Search Results (600)

This study addresses a significant problem: it is difficult to choose a suitable mobile learning platform effectively since many learning platforms are readily available for users. For this purpose, the study proposes an efficient way to rank and choose the most suitable mobile learning platform by integrating risk analysis and multi-criteria decision-making methods. The selection of a suitable mobile learning platform is challenging due to the vast collection of available platforms. Traditional decision-making approaches often struggle to manage the inherent uncertainty and subjectivity in platform evaluation. To address this, we propose an enhanced methodology that combines grey relational analysis (GRA) and fuzzy analytic hierarchy process (FAHP), leveraging their complementary strengths to provide a robust and adaptive solution. The study employs ISO/IEC 9126 software quality standards to locate the most suitable mobile learning platform. FMEA is based on three risk factors: occurrence, severity, and detection. The fuzzy analytical hierarchy process (FAHP) is used to determine the relative weight of each risk factor to identify the grey risk priority number that can be calculated for each criterion. Mobile learning platforms are then ranked based on their grey risk priority number. The method was applied to five widely used mobile learning platforms with three decision-makers. In addition, the multi-criteria decision-making software was developed to aid users, educators, and administrators in their decision-making processes. The integrated FMEA-GRA-FAHP technique, using ISO/IEC 9126 standards, provides an effective way of locating the most suitable mobile learning platform and ranking them according to their reliability. This research is believed to be the only study applying an integrated FMEA-GRA-FAHP approach to evaluate the risks and quality of mobile learning platforms. The unique approach overcomes certain limitations of the standalone methods such as FMEA and FAHP, making it a valuable tool for identifying the suitability of mobile learning platforms. In addition, the study underscores the importance of inclusivity and equity in ensuring high-quality education and creating an environment conducive to lifelong learning for all. Full article

In the context of healthcare technology management (HTM) in Saudi Arabia and the Gulf region, this study addresses a significant gap by exploring both external and internal risk factors affecting HTM performance. Previous studies have primarily focused on modeling or predicting failures in medical devices, mostly examining internal (endogenous) factors that impact device performance and the development of optimal service strategies. However, a comprehensive investigation of external (exogenous) factors has been notably absent. This research introduced a novel hierarchical risk management framework designed to accommodate a broad array of healthcare technologies, not limited to just medical devices. It significantly advanced the field by thoroughly investigating and validating a comprehensive set of 53 risk factors and assessed their influence on HTM. Additionally, this study embraced the perspective of enterprise risk management (ERM) and expanded it to identify and incorporate a wider range of risk factors, offering a more holistic and strategic approach to risk assessment in healthcare technology management. The findings revealed that several previously underexplored external and internal factors significantly impacted HTM performance. Notably, the Fuzzy AHP survey identified “design risk” under facility and environmental risks as the highest risk for HTM in this region. Furthermore, this study revealed that three out of the top ten risks were related to “facility and internal environmental” factors, six were related to technological endogenous factors, and only one was related to managerial factors. This distribution underscores the critical areas for intervention and the need for robust facility and technology management strategies. In conclusion, this research not only fills a critical void by providing a robust framework for healthcare technology risk assessment but also broadens the scope of risk analysis to include a wider array of technologies, thereby enhancing the efficacy and safety of healthcare interventions in the region. Additionally, the proposed hierarchy provides insights into the underlying risk factors for healthcare technology management, with potential applications extending beyond the regional context to a global scale. Moreover, the equation we proposed offers a novel perspective on the key risk factors involved in healthcare technology management, presenting insights with far-reaching implications applicable not only regionally but also on a global level. This framework also supports sustainability goals by encouraging the efficient and responsible utilization and management of healthcare technologies, essential for ensuring the long-term economic and environmental sustainability of medical technology use. This research is of an exploratory nature, with the findings from the Fuzzy AHP analysis being most applicable to the specific geographic regions examined. Additional research is required to validate these results and to confirm the trends observed in various other regions and contexts. Full article

Based on the lining structure of an operational tunnel, the AHP and Fuzzy mathematical models were used to determine the weight of the evaluation index and solve the membership matrix. The weighted-average Fuzzy comprehensive function was used to combine the two, and the Fuzzy–AHP evaluation model was built and programmed. According to the self-developed Fuzzy–AHP evaluation-programmed model, a visualized structure safety evaluation system for operational tunnels was developed by using MATLAB. The system’s functional design, program development, and computational visualized interface were implemented, and key codes were provided. The system can be divided into four modules: data management, fuzzy computation, predictive analysis and key disease indexes to focus on. In addition, the system can easily edit and modify the evaluation function, which includes not only the Fuzzy evaluation but also other types of evaluation functions applicable to other practical engineering projects, improving the applicability of the system. After that, the system was applied to the structure safety evaluation of a mountain tunnel, which provided the evaluation results and key indexes to focus on in the tunnel. Finally, the rationality of the system design was verified by constructing the corresponding BP–RBF combined neural network. This study provides a reference for the establishment of an intelligent structure safety warning system for operational tunnels. Full article

Open-pit mining seriously damages the original vegetation community and soil layer and disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental awareness and the influence of related policies, the ecological restoration of open-pit mines has been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and small scale of single mines. However, the carbon sequestration capability of mines after ecological restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current status were determined through field investigation, the methods for sampling and data collection for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting of three aspects and nine evaluation indexes was established by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following: the establishment of the judgment matrix, calculation of the index weight, determination of the membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation results of the restoration mines were determined with the ‘excellent, good, normal and poor’ grade classification according to the evaluation standards for each index proposed considering the data of the field investigation and laboratory tests. The results indicated that (1) the evaluation results of the mines’ carbon sequestration capacity were of excellent and good grade at a proportion of 62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547, indicating the stability of the system is the main factor in the carbon sequestration capacity of the mines and the sustainability of the vegetation community and the stability of soil fixation on the slope. The proposed evaluation system effectively evaluates the short-term carbon sequestration capability of the restoration mining system according to the visual effects and the laboratory testing results, objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative analysis. The evaluation method is relatively applicable and reliable for restoration mines and can provide a reference for similar ecological restoration engineering. Full article

Natural aquifers used for human consumption are among the most important resources in the world. The Locumba basin faces significant challenges due to its limited water availability for the local population. In this way, the search for possible aquifer recharge zones is crucial work for urban development in areas that have water scarcity. To evaluate this problem, this research proposes the use of the hybrid Fuzzy AHP methodology in conjunction with the TOPSIS algorithm to obtain a potential aquifer recharge map. Ten factors that influence productivity and capacity in an aquifer were implemented, which were subjected to Fuzzy AHP to obtain their weighting. Using the TOPSIS algorithm, the delineation of the most favorable areas with high recharge potential was established. The result shows that the most influential factors for recharge are precipitation, permeability, and slopes, which obtained the highest weights of 0.22, 0.19, and 0.17, respectively. In parallel, the TOPSIS result highlights the potential recharge zones distributed in the Locumba basin, which were classified into five categories: very high (13%), high (28%), moderate (15%), low (28%), and very low (16%). The adapted methodology in this research seeks to be the first step toward effective water resource management in the study area. Full article

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes damage to the environment. The most important cause of air pollution is the amount and density of substances called “particulate matter” above guidelines. Particulate matter (PM) are mixed liquid droplets and solid particles with advective diameters less than 2.5 μm (PM_2.5—fine particles) and between 2.5 and 10 μm (PM_2.5–10—coarse particles). PM₁₀ is defined as one that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. It is important to identify the underlying causes of air pollution caused by PM₁₀. In this context, these criteria need to be evaluated to minimize the negative effects of PM₁₀. In the study, monthly average PM₁₀ data obtained from the Air Quality Monitoring Station in Kocaeli, Türkiye, between 2017 and 2023 are used. After determining the criteria for PM₁₀, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking decision-maker opinions. The proposed decision-making model aims to guide obtaining and focusing on the important causes of out-of-limit and dangerous PM₁₀ concentrations in the air. Additionally, PM₁₀ data is analyzed in the context of COVID-19 and a statistical analysis is conducted. One-way Analysis of Variance (ANOVA) is used to evaluate whether there is a significant difference in average monthly data over the years. The Games–Howell test, one of the post-hoc tests, is used for determining differences between groups (years). In addition, monthly PM₁₀ values for the future are estimated using the Expert Modeler tool in the software IBM^® SPSS^® Statistics 22. The study is important in that it provides a focus on the criteria affecting PM₁₀ with an intuitionistic fuzzy perspective, along with statistical analysis. Full article

Restorative environments have a positive impact on improving the physical and mental health of the elderly. In China, the proportion of elderly residents in aging urban communities is significantly higher than in newly constructed ones, making it essential to understand the restorative potential of public open spaces (POSs) in these settings to promote the development of aging-in-place models. To investigate this issue, we employed the Fuzzy Delphi Method (FDM) and the Analytic Hierarchy Process (AHP) to construct an evaluation system for the restorative potential of public open spaces. Subsequently, we assessed the restorative effects of POSs in the Dabeizhuang community using 320 residents’ questionnaires and the fuzzy comprehension evaluation method. The results indicate that the dimension of safety is the most critical factor in creating restorative environments. In contrast, the dimension of comfort is the primary reason affecting the current evaluation of the community’s restorative environment. By establishing a restorative environment evaluation system, this research will facilitate the creation of more restorative environments in urban areas, thereby promoting active and healthy aging among elderly residents. Full article

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. Full article

The objective of this study is to investigate the potential risks posed by snowdrifts, a prevalent cause of natural disasters in northern China, on railway subgrades, and to assess their risk level. As a wind-driven process of snow migration and redeposition, snowdrifts pose a significant threat to the safety of transportation infrastructures. This study focuses on the Afu Railway in Xinjiang, situated on the northern slopes of the Eastern Tianshan Mountains, where it experiences periodic snowdrifts. We employed a combination of the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation (FCE) to construct an integrated evaluation system for assessing the risk of snowdrift to railway subgrades. The results indicate that subgrade design parameters and regional snowfield conditions are two key metrics affecting the extent of snowdrift disasters, with topography, vegetation coverage, and wind speed also exerting certain impacts. The evaluation method of this study aligns with the results of on-site observations, verifying its accuracy and practicality, thereby providing a solid risk assessment framework for snowdrifts along the railway. The scientific and systematic hazard assessment method of railway subgrades developed in this research provides basic data and theoretical support for future research, and provides a scientific basis for relevant departments to formulate countermeasures, so as to improve the safety and reliability of railway operations. Full article

Off-site construction (OFC) is a hot topic to remedy the chronic issues of the construction industry, such as low levels of productivity, waste, safety risks, environmental pollution, poor quality, and time and cost issues. However, the lack of standards and knowledge about OFC projects hamper the adaptation process. Disputes are one of the most important hampering factors. Therefore, this study aims to identify contractual and financial disputes and to detect the importance level of disputes in OFC projects. In the study, the Focus Group Discussion (FGD) technique, Pythagorean fuzzy AHP, and fuzzy TOPSIS were employed. As a result of FGD, 42 dispute causes for off-site construction projects were found. The Pythagorean fuzzy AHP method was used to calculate the weights of the criteria (occurrences, severity, and detection) that were used in the evaluation of dispute causes. The Pythagorean fuzzy AHP analysis results indicated that “detection” is more important than other criteria in the evaluation of off-site construction dispute causes. After that, the fuzzy TOPSIS method was used to determine the importance level of off-site construction dispute causes. The analysis results showed that “Increase in contract value due to revision in scope of work” in the contractual factor group and “Extra money for the additional works” in the financial factor group are the most important dispute causes, respectively. The study findings can be used for the evaluation and analysis of OFC project contracts. Full article

In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific production process is a complex task. There are numerous criteria to consider, often conflicting with each other, making decision-making challenging. In order to tackle this problem, the multi-criteria decision-making (MCDM) method is employed, which aids in ranking decisions based on criteria weights. However, traditional MCDM methods are now considered outdated, and researchers are concentrating on hybrid models that include multiple MCDM techniques to tackle decision-making problems effectively. This study presents an effective MCDM model that integrates Fuzzy-AHP-TOPSIS to evaluate and choose the best robot. The Fuzzy-AHP is utilized to establish a set of weights for the evaluation criteria. Subsequently, the proposed technique analyzes, prioritizes, and chooses the best robot option from the ranking list for the factory. The experimental results demonstrate that by employing the integrated fuzzy analytical hierarchy process, taking into account parameter weights and expert judgment, the robots are identified in order of best to worst alternatives to factories. The outcomes of this research possess significant implications for robot selection and can be applied in various fields to cater to production requirements. Full article

Tunnel boring machines (TBMs) are preferred for constructing tunnels, particularly for underground mass transit railways, because of their speed, minimal environmental impact, and increased safety. However, TBM tunneling involves unavoidable risks, necessitating careful assessment and management for successful project completion. This study presents a novel hybrid risk-analysis method for tunnel construction using TBMs. The proposed method integrates fault tree analysis (FTA) and the fuzzy analytic hierarchy process (fuzzy AHP). FTA was employed to calculate the probabilities of risk occurrences, while fuzzy AHP was utilized to determine the consequences of the risks. These probability and consequence values were used to calculate continuous risk levels for more accurate risk analysis. The proposed method was applied to a real case of metro line construction. The results demonstrated that the proposed method effectively analyzes the risks, accurately reflecting decision support data. The risks were categorized based on the continuous risk levels in descending order. The most significant risk was the deterioration of the TBM. The benefits of this study provide project managers and stakeholders involved in underground construction with a new risk-analysis method that enhances work safety and facilitates the timely execution of urban tunnel construction projects. Full article

Mining is a high-risk industry where occupational accidents are common due to its complex nature. Therefore, providing a more holistic and dynamic risk assessment framework is essential to identify and minimize the potential risks and enhance safety measures. Unfortunately, traditional risk assessment methods have limitations and shortcomings, such as uncertainty, differences in experience backgrounds, and insufficiency to articulate the opinions of experts. In this paper, a novel risk assessment method precisely for such cases in the mining sector is proposed, applied, and compared with traditional methods. The objective of this study is to determine the risk scores of Turkish Coal Enterprises, based on non-fatal occupational accidents, which operates eight large-scale open-cast coal mine enterprises in Türkiye. The causes of the accidents were categorized into 25 sub-criteria under 6 main criteria. The risk scores for these criteria were computed using the Pythagorean fuzzy Analytical Hierarchy Process (PFAHP) method. The first shift (8–16 h) (0.6341) for the shift category is ranked highest out of the 25 sub-risk factors, followed by maintenance personnel (0.5633) for the occupation category; the open-cast mining area (0.5524) for the area category, the 45–57 age range (0.5279) for employee age category, and the mining machine (0.4247) for the reason category, respectively. The methodologies proposed in this study not only identify the most important risk factors in enterprises, but also provide a mechanism for risk-based rankings of enterprises by their calculated risk scores. The enterprises were risk-based ranked with the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method and Paksoy approach based on interval type-2 fuzzy sets (IT2FSs). The findings indicate that the first three risk score rankings of enterprises are the same for both approaches. To examine the consistency of the applied methods, sensitivity analyses were performed. The results of the study also indicate that the proposed approaches are recommended for effective use in the mining sector due to their ease of application compared to other methods and their dynamic nature in the risk assessment process. Full article

The evaluation and management of frontier fundamental research projects for future advanced aircraft engines are challenging due to the need to balance assessing the innovative potential and technical risks with considering their long-term effects and inherent uncertainties. This study presents a comprehensive evaluation indicator system for evaluating frontier fundamental research projects for future advanced aircraft engines, integrating the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation (FCE) to balance innovative potential with technical risks. The AHP is used to determine weights for the evaluation indicator system based on a survey of technical experts. By incorporating expert ratings and weighted criteria, the FCE method synthesizes comprehensive evaluations and effectively avoids traditional scoring biases and simplistic averaging methods. A case study on a major project is conducted to demonstrate the effectiveness of the proposed method in highlighting the significant achievements and potential for innovation gaps. The results show that the AHP-FCE method proves robust in identifying complex, prospective research, providing a strategic tool for policymakers to prioritize impactful aircraft engine research and ensuring investment in projects with significant breakthrough potential. Full article

As the air–rail integration continues to emerge around the globe, the successful and maintainable implementation of such schemes can be influenced by many factors within administrative, social, infrastructural, and economic aspects. With the existing airport rail link system that shifted from air–rail integration at beginning to air–rail cooperation at present, this work aims to assess the success factors of air–rail integration development in Thailand and evaluate the partnership level required to achieve a long-term and indefinite horizon of relationship based on an ongoing airport rail link project in the country. The factor assessment results from using fuzzy analytical hierarchy process (AHP) revealed different perspectives from regulators and operators, while directing the high influence of administrative, economic, and infrastructural factors. The partnership evaluation suggested the highest level of partnership; although, the operators still express doubt whether the competitive advantages incurred from the partnership and the partnership itself would be sustainable. Full article