Search

This paper presents a hybrid Machine Learning (ML)-based feature selection algorithm to create an Intrusion Detection Systems (IDS) model in distributed computing environments such as in Internet of Things (IoT) requests. IoT applications need frequent network connections to reach either the cloud or fog resources. Since reliability and trust are very prominent attributes in distributed systems, utilizing the ML-based algorithm is vital to efficiently detect malicious/attack behaviors at the earliest time with high accuracy. To address the issue, a hybrid ML-based algorithm is presented that includes four phases. In the first phase, the pre-processing including cleansing and normalization on the relevant dataset is performed. Then, the two next phases, namely heuristic-based and meta-heuristic-based approaches, are passed for the training step. In the second phase, three effective filter-based heuristic feature rankers are utilized to sort features according to their importance. Afterward, the fuzzy TOPSIS approach is also applied to prepare a consensus among them returning top-K features. In the third phase, to optimize the consensus feature subsets, a novel Discrete Gray Wolf Optimization Algorithm (DGWA) as a meta-heuristic approach is designed. It leads to a balance in exploitation and exploration searching areas. The effectiveness of the proposed hybrid model is tested in the fourth phase on the famous NSL-KDD and UNWS-NB15 datasets against some state of the art. The simulation results of running different scenarios prove that the proposed hybrid ML-based model gives an average 10.60%, 15.85%, 3.30%, 4.39%, and 2.03% improvement in testing datasets in terms of accuracy, precision, recall, F-cost, and specificity against other comparative state of the art in the same conditions. [ABSTRACT FROM AUTHOR]

Purpose: Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being. Design/methodology/approach: A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation. Findings: Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them. Originality/value: The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry. [ABSTRACT FROM AUTHOR]

The "Zero Waste City" program is a waste management policy initiative in Depok City that has been implemented from 2016 to 2024. The primary objective of this program is to make the entire Depok City area free from all types of waste. However, the program's implementation faces challenges, leading to some areas in Depok City still struggling with waste issues. Universitas Indonesia, as a higher education institution located in Depok City, acknowledges the waste-related challenges in its surrounding environment. Therefore, the university is committed to actively participating in solving waste issues originating from its campus environment. This research aims to address waste issues at the source, particularly by applying appropriate technology based on the criteria and sub-criteria at Universitas Indonesia. This step is expected to contribute to reducing waste-related problems in Depok City and serve as an example of effective waste management from its source. Notably, Universitas Indonesia contributes to the accumulation of residual waste at TPA Cipayugn Depok, which has become increasingly concerning over time. The analysis in this study employs the Analytical Hierarchy Process (AHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) methods to evaluate suitable waste management technologies for addressing the issues of residual waste accumulation. Additionally, technoeconomic analysis is conducted to assess the feasibility of the selected technology. The project's feasibility evaluation parameters include Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit Cost Ratio (BCR). [ABSTRACT FROM AUTHOR]

Background: This study introduces a novel supply chain management (SCM) model tailored for the hospital industry in Thailand. The model emphasises the integration of third-party logistics (3PL) providers to streamline supply-processing-distribution (SPD) functions. By outsourcing non-core activities like SPD to 3PL providers, hospitals can enhance their operational efficiency, allowing healthcare professionals to focus on core tasks and ultimately improving service delivery. Methods: This research employed a dual methodology, combining an analytic hierarchy process (AHP) with a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). These approaches evaluated various SCM models based on multiple hospital logistics performance attributes. Results: The AHP results highlighted on-time delivery, patient safety, utilisation rate, and emergency procurement as critical criteria for selecting the optimal model. Fuzzy TOPSIS analysis identified the SCIII: W-G-H model as the most suitable for implementation in Thai hospitals. This model incorporates a centralised warehouse for negotiation leverage, a Group Purchasing Organisation (GPO) for cost efficiency, and regional SPD hubs for effective inventory management and rapid responses to demand fluctuations or emergencies. Conclusions: Adopting this SCM model is expected to significantly enhance supply chain performance, reduce operational costs, and improve the quality and safety of patient care in Thai hospitals. [ABSTRACT FROM AUTHOR]

Ho Chi Minh City (HCMC), Vietnam's largest urban center, is home to over 9 million people and faces significant challenges due to rapid urbanization and the heavy reliance on personal vehicles. Over 95% of urban passenger transport in HCMC relies on high-emission cars, contributing to severe air pollution and slowing progress toward the United Nations Sustainable Development Goals (SDGs). Greening HCMC's transport system is essential to reducing emissions and achieving SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Despite ongoing efforts, many barriers continue to hinder this transition. This paper aims to identify and prioritize these barriers using the fuzzy TOPSIS method. The results highlight limited infrastructure investment, a lack of charging stations, and unclear policies as the top obstacles. Other challenges include poor service quality, dependence on private vehicles, low community awareness, high fuel prices, and the cost of green vehicles. These findings offer a foundation for proposing solutions and prioritizing actions to build an eco-friendly transport system. This would directly support the SDGs related to sustainable cities, climate action, and public health. A thorough analysis of these barriers and their impact is vital to expedite and enhance the "greening" process by focusing on factors with significant and decisive influence. [ABSTRACT FROM AUTHOR]

Decision-making involves selecting the best alternative based on evaluation criteria while considering environmental impacts. The translation of environmental factors into quantifiable mathematical expressions is challenging due to the inherent uncertainties. Decision-makers can address the subjective characteristics of alternatives by incorporating fuzzy set theory into decision-making processes where uncertainty and ambiguity exist. Game theory is introduced as another approach to enhance the robustness of decision-making models, leading to more informed and flexible decision outcomes. This approach promotes strategic thinking and aids decision-making by allowing individuals to visualize the potential consequences of different decisions under various conditions. This study proposes a fuzzy multi-criteria decision support system that provides a structured framework to address the complexities of agricultural land selection. The decision support system employs a two-person zero-sum game to identify the optimal land management option, considering the strategic interactions between players. The results from the payoff matrix reveal the equilibrium point, providing an ideal solution for more effective land use planning decisions. [ABSTRACT FROM AUTHOR]

Small and Medium Enterprises (SMEs) have a crucial role in driving economic growth and innovation. In the modern business landscape characterized by uncertainty, the adoption of lean philosophy has gained prominence as a means to continuously improve SMEs' operational efficiency and competitiveness. A fuzzy assessment model was proposed as comprehensive framework and guide for SMEs during their lean journey that utilizes the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL), and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to serve many objectives; identifying the most important and influence lean dimensions on SMEs leanness; assessing the level of adoption of lean philosophy in SMEs for five essential lean dimensions, namely management, process, supplier, customer, and employee; identifying the interrelationships among them; and identifying the driving dimensions for the improvement process to focus SMEs limited resources on it in improvement process due to its direct influence on the other lean dimensions. The proposed fuzzy model was applied in Iraqi medium-sized enterprise producing healthy water, juice, and soft drinks to identify the most important and influence lean dimension on enterprise leanness, identify the driving dimension of the improvement process, and assess the level of leanness of these five lean dimensions. The results showed that the management dimension has the highest level of importance and influence on SMEs' leanness, equal to 41.31%, and is distinguished as a driving dimension that has a high influence on improving the driven dimensions of process and customer. Although the level of importance and influence of the process dimension is 19.96%, it is an effect dimension and is considered a driven dimension. Plotting the interrelationship among the five lean dimensions in the diagram shows management has an influence on all lean dimensions and, at the same time, has a mutual relationship with employees and customers. An employee has a mutual relationship with the process dimension. In addition, the results showed that enterprise has deficiency in adopting a lean philosophy, where its level of leanness equal 0.39. [ABSTRACT FROM AUTHOR]

Abstract\nIMPLICATIONS FOR REHABILITATIONObjectives: Children with intellectual disabilities struggled to perform basic tasks in the educational platform due to their deferred learning pace and individual challenges. To assist such needs, the child’s individual education program (IEP) team, spearheaded by the school administrators, continues to offer supplementary educational technologies. However, selecting an appropriate technology does not follow an objective system, making the decision process tedious and unreliable. Therefore, this paper provides a new perspective on analyzing the educational technologies for children with intellectual disabilities with respect to design factors that also constitute the technical specifications and ergonomic principles of technology design.Method: To analyze the educational technologies, fuzzy best-worst method (BWM) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) method are employed in a case study in the Philippines.Impact: Results reveal that user perception is the most important design factor to be considered when selecting educational technology for children with disabilities. Furthermore, the adaptive educational platform has been found to best fit the needs of children with autism.The paper provides priority weights to design factors involved in developing educational technologies for children with intellectual disabilities (i.e., ADHD, cerebral palsy, down syndrome, and autism). These priority weights can be utilized by school administrators, technology developers, and parents of children with intellectual disability in designing an educational technology that best fits the individual needs of these children.The suitability of the educational technologies (i.e., sensor-based AI systems, assistive AI robots, and adaptive educational platforms) is assessed according to the design factors. The paper specifically proposed a ranking of alternative educational technologies that can optimize learning and development among children with intellectual disabilities. Furthermore, such results can also overcome the learning limitations among children with intellectual disabilities.The paper provides priority weights to design factors involved in developing educational technologies for children with intellectual disabilities (i.e., ADHD, cerebral palsy, down syndrome, and autism). These priority weights can be utilized by school administrators, technology developers, and parents of children with intellectual disability in designing an educational technology that best fits the individual needs of these children.The suitability of the educational technologies (i.e., sensor-based AI systems, assistive AI robots, and adaptive educational platforms) is assessed according to the design factors. The paper specifically proposed a ranking of alternative educational technologies that can optimize learning and development among children with intellectual disabilities. Furthermore, such results can also overcome the learning limitations among children with intellectual disabilities. [ABSTRACT FROM AUTHOR]

Avoiding error in handling artifacts is crucial for achieving a high level of system reliability and safety assessment. This study develops a predictive and ranking model of use error (PRUE). In the first phase, use errors are systematically detected and anticipated via an inquiry process in two levels (activity and function). In the second phase, the fuzzy best–worst method (F-BWM) is employed to determine relative weights of three criteria including consequence of use error (CUE), detection of use error (DUE) and probability of use error (PUE). Fuzzy TOPSIS is then employed to rank use errors according to their risk level. The use errors of an infant ventilator device are assessed to demonstrate applicability of the PRUE method. The results of the present study confirm the reliability and applicability of this approach to assess artifact use errors. Moreover, the PRUE method can be utilized in investigation of user interface design. [ABSTRACT FROM AUTHOR]

People with disabilities (PWD), in their routine commutes, confront hindrances associated with road infrastructure in busy urban centers. The present study developed a user perception-based methodology to evaluate optimal routes for PWD in urban settlements in the Kingdom of Saudi Arabia (KSA). A survey captured the preferences for 105 PWD, consisting of 37 powered wheelchair users, 62 manual wheelchair users, and 6 artificial limb users. The multi-criteria decision analysis evaluated the accessibility index for PWD based on four criteria: length, number of junctions, absence of footpath, and slope. This study revealed that manual wheelchair users prefer the length criterion, powered wheelchair users emphasized the absence of footpaths, and artificial limb users were concerned about slope. The result showed that only two routes out of ten showed medium, while those remaining exhibited low accessibility. Most routes were relatively long for people with disabilities, focusing on the need for public transportation with special arrangements in most small and medium-sized cities, like the study area of Hail and Qassim province of the KSA, to reduce the distance and travel time. The proposed framework provides valuable insights to route evaluation for persons with special needs in the KSA and elsewhere. [ABSTRACT FROM AUTHOR]

With the rapid growth of green construction projects (GCPs) in Saudi Arabia, managing the associated risks has become crucial to ensuring project success and sustainability. These projects face a range of challenges, including socio-economic, environmental, and technical risks that need to be carefully identified and prioritized. This study systematically identifies and prioritizes the risks in GCP using a hybrid model combining fuzzy TOPSIS and an Emotional Artificial Neural Network (EANN). The focus of this study is on the risk management of the green construction industry in Saudi Arabia. Based on expert evaluations, low-quality materials and equipment (Likert scale mean is 4.71) and stakeholder resistance to adopting green ideas (4.67) emerged as the most critical risks. The fuzzy TOPSIS analysis assigned the highest weight to risk probability (0.174), followed by outcome (0.137), and vulnerability (0.123). The EANN refined the risk rankings, confirming the importance of these risks. The findings suggest that risk management strategies should prioritize material quality and stakeholder engagement, while environmental risks, ranked lower, are less of a concern. This hybrid model provides a robust framework for effective risk management, with practical implications for enhancing the sustainability and success of GCP. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this article is to propose and practically verify the use of the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for comparing and evaluating occupational risk management in manufacturing companies. Design/methodology/approach: The adopted approach was verified through a questionnaire survey conducted at four furniture industry companies with similar employment size, product mix and volume. The evaluation criteria were eight detailed occupational risk assessment processes that make up the risk management process. The assessments were carried out by independent experts. Verbal and scoring assessments were used. Conclusions: With the help of the ordered fuzzy TOPSIS method, the surveyed companies were ranked and the best and worst implemented occupational risk assessment process was identified. The research confirmed the usefulness of the fuzzy TOPSIS method in the area of occupational risk management. Limitations of the study: The main limitations relate to subjectivity in the selection of experts and subjective expert judgment. This work may inspire the verification of other multi-criteria decision-making methods for their suitability in the field of occupational safety, including their fuzzy counterparts. Practical implications: The presented approach can be an important tool for improving occupational risk management and, in general, occupational health and safety management in manufacturing companies. Social implications: In the area of occupational risk management, there is a lack of practically validated tools that decision-makers can use in their decision-making process for shaping safe and healthy working conditions. Originality/Value: The use of the fuzzy approach in OHS management is particularly beneficial, as it allows experts to evaluate various criteria using the most commonly used linguistic variables. The fuzzy TOPSIS method has already been used for many years to evaluate alternatives in many different areas, while the application, with ordered fuzzy numbers in the field of occupational risk management, is original. [ABSTRACT FROM AUTHOR]

With the growing number of anxiety disorder cases among university students in different countries, it is important to evaluate, compare and demonstrate different ways to obtain information about the mental health status of these students. This paper evaluates the prevalence and compares anxiety disorders in a population of university students to provide information to aid the formulation of public health policies and develop actions to support students. The study was conducted using primary data (n = 427), composed of students from two universities in Latin America. The fuzzy TOPSIS method was used to analyze the collected data, with the research instruments Generalized Anxiety Disorder -GAD-7 and the State-Trait Anxiety Inventory- STAI. The originality of this research lies in the fact that it is the first article in the literature to use the proposed methodology to analyze anxiety disorders in university students in Latin America countries. The data were adapted for cloud analysis, particularly the multicriteria method, which contributes to public health studies. Together with the instruments, the method generated insights that corroborate the World Health Organization (WHO) data. This study may assist public policymakers in providing greater support for citizens with anxiety disorders. It is hoped that the results of this study can assist managers in public health decision making by proposing public policies that mitigate anxiety disorders that affect thousands of university students worldwide. [ABSTRACT FROM AUTHOR]

Aluminum parts are used in the aviation industry because of the need for light. However, in addition to lightness, critical parts that must have high strength properties have also been developed. The corrosion resistance, resistance to high temperatures, and workability were investigated in this case. It becomes difficult to choose among many aluminum materials that can be alternatives to each other when these features are included. The developed approach, which considers many of the features listed above and ultimately recommends to the user the most suitable aluminum material for the relevant critical part, will be used in overcoming the difficulties in this process. A material selection model is proposed in this paper for this purpose, and the decision-making model is demonstrated with examples from the aviation industry. Therefore, the developed model, which will enable the selection of the most suitable materials among alternative materials, especially for critical parts in the aviation industry, will guide professionals working in this field. For this purpose, the fuzzy TOPSIS method is used in the study, and suitable alternatives are determined. Finally, a robustness analysis is proposed to determine the most suitable aluminum material for highly uncertain situations. We apply a stability evaluation study based on process control theory in the robustness analysis. [ABSTRACT FROM AUTHOR]

Prototyping with incremental sheet forming is becoming the newest trend in the manufacturing industry due to its flexibility and low cost for generating customized components based on client needs. The main goal of this paper is to minimize the manufacturing time, surface roughness, and forming force requirement in the incremental forming process by optimizing the process parameters. The incremental forming experiments were performed on a CNC milling machine as per Taguchi L9 (33) orthogonal array by varying the tool diameter, step depth and wall angle in three levels. The optimization of the selected performance characteristics has been performed by the application of integrated fuzzy analytic hierarchy process and fuzzy technique for order of preference by similarity to ideal solution methods. Finally, analysis of variance has been used to understand the effect of process parameters on the individual response variables. The result shows that step depth was a highly significant parameter with the highest contribution of 70.31% followed by wall angle at 20.71%. Tool diameter is the least significant parameter with an 8.97% contribution. The effectiveness of the suggested technique was further verified by performing the confirmation experiment with optimum parameter. [ABSTRACT FROM AUTHOR]

Abstract This study evaluates sustainable water management strategies using TOPSIS and Fuzzy TOPSIS (FTOPSIS) to address global water scarcity by comparing rainwater harvesting, water recycling, and desalination across five criteria: water efficiency, cost-effectiveness, environmental impact, social equity, and technological feasibility. The results show Rainwater Harvesting as the most balanced option with a relative closeness value of $${C}_{i}^{+}$$ C i + =0.640, excelling in social equity and environmental sustainability. Water Recycling ranks closely behind ( $${C}_{i}^{+}$$ C i + =0.608), highlighting its adaptability and technological feasibility, while Desalination, though highly efficient, is hindered by lower cost-effectiveness ( $${C}_{i}^{+}$$ C i + =0.578). By integrating TOPSIS and FTOPSIS, the study addresses uncertainties and subjective criteria, providing a robust multi-dimensional assessment framework for resource management. This methodology aids decision-makers in identifying strategies that align with sustainable development goals and adapt to regional priorities. Future work can expand this framework to include stakeholder engagement and policy factors, enhancing water management strategies for resilient, long-term solutions.

Abstract Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers’ behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Abstract The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The ‘cleaning’ factor has the highest weight, and ‘updating’ is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

Abstract The literature has explored various methods for assessing minimum environmental flow. Implementing holistic approaches proves to be prohibitively expensive and impractical for many small and medium projects. Hence, desktop and cost-effective methods are commonly employed without an integrated decision-making system to justify the assessed values. This study introduces a systematic decision-making framework aimed at selecting the most suitable method for assessing the actual needs of river habitats. Employing a fuzzy technique known as the Order Preference Similarity to the Ideal Solution (FTOPSIS), the study considers factors such as physical, thermal, and dissolved oxygen habitat suitability, maximum habitat area, and water demand loss function to determine the most appropriate method among established ones, including the Tennant method, flow duration curve analysis method, wetted perimeter method, and physical habitat simulation method. The results prioritize physical habitat simulation, wetted perimeter by slope method, and flow indices of 70%, 75%, and 80% by flow duration curve analysis method as the optimal approaches for assessing minimum environmental flow. This proposed decision-making system offers a viable platform to explore the applicability of existing cost-effective methods for assessing minimum environmental flow. It also serves as an effective mechanism for reducing negotiations among stakeholders by comprehensively considering all relevant aspects in the environmental management of river ecosystem requirements.

Abstract This study addresses the growing anxiety and depression among Chinese university students by evaluating and ranking music education strategies to alleviate these issues. We integrates Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FAHP was utilized to determine the weight of factors such as academic pressures, social relationships, and cultural norms, while fuzzy TOPSIS ranked the effectiveness of music education interventions based on these weights. The results revealed that ‘Mental health stigma’ and ‘Academic Pressures and Rigidity’ are among the highest weighted factors, significantly impacting student anxiety. ‘Music Appreciation and Music-Based Self-Care’ emerged as the most effective strategy. These results highlight the importance of direct involvement in music-related activities for improving student mental health.

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers' behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing C O 2 emissions in Romania from 1990 to 2023 using the Autoregressive Distributed Lag (ARDL) model. Before the ARDL model, we identified a set of six policies that were ranked using Fuzzy Electre, Topsis, DEMATEL, and Vikor. The multi-criteria decision-making (MCDM) methods have highlighted the importance of a circular policy on C O 2 emission reduction, which should be a central focus for policymakers. The results of the ARDL model indicate that, in the long term, renewable energy production reduces C O 2 emissions, showing a negative relationship. Conversely, an increase in patent applications and urbanization contributes to higher C O 2 emissions, reflecting a positive impact. In total, five key factors were analyzed: C O 2 emissions per capita, patent applications, gross domestic product, share of energy production from renewables, and urbanization. Notably, GDP does not significantly explain C O 2 emissions in the long run, suggesting that economic growth alone is not a direct driver of C O 2 emission levels in Romania. This decoupling might result from improvements in energy efficiency, shifts towards less carbon-intensive industries, and the increased adoption of renewable energy sources. Romania has implemented effective environmental regulations and policies that mitigate the impact of economic growth on C O 2 emissions. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The construction industry has recognized the potential benefits of Lean methodology for improving project outcomes. However, the full utilization of Lean principles remains challenging in the industry. This study aims to address this issue by exploring integrating Building Information Modeling (BIM) into Lean Construction practices to enhance project success. The concept of success in construction projects encompasses various dimensions, including sustainability, time efficiency, costeffectiveness, and budget adherence. This study focuses on measuring success through the lens of sustainability, emphasizing the need to make construction projects more sustainable and effective. To achieve this, a survey was conducted to collect data on sustainable factors that influence project success. The research analysis focused on a selected company's capacity to utilize its existing BIM software to improve critical sustainable factors. The sustainable factors identified as crucial for project success were the relationship with subcontractors, site cleanliness and tidiness, safety, and solid waste production. Projects can enhance their overall success and sustainability by effectively managing these factors. To evaluate different BIM software options, a comprehensive review of available software was undertaken based on their ability to address the identified sustainable factors. The Fuzzy Weighted LC-BIM TOPSIS was proposed as the evaluation method to determine the best BIM software that could maximize project success. The findings of this study highlight the importance of considering sustainable factors in construction projects and leveraging BIM software to manage them effectively. Integrating BIM into Lean Construction practices can improve project success by enhancing collaboration, streamlining processes, and optimizing resource allocation. The implications of this study suggest that construction industry stakeholders should prioritize adopting lean construction principles and leveraging BIM as a valuable tool to enhance project success. Integrating BIM and Lean methodologies can drive sustainable practices, optimize project performance, and contribute to the long-term success of the construction industry. [ABSTRACT FROM AUTHOR]

Noise pollution in hospitals has increased over the last few years to a level that can threaten the health and productivity of staff and patient safety. There are many control measures to reduce hospital noise. However, there is still no consensus on the best measures. This study aims to prioritize the control measures for reducing hospital noise. The work is divided into three phases. The first phase identifies and categorizes noise sources in hospitals through a review of the state-of-the art literature using Scopus®, ProQuest, PubMed, Google Scholar, Embase,™ and Web of Science™. The second phase identifies possible strategies for reduction of hospital noise and the best criteria for their adoption using findings from the literature review and interviews with corresponding experts. The third phase uses Fuzzy Analytic Hierarchy Process (FAHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) method to weigh the criteria and to prioritize the control measures. Based on the results, hospital noise sources were classified into four groups: outdoor noise sources (29.7%), noise produced by domestic facilities (20.8%), indoor noise from human activities (27.5%), and noise produced by diagnostic and treatment equipment (22%). The study further arrives at a set of 9 criteria and 22 alternatives ranked using FAHP and fuzzy TOPSIS. The criteria's weights were determined using the FAHP method, with feasibility (0.175), effectiveness (0.143), and interference with staff activities (0.140) being the most important criteria. It was found that engineering controls such as substitution of noisy equipment (rank = 1), using acoustic enclosures (rank = 2), using double-glazed windows (rank = 2), and soundproofing walls, doors, and windows (rank = 3) have priority for reducing hospital noise. [ABSTRACT FROM AUTHOR]

Pilot recruitment is critical as they pose a multifaceted challenge for civilian and military organizations due to the complex traits impacting their missions and performance. In this study, a novel set of criteria and sub‐criteria were determined to compare twelve candidate pilots. Numerically immeasurable, imprecise, and non‐linear continuous fuzzy linguistic traits (variables) were studied which make the work unique and challenging due to individual preferences and disagreements between decision‐makers (DMs). The outcomes of three distinct fuzzy multiple criteria decision‐making (MCDM) approaches; fuzzy TOPSIS, fuzzy VIKOR, and fuzzy PROMETHEE were evaluated with trapezoidal fuzzy numbers (TFNs) to sort the positions of candidate pilots. Moreover, a unique defuzzification ranking method was employed to adjust the results of fuzzy MCDM methods for the synthesis and evaluation of outcomes of the pilot selection problem. All these efforts make the paper original and outstanding. Our findings and analysis suggested that fuzzy TOPSIS and PROMETHEE methods' outcomes showed maximum close similarity for ranking positions. However, substantial distinctions were noted when comparing these outcomes with the fuzzy VIKOR approach. Yet, the mission of predicting and revealing the best candidates is related to several traits, their weights, and the methods selected. Therefore, since vague information and ambiguous preferences match fuzzy superiority, a comprehensive and unbiased evaluation was achieved, ensuring the integrity of the decision‐making process. The results can be employed to enhance the safety and efficiency of airline operations and ensure that the most qualified and competent pilots are selected for the job. [ABSTRACT FROM AUTHOR]

Every new storage tank in the oil sector is required to be calibrated before using them for oil storage and also to be re-calibrated as statutorily required. Depending on the prevailing regulatory guidelines in the country, either a wet or geometrical method of calibration is adopted. This study examines various geometrical methods of tank calibration vis–a–vis their strengths and weaknesses. Tank farm owners (operators) are always faced with the challenge of selecting the best geometrical method of tank calibration while considering some number of factors. To address this aforementioned issue, this study was embarked upon to rank the known four (4) geometrical methods of tank calibration using Fuzzy TOPSIS (F-TOPSIS) approach. Three different experts were drawn from reputable calibration companies to respond to the questionnaire based on the following criteria: Accuracy; Hazard involved; Time consumed; Drudgery involved; and Cost. The interdependencies among the criteria were considered, and a triangular fuzzy set was adopted. The results revealed that the Electro-Optical Distance Ranging (EODR) is the best alternative with a closeness coefficient of 0.974, while the Optical Reference Line Method was ranked least with a closeness coefficient of 0.197. To validate the result of rating by F-TOPSIS, another hybrid MCDM, Fuzzy Analytic Hierarchy Process (FAHP) was used to rank the alternatives, and EODR was also ranked as the best alternative. Sensitivity analysis was carried out for five different scenarios to validate the robustness of the decision-making tool used in this study. All the scenarios considered for the sensitivity analysis ranked EODR and OTM (Optical Triangulation Method) first and second, respectively. So, it can be concluded that EODR is the best geometrical method of tank calibration. Though the cost of using EODR might be higher than other methods, this is being compensated for by higher accuracy, less time with less exposure to hazards. It can also be confirmed that F-TOPSIS is a formidable MCDM tool that finds its usage in every facet of life for a robust decision-making process. [ABSTRACT FROM AUTHOR]

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The 'cleaning' factor has the highest weight, and 'updating' is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals. [ABSTRACT FROM AUTHOR]

Prioritizing Lean Six Sigma (LSS) projects that align with company objectives is crucial, yet traditional methods struggle with associated subjective criteria. This study proposed a hybrid Best Worst Method - Fuzzy TOPSIS approach to prioritize LSS projects for a project consulting company. The method integrates expert opinion from 3 decision-makers on 7 main criteria and 24 sub-criteria to select the optimal LSS projects in project management consulting company. Triangular fuzzy numbers were used to describe the responses. The fuzzy positive and negative solutions of the five alternatives were calculated. Results indicate project alternative 3 (ERP Deployment Project) is the optimal choice with the highest closeness coefficient (0.68651), while project alternative 2 (Warehouse Automation Project - 0.54077), project 1 (Data Warehousing Project -- 0.46731), project 4 (Battery life improvement -- 0.54077), and project 5 (Improvement of OEE -- 0.34093) follow closely, thus ensuring efficient project selection. Emphasis should be placed on project 3 when considering the 7 criteria while the other projects are closely monitored in the ranking order. Future research can explore the combination of other multi-criterion decision making approaches that enrich criteria weights and address the subjectivity of decision-makers' opinion. The hybrid methodology used in this work is applicable in other disciplines where selection and ranking problems exists. [ABSTRACT FROM AUTHOR]

Previous studies in the literature have shown that Digital Marketing (DM) can be a factor influencing competitiveness and the performance of marketing actions so that a company can correctly reach the target audience. From this literary assumption, it is imperative to evaluate tools that can help marketing managers in decision-making to select the most social media platform to use the DM. In this context, this article aims to analyze the performance of social media platforms for using the strategies DM in organizations. from the point of view of experts from different organizations that use DM for marketing actions. To provide empirical evidence, the selection of criteria was based on the eight dimensions of the DM mix to the lens of multi-criteria decision-making based on the inter-criteria correlation method (CRITIC). This method adhered to the order of preference by similarity technique to find an ideal solution with the help of the Fuzzy TOPSIS environment. The results discovered corroborate the previous literature and stand out, mainly by showing that the main criterion with greater weight among the eight analyzed dimensions was promotion (Cr5). Based on this finding, these results indicate that the best performing alternative for DM actions for the specific case was the A1-Facebook platform. This article brings reflections, and presents a robust tool of quantitative analysis in addition to bringing theoretical and managerial contributions that can direct DM strategies in the consumer's journey through social media platforms. [ABSTRACT FROM AUTHOR]

This study addresses the growing anxiety and depression among Chinese university students by evaluating and ranking music education strategies to alleviate these issues. We integrates Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FAHP was utilized to determine the weight of factors such as academic pressures, social relationships, and cultural norms, while fuzzy TOPSIS ranked the effectiveness of music education interventions based on these weights. The results revealed that 'Mental health stigma' and 'Academic Pressures and Rigidity' are among the highest weighted factors, significantly impacting student anxiety. 'Music Appreciation and Music-Based Self-Care' emerged as the most effective strategy. These results highlight the importance of direct involvement in music-related activities for improving student mental health. [ABSTRACT FROM AUTHOR]

The literature has explored various methods for assessing minimum environmental flow. Implementing holistic approaches proves to be prohibitively expensive and impractical for many small and medium projects. Hence, desktop and cost-effective methods are commonly employed without an integrated decision-making system to justify the assessed values. This study introduces a systematic decision-making framework aimed at selecting the most suitable method for assessing the actual needs of river habitats. Employing a fuzzy technique known as the Order Preference Similarity to the Ideal Solution (FTOPSIS), the study considers factors such as physical, thermal, and dissolved oxygen habitat suitability, maximum habitat area, and water demand loss function to determine the most appropriate method among established ones, including the Tennant method, flow duration curve analysis method, wetted perimeter method, and physical habitat simulation method. The results prioritize physical habitat simulation, wetted perimeter by slope method, and flow indices of 70%, 75%, and 80% by flow duration curve analysis method as the optimal approaches for assessing minimum environmental flow. This proposed decision-making system offers a viable platform to explore the applicability of existing cost-effective methods for assessing minimum environmental flow. It also serves as an effective mechanism for reducing negotiations among stakeholders by comprehensively considering all relevant aspects in the environmental management of river ecosystem requirements. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To maximize its synergetic effect across the cycle from prevention to response to post-crime management, crime prevention requires a balanced combination of spatial urban design and advanced crime prevention technologies for crime prediction and real-time response. This study derived intelligent Crime Prevention Through Environmental Design (CPTED) services and suggested a decision model based on the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to implement these services in municipal control centers. The analysis results are summarized as follows. First, this study established a fuzzy TOPSIS-based decision-making support model enabling local government control centers to effectively select intelligent CPTED service elements. Second, overall, operator-led Closed-Circuit Television (CCTV) and platform control technologies were identified as significant components of intelligent CPTED service elements. Third, a comparison by city size revealed that large cities in the Seoul metropolitan area rated system services for control based on advanced crime prevention infrastructure (e.g., the crime monitoring systems and real-time control drones/robots) relatively higher. In contrast, small and medium-sized cities in other provinces rated services that were perceptible to residents and improved crime-prone environments (e.g., artificial intelligence (AI) video analysis for living safety) relatively higher. [ABSTRACT FROM AUTHOR]

Heat pipes have the potential to benefit from nanofluid flow between coaxial cylinders. Heat is effectively transferred from one place to another by means of heat pipes. Heat pipes can be used for electronics cooling, spacecraft thermal management, and heat recovery systems by adding nanofluids, which enhances the heat pipe's thermal conductivity and heat transfer capability. This work aims to discover an approximate solution for the flow of a trihybrid nanofluid (THNF) consisting of graphene, copper, and silver between two coaxial cylinders in magneto-hydrodynamics, taking into account the broad variety of applications. The nanomaterial is tested in a system with a fixed inner cylinder and a rotating outer cylinder. It contains graphene, copper, silver, and kerosene oil as the base fluid. For examining the flow characteristics, magnetic field is applied along radial direction of the cylinder, while inner cylinder is fixed, and outer cylinder is rotating. Moreover, temperature of the outer cylinder is higher than the lower cylinder. The objective of this study is to develop a mathematical model of the problem and solve the governing equation numerically using the MATLAB built-in routine called bvp4c. Additionally, we identify the most effective physical parameter to optimize the heat transfer rate using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Using a variety of factors, we calculate fluid velocity, skin friction, temperature, and Nusselt number graphically. According to the study, higher Brinkman numbers (Br) and magnetic parameter (M) characteristics lead to higher temperatures. Furthermore, Fuzzy TOPSIS shows that alternative A11 (ϕ=(0.9,1.0,1.0),M=(0.5,0.7,0.9),Br=(0.9,1.0,1.0)) has the maximum heat transfer rate, while another A8 (ϕ=(0,0,0.1),M=(0,0,0.1),Br=(0,0,0.1)) has the lowest.

The emergence of Industry 4.0 (I4.0) is reshaping industries worldwide, driven by rapid technological progress and the need for open innovation. This study focuses on understanding the interdependencies of driving factors of I4.0 maturity in developing countries using Fuzzy Multi-Criteria Decision Analysis (MCDA) methods. By analyzing Indonesia, Pakistan, and Venezuela, the research aims to foster open innovation and address the unique challenges these nations face in adopting I4.0 technologies. I4.0 maturity models are essential for evaluating current maturity levels and identifying areas for improvement. However, the complexity and interdependence of various factors—ranging from data science and technology to policy, governance, and open innovation dynamics, such as social open innovation and the role of SMEs—complicate this process. This study employs Fuzzy TOPSIS and Fuzzy DEMATEL to identify the critical factors influencing I4.0 maturity and analyze their interdependencies and prioritization. The results indicate that 'Data and Information' and 'Willingness to Change' are crucial across all countries, while strategic differences between large enterprises and SMEs highlight the need for tailored approaches. This research highlights the importance of continuous IT investment, digital leadership, collaborative ecosystems, and agile strategies in fostering open innovation and driving I4.0 adoption. This research contributes to the theoretical and practical understanding of I4.0 maturity, offering valuable insights for practitioners and academics to explore the dynamic interactions of I4.0 factors and their impact on operational efficiency.

This study investigates the extent to which circular economy (CE) practices are adopted within supply chains in the Brazilian context. An expert survey was conducted to assess eight critical factors for the integration of CE practices in supply chains. The collected data was analyzed using a modified version of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), specifically the Fuzzy TOPSIS class. Sensitivity analyses were performed to validate the robustness of the results. The findings indicated that practices related to economic and risk management, cleaner production, top management support, and knowledge management were categorized as having medium maturity. Conversely, practices associated with governance, product development, infrastructure, and social and cultural changes are still in an early stage. This research provides insights for organizations by identifying areas of strength and areas requiring attention in the adoption of CE practices within Brazilian supply chains. Policymakers can use the findings to design targeted policies and incentives that encourage and facilitate the development of circularity in supply chains, thus promoting CE principles. This research highlights the importance of addressing specific factors related to CE practices within supply chains, providing a framework for further investigations and strategies to bridge the gap between CE and supply chain management. The findings suggest that there are social and cultural challenges associated with the adoption of CE practices, implying a need for awareness campaigns, education, and community involvement to drive positive social change and adoption of CE principles. [ABSTRACT FROM AUTHOR]

Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities. [ABSTRACT FROM AUTHOR]