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.
Open-source intelligence in aerospace technology often contains lengthy text and numerous technical terms, which can affect classification accuracy. To enhance the precision of classifying such intelligence, a classification algorithm integrating the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models was proposed. Initially, key features within the intelligence were extracted through the deep structure of the BERT model. Subsequently, the XGBoost model was utilised to replace the final output layer of BERT, applying the extracted features for classification. To verify the algorithm's effectiveness, comparative experiments were conducted against prominent language models such as Text Recurrent Convolutional Neural Network (TextRCNN) and Deep Pyramid Convolutional Neural Network (DPCNN). Experimental results demonstrate that, for open-source intelligence classification in aerospace technology, this algorithm achieved accuracy improvements of 1.9% and 2.2% over the TextRCNN and DPCNN models, respectively, confirming the algorithm's efficacy in relevant classification tasks.
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.
Manufacturing industry clusters contribute to the optimization of regional industrial structures and the improvement of economic growth efficiency. The role of inter-county carriers can further enhance the cluster effects of the manufacturing industry in Sichuan Province, promoting coordinated development across industries and accelerating the transformation of new productive forces. This study evaluates the degree of industrial agglomeration of the manufacturing industry in 183 counties in Sichuan Province. The results indicate that Sichuan's manufacturing industry exhibits a clear clustering effect, with a particularly pronounced structural agglomeration centered around Chengdu. However, economic development across counties in the province remains unbalanced. The Generalized Method of Moments (GMM) regression analysis confirms that total factor productivity (TFP) and industrial interlinkages exert a significant positive influence on the agglomeration of the manufacturing industry.
The Riung Participatory Design (RPD) model has been developed as a strategy for fostering sustainable community development and regeneration in Indonesia’s urban kampongs. This model integrates participatory design principles with design thinking methodologies to address the complex challenges faced by urban kampongs. The first stage of the RPD model, the Re-visit Participatory Art approach, investigates key dimensions such as relational dynamics, regional vitality, socio-cultural engagement, and territorial identity. Research findings highlight four significant factors: (1) robust community ties, wherein local residents collaboratively engage in problem-solving through the Indonesian cultural practice of ‘gotong royong’; (2) the preservation and promotion of local traditions by the community; (3) the presence of symbolic artifacts that reflect local values and wisdom; and (4) the articulation of distinct territorial narratives within the kampong environment. In the second stage, the model employs a hybrid approach that combines participatory art with design thinking, mobilising communities for active participation in place-making processes. This approach was applied across three case study locations: Kampong Pelangi in Semarang (Central Java), Kampong Sukapura in North Jakarta, and Kampong Pondok Pucung in South Tangerang (Banten, West Java). The participatory evaluation conducted during the regeneration of these urban kampongs revealed the critical role of local actors in driving sustainable urban transformation. The study assesses community participation through the lens of eight characteristics of sustainable communities, thereby demonstrating the relevance of the RPD model in urban kampong regeneration. The findings indicate that an integrated and contextually adapted participatory design model is essential for addressing the unique socio-cultural and territorial dynamics of Indonesian urban kampongs. This research contributes to the understanding of how participatory design can be effectively employed to regenerate urban spaces while fostering sustainable, community-driven development.
The rise of the gig economy and the increasing demand for flexible, remote work have transformed the freelancing landscape. This paper presents the development of a mobile application designed to streamline the process of project bidding, user management, and service matching for freelancers. Built using Java and Android Studio, the application employs Agile development methodologies to ensure robust performance and a seamless user experience. Key features include user registration and verification, a secure project-bidding platform, and efficient database management with SQLite. The app also utilises the Glide library for optimised image handling, ensuring smooth interaction for freelancers and clients alike. Initial results indicate that the platform effectively connects clients with freelancers by providing an intuitive, reliable, and secure service-matching environment. This paper explores the technical design, challenges, and future directions of the application, emphasising its potential to improve freelancing workflows and address key issues such as decision fatigue and trust in digital platforms.
The strategic location of emergency supply depots is critical for enhancing pre-disaster preparedness and post-disaster relief efforts. Given the inherent uncertainties and risks associated with natural and man-made disasters, ensuring the swift and effective delivery of relief materials to affected areas is pivotal for minimizing disaster impacts and safeguarding lives and property. This review synthesizes the current body of research on the siting of emergency stockpiles, providing a comprehensive analysis of the factors influencing site selection. Key factors such as the geographic scope of disaster response, hydrographic conditions, transportation infrastructure, and accessibility to affected populations are examined. Various siting models are evaluated to optimize resource allocation, minimize logistics costs, and improve supply chain responsiveness during emergencies. This review also identifies key challenges within the existing literature, including limitations in model algorithms, disaster stage considerations, optimization criteria, and the degree of stakeholder involvement in decision-making. Notably, while previous research has often focused on isolated factors, this study emphasizes the need for an integrated approach that accounts for dynamic, diversified, intelligent, and human-centered considerations. Dynamic models are essential to adapt to the unpredictable nature of disasters, while diversified approaches are necessary to address the varying needs of different disaster types and affected populations. Intelligent decision-making tools, incorporating data analytics and real-time information, can enhance the efficiency and accuracy of site selection processes. Human-centric models, focusing on the actual needs of disaster-affected communities, are critical for ensuring the effectiveness of relief operations. The review concludes by outlining future research directions, emphasizing the importance of developing adaptable, sustainable, and context-specific siting models. Future investigations should focus on the practical application of emerging technologies, such as big data analytics, artificial intelligence, and remote sensing, to refine siting models and improve their responsiveness in a rapidly changing global landscape. These advancements are expected to contribute to more efficient and cost-effective emergency supply systems, better equipped to address the evolving challenges of global disaster risks.
The shear connection behaviour of steel-concrete composite beams is primarily governed by the strength of the connectors and concrete. Modern seismic evaluations and vibrational analyses of composite beams, particularly concerning their load-slip characteristics and shear strength, predominantly rely on push-out test data. In this study, the Finite Element Method (FEM) has been employed to simulate and analyse the shear, bending, and deflection responses of composite beams subjected to various load conditions, in accordance with Eurocode 4 standards. Failure modes, ultimate loads, and sectional capacities were examined in detail. The results indicate that increased strength of both steel and concrete significantly enhances the beam’s capacity in bending. Specifically, flexural and compressive resistance showed marginal improvements of 3.2%, 3.1%, and 3.0%, respectively, as concrete strength increased from 25 N/mm² to 30, 35, and 40 N/mm², while steel strength increased by 27% and 21%, with yield strengths of 275 N/mm², 355 N/mm², and 460 N/mm², respectively. Under seismic loading, however, the ultimate flexural load capacity exhibited a reduction with a fixed beam span, irrespective of steel strength. The shear capacity remained constant across varying beam lengths but demonstrated significant improvements with increased steel yield strength, with enhancements of 29% and 67% as steel yield strength increased from 275 N/mm² to 355 N/mm² and 460 N/mm², respectively. A detailed vibration analysis was also conducted to investigate the dynamic behaviour of these composite beams under seismic conditions. These findings underscore the critical influence of material strengths and loading conditions on the performance of steel-concrete composite beams, particularly in seismic scenarios, providing valuable insights for the design and assessment of such structures in seismic-prone regions.
Open Access
