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Artificial intelligence (AI) is considered a mechanism that can improve supply chain resilience. Organisations around the world are investing in implementing AI systems to improve their supply chain and become more resilient to pandemics and disruption. At the same time, practitioners are not fully aware of the factors that impact the implementation of these systems. Alongside this, the extant literature lacks a comprehensive study that evaluates the enablers impacting the implementation of AI in production systems. This research fills this gap by identifying, defining, and evaluating the critical enablers influencing the adoption and implementation of AI in production systems. We extracted twelve enablers, created a conceptual model, and categorised the enablers based on the Technology, Organization, and Environment (TOE) framework. After categorisation, we used the analytical hierarchy process to assess the importance of the enablers presented in the model using data collected from eight experts. The results revealed that technology, as a category, is more crucial than organisation or environment. The findings also indicated that project management is the most critical of all twelve enablers. We discuss the implications of the analysis for practitioners and researchers. We also offer twelve propositions that researchers can empirically assess in future studies. [ABSTRACT FROM AUTHOR]

The ever-happening disruptive events interrupt the operationalisation of manufacturing organisations resulting in stalling the production flow and depleting societies with products. Advancements in cutting-edge technologies, viz. blockchain, artificial intelligence, virtual reality, digital twin, etc. have attracted the practitioners' attention to overcome such saddled conditions. This study attempts to explore the role of artificial intelligence (AI) in building the resilience of production function at manufacturing organisations during a COVID-19 pandemic. In this regard, a decision support system comprising an integrated voting analytical hierarchy process (VAHP) and Bayesian network (BN) method is developed. Initially, through a comprehensive literature review, the critical success factors (CSFs) for implementing AI are determined. Further, using a multi-criteria decision-making (MCDM) based VAHP, CSFs are prioritised to determine the prominent ones. Finally, the machine learning based BN method is adopted to predict and understand the influential CSFs that help achieve the highest production resilience. The present research is one of the early attempts to know the essence of AI and bridge the interplay between AI and production resilience during COVID-19. This study can support academicians, practitioners, and decision-makers in assessing the AI adoption in manufacturing organisations and evaluate the impact of different CSFs of AI on production resilience. [ABSTRACT FROM AUTHOR]

Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework. [ABSTRACT FROM AUTHOR]

The pulp and paper industry has significant sustainability implications and necessarily requires a careful consideration of direct and lower-tier suppliers for effective sustainable supply chain management. This paper utilized an extensive literature review and semi-structured interviews of experts in the Chinese pulp and paper industry to unearth the factors that highly influence multi-tier supply chain sustainability. A Technological-Organizational-Environmental (TOE) and Human-Organizational-Technological (HOT-fit) based theoretical framework was employed to classify the identified factors. Then, an Analytical Hierarchy Process (AHP) based methodology was applied to determine the relative importance of the factors. A comparison analysis of the relative importance of the factors as determined by the experts in the focal companies, Tier-1 suppliers and Tier-2 suppliers of the Chinese pulp and paper industry is presented. The results show that institutional and technological factors are most critical to actualizing multi-tier supply chain sustainability. Thus, the study outcomes present relevant theoretical and practical implications for the managers and practitioners in the pulp and paper industry on how to facilitate multi-tier supply chain sustainability for increased competitiveness. Furthermore, this study provides guidelines for other industries as well and sets the stage for subsequent theorization and exploration of multi-tier supply chain sustainability for expected performance gains. [ABSTRACT FROM AUTHOR]

As an innovative form of digital online education, educational games have potentially significant impacts on preschool children's enlightenment education. Identifying key factors for effective design is crucial in game development. This study identifies key factors for the design of educational games aimed at preschool children's enlightenment through decision analysis methods. The specific research steps include using SET analysis method to identify gaps in development opportunities, employing the Analytic Hierarchy Process to construct an interactive system design model for enlightenment educational games, calculating the weight value of each factor, and pinpointing the key factors for design practice. Based on this methodology, an enlightenment educational game has been developed, and Fuzzy Comprehensive Evaluation method has been utilized to assess the practical implementation. The approach outlined in this study enables designers to swiftly recognize the critical elements in designing educational games for preschool children's enlightenment, facilitating a responsive design process, achieving effective game designs, and ultimately enhancing preschool children's learning interest and outcomes. [ABSTRACT FROM AUTHOR]

Spatial evaluation of the region is associated with the assessment of the Quality of Life (QoL). Despite numerous research endeavoring to define, measure, quantify, and map the quality of life, there exists a consistent fault in Sri Lanka. Hence, the objective of this study was to construct a QoL index and determine the spatial disparities of QoL from the Polpitigma town to its periphery. The assessment was conducted by employing 20 geographical factors that quantify QoL using the Geographic Information Systems (GIS). The evaluation assigned weights to each criterion based on the assessments of both local residents and experts, utilizing the Multi-Criteria Decision Analysis (MCDA) and the Analytical Hierarchy Process (AHP). The findings indicated that cultural factors made a greater contribution compared to the environment,service functions,security and socioeconomic factors. Within the study area, the region with a higher quality of life (HQoL) only covered 4.5% (17.3 km2), whilst the lower QoL zone encompassed 63.8% (252 km2). And also, the distance from the town is a crucial factor in determining the spatial variations in QoL. The derived model can serve as a road map for local-level planning, as it has been validated and shown to have an accuracy of 74% through the Receiver operating characteristic (ROC) curve. Considering the lack of previous research in this field, this study offers a crucial contribution in enhancing the QoL for underprivileged communities in the study area by improving employment, income, and accessibility to physical infrastructure, public utility services, and cultural and recreational facilities. Especially the findings of this study can efficiently guide decisions for the distribution of financial resources to enhance the QoL in impoverished rural communities on the rural periphery of DS. [ABSTRACT FROM AUTHOR]

Toppling deformation can be classified into deep toppling (DT) and shallow toppling (ST) based on deformation mechanisms and development depth of rock mass under different soft and hard rock conditions. Currently, the toppling zoning indicators and quantitative criteria are not uniform, and human factors have a significant influence on the toppling zoning indicators. Summerizing and analyzing the existing toppling cases and toppling zoning researches, this study selects rock layer toppled angle, maximum tension within layer, unit tension within layer, and longitudinal wave velocity as indicators for toppling zoning. Considering the differences in the characteristics of deep toppling (DT) and shallow toppling (ST), the quantitative criteria for the deep and shallow toppling zoning indicators are determined respectively. This study employs the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method to establish toppling zoning evaluation models. The deep toppling dam site slope at Miaowei hydropower station and the shallow toppling bank slope of Xingguang Π formation at Xiluodu Hydropower Station were tested, respectively. These results are compared with toppling zoning of field surveys to verify the rationality and applicability of the models. This achievement holds significant reference value for the toppling zoning of rock masses in engineering slopes, especially in the construction, development, and engineering management of toppling slopes. [ABSTRACT FROM AUTHOR]

Global literature highlights risks in the seafood trade and suggests mitigation methods, but these issues are often overlooked in developing countries, particularly in Pakistan, due to ineffective policy implementation. This underlines the urgent need for a thorough investigation into Pakistan's seafood trade to address its multifaceted risks and revive this agricultural sector. This study is notable for being the first to explore these uncharted risks in Pakistan's seafood trade, which can help achieve the sustainable development goals (SDGs) of the United Nations, particularly SDG 14 (life underwater) and SDG 2 (zero hunger). Primary data were gathered from 626 respondents using snowball sampling and structured questionnaires from July 13, 2023, to December 27, 2023. The study used multi-criteria decision analysis, including fuzzy Analytic Hierarchy Process (AHP) and Importance Performance Analysis (IPA), and multivariate analysis, comprising Analysis of Moment Structures (AMOS), to analyze the data. The findings revealed that 'environmental risks' were the most significant, followed by 'infrastructure and logistic risks'. The biggest subrisk identified for managerial focus includes overfishing. Controlling overfishing is critical for ensuring marine conservation and reviving the seafood trade. Several sub-risks, like seafood prices, marketing strategies, consumer preferences, and tastes, are critical but never addressed in the regulations. Furthermore, risk perception mediates the relationship between risk management and risk performance. The survey respondents reported low-risk perception and inadequate management measures. Besides, this study expounds on ramifications, shortcomings, and areas for further research. [ABSTRACT FROM AUTHOR]

This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation. [ABSTRACT FROM AUTHOR]

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed. [ABSTRACT FROM AUTHOR]

A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future. [ABSTRACT FROM AUTHOR]

Industrial development has led to an increase in demand for friction materials, resulting in increasingly prominent environmental issues throughout their entire life cycle, and developing green and eco‐friendly brake friction materials has become a focus in the braking field. This paper explores the mechanical and tribological properties of fly ash (industrial waste) and sisal fiber (natural fiber) as eco‐friendly components to enhance brake friction materials. Four samples containing different ratios of fly ash and sisal fiber were prepared and tested with a variable‐speed friction testing machine. The results show that the eco‐friendly alternative combination can effectively improve the friction coefficient, and reduce friction fluctuations and thermal degradation, but the wear rate will also increase accordingly. In addition, the worn morphology reveals the formation of the contact platform and wear mechanism. A hybrid integration of analytic hierarchy process (AHP) and multi‐objective optimization by ratio analysis (MOORA) was used to weigh various evaluation indicators objectively and rank the samples. The sample with 8% fly ash and 6% sisal fiber exhibited the best comprehensive tribological performance. Highlights: Development of eco‐friendly brake friction composites with natural and waste additives.Investigation of the effect of the natural wastes' ratio on prepared composites' physical, mechanical, and tribological properties.Evaluation of brake friction composites with AHP‐MOORA.Sample S6F8 exhibits the best comprehensive tribological performance. [ABSTRACT FROM AUTHOR]

Rumors are unverified pieces of information whose veracity status is unknown at the time of circulation. Preventing them at an early stage helps to mitigate the consequential loss and reduces the cost of recovering the nodes affected by rumors in the social network. However, existing research focuses more on rumor detection and control than on rumor prevention. In this article, we propose a Multi-Criteria Decision Making (MCDM) integrated approach for rumor prevention that addresses three issues. Firstly, the need for a rumor prevention model to debunk them at an early stage. Secondly, introduction of counter-rumor in the system as soon as a rumor is detected to reduce the time lag between rumor diffusion and counter-rumor diffusion. Thirdly, selection of trusted authorities as key nodes to initiate the counter-rumor diffusion. The proposed method is two-fold. Firstly, we propose Analytic Hierarchy Process - Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) method, an MCDM-based approach, to select the important nodes and host agents on them. Secondly, using agent nodes, we propose a counter-rumor diffusion model Susceptible-Infected-Recovered-Prevented-Agent (SIRPA) which is an improved variant of the popular epidemic model Susceptible-Infected-Recovered (SIR). The proposed SIRPA model is simulated on six social network datasets and multiple experiments are performed on these networks to validate the proposed work. The results show that our proposed SIRPA model is effective in preventing rumors in social networks and outperforms the baseline model. [ABSTRACT FROM AUTHOR]

Introduction: In the modern competitive landscape of football, clubs are increasingly leveraging data-driven decision-making to strengthen their commercial positions, particularly against rival clubs. The strategic allocation of resources to attract and retain profitable fans who exhibit long-term loyalty is crucial for advancing a club's marketing efforts. While the Recency, Frequency, and Monetary (RFM) customer segmentation technique has seen widespread application in various industries for predicting customer behavior, its adoption within the football industry remains underexplored. This study aims to address this gap by introducing an adjusted RFM approach, enhanced with the Analytic Hierarchy Process (AHP) and unsupervised machine learning, to effectively segment football fans based on Customer Lifetime Value (CLV). Methods: This research employs a novel weighted RFM method where the significance of each RFM component is quantified using the AHP method. The study utilizes a dataset comprising 500,591 anonymized merchandising transactions from Amsterdamsche Football Club Ajax (AFC Ajax). The derived weights for the RFM variables are 0.409 for Monetary, 0.343 for Frequency, and 0.248 for Recency. These weights are then integrated into a clustering framework using unsupervised machine learning algorithms to segment fans based on their weighted RFM values. The simple weighted sum approach is subsequently applied to estimate the CLV ranking for each fan, enabling the identification of distinct fan segments. Results: The analysis reveals eight distinct fan clusters, each characterized by unique behaviors and value contributions: The Golden Fans (clusters 1 and 2) exhibit the most favourable scores across the recency, frequency, and monetary metrics, making them relatively the most valuable. They are critical to the club's profitability and should be rewarded through loyalty programs and exclusive services. The Promising segment (cluster 3) shows potential to ascend to Golden Fan status with increased spending. Targeted marketing campaigns and incentives can stimulate this transition. The Needs Attention segment (cluster 4) are formerly loyal fans whose engagement has diminished. Re-engagement strategies are vital to prevent further churn. The New Fans segment (clusters 5 and 6) are fans who have recently transacted and show potential for growth with proper engagement and personalized offerings. Lastly, the Churned/Low Value segment (clusters 7 and 8) are fans who relatively contribute the least and may require price incentives to potentially re-engage, though they hold relatively lower priority compared to other segments. Discussion: The findings validate the proposed method's utility through its application to AFC Ajax's Customer Relationship Management (CRM) data and provides a robust framework for fan segmentation in the football industry. The approach offers actionable insights that can significantly enhance marketing strategies by identifying and prioritizing high-value segments based on the club's preferences and requirements. By maintaining the loyalty of Golden Fans and nurturing the Promising segment, football clubs can achieve substantial gains in profitability and fan engagement. Additionally, the study underscores the necessity of re-engaging formerly loyal fans and fostering new fans' growth to enable long-term commercial success. This methodology not only aims to bridge a research gap, but also equips marketing practitioners with data-driven tools for effective and efficient customer segmentation in the football industry. [ABSTRACT FROM AUTHOR]

Introduction: Rural domestic sewage treatment is an important starting point to improve the quality of the rural ecological environment, an important part of new rural construction, and an inherent requirement to promote rural economic development. The operation of rural sewage treatment facilities is not good, and there is a lack of long-term operation guarantees and supervision mechanisms. It is urgent to carry out research on the evaluation index system, evaluation method, and evaluation benchmark of the operational effectiveness of rural sewage treatment facilities. Methods: This article used rural sewage treatment facilities in a city in northern China as the research object and constructed an evaluation method for the operational effectiveness of rural sewage treatment facilities. This study selected evaluation indexes from three perspectives, namely, economy, technology, and management, which are divided into two stages, namely, planning and operation. Ajudgment matrix was constructed using the analytic hierarchy process (AHP), and index weights were calculated using Yaahp10.3 software to determine the evaluation criteria. Fifteen rural sewage treatment plant stations were selected to evaluate their planning and operation effectiveness. Results: The results of the weight assignment show that the weight of the COD removal rate, operating load rate, and operating cost indexes are high, which is in line with the actual evaluation of the effectiveness of rural sewage treatment facilities at different stages. The empirical calculation results showed that the rural sewage treatment facilities have a comprehensive score of more than 80 points in 7 cases and 60-80 points in 8 cases, with an average score of 79.05 points; the overall performance of the score in the operation stage was better than that in the planning stage, and the overall operation effect was good. Discussion: The calculation results were consistent with the actual operation, verifying the scientific nature and availability of the selected indices, the evaluation method constructed, and the evaluation benchmark determined. The research results can provide technical methods for evaluating the operational effectiveness of rural sewage treatment facilities in similar areas and provide technical support for the planning, design, optimization, upgrading, and transformation of rural sewage treatment plants. [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]

With the outbreak and continued spread of the COVID-19 pandemic, people's demand for daily disinfection products has increased rapidly, and its innovative design has received widespread attention. In this context, this study aims to propose a design methodology for home entrance disinfection devices based on AHP-FAST-FBS. Firstly, the design requirements of the home entrance disinfection device were collected and analyzed through in-depth interviews and the KJ method, and a hierarchical model of design demand indicators was constructed. Secondly, the Analytical Hierarchy Process (AHP) was employed to quantify these design demand indicators, and core design demands for home entrance disinfection devices were identified by weight calculations. On this basis, the Functional Analysis System Technique (FAST) method was combined to rationally transform the design demands into product functional indicators, constructing a functional system model for the home entrance disinfection device through systematic decomposition and categorization. Lastly, based on the Function-Behavior-Structure (FBS) theoretical model, the mapping of each function of the product to its structure was realized, the product structure modules were determined, and the comprehensive design and output of the innovative design scheme for the home entrance disinfection device were completed. The results of this study indicate that the design methodology combining AHP-FAST-FBS can effectively improve the scientific rigor and effectiveness of the home entrance disinfection device design, thereby generating an ideal product design scheme. This study provides systematic theoretical guidance and practical reference for designers of subsequent related disinfection products and also offers a new path for improving social health and safety. [ABSTRACT FROM AUTHOR]

Lifestyle is an important factor affecting people's health. Evaluating and promoting healthy lifestyles among occupational population can not only improve work ability and efficiency, but also contribute to the prevention a variety of potential diseases. This study developed a preliminary index system based on literature review and group discussion. Two rounds of expert consultation were conducted on 12 experts using Delphi method. The questionnaire recovery rate of the two rounds of Delphi survey was over 85%, the expert authority coefficient was over 0.70, and Kendall W was 0.450 and 0.446, which were significant (P < 0.001). The weighting coefficient of each indicator was calculated using the hierarchical analysis method. Among them, the weighting coefficients of physical health lifestyle, mental health lifestyle, social adaptation lifestyle and occupational health lifestyle were 0.4133, 0.2922, 0.1078 and 0.1867, respectively. The consistency index CI = 0.024 and the consistency ratio CR = 0.027 of the first-level index judgment matrix. The weighting coefficient is acceptable and the indicators do not cause logical confusion. The healthy lifestyle assessment tool of the occupational population constructed in this study consists of 4 primary indicators, 13 secondary indicators and 45 tertiary indicators, which can provide a standardized and operable assessment tool for monitoring and evaluating the healthy lifestyle of the Chinese occupational population. At the same time, the weight analysis of various indicators through the analytic hierarchy process can also provide reference for the key areas of occupational health intervention. [ABSTRACT FROM AUTHOR]

Flooding is a global natural disaster that occurs when water rises over normal levels and damages infrastructure, buildings, and land. Lately, a substantial rise has occurred in the frequency and severity of floods in Nigeria due to urbanization, population growth and climate change. This study aims to identify areas in the Osun River Basin (ORB) in southwest Nigeria that are at risk of flooding as a result of increased rainfall patterns that can induce river flooding. 10 flood factors contributing to flood susceptibility were obtained around the study area. These remote sensing data were analyzed using a weighted overlay on ArcGIS. The Analytic Hierarchy Process (AHP) was particularly applied in analysing the flood factors and creating the flood susceptibility maps. Results obtained showed that flood events are probable in areas along the river bank, some areas that are low-lying terrains and areas where there is high rainfall. Ogun State falls within the areas with the lowest digital elevation, therefore the state is very highly susceptible to flooding from the tributaries of the Osun River. Areas such as Ijebu North, Ijebu North East, Ijebu East and Ijebu Ode were identified as highly susceptible to flooding from the maps created. This study will further help stakeholders and policymakers in reducing the impact of flooding in these areas. [ABSTRACT FROM AUTHOR]

Establishing a differentiated green mine evaluation system for different types of mines is of significant practical importance. This study constructs a green mine evaluation system for the gold industry based on the analytic nierarchy process (AIP). The evaluation system sets the green mine evaluation index system for the gold industry as the target level, with 6 indicators as the criteria level such as mining area environment, resource development methods, and comprehensive resource utilization, 22 indicators as the index level such as functional zoning, mine landscape, and mining area greening. The AIP method was employed to determine the weight ranking of different indicators, identifying important criteria levels such as resource development methods, energy conservation and emission reduction, and comprehensive resource utilization. As for the index level, key indicators include associated mineral resource utilization, green development, energy conservation and consumption reduction, mining recovery rate, Deneficiation (metallurgy) recovery rate, and simultaneous mining and reclamation. The evaluation results provide a reference for the construction of green mines in the gold industry. [ABSTRACT FROM AUTHOR]

In container transportation by sea, it is crucial to define the transshipment design. Liner operators aim not only to reduce operational costs but also to maximize customer satisfaction. Therefore, selecting a transshipment hub port becomes paramount for achieving efficient transshipment capabilities. The study aims to propose an alternative approach to determine the best transshipment hub port for a northbound container ship entering the Eastern Mediterranean basin through the Suez Channel. The proposed model evaluates the container ports with a novel quantitative method. The choice criteria were weighted with the AHP (Analytic Hierarchy Process) method and numerical data acquired with various scientific methods regarding each criterion was used to rank the alternatives based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions). Additionally, to generate data and utilize it as a criterion a cross-sectional efficiency snapshot regarding the evaluated container ports was also taken using the bootstrap DEA (Data Envelopment Analysis). Thus, as a decision-maker, the line operator can determine the transshipment port quickly using an easily applicable method upon determining the port calls and the schedules. The findings imply that connectivity is the most important criterion for transshipment hub port choice for containerized goods. The location was also considered important for this choice, but to improve transshipment function, the proper way may be to draw beneficial strategies on maritime connectivity. Conversely, capital investments such as superstructure improvement were found to be less critical for an attractive transshipment hub. [ABSTRACT FROM AUTHOR]

The escalating adversities of climate change increasingly jeopardise agriculture in coastal Indian states like Odisha. The significance of the agriculture sector for the state necessitates effectively mitigating the adverse climatic impacts. Strengthening the resilience of agriculture has been widely acknowledged as one of the most effective strategies for mitigating negative climatic impacts. Framing and implementing essential resilience-enhancing measures depends on a comprehensive preliminary assessment of existing resilience. This study estimates agricultural resilience to climate change in Odisha by constructing district-level composite indicators. The Principal Component Analysis and Analytic Hierarchy Process are employed to assign weights to a multidimensional set of indicators and aggregate them into composite indicators. In addition, the Cluster Analysis is employed to identify heterogeneity among the districts in terms of their agricultural resilience. The study finds that the coastal districts in the state have the lowest agricultural resilience, which may be attributed to the higher vulnerability of these districts to a number of climatic risks. The composite indicators further highlight the need for region-specific interventions. Similarly, the interplay of multiple social and environmental factors is found to influence resilience, underscoring crucial implications for public decision-making. [ABSTRACT FROM AUTHOR]

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model. [ABSTRACT FROM AUTHOR]

Hydrogen has great growth potential due to its green, carbon-neutral nature, but public acceptance is low due to negative perceptions of the dangers associated with hydrogen energy. Safety concerns, particularly related to its flammability and explosiveness, are an obstacle to hydrogen energy policy. In South Korea, recent hydrogen-related explosions have exacerbated these concerns, undermining public confidence. This study developed public relations (PR) strategies to manage risk perception and promote hydrogen energy acceptance by analyzing the opinions of government officials and experts using SWOT factors, the TOWS matrix, and the analytic hierarchy process. The findings highlight the importance of addressing weaknesses and threats in PR efforts. Key weaknesses include Korea's technological lag and the low localization of core hydrogen technologies, both of which hinder competitiveness and negatively impact public perception of hydrogen energy. Notable threats include deteriorating energy dependency and expanding global carbon regulations. This information can be used to influence attitudes and foster public acceptance of hydrogen energy policies. Emphasizing weaknesses and threats may result in more effective PR strategies, even if they do not directly address the primary concerns of scientific experts. The persuasive insights identified in this study can support future policy communication and PR strategies. [ABSTRACT FROM AUTHOR]

The high availability of solar energy in the Gulf Cooperation Council (GCC) makes it the most attractive source of energy in this region, especially due to the global shift toward eco-friendly systems. A significant increase in the implementation of solar PV projects has been noticed in the United Arab Emirates (UAE). For this reason, this study conducted a multi-criteria decision-making process to compare four locations for building a solar PV park in the UAE, namely, Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain. Various criteria were taken into account, including the solar radiation, wind speed, distance from the electricity grid, distance from seaports, and land cost. A hybrid AHP-TOPSIS model was employed to evaluate the criteria weights and alternatives' scores, which were also compared with the no priority-TOPSIS to check the effects of the criteria weights on the overall ranking. According to the findings, Dubai and Abu Dhabi were ranked first and second, with final scores of 0.7108 and 0.4427, respectively, when using the AHP-TOPSIS model. Furthermore, Umm Al Quwain scored slightly less than that of second place, with a value of 0.4252. The top two ranked alternatives were consistent between the two compared models, as Dubai also performed the best among all locations, which confirmed the reliability of the proposed approach and verified the obtained results and rankings. [ABSTRACT FROM AUTHOR]

A fairly high breakage rate can often occur in the cold-chain logistics of fresh eggs. In this study, the compression and freshness detection were designed for fresh eggs, with the storage temperature of the eggs and the coating material on the surface as the main parameters, and the storage time as the boundary conditions. The results demonstrated that the temperature of 2-8 ℃ retarded the rottenness of eggs for better pressure resistance. Whether at room temperature or low temperature, the relative elastic deformation with time was maintained at a high value. There was a small variation in the type of coating agent. The rottenness of eggs after coating was decelerated at room temperature. While the pressure resistance of eggs was improved to compromise at 2-8 ℃. Both the coating agent and the low temperature were superimposed to slow down the decrease of its freshness. A professional experimental group was organized to conduct the sensory scoring on different groups of eggs under various storage times. The Huff unit value and sensory scoring were similar to each other. Therefore, the freshness of fresh eggs decreased with the extension of storage time. After 14 days of storage, a small amount of egg contents were solidified in the non-coated group of eggs stored at 2 ℃. The sensory scores of the group were lower during that time period. A comprehensive evaluation model was established for the parameters of the cold-chain transportation using the analytic hierarchy process, particularly with the ultimate load, the relative elastic deformation, the Haugh unit value, and the sensory score of eggs as the decision-making indicators. The relative importance of each index was ranked to determine the importance discrimination matrix. The weights were calculated corresponding to each decision index, thus reflecting the importance of the decision index to the final. Four decision indicators were ranked in descending order of the Hough unit value, sensory score, ultimate load, and relative elastic deformation, according to the priority. The optimal parameters were achieved in the coldchain logistics temperature and the coating material for different transportation durations as follows. In the cold-chain logistics duration of 0-4 days, the transportation temperature was at 2 ℃ with no paints applied; In the duration of 4 -7 days, the transportation temperature was 5 ℃, with the chitosan solution chosen as the paints. In the duration of 7-10 days, the transportation temperature was selected at 2℃ with the polyvinyl alcohol solution chosen as the paint. For the duration of 10 to 14 days, the transportation temperature was 5 ℃ with no paints applied. The quality of stored eggs was generally better at low temperatures than that at room temperature. The coated eggs at room temperature were generally better than the uncoated eggs. While the coated eggs at low temperature were less than the uncoated eggs. The coating layer protected the eggs to reduce the damage from the external physical collisions in the cold-chain logistics of a short or medium distance, indicating the less rottenness of eggs. However, the coating layer on the eggs failed to protect them in the process of long-term cold-chain transportation. The coating layer deteriorated and even fell off in the low-temperature environment for a long time, leading to less pressure resistance and maintenance of the egg quality. The findings can provide theoretical support for the cold-chain logistics of fresh eggs. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the realization of users ‘ information rights (IRs) in the smart service of university libraries. Based on the LibQUAL model, principal component analysis (PCA) is used to measure the quality of smart services in university libraries. The contribution weight of various services to the realization of IRs is calculated by analytic hierarchy process (AHP), then the degree of IR realization is obtained. Users have the highest right to express information, the lowest right to equality, and other rights tend to be moderate. Suggestions to improve the realization of IRs are put forward at the end. [ABSTRACT FROM AUTHOR]

In order to identify the greater risks in the implementation of the new construction technology of 'Embedded positioning construction technology of rotary digging pile hole casing'. And further explore the construction technical risk factors and the relationship between them. On the basis of the existing research and related standards, this paper uses the work decomposition method (WBS-RBS) to construct the construction technical safety risk index system from four dimensions: natural environment, material and equipment, construction quality and working personnel. And use the decision laboratory method (DEMATEL) to establish the internal relationship between the risk evaluation indicators, and then use the network analytic hierarchy process (ANP) method to determine the weight of the risk evaluation index, establish a risk assessment model based on DEMATEL-ANP and carry on the example operation. The results show that the burying stability, fixing mode, type, upper position and installation stability of the reamer are the key work to ensure the normal implementation of the technology. [ABSTRACT FROM AUTHOR]

The traditional e-commerce's traffic dividend is gradually declining, while social e-commerce as a new business model has attracted numerous enterprises to jump into by virtue of the advantages of viral traffic aggregation and efficient conversion. How to improve the core competitiveness and stand out from the Red Ocean battle of traffic competition is of great significance for the healthy and sustainable development of social e-commerce platforms. In view of this, this study focuses on the issues related to the attractiveness of social e-commerce platforms from the consumer's perceptive, with the aim of providing theoretical support for social e-commerce platforms to enhance their core competitiveness and formulate relevant development strategies and decision-making mechanisms. First, four key factors affecting the attractiveness of social e-commerce platforms based on the AISAS model are proposed: the ability to attract consumers to access, promote consumers' purchase conversion, maintain consumers' platform loyalty, and attract to share experiences. Second, an attractiveness assessment model for social e-commerce platform including four secondary indicators and corresponding 14 tertiary indicators is constructed by using Analytic Hierarchy Process, and an arithmetic example is demonstrated. Finally, management suggestions to enhance the attractiveness of social e-commerce platforms are presented. [ABSTRACT FROM AUTHOR]

Forestry listed companies are an important force in the development of the forestry industry, and their human capital level is related to organizational innovation driving ability, comprehensive performance, and thus affects the development of the forestry industry. There is little existing research on this topic. From the perspective of organizational resilience improvement, this article constructs an evaluation index system for human capital of Chinese forestry listed companies based on the annual report data of 61 Chinese forestry listed companies, focusing on knowledge human capital, capacity human capital, and proactive human capital. On the basis of determining the weights of each indicator in the evaluation index system using analytic hierarchy process and entropy value method, the final weights are determined using the combined weights method. Obtain a comprehensive evaluation value of the human capital level of each forestry listed company and conduct cluster analysis on this basis. In terms of research objects, this article focuses on human capital of forestry listed companies; In terms of research content, establish a systematic evaluation index system; At the research level, quantitative analysis was conducted to achieve innovation. The research results indicate that the comprehensive evaluation value of the overall human capital level of each forestry listed company has a maximum value of 0.8245 and a minimum value of 0.0801, with an average score of only 0.2692. Overall, there is room for improvement. A total of 56 companies were relatively average and poor, reaching 92%. The overall human capital of forestry listed companies is relatively low, which is closely related to the weakness, core competitiveness, and high risk of forestry. Therefore, the following policy recommendations are proposed: attach importance to the reserve of knowledge-based human resources and increase the proportion of employees with junior college degree or above in total employees; strengthen the construction of talent for technological breakthroughs and increase the proportion of R&D technicians ; pay attention to the improvement of the abilities of senior executives, directors, and supervisors, and optimize the structure of professional and technical titles; appropriately increase employee compensation levels and enhance employee proactive human capital utilization. [ABSTRACT FROM AUTHOR]

Cities have emerged as the epicenters of economic activities, innovation, and cultural exchange in the era of urbanization and globalization. The concept of urban competitiveness has garnered increasing attention as a means of understanding the factors driving cities to compete locally and globally. Given cities represent the driving force of economic, social, and cultural development, there is an increasing need to identify the stages of development and establish a system for ranking and positioning cities and regions in this process. Therefore, our study attempts to help Egyptian cities enhance their competitiveness by assessing the competitiveness of 42 cities. These cities have been identified as the most competitive by the government's Economic Development Strategy 2030 and the National Plan 2050. A total of 10 global power city index indicators for each city during 2015–2022 represent five categories: cultural interaction, economics, research and innovation, environment, and connectivity. The indicators were selected based on data availability by 11 experts. The indicators and alternatives were weighted and ranked using the fuzzy analytic hierarchy process and multi-criteria optimization and compromise solution (AHP-VIKOR) hybrid method. The geographic information system was then used to map Egyptian cities into four categories. Our findings, which are compatible with previous articles, reveal that in the case of Egyptian cities, high-government expenditure-based metropolitan cities are more competitive than small and natural resource-based peripheral cities. Egyptian cities' most effective competitiveness indicators are the gross domestic product (GDP) and unemployment rates of 0.58 and 0.26, respectively. At the same time, the lowest effectiveness is the number of universities among the 50 best in the world as well as the number of hotel rooms, which are 0.039 and 0.044, respectively. Through the proper utilization of opportunities following the competitiveness indicators, medium-sized Egyptian cities with potential will be cities with national and even transnational competitiveness within 10 years. Our results can potentially provide beneficial insights to enhance our comprehension of Egyptian cities' weaknesses and assist in formulating urban policies for enhancing urban singularity and competitiveness. [ABSTRACT FROM AUTHOR]

• Proposed a method for multi-axis random vibration topology optimization. • Enhancing vibration resistance and extending life during the topology phase. • Prompting the structure to add load-bearing paths to resist random vibration loads. • Guiding the topology towards forming a vibration-resistant structure. • Expanding HCA into the field of random vibration performance topology optimization. Considering the lack of effective solutions for multi-axis random vibration topology optimization problems, and recognizing that multi-axis random vibration excitation is the most common loading conditions experienced by structures during their operational life, this paper proposes a multi-objective topology optimization method for multi-axis random vibration. By combining Hybrid Cellular Automata (HCA) and Analytic Hierarchy Process (AHP), this method aims to enhance the vibration resistance of structures and extend their fatigue life during the topology optimization phase. To validate the effectiveness and engineering practicality of this method, two engineering cases were designed: a cantilever beam case and an automotive steering knuckle case. Multi-objective topology optimization were conducted with objectives including mass, stiffness, and vibration intensity metric (root-mean-square stress). The two cases demonstrate that the control group's topology model exhibits weaker resistance to random vibrations and shorter fatigue life. In contrast, the validation group's topology model introduces an additional load path near the excitation point, effectively dissipating the impact of vibration excitation. This enhances the structure's vibration resistance and significantly extends its fatigue life. [ABSTRACT FROM AUTHOR]

This study presents a detailed comparative analysis of online evaluation platforms aimed at identifying the most suitable one for integrating an adaptive revision plugin. The study uses both the analytic hierarchy process (AHP) and multi-criteria decision making (MCDM) to give a structured and thorough evaluation of different learning management systems (LMSs). By scrutinizing the capabilities, interfaces, and underlying technologies of these systems, the analysis seeks to pinpoint the platform that offers optimal compatibility, flexibility, functionality, and user experience. The central objective of this study is to enhance the effectiveness of adaptive learning tools within educational technologies. This is achieved by evaluating different platforms to ascertain which one best supports the integration of adaptive revision functionalities. Our comprehensive analysis clearly identifies Moodle as the superior platform due to its robust adaptability and enhanced customization capabilities, which are essential for implementing effective adaptive learning tools. The results underscore Moodle's potential to significantly enhance the educational experience by supporting tailored learning paths and dynamic content adjustments. This study not only highlights Moodle's advantages but also sets the groundwork for future advancements in adaptive educational technologies. [ABSTRACT FROM AUTHOR]

This paper aims to assess the extent to which Brazilian companies adhere to the accuracy, balance, clarity, comparability, reliability, and timeliness criteria recommended by the Global Reporting Initiative (GRI) standards. A survey was conducted with experts in sustainability reporting with extensive experience in the Brazilian corporate context. The analytic hierarchy process was employed to assign weights to the criteria, and a grey fixed weight clustering model was proposed to analyze the collected data. Clarity and accuracy received the highest weights, which is concerning given the quality observed in sustainability reports. A lower emphasis on timeliness in sustainability reporting reflects many companies' publishing practices, prompting more efficient reporting. The majority of respondents perceive the quality of information in sustainability reports from Brazilian companies as falling within the medium maturity range, suggesting significant room for enhancement. The originality of this study lies both in the methodological approach, which combines multicriteria decision techniques capable of incorporating uncertainty related to data on sustainability reports, as well as in the results that provide information for improving sustainability reporting practices. The findings provide a foundation for companies and policymakers to enhance sustainability reporting, particularly in developing nations, promoting transparency, accountability, and international standard compliance. [ABSTRACT FROM AUTHOR]

In response to heightened competition arising from globalization, companies are crafting strategies to sustain their operations. However, these strategies also introduce risks that require meticulous management. The onset of the COVID-19 pandemic has exacerbated disruptions in supply chains, including the vulnerable food supply chain (FCS), strained further by escalating food prices and resource depletion in recent times. Within this context, the vulnerability of global FSCs has escalated significantly due to government-imposed lockdowns during the pandemic. This study aims to comprehensively investigate the multifaceted disruptions in global FSCs caused by the COVID-19 pandemic. By delving deep into the complexities of these disruptions, it seeks to uncover the key factors contributing to the vulnerability of supply chains. Employing a blend of literature review and expert opinions, the study identifies and prioritizes factors using the Fermatean Fuzzy Analytical Hierarchy Process (FF-AHP). A two-level criteria framework consisting of three main criteria and eleven sub-criteria has been developed, taking into account expert recommendations and previous studies. According to the results obtained, it has been revealed that the Managerial factors within the main criteria are the most significant factors in the fragility of the FSC. Among these factors, it has been observed that Technology, Corporation, and Inventory Management are the leading criteria causing to the vulnerability of the FSC. This is the first study to investigate the vulnerabilities of FSC using fuzzy logic. The research underscores the imperative of comprehensive risk management strategies that encompass all stakeholders within the supply chain, particularly during unanticipated crises like pandemics. [ABSTRACT FROM AUTHOR]

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In light of the pressing global challenges related to greenhouse gas emissions from the construction industry, current evaluation systems for green building construction sustainability remain limited, often overlooking sustainability domains. This study innovatively established an evaluation framework by exploring five critical domains: environmental sustainability, economic benefits, sociocultural impacts, technological innovation, and health and well-being. Sixteen key evaluation indicators were identified using the Delphi method, with the novel inclusion of a carbon emission reduction target achievement indicator, thereby promoting the goal of carbon neutrality in green buildings. To determine a more reasonable weight distribution, this paper combined the fuzzy analytic hierarchy process (fuzzy AHP) with the entropy weight method. Additionally, the study employed a fuzzy matter-element method enhanced by genetic algorithms for precise evaluation of green building construction sustainability. The feasibility and effectiveness of the proposed model were validated through an empirical analysis of a green building project in Beijing. The results of this research provide innovative theoretical references and practical guidelines for green building construction sustainability evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Even though many studies have been thus far devoted to landscape services (LS) or vulnerability assessment (VA) alone, the relationship between these two concepts has been less investigated. The current study attempts to model the spatial relationship between LS and VA in the Tarhan area (west of Iran). For this purpose, a combination of the analytic hierarchy process (AHP), remote sensing (RS), and geographic information system (GIS) techniques are applied to assess vulnerability. Variogram models and geostatistical simulations are used for mapping and evaluating landscape services. Moreover, the geographically weighted regression (GWR) is operated to predict the relationship between LS and VA. The results indicate that landform and socioeconomic variables are also two main factors shaping variations in VA. As well, the levels of services provided in the possible landscape are affected by the vulnerability. The assessment accordingly establishes that natural and cultural values have significant effects on services, in terms of their spatial distribution and nature. Exploring the relationship between LS and VA correspondingly depicts that VA has a direct influence on the capacity and entity of LS provision (adjusted R2 = 0.67). The outcomes of the study provide a foundation for nature management and conservation, although it is less able to analyze the trade-off between ecosystem degradation and future development trends. The nexus of LS and future ecological processes in an integrated system can be thus the subject of further research. [ABSTRACT FROM AUTHOR]

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22 years using Mann–Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments. [ABSTRACT FROM AUTHOR]

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure. [ABSTRACT FROM AUTHOR]

The development and application of virtual reality (VR) technology significantly enhances consumer immersion. Exploring a multi-sensory evaluation model for virtual packaging is valuable for integrating VR technology with packaging. This study developed a multi-sensory evaluation model for virtual packaging using the analytic hierarchy process (AHP). Eye-tracker experimentation was conducted to identify consumer attention indicators when interacting with virtual packaging. These indicators were quantified using Saaty's nine-level importance scale and expert input, resulting in a comprehensive multi-sensory evaluation model. Subsequently, a VR shopping system focused on potato chips and cola as packaging design objects. This system was evaluated using the established model, and the results were analyzed. Based on the findings, improvements were made, and the system was re-evaluated using the modified model. The post-improvement evaluation demonstrated significantly enhanced sensory experiences. These results validate that the developed evaluation model effectively guides multi-sensory design approaches for packaging within a VR environment. [ABSTRACT FROM AUTHOR]

In today's world, air transport has become a favored choice for enhancing the value of a national economy, driven by advancing technology, escalating volumes of national and international trade, and population growth. The proliferation of airport transfer centers, particularly within air transport, plays a pivotal role in fostering the advancement of the aviation sector. Therefore, the selection of these hubs is of great importance. This study evaluated the New Çukurova, Antalya, Sivas Nuri Demirağ, Erzurum and Muğla Airports in Türkiye for the selection of a new airport transfer center in terms of criteria such as airport costs, airport terminal and apron facilities, airport passenger transportation services, airport operating capacity, airport location, demand factors in the service region and other factors. The study employed three methods for evaluating alternative international airports: AHP (Analytic Hierarchy Process), MOORA (Multi-Objective Optimization by Ratio Analysis) and ELECTRE (Elimination and Choice Translating Reality). In the initial phase, the priority ranking of criteria was established based on expert opinions. Subsequently, Antalya Airport was the most suitable airport transfer center according to the ELECTRE method, while New Çukurova Airport emerged as the preferred choice according to the MOORA method. Both airports secured top rankings in both evaluation methods. [ABSTRACT FROM AUTHOR]