Least Squares (PLS) software, which revealed significant findings regarding the influence of financial risk management on CSR and sustainable development. The results underscore the crucial role of effective financial risk management in facilitating CSR initiatives and enhancing the sustainability of petrochemical companies. Additionally, CSR was found to positively affect sustainable development, with a particular emphasis on the integration of social activities, product and service innovation, and human resource management practices. It is concluded that prioritizing CSR, along with strategic financial risk management, is essential for achieving long-term sustainability in the petrochemical sector. These findings offer valuable insights for both academic research and industry practice, contributing to the development of more effective risk management frameworks in the context of sustainable development.
In response to the complex characteristics of gearbox vibration signals, including high frequency, high dimensionality, non-stationarity, non-linearity, and noise interference, this paper proposes a data processing method based on improved compressed sensing. First, the K-means Singular Value Decomposition (K-SVD) dictionary is used for sparse representation, ensuring good sparsity in the frequency domain. Next, a random convolution kernel measurement matrix is employed in place of the traditional Gaussian random matrix, satisfying the equidistant constraint while enhancing both computational and hardware implementation efficiency. Finally, the Generative Flow (GLOW) model is introduced, incorporating the measurement matrix, dictionary matrix, and sparse coefficient matrix into a unified optimization framework for joint solving. Through reversible mapping and probabilistic distribution modeling, the method effectively addresses noise interference and the challenges posed by complex signal distributions. Experimental results show that, compared with traditional compressed sensing methods, the proposed method offers superior signal reconstruction quality and better noise robustness.
The Prapatan coastal area, located along Jalan Jenderal Sudirman in Kelurahan Prapatan, Balikpapan City, is an area of significant urban and environmental potential, particularly in the context of waterfront city development. This area is strategically positioned as an environmental service centre within the city’s broader spatial structure plan, which identifies it as a key region for coastal development. Given the growing pressures on Prapatan Beach, particularly in light of the anticipated urban congestion due to the city’s role as a buffer for Indonesia’s new capital (IKN), there is a need for comprehensive planning to manage urban expansion and preserve the coastal ecosystem. This study employs a combined approach, integrating the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) analysis, to assess land suitability for waterfront development. The results of this analysis are then visualized through a WebGIS platform, enabling dynamic mapping of the area's environmental and spatial characteristics. The spatial analysis provides a framework for informed decision-making, highlighting areas with the greatest potential for sustainable development while addressing the challenges posed by urbanisation, environmental preservation, and infrastructure development. Ultimately, the research aims to contribute to the strategic planning of the coastal area, ensuring alignment with regional spatial policies and fostering the sustainable development of Balikpapan as a model waterfront city. The proposed spatial development concepts offer insights for future planning processes, assisting in the identification of potential risks and opportunities.
Risk management in the transportation of dangerous goods is critical for safeguarding human health, the environment, and infrastructure. This study explores systematic methodologies for risk assessment in the context of hazardous materials transit, with a particular focus on the transport of bitumen in Hungary. Key techniques, including Failure Mode and Effect Analysis (FMEA), Hazard and Operability Analysis (HAZOP), and Bow-Tie Analysis, are employed to identify, evaluate, and prioritize risks associated with the transportation process. These approaches enable the systematic breakdown of potential failure points, the evaluation of their effects, and the identification of mitigation strategies. The case study on bitumen transport highlights several significant risk factors, including operational failures, human errors, and vehicle-related incidents. The analysis reveals the importance of robust safety measures, such as enhanced driver training, real-time monitoring systems, and comprehensive documentation protocols, in reducing the likelihood and impact of such incidents. Furthermore, the study advocates for the continuous improvement of risk assessment procedures, emphasizing the need for adaptation to evolving regulatory standards and emerging challenges in hazardous materials transport. The findings underscore the importance of a proactive safety culture that integrates both technical solutions and organizational practices, ensuring a comprehensive approach to risk management in the transport of dangerous goods (TDG).
This scoping review aims to investigate the current applications of Digital Twin (DT) technology within the field of medical education, evaluating its advantages, limitations, and implications for future research and practice. A comprehensive search was conducted across seven authoritative databases, including PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI), covering the period from the inception of each database until November 20, 2024. Data extraction was carried out using NoteExpress and EndNote software, and studies were selected based on strict inclusion and exclusion criteria. A total of 112 articles were identified in the initial search, of which eight met the criteria for final inclusion in the analysis. These studies predominantly addressed the application of DT in medical imaging education, critical care training, and medical education for individuals with disabilities. The findings reveal that DT technology has shown significant promise in enhancing teaching effectiveness, improving student engagement, and increasing overall satisfaction. However, several limitations were identified, including the nascent stage of the technology, challenges related to system integration, high resource demands, and the difficulties faced by educators in mastering and implementing the technology. Despite these challenges, the application of DT in medical education is progressing, demonstrating substantial potential to advance the modernization of educational practices, improve learning outcomes, and enhance educational efficiency. To fully realize the benefits of DT, further research is needed to address the technological, economic, and pedagogical barriers currently limiting its widespread adoption. Additionally, the development of more effective “digital-physical fusion” teaching models is essential for maximizing the utility and scalability of DT technology in medical education.
Pre-stressed concrete continuous box girder bridges are widely used in bridge engineering due to their excellent mechanical properties. However, as the service life of the bridge increases and heavy vehicles exert additional loads, cracks may develop in the structure, leading to pre-stress loss and affecting its safety. This paper focuses on the reinforcement of an actual bridge and determines the pre-reinforcement stress state and stiffness degradation through load testing. The test results are combined with numerical simulations to analyze the stiffness of the box girder section. When the section stiffness is reduced by 5%, the deflection at the mid-span control section of the box girder is 11.7 mm, which is in good agreement with the actual condition. By integrating the bridge's appearance inspection results with numerical simulations, pre-stress loss in the box girder is analyzed. When the pre-stress loss reaches 10%, transverse cracks appear at the bottom of the main girder, similar to the results of field inspections. Based on this, the analysis considers a 5% stiffness reduction and a 10% pre-stress loss to evaluate the box girder.
Manufacturing firms face increasing pressure to enhance their competitiveness, penetrate new markets, and prioritise customer satisfaction in an increasingly dynamic global business environment. To remain competitive, these firms must adopt innovative strategies that address the evolving demands of customers. In this context, a firm’s capacity to innovate is critical, as it directly influences both the development and implementation of strategic initiatives. Innovation capacity in manufacturing companies is shaped by numerous interrelated factors, each contributing to a firm's ability to respond to technological advancements, market shifts, and changing consumer expectations. This study aims to identify the key determinants of innovation capacity in manufacturing firms based in Ordu Province, Turkey, with a focus on the role of corporate identity. A multi-criteria decision-making (MCDM) approach, specifically the Criteria Importance Assessment (CIMAS) technique, is employed to determine the relative importance of these factors. The findings suggest that “clustering and international networking activities” emerge as the most significant factor influencing innovation capacity, while the “level of entrepreneurship” is found to have the least impact. These results underscore the importance of collaboration, international connections, and strategic partnerships in driving innovation, while highlighting the comparatively limited role of entrepreneurship in fostering innovation within the studied region. The findings have significant implications for manufacturing firms, particularly in terms of strategy development, resource allocation, and the identification of key areas for improvement in innovation processes. Additionally, the research provides valuable insights for policymakers seeking to enhance the innovation capacity of manufacturing sectors in emerging markets.
The effective utilisation of equipment is essential for achieving the operational goals within production sectors, particularly in industries involving heavy machinery. Throughout its lifecycle, equipment is exposed to dynamic loads and harsh operational environments, leading to potential failures that may significantly shorten their service life. Therefore, evaluating equipment reliability is crucial for mitigating production losses and ensuring continuous operations. This study presents a comprehensive reliability analysis of underground mining machinery, with a focus on Load-Haul-Dump (LHD) systems, which are key to material handling in mining operations. Reliability assessments are performed using methodologies based on the series configuration of repairable systems. The reliability of each LHD system is quantitatively evaluated by employing a feed-forward back-propagation artificial neural network (ANN) model implemented in MATLAB. This model is designed to predict the optimal responses of each LHD machine under varying operational conditions. The results obtained from the ANN model are compared with the calculated reliability values, demonstrating a high degree of correlation between the predicted and observed outcomes. This strong alignment underscores the potential of ANN-based models in accurately forecasting system reliability. Based on the analysis, recommendations are made to identify the most critical components contributing to the system's unreliability, thereby enabling targeted corrective actions. The findings provide valuable insights for engineers seeking to enhance the performance and operational efficiency of mining machinery through more informed maintenance and operational strategies.
In today’s volatile and competitive global markets, organizations face numerous challenges to their survival and growth. To navigate these challenges effectively, the adoption of future-oriented, environment-based planning strategies is essential. Such strategies must not only address the identification of key environmental factors but also assess their long-term impacts on the organization, alongside its interaction with these external variables. The survival and sustainable development of an organization depend on a timely understanding of emerging opportunities and market dynamics, the formulation of strategic plans, and the selection of appropriate, effective strategies. This study presents an integrated model designed to evaluate the factors influencing a construction company’s performance, with a focus on conducting a comprehensive risk analysis. The model prioritizes and quantifies the significance of each element within the strengths, weaknesses, opportunities, and threats (SWOT) analysis of the company’s operational context. Furthermore, two fuzzy logic-based Multiple-Attribute Decision-Making (MADM) methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchy Process (AHP), were employed to rank the identified factors. Based on the analysis of the collected data, the final strategic course for the company was derived. The results indicated that the TOPSIS method placed a greater emphasis on the organization's strengths and opportunities, while the AHP approach, despite prioritizing long-term safety considerations, underscored the significance of addressing weaknesses and mitigating threats. This research contributes to the understanding of how fuzzy MADM techniques can be applied to strategic planning in the construction industry, facilitating more informed decision-making processes that align with the evolving demands of the market and ensure organizational resilience.
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