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Before integrating Autonomous Vehicles (AVs) into transportation systems, it's important to evaluate various aspects to facilitate their adoption. Numerous factors are likely to be crucial in this process. This research investigates the critical success factors influencing the adoption of AVs in sustainable urban transportation through a hybrid fuzzy Multiple-Attribute Decision-Making (MADM) approach. The primary aim is to identify key criteria and sub-criteria perceived by transport experts, shedding light on the complex dynamics of AV adoption. Methodologically, a fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) is employed to unveil cause-and-effect relationships among the main criteria, determining their respective weights. Subsequently, fuzzy Analytic Network Process (ANP) is applied to calculate the weights of criteria and sub-criteria based on the fuzzy DEMATEL results. The study considers seven major factor dimensions: economic, technical, operational, environmental, safety and risk, social and regulatory, and user acceptance. Key findings underscore user acceptance, financial costs, and environmental impact as the most pivotal criteria, emphasising their significance in the eyes of transportation experts. Contrarily, technological and operational criteria hold the lowest weight in the considered criteria for AV adoption in transportation networks. This research provides valuable insights into critical barriers and opportunities in the adoption of AVs, contributing to the advancement of sustainable urban transportation. • Identification of critical success factors for AV adoption in urban transportation networks. • Fuzzy DEMATEL analysis reveals cause-and-effect relationships among criteria. • Integration of fuzzy ANP enables weighted evaluation and ranking of criteria. • User acceptance, financial costs, and environmental factors are crucial for AV adoption. [ABSTRACT FROM AUTHOR]

The consistency analysis in AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process) methods is a critical element that helps evaluate how consistent the pairwise comparisons of criteria or decision alternatives made by the decision-maker are. Inconsistent evaluations can lead to erroneous or misleading results. The article presents a simple method of graphical analysis of the consistency of these evaluations (occurring in the so-called pairwise comparison matrix). The proposed concept of spatial visualization of the relative positioning of the compared pairwise criteria or decision alternatives offers analysts, on the one hand, a straightforward and visually appealing way to formulate always consistent evaluations. On the other hand, it facilitates the identification (seeing), understanding, and correction of undesirable inconsistencies in situations where the classical method of evaluation formulation, recommended by Saaty [1], has been used, where each compared pair of elements is considered separately. [ABSTRACT FROM AUTHOR]

The fire hazard assessment of polymers and the effect of flame retardants constitutes the foundation for preventing and managing fire risks to public safety. This study addresses key challenges by considering intrinsic hazards, indicator volatility and correlations, while overcoming subjectivity in weight assignment and the neglect of flame-retardant treatments. Firstly, we initiate two novel steps of Pearson correlation analysis and predefined comparative scales for indicators, which enhance the objectivity, scalability and repeatability of analytic network process (ANP) weights. Secondly, we utilize the CRITIC method to incorporate indicator volatility and correlations, surpassing the limitations of separate calculations and subsequent combinations. The newly developed pure sample normalization method enhances the interpretability and scientific validity of CRITIC weights. Combining the ANP and CRITIC weights using Nash equilibrium theory, the results rectify the underestimated T -5% from 0.72% to 3.64%, reducing the overestimated PCO 2 , PSP and TSP from 11.98%, 9.62% and 9.59–7.25%, 9.12% and 5.69%, respectively. Finally, the fire hazards value effectively captures the increased fire hazards resulting from specific degraded indicators following flame retardant treatments. The research findings provide a fresh perspective for the safe design of flame-retardant polymers and fire hazard assessment, offering valuable insights for fire risk prevention and management. [ABSTRACT FROM AUTHOR]

Fast tool servo (FTS) is a promising technique for the high-speed diamond turning of micro-structured functional surfaces due to its fast response capacity. However, the inherent actuator non-linearity, the lightly damped vibration mode and the external disturbance of the FTS would degrade its tracking performance severely and thus restrict its machining ability. To overcome this limitation, a band-stop-filter-based repetitive control (BSF-RC) scheme is proposed in this article to improve the operating performance of the FTS. Firstly, the spectra of the FTS tool paths (FTS-TPs) for machining typical micro-structured functional surfaces with the cylindrical coordinate machining method are analyzed in detail. The results reveal that the frequency components concentrate on the tiny neighborhoods of the rotational frequency of the spindle and its harmonic frequencies. Afterwards, the comb-like band-stop filter used by the BSF-RC is elaborately designed via the cascade of three comb-like notch filters to generate a comb-like band-stop loop shape for covering main spectral components of the FTS-TPs. The analytical expression of the comb-like band-stop filter amplitude as well as the stability condition of the closed-loop control system are derived to provide effective criteria for the parameter selection of the BSF-RC. The experimental validation of the developed BSF-RC scheme is conducted on a five-axis ultra-precision lathe equipped with a self-developed FTS via the diamond turning of three typical micro-structured functional surfaces. The experimental results show that good surface qualities with the maximal form error of a few hundred nanometers and the surface roughness of a several nanometers are achieved for all the obtained micro-structured surfaces by using the BSF-RC scheme under a high spindle speed. • A novel repetitive controller is proposed for fast tool servos to fabricate the micro-structured surfaces. • The frequency components of fast tool servo tool paths for the micro-structured surfaces are analyzed. • The design methods and the stability analysis of the proposed repetitive controller are presented. • Three typical micro-structured surfaces are fabricated by fast tool servo diamond turning. • A submicron form error and a nanometric surface roughness are obtained for all the generated surfaces. [ABSTRACT FROM AUTHOR]

This article presents a zone-based evaluation method to measure the level of transit-oriented development (TOD) within a zone. The method is an analytic network process (ANP) and contributes to the literature on TOD evaluation in dealing with the interdependencies among criteria and zones. The method was applied to the evaluation of 456 zones in Taipei City, Taiwan to confirm the method's applicability, verify the necessity of considering interdependences in TOD evaluation, and examine the validity of evaluation results. The evaluation results of the case study suggest that considering zonal interdependences in TOD evaluation raises the correlation between measured TOD levels and mode shares of transit systems among zones and provides action directions to the local administration in developing a transit-oriented environment for a neighborhood. • A valid and applicable method for zone-based TOD evaluation is suggested. • The method addresses interdependences among criteria and zones. • Effectiveness of the method and results was verified. • The method is effective in developing TOD plans for zones of poor TOD-ness. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a quantitative methodology for the management of work teams through fuzzy logic and analytical network processing (ANP) for managerial decision-making in companies in the small and medium-sized business sectors. Through bibliographic research, the most influential variables were identified. The occurrences of these events were determined by the environment and their respective relevant variables for inclusion in the model. Qualitative data were transformed into quantitative data through fuzzy logic. The interactions of the variables among themselves in ANP were evaluated to estimate the best work teams or the members of each of them. [ABSTRACT FROM AUTHOR]

Pumped storage power stations'(PSPSs) construction sites are widely concentrated in mountainous rural areas, which brings significant benefits to the areas' development. China encourages the development of PSPSs, and also supports the development of rural areas driven by PSPSs. Scientifically evaluating the benefits of PSPSs boosting rural revitalization is helpful in timely discovering the deficiencies of PSPSs promoting rural development, and further playing the role of PSPSs in rural revitalization strategy in China. So, this paper proposes methodology to scientifically evaluate the benefits of PSPS boosting rural revitalization: uses context, input, process, product (CIPP) model to construct an evaluation indicator system; combines Fuzzy decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) with analytic network process (ANP) to determine the indicator weights; uses the improved matter-element extension model (IMEM) to comprehensively evaluate the benefit of PSPS boosting rural revitalization in the surrounding areas. The example analysis results show that the benefit levels of 10 PSPSs are: good, relatively good, moderate, moderate, moderate, moderate, relatively good, moderate, relatively good, relatively good; the corresponding grade closeness is: 0.99932, 0.99921, 0.99883, 0.99930, 0.99908, 0.99871, 0.99886, 0.99894, 0.99902, 0.99898. Validity tests prove that the models are scientific and effective. Recommendations for PSPS to promote rural revitalization are presented. [Display omitted] [ABSTRACT FROM AUTHOR]

Forest fires significantly disrupt global ecosystems. Many forecasting techniques predict fire activity and allocate prevention resources, but various factors are missing from the assessments, and multi-criteria decision approaches alone are insufficient. This work introduced a novel methodology combining artificial neural networks (ANN) with the analytical hierarchy process (AHP), fuzzy AHP multi-criteria methods, and spatial data to detect potential forest fire vulnerability areas using twenty variables. The results from AHP or fuzzy AHP, were processed using a multilayer perceptron with a backpropagation algorithm. The final ANN model has two objectives: first, to create a forest fire vulnerability with four classes (low, moderate, high, and very high), and second, to classify burned area sizes in regions highly or very highly vulnerable to fire. Evaluation metrics were also applied for validation. The fire model was tested using both literature review data and in situ observations. Comparative analysis showed that the burned area size model performed better than other machine learning methods, achieving an accuracy score of 89%. Meanwhile, the fire vulnerability model scored 82%. The study addresses the problem of prediction and provides an algorithm for classifying fire risk based on historical data in the Czech Republic, offering a master model for future forest management. Therefore, the ANN model, once trained, validated, and tested, does not require resetting and is effective for estimating forest fire vulnerability. Moreover, it produces results quickly, providing rapid insights into complex forest ecosystems, saving time and enhancing understanding for decision-makers. [ABSTRACT FROM AUTHOR]

Virtual reality technology has rapidly gained popularity as an entertainment medium, drawing interest from diverse age groups. However, its widespread adoption depends on effectively addressing public concerns and achieving market acceptance. While some studies have acknowledged these concerns, a significant gap persists in comprehensive research that incorporates both individual and expert perspectives. Consequently, certain underlying social issues related to virtual reality systems remain unexplored and unprioritized. To address this gap, this paper proposes a methodology that utilizes Latent Semantic Analysis (LSA) to identify and assess social concerns from various sources, including user perspectives. Large Language Models (LLMs) assist in retrieving relevant chunks of articles during analysis, enhancing data quality. Furthermore, we introduce a novel decision-making tool, the Hierarchized Analytic Network Process (HANP) and its fuzzy form, to effectively rank these concerns. This approach addresses a limitation of the traditional Analytic Network Process (ANP), which can overemphasize dependent attributes, potentially leading to zero-weighted, less important attributes and making comparisons impossible. By prioritizing social concerns based on their significance, our approach aims to facilitate broader social acceptance of virtual reality technologies among the general public. To further demonstrate the advantages of our proposed approach, the results obtained from F-HANP (in situations where fuzzy judgments are available) and HANP are compared with other popular decision-making methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • The land degradation hazard and vulnerability maps were combined to identify high-risk areas. • Socio-environmental vulnerability to land degradation was assessed using the Analytic Network Process (ANP). • Low-altitude areas with low rainfall and high temperatures are most susceptible to land degradation hazard. • Land use, vegetation cover, and population density were identified as key factors contributing to vulnerability. • The final risk map shows that approximately 7.56% of the region is classified as high-risk. The degradation of land (LD) is a major concern for the health and sustainability of natural resources. It is primarily caused by the deterioration of vegetation and soil. In this study, we focus on assessing the risk of LD in the Bakhtegan basin in Iran, one of the arid and semi-arid ecosystems. Our objective is to predict LD hazard and vulnerability maps, and then combine them to identify areas at high risk. To predict LD hazard, the Support Vector Machine (SVM) algorithm was used with 179 LD locations and twelve variables, including land use, lithology, rainfall, temperature, distance to the stream, elevation, aspect, slope, curvature, distance to the road, Normalized Difference Moisture Index (NDMI), and population density. The LD hazard map was evaluated using five error statistics extracted from the contingency table. For LD vulnerability mapping, eight criteria were deployed, including land use, population density, Normalized Difference Vegetation Index (NDVI), livestock density, groundwater quantity and quality, Salinity Index (SI), and migration. These criteria were weighted using the integrating eDPSIR framework and Analytic Network Process (ANP) methods. The results show that low-altitude areas which have low rainfall and high temperatures face the highest LD hazard. Additionally, the western and northwestern regions of the basin are more vulnerable compared to other areas due to factors such as land use and vegetation cover. Lastly, the LD risk map reveals that some 7.56 % of the region falls into the high-risk classification, totaling 2413.37 km2. Notably, salt lands emerge as the most at-risk land use, with 77 % under high risk, with rain-fed agricultural land following as the second-highest risk class. These findings underscore the importance of considering LD risk in land management strategies. [ABSTRACT FROM AUTHOR]

• This study focuses on the materialization stage of large public buildings to unlock the carbon reduction potential of this stage. • The Life Cycle Assessment is a basic theory. Analytic Network Process and Fuzzy Comprehensive Evaluation combined method were used for carbon measurement and evaluation, and Super Decision is used to assist with calculations. • The materialization stage in large public buildings shows a "Good" carbon grade, while the building materials production stage, which shows an "Average" grade, has the most significant reduction potential. • Research expands carbon modeling for large public buildings, providing a framework for effective regulation during the materialization stage. The greenhouse gas emission during its materialization stage is characterized by time-concentrated and large emissions. This study first divided the materialization stage into three segments according to the life cycle theory: building materials production, building materials transportation, and construction. A carbon emission measurement model for the materialization stage of large public buildings was established by integrating the carbon emission coefficient method, and the emission reduction rate (ERR) was proposed as an indicator for quantitatively assessing carbon emissions during the materialization stage of large public buildings. After the initial indicator system was framed, the Analytic Network Process and Fuzzy Comprehensive Evaluation methods were used to establish the evaluation model for large public buildings at the materialization stage. Last, the model was verified at a convention and exhibition center in Xiamen, China, and it shows that the production stage of building materials has a large potential for emission reduction. [ABSTRACT FROM AUTHOR]

A methodology was developed to prioritize stakeholders of a collaborative research and innovation (R&I) project in the circular bioeconomy area, towards enhancing its benefits from a multi-perspective point of view. The concept of R&I project benefits was broken down into criteria, evaluating different attributes related to the project outputs and outcomes, to the project management processes, and to the social, environmental and economic dimensions. The devised methodology was based on a combination of the analytic network process multicriteria decision making method and the key benefit categories from the P5 standard for sustainability in project management. The P5 standard has been shown to adequately frame the benefits to stakeholders of R&I projects in the topic of circular bioeconomy. Key benefits identified by the experts relate to the categories "society and costumers" and "consumption". The following stakeholders should have priority in the development of the project stakeholder management plan: research team members, leaders at the consortium organizations, project management team members and environmental NGOs. Future research will include a longitudinal study of the perceived stakeholder and benefit categories priority over time [ABSTRACT FROM AUTHOR]

In the field of machinery manufacturing, green transformation has become an essential objective for corporate governance. Implementing environmentally conscious practices not only contributes to ecological preservation and sustainable growth but also strengthens corporate reputation, lowers operational expenses, and fosters innovation. Despite these advantages, further investigation is required to identify the key factors influencing Green Business (GB) initiatives within the machinery manufacturing industry and to evaluate the practical significance of the strategies essential for advancing sustainable development. Additionally, it is crucial to determine which strategies enable firms to gain and sustain a competitive edge. Currently, the industry lacks a comprehensive methodology and an integrated framework for GB models. This study addresses this gap by systematically identifying critical indicators shaping GB practices. The rough-fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, combined with the Analytic Network Process (ANP), referred to as DANP, is employed to analyze the interrelationships among these indicators and to assign relative weights. The use of a rough-fuzzy-based approach not only synthesizes expert judgments but also accommodates information uncertainty. The findings indicate that "green value realization" is the most pivotal dimension of the GB model. Moreover, "green value capture," "green value proposition," and "sustainable value creation" emerge as the three primary areas for enhancement. This study offers a strategic roadmap for decision-makers in the machinery manufacturing sector to progress towards GB practices. By embedding green transformation strategies into manufacturing operations and supply chain management, enterprises can promote sustainable development and reinforce their market position. [ABSTRACT FROM AUTHOR]

• A novel information fusion framework is proposed to handle knowledge-intensive tasks. • The framework can evaluate coexisting-cooperative-cognitive capabilities of cobots. • A evaluation index system fuses expert knowledge and text information is constructed. • Test tasks are designed and FANP is improved to locally evaluate cobot performance. • A combination weighting method is proposed to globally evaluate cobot performance. To effectively address the requirements of deeply coexisting-cooperative-cognitive (Tri-Co) interactions among humans, robots, and the environment within the context of Industry 4.0, it becomes crucial to conduct evaluation research on Tri-Co capabilities (TCCs) of collaborative robots (cobots), which is a knowledge-intensive task. However, existing research on performance evaluation of cobots has not yet established a common method for constructing a complete performance evaluation index system. The testing methods lack a design basis and fail to evaluate the performance of cobots through operational tasks in industrial production or daily life from the perspective of test tasks. Furthermore, these methods do not facilitate the fusion of multi-source subjective and objective decision-making information, which includes both the knowledge of experts and fundamental parameters of cobots. To this end, this study proposes a multi-source decision-making information fusion framework for evaluating the TCCs of cobots. This framework includes a construction method for an evaluation index system that fuses subjective and objective elements based on statistics, text clustering, and closed-loop feedback mechanism. It also incorporates TCCs test tasks, and an improved fuzzy analytic network process (IFANP). Additionally, it incorporates a combination weighting method that aims to minimise both subjective and objective weights deviations. This framework effectively integrates the subjective knowledge of experts with the objective fundamental parameters of cobots. It accomplishes local TCCs evaluation from the perspective of test tasks. Additionally, it achieves global TCCs evaluation by combining basic performance evaluation indices and the performance of completing the test tasks. A case by Rethink Sawyer (Sawyer) is presented to demonstrate the application process and viability of the developed framework. The experimental results and calculation examples indicate that the research has the potential to provide a certain theoretical basis and reference to solve the performance testing and evaluation issues for other types of robots in engineering applications. [ABSTRACT FROM AUTHOR]

Identifying districts' potential to become Positive Energy Districts (PED) is challenging but strategic since they are considered critical enablers for cities' carbon neutrality. PEDs are city areas with a positive annual energy balance, achieved primarily through energy efficiency and renewable energy generation while ensuring sufficient energy flexibility. This investigation introduces a methodological framework designed to prioritise and comprehend the potential PED status of diverse districts within a city, drawing upon predetermined criteria and expert insights. The study employs a combination of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methodologies to scrutinise the city's various districts and the influencing criteria. The method's applicability is tested through to the specific case of Valencia City. The study reveals that the importance of specific criteria in attaining PED varies according to the distinctive attributes of each district. Furthermore, variations emerge based on the perspective and expertise of the contributing experts. The results of this application allowed the selection of the 19 most influential criteria, organised into technical, social, urban, environmental and economic clusters. The two economic criteria (Investement and Grant or projects), one social criterion (Interest or acceptance) and one technical (Potential for retrofitting the buildings), are the most influential overall. The evaluation of the 19 administrative districts of Valencia for each criterion allowed the identification of the districts on the city's outskirts as having the greatest potential to be energy-positive. In conclusion, the proposed methodology aids decision-making in a city's urban energy planning on a district-by-district basis. • Positive energy districts are cities' areas with a positive annual energy balance • A Multi-Criteria Decision-Making method to assess the potential PEDs • Different strategies to implement to become PED related to the district's idiosyncrasy • Approach and conclusions suitable for Mediterranean cities. [Display omitted] [ABSTRACT FROM AUTHOR]

This study explores the complexities surrounding the adoption of decentralised Renewable Energy Technologies in Spain, crucial for transitioning towards a renewable energy-driven economy. Through a systematic review of both scientific and grey literature, key factors influencing adoption were identified. Utilising the Analytic Network Process method, this research highlights political will, technological maturity, and fiscal incentives as primary drivers. These findings underscore the importance of mature technologies and incentivisation strategies in expediting adoption. Conversely, addressing barriers necessitates a multifaceted approach, presenting challenges for policy formulation. However, the analysis reveals a positive cascade effect, wherein strengthening primary drivers positively impacts others within their domain. This pattern is mirrored in the barriers. Furthermore, the study reveals consistent factors across technologies, adopter types, and regions. The three-axis analysis shows the largest differences in terms of the type of adopter, followed by the type of technology. The smallest differences are found by region, emphasising the unifying influence of the EU framework. This suggests a unified approach to policy design and promotion efforts. • The study assesses drivers and barriers using the Analytic Network Process. • No relevant differences in prioritisation across regions, technologies, or adopters. • Technological maturity is key for adoption, needing political will and incentives. • The three main drivers account for 41% of the total prioritisation weight. • Barriers are more diverse and need to be addressed from various perspectives. [ABSTRACT FROM AUTHOR]

• Developed a structural model to assess the service efficacy of the UOS system. • Quantified and characterized the relationships between subsystems. • Identified and determined the weighting of indicators for subsystem analysis. • Integrated Evaluation of Service Efficacy in the UOS System. • Service efficacy of UOS in the central districts of Nanjing: Overall moderate but in need of improvement. This study assessed the service efficacy of Nanjing's urban open space (UOS) system, emphasizing the complex structural characteristics of UOS services, specifically, the impact of interactions between different subsystems on the overall service efficacy of the UOS system. The objective was to understand whether and how a UOS system can effectively and efficiently meet urban needs under constrained conditions. The dimensions of UOS services and the interrelationships between services offered by different subsystems were examined, and a structural model for assessing the service efficacy of the UOS system was developed. A multidimensional analytical approach was applied to quantify and characterize the relationships between subsystems and identify and determine the weighting of subsystem analysis indicators. The findings indicate that subsystems and their respective indicators differ in importance across dimensions, influence each other, and collectively form the overall service efficacy of a UOS system. Furthermore, while service efficacy across different administrative districts in Nanjing's main urban areas generally remains moderate, significant differences in evaluation values exist among districts and subsystems, offering direction for future urban planning and resource allocation. This study offers detailed insights into the efficiency and optimization of UOS services, helping urban planners improve UOSs globally. [ABSTRACT FROM AUTHOR]

Given its widespread potential and technological advancements, it is critical to use the greatest amount of wind power in reaching the 100% renewable energy target in power grids. SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is frequently used to evaluate alternative strategies in multi-criteria decision issues. This paper employs a hybrid Fuzzy Analytic Network Process (FANP) approach based on SWOT to assess the future development of wind power capacities in Turkey in light of the sectoral effects of the Covid-19 outbreak in 2020. To validate the proposed approach, the results are compared to the results of the SWOT-based Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and fuzzy AHP (FAHP). According to the findings, "Development of domestic and efficient technologies (ST1)" and "Sustaining support mechanisms in investments and technological research (SO2)" are the best alternative strategies in all analysis models. While the priority ranks of other alternative strategies are the same in AHP and FAHP hierarchical techniques, FANP differs from ANP when the linguistic assessment process is taken into account. This study proposes long-term strategies for increasing wind power capacity and sector sustainability, and it demonstrates that FANP could be an appropriate approach for prioritizing these strategies in current scenarios. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach to forecast a transhipment port's competitiveness on the basis of market share. The potential reasons for escalating or diminishing competitiveness are explored by utilising a multi-criteria decision-making (MCDM) methodology. We assess the competitiveness of transhipment ports by seven major components: connectivity, port facility, efficiency, cost factor, policy and management, information systems and green port management, and contribute to the understanding of the interdependence among the components. We investigate the Bangladesh container market due to the uniqueness of its regional context and dimensions of competitiveness. Bangladesh has direct connections to four regional hubs, namely, Singapore, Colombo, Port Klang and Tanjung Pelepas. These regional hubs compete to grasp a significant volume of container movements in line with the rapid growth of Bangladesh's industrialisation and national economy. We find that Singapore outperforms others in terms of competitiveness, but all ports perform below expectations in green port management practices. These findings would benefit port authorities for improving their competitiveness, and liner shipping companies and shippers in making strategic port choice decisions. • The case of four transhipment ports in the Bay of Bengal. • Detail calculation steps in forecasting transhipment port's market share using ANP. • Singapore outperforms other three transhipment ports in terms of competitiveness. • Evidence of intense competition between the Port of Colombo and Port Klang. • Port policy and management drives green port management practices. [ABSTRACT FROM AUTHOR]

State synchronization with uncertain disturbances is an important issue in networked heat equations, especially in certain industrial production processes. A synchronizing controller based on active disturbances rejection control (ADRC) is developed to deal with this work. First, an observer is designed rely on the boundary output information of each subsystem to estimate/offset the total disturbances which are made of structured time-varying perturbations and unknown disturbances. Then, an adaptive gain controller based on consensus protocol is used to achieve synchronization between each subsystem and the reference system. Plus, the convergence of the networked heat equations to the reference systems' state is established by using Lyapunov functional method, and the well-posedness of the closed-loop system is shown by using established results on an analytic semigroup approach. The simulation example of five networked heat equations with different time-varying disturbances, demonstrates the effectiveness and robustness of the proposed method. • Synchronizing controller design for networked heat equations. • An observer-based controller to estimate/offset the time-varying disturbances. • An adaptive gain control based consensus protocol is used to achieve synchronization. [ABSTRACT FROM AUTHOR]

• Thermodynamic modeling of an IX-SAHP system is developed and verified. • Artificial Neural Network (ANN) method is used to solve the IX-SAHP model. • Performance of the system is assessed through COP and the SCE factors. • The parametric analysis of input design parameters is studied using OVAT. • The IX-SAHP performance is optimized using MOPSO algorithm. • ANP decision-making is used to select the optimum configuration of the IX-SAHP. The current research intends to design and develop the simulation-based multi-criteria optimization (MCO) of the indirect-expansion solar-assisted heat pump (IX-SAHP) used to supply sustainable warm water for buildings. The developed IX-SAHP is verified and its performance is studied in the temperate climate of Iran every month of the year. The parametric analysis (PA) is performed using one-variable-at-a-time (OVAT) approach to analyze the impacts of input variables of the IX-SAHP on the system performance. Furthermore, the single and multi-objective particle swarm optimizations (MOPSO) are integrated with the designed IX-SAHP to maximize the coefficient of performance (COP) and the solar collector efficiency (SCE), independently and concurrently to study the trade-off in the COP and SCE of the system, to achieve the optimal non-dominated solutions in the form of Prato front, and to define the optimal configuration and operating parameters of the system. Eventually, the final optimum configuration is chosen through the Analytic Network Process (ANP) multiple-criteria decision-making (MCDM) method integrated with the MOPSO algorithm using MatLab coupling functions. The comparison of achieved optimum solutions indicates that the MCO scheme provides better comprehensive performance, more appropriate and reliable solutions in comparison with the single-criterion optimization (SCO) schemes. The results of the MCO process indicate that whereas the SCE relatively lessens from 52% to 47%, the COP enhances considerably from 3 to 7 in comparison with the baseline design so that the total operating hour of the optimized IX-SAHP lessens close to 282 h throughout the year that leads to considerable lessening of the total power consumption of the system. This study demonstrates the importance of MCO, OVAT, and MCDMs in the design stages of energy systems to maximize the performance and minimize power consumption of the system. [ABSTRACT FROM AUTHOR]

• Global transportation systems (GTS) resiliency factors have been extracted by SLR. • A conceptual framework of 12 factors and 22 subfactors of GTS resiliency. • A novel spherical fuzzy (SF) Delphi has been developed to screen critical factors. • A novel SF-DEMATEL-ANP approach has been designed to analyse screened factors. • A critical analysis of the policies of an emerging economy toward GTS resiliency. Although transportation systems play a critical role in the global socio-economic facets, they are acknowledged as vulnerable systems directly impacted by unexpected events, e.g., natural calamities, war, traffic accidents, terrorist attacks, and public health. In this respect, improving the resiliency of transportation systems under uncertainty is a controversial global challenge that this study could underpin. To do so, a systematic literature review (SLR) extracted a list of resiliency factors for resilient transportation systems. Next, a novel version of spherical fuzzy Delphi (SFD) screened factors, considering the case of Iran's international maritime transportation system. Moreover, the causal network relationship of the finalised factors was analysed by a novel hybrid spherical fuzzy approach, including a decision-making trial and evaluation laboratory (DEMATEL) and the analytic network process (ANP). Later, the unexpected events that occurred after 2000 were investigated. The SLR deeply investigated 51 of the top relevant articles. As a result, 12 factors and 22 subfactors that affect transportation systems resiliency were extracted. Notably, the rest of the findings primarily apply to the Iranian context. By implementing the SFD, ten factors were screened for Iran's international maritime transportation system and then analysed by SF-DEMATEL. After, the analysed factors were weighted by SFANP, where "recoverability" was selected as the most critical factor, and the "technological and communicational" factor was chosen as the least critical factor. Furthermore, the results provide a critical analysis of the policies adopted by Iran's international maritime transportation system to enhance resiliency under disruptive events. [ABSTRACT FROM AUTHOR]

Fire hazards are a primary concern in aircraft accidents, and controlling combustible materials is pivotal in managing cabin fire risks. This work aims to elucidate the impact of widespread measures that introduce phosphorus-based flame retardants (PFRs) on the fire safety of carbon fiber epoxy composites (CF/EP). Firstly, the hazardous indicators for flame retardant CF/EP composites exhibit two categories of optimization and degradation, which are caused by the intrinsic feature, pyrolysis characteristics and chemical valence states of PFRs. Secondly, the fire hazards of composites are assessed by fusing the analytic network process and information weights guided by Nash Equilibrium theory. The method combines the merits of subjective and objective weights from dimensions of intrinsic hazards, interrelationships and data dispersion of indicators. Finally, the assessed fire hazard rankings under 35 and 65 kW/m2 both are EP > CF/EP16%DOPO-HQ > CF/EP > CF/EP7%DPO. In conclusion, DOPO-HQ (+1) primarily increases the fire hazard of CF/EP due to gas-phase flame-retardant effects, while DPO (+3) predominantly reduces the fire hazard of CF/EP by contributing to condensed-phase effects. This work highlights the contradictory effects of PFRs on the fire safety of CF/EP composites due to structure and thermal stability. The proposed ANP and IW hybridization method provides a theoretical foundation for selecting flame retardants and assessing material fire hazards comprehensively. [ABSTRACT FROM AUTHOR]

• Land use change is one of the most significant drivers threatening wildlife habitats. • There is spatial nonstationarity between bird habitats and land use patterns. • We used expert knowledge to assess bird habitats and the impacts of land use changes. • We determined suitable bird habitats and an effective farmland conservation strategy. • An effective conservation strategy could more than double suitable bird habitat. Land use changes negatively impact agroecosystems, where the loss of wildlife habitats is increasingly threatening global biodiversity. Ecological conservation strategies that seek to reduce habitat destruction are important and strongly rely on understanding the spatial and temporal dynamics of land use change. In recent years, an expert-based approach using the Analytic Network Process (ANP) has been applied commonly to wildlife habitat assessment, and the Conversion of Land Use and its Effects at Small regional extent (CLUE-s) model is a well-known simulation tool used to predict land use dynamics. Although their effectiveness has been proven widely, existing studies have yet to integrate the two approaches into ecological modeling for conserving bird habitats under the influence of land use changes. Thus, there is a notable research gap regarding the potential synergies of the ANP and CLUE-s models to evaluate the spatial nonstationarity between bird habitats and land use patterns. In this study, we integrated use of the ANP to identify suitable habitats for birds on farmland with the CLUE-s model to predict future changes in habitat suitability by 2035. Our study aims to evaluate the efficiency of various conservation strategies for bird habitat in farmland ecosystems within the Zhuoshui River basin in Taiwan. We formulated conservation strategies to protect areas of farmland based on their relative suitability as bird habitats. The research findings reveal a significant reduction of farmland habitats coinciding with expanding built-up areas and forests, while designating farmland areas for conservation purposes effectively ensures the maintenance of bird habitats amidst these transformations. Conserving highly suitable farmland habitats (those whose suitability values ranked at or above the 75th percentile) presents the most effective strategy to mitigate the impacts of land use change on bird habitats. Under this strategy, changes in farmland habitats lead to a notable decrease of 16.4 % in low and very low suitability, whereas farmland habitats with moderate and high suitability significantly expand by 879.8 % and 225.9 %, respectively. Farmland habitats characterized by high suitability are concentrated in the western region of the Zhuoshui River basin, accounting for 2289 hectares. These findings underscore the magnitude of habitat loss resulting from land use changes outside conserved areas. Our research emphasizes the importance of implementing conservation strategies for bird habitats, providing policymakers with a framework to manage farmland ecosystems effectively. [ABSTRACT FROM AUTHOR]

Renewable energy continues occupying the global agenda for the low-carbon transition. Identifying suitable sources for renewable energy involves different criteria and alternatives, which can be defined as a Multi-Criteria Decision-Making (MCDM) problem. MCDM methods can be enriched with Group Decision Making (GDM) to merge individual judgments into group opinions, as well as with fuzzy sets to better simulate human opinions. To handle uncertainties of DMs' preferences, a recently developed fuzzy set extension, the Spherical Fuzzy Sets (SFSs), is combined in this study with different MCDM tools to establish a decision support tool for the energy source selection problem. This article proposes a novel MCDM approach that integrates SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory), SF-ANP (Spherical Fuzzy Analytic Network Process), and SF-VIKOR (Spherical Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje) algorithms in a GDM environment and presents its application in a case study for selecting renewable energy sources in Turkey. Comparing wind, geothermal, solar, hydropower, and biogas, the proposed method highlighted both wind and solar energy as a compromise solution as the most suitable options for Turkey. It has been found that compared to similar fuzzy sets, SFS can better reflect the hesitation degree to address the lack of knowledge and errors in the membership function definitions. The robustness and plausibility of these findings are demonstrated with sensitivity and comparative analyses, suggesting this MCDM method can also be applied to similar problems in other settings. • A new integrated MCDM approach is proposed for selecting renewable energy sources. • The method combines SF-DEMATEL, SF-ANP and SF-VIKOR with GDM. • Its plausibility and usefulness are demonstrated on a case study in Turkey. • Results suggest a compromise solution for wind and solar energy for Turkey. • The method is found to be robust and can be applied to similar energy selection problems. [ABSTRACT FROM AUTHOR]

Consumers across demographics have diverse digital lifestyles and rely heavily on e-commerce to purchase necessities including food and beverages. The COVID-19 pandemic has habituated consumers to making mobile payments at an accelerated pace. Despite rising consumer demand for mobile payments, comparable trends in the supply side for businesses remains crucial for the future sustainability of mobile payments. However, businesses must remain willing to accept mobile payments and engage in continued marketing to promote mobile payment. Few studies thus far have applied multi-criteria decision-making analyses to evaluate the critical determinants of intention and motivation to use mobile payment; quantitative analysis methods, such as factor, regression, and variance analyses, are more commonly applied. This study examined the critical evaluation constructs and criteria that affect retailers and service providers in outlying islands using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as our theoretical basis. It then established a multi-criteria decision-making framework for understanding the factors influencing businesses' mobile payment use. This study uses the multi-criteria evaluation of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and DEMATEL-based Analytic Network Process (DANP) to create network relation maps of the constructs and criteria for the willingness of outlying island retailers and service providers to continue accepting mobile payments. Penghu County is located on the outlying islands of Taiwan and has witnessed a substantial shift in transaction practices among its small and medium-sized enterprises following the outbreak of the pandemic. It experienced a marked decrease in tourist influx during the COVID-19 pandemic, significantly impacting local businesses. This study aimed to better understand the opinions and motivations of businesses in Penghu County regarding mobile payment services. Insights from this perspective should help improve local businesses' adaptability and usage habits. The findings offer significant strategies for the government to provide specific training programs and subsidies or incentives for businesses adopting mobile payment technologies, ultimately expanding mobile payment services across small businesses that target tourists. • E-commerce has become a vital part of consumers' diverse digital lifestyles across demographics, particularly due to the pandemic, which has necessitated the purchase of necessities, including food and beverage delivery. The pandemic has rapidly habituated consumers to the use of mobile payments, increasing the demand for this mode of payment. • Despite rising consumer demand, the sustainability of mobile payments depends on business owners' acceptance of this method. Their willingness to adopt mobile payments is an area that needs more focused marketing efforts. • Most research currently employs quantitative analysis methods, with few studies applying multi-criteria decision-making analysis to evaluate critical determinants behind the intention and motivation to use mobile payments. • This study aims to fill the gap by using the UTAUT2 as a theoretical basis to examine key evaluation constructs and criteria affecting retailers and service providers in outlying islands. A multi-criteria decision-making framework is used to understand the factors that influence the use of mobile payments by business owners. • The study identifies the key constructs and criteria influencing mobile payment adoption, and further examines the potential barriers and facilitators to mobile payment adoption, including technological dependencies, cost implications, and the impact of social influence constructs. • Our research provides practical implications for businesses, policymakers, and stakeholders, offering strategies for expanding mobile payment services and enhancing digital payment ecosystems. services across small businesses targeting tourists. [ABSTRACT FROM AUTHOR]

Driven by the development of global Energy Internet and the requirement of energy transition, the sharing economy mode in energy sector has turned into a novel inspiration. This study aims to analyze an innovative form as a new attempt of sharing economy in China's renewables. The Analytic Network Process method is used as an exploratory approach. A feasibility evaluation system for the Uber-like mode plus renewables project is established from the aspects of technical-economic feasibility, social-environmental feasibility, energy feasibility and reliability feasibility. The Uber-like mode plus wind project, as one specific application, is chosen as a case study to verify the effectiveness of the evaluation model. Based on the evaluation results, SWOT is adopted to discuss the internal conditions and the external environment for Renewables' Sharing Economy in China. The results show that: (1) The Energy with Uber-like Mode is theoretically feasible, and it can be a prime example of the "Energy Internet and Sharing Economy" scheme. (2) The feasibility evaluation system can be applied to regulate the key problems in the Renewables' Sharing Economy. (3) Analyzed by the SWOT, the unfavourable factors should be paid more attention to the Uber-like mode plus renewables project than the favorable factors. • Uber-like mode plus renewables as a development scheme of Renewables' Sharing Economy. • Incorporation of ANP model into Feasibility Evaluation analysis. • A case study on the Uber-like mode plus wind project. • Application of SWOT analysis method for Uber-like mode plus renewables project. [ABSTRACT FROM AUTHOR]

Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naïve Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naïve Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%). Unlabelled Image • Landslide susceptibility modeling plays a significant role in disaster prevention. • Three models (FDEMATEL-ANP, NBC, and RFC) were applied and compared. • The ROC curve was used for evaluating and comparing the performance of the results. • Random forest classifier (RFC) produced the best result for susceptibility assessment. • An accurate model of susceptibility is helpful for landslide risk mitigation and planning. [ABSTRACT FROM AUTHOR]

Objectives: The quality-adjusted life-year (QALY) has been long debated, but alternative estimation approaches have not been comprehensively evaluated. Our objective was to identify alternatives, characterize them by implementation feasibility, and evaluate the impact of implementing feasible options in cost-effectiveness models developed for the Institute for Clinical and Economic Review reports.Methods: We conducted a literature review combining keywords relating to QALYs, methodology alternatives, and cost-effectiveness in PubMed, EconLit, Web of Science, and MEDLINE. Articles that discussed alternatives to the conventional QALY were included. Alternatives were characterized by type, data availability, calculation burden, and overall implementation feasibility. The subset of feasible alternatives, that is, sufficient data and methodology compatible with incorporation into common modeling approaches, were evaluated according to impact on incremental QALYs, incremental net monetary benefit (iNMB), intervention rankings, and proportion of interventions with a positive iNMB.Results: We identified 28 articles discussing 9 alternatives. Feasible alternatives were using patient preference (PP) data; equity weighting according to baseline utility, fair innings, or proportional QALY shortfall; and the equal value of life-years-gained approach. All alternatives affected the incremental QALY and iNMB outcomes, rankings, and proportion of interventions with a positive iNMB. The PP alternative had the largest and most consistent impact. The PP impact on the proportion of interventions with a positive iNMB, was in the negative direction.Conclusions: Our work is the first comprehensive evaluation of proposed alternatives to the conventional QALY. We found robust literature but few options that were feasible to be implemented in current healthcare decision-making processes. [ABSTRACT FROM AUTHOR]

This study uses industrial upgrading (I), export development (E), and investment promotion (K), or collectively, the IEK model to identify critical indicators to asses competitiveness of the service industry in Taiwan. The proposed development and evaluation model for the service industry uses the DEMATEL (decision-making trial and evaluation laboratory) method to construct an influential relationship between the dimensions and criteria; a DEMATEL-based analytic network process (DANP) to obtain influential weights; and a modified Vlšekriterijumska Optimizacija I Kompromisno Resenje (m-VIKOR) method to assess gaps in industry development performance. It illuminates current industry demands and helps to improve service firms' operations through fewer developmental gaps and stronger policy and strategy. Further, an influential network relationship map (INRM) and influential weights can unravel the complex assessment system and prioritise industry improvements. The results demonstrate that upgrades must occur first to the industry, then to promoting of investments, and lastly, in developing exports. Finally, the practical implications for managing service firms have been discussed. [ABSTRACT FROM AUTHOR]

China has been at the global forefront of alternative fuel vehicle development, including methanol vehicles, in a bid to achieve economic benefits and improve environmental outcomes for the nation. Methanol vehicle pilot projects completed in ten Chinese cities have demonstrated that methanol vehicles can reduce costs and emissions. In considering whether methanol vehicles should be deployed more widely in China, there is a role for bespoke decision-making methods as an aid for assessing the prospects for such an expansion and developing the strategies needed for implementation. In this paper, a FANP (fuzzy analytic network process) - SWOT (strengths, weaknesses, opportunities, and threats) method was proposed and applied to assess the prospects for methanol vehicles in China. Based on the proposed method, an evaluation criteria system that includes 12 criteria from social, technological, economic, environmental, and political fields was established. Each criterion was weighted based on interviews with 16 experts who have extensive experience and expertise related to policy development for methanol vehicles in China. As a result, 10 strategies for the further implementation of methanol vehicles in China were proposed and prioritized. The results form the basis for six proposed policy implications for the further deployment of methanol vehicles. Policies for developing core technologies for methanol vehicles to achieve socioeconomical and environmental benefits are given the highest priority. • Novel fuzzy analytic network process combined with SWOT analysis is proposed. • The proposed method is applied to assess prospects of methanol vehicles in China. • Strategies for methanol vehicle deployment in China are proposed and prioritized. • Six policy implications for further deploying methanol vehicles are proposed. [ABSTRACT FROM AUTHOR]

In part I of this series of articles, the concept of overall system reliability was presented for two applications: reliable estimation of variables for steady state linear flow processes, and reliable fault detection and diagnosis for any process. In this part, systematic generation of the proposed system-wide reliability expression is discussed. In particular, an approach for generating the system reliability using the sum of disjoint product method is presented. This serves as the objective function to be maximized in various constrained optimization formulations for sensor network design, which are also proposed in this work for both applications. A heuristic is proposed to solve the resulting nonlinear integer programming problems. The sensor network design formulations are applied to two benchmark case studies: (1) Tennessee Eastman process, (2) Steam metering process. Results indicate the utility of the proposed sensor network design approach in designing optimal sensor networks that maximize system reliability. • System reliability objective is efficiently generated for sensor network design. • Sum of disjoint product based algorithm is proposed for this efficient generation. • Optimization formulations for sensor network design presented. • Heuristic to solve the resulting nonlinear integer programming problem proposed. • Sensor network design approach applied to two benchmark case studies. [ABSTRACT FROM AUTHOR]

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks. • New definition of system reliability objective proposed for sensor network design. • Idea demonstrated for fault detection and diagnosis, and estimation of variables. • Dual approaches for system reliability computation proposed. • Analogies between proposed and classical reliability definitions presented. • Ideas demonstrated using illustrative and comparative studies. [ABSTRACT FROM AUTHOR]

Eighty per cent of the people without access to electricity live in rural areas. Due to high investment costs in the grid, the solution to providing electricity to these people will mainly rely on the installation of islanded hybrid microgrids. Designers need to consider a variety of factors for the optimal design of hybrid microgrids. However, many of these criteria are qualitative or uncertain. This paper provides a novel methodology to assess the influence of such criteria in the design of a Hybrid Microgrid of Renewable Energy Sources (HRES). The method combines context analysis, literature review and the Analytic Network Process (ANP) through panels of experts and surveys. The methodology ranked the criteria and helped to design a HRES in an isolated Honduran rural community in the Mesoamerican Dry Corridor. The study presents a review and classification of the main criteria and energy technologies considered for the design of HRESs in rural communities. The most influential factors turned out to be the institutional support, the possible expansion of the grid to the community and the availability of local energy resources. Regarding energy technologies, photovoltaic and wind power ranked as the preferred followed by a biomass gasifier as backup. • Access to electricity will largely depend on new microgrids. • The article presents a novel methodology to select energy technologies of a HRES. • Institutional support largely affects the design of hybrid microgrids. • Solar PV is the preferred energy technology for a microgrid in Honduras. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making (MCDM) methods have become increasingly popular in site selection decision-making of renewable energy power plants because they consider multiple conflicting goals and decision-maker preferences. In this paper, a systematic literature review of MCDM applications for renewable energy site selection is performed, covering a total of 85 papers published from 2001 to 2018 in high-level journals (chosen from the ScienceDirect database). This paper has summarized the exclusion criteria and evaluation criteria of site selection for five energy sources. The five site selection stages, criteria selection, data normalization, criteria weighting, alternative evaluation and result validation, are revealed by content analysis. All papers are further classified by the date, energy source, journal of publication, country of author affiliation, and study area. It is found that different energy sources emphasize different criteria; however, some similarities exist. Literature surveys and expert opinions are most common criteria selection method. Reclassification is most frequently used data normalization method. The analytic hierarchy process is popular for weighting. In alternative evaluation, geographic information systems and weighted linear combination are the most popular tools. Varying the criteria weights is most commonly used result validation method. • This is the first review of MCDM applications for renewable energy site selection. • It covers five energy sources: solar, onshore wind, offshore wind, wave and tidal. • It focuses on two types of criteria and methods in the five site selection stages. • It also involves methods for data normalization and result validation. • The combination of GISs and MCDM is the most popular tool in this research area. [ABSTRACT FROM AUTHOR]

• Choosing a landfill location is a complex process. • Many influential factors must be assessed and evaluated. • The factors could be physical, environmental, social, or economical. • Multi Criteria Decision Making (MCDM) can help the decision making process. • With enough time and resources, Fuzzy Analytical Network Process is most effective. Although a lot of research has focused on energy recovery and recycling of valuable materials from wastes to reduce loads on landfills, landfilling is still an inseparable part of solid waste management. Since landfills potentially pose a risk to human health and the surrounding environment, landfill site selection is an important endeavor that has to be carried out with great care. Choosing a suitable location for landfills depends on several factors and is a complicated procedure. In this paper, a comprehensive review of landfill site selection methods, with an emphasis on multi criteria decision making (multiple criteria decision analysis) has been presented. Assessments indicate that the most frequently used methods are weighted linear combination (WLC), ordered weighted averaging (OWA), analytic hierarchy process (AHP), fuzzy analytic hierarchy process (F-AHP), TODIM, fuzzy TODIM, analytic network process (ANP), fuzzy-analytic network process (F-ANP), and grey systems theory, each of which is discussed. The most widely used factors for selecting a landfill site are groundwater depth, surface water vicinity, elevation, land slope, soil permeability, soil stability, flooding susceptibility, lithology and stratification, faults, land use type, nearby settlements and urbanization, cultural and protected site vicinity, wind direction, roads, railways, proximity to building materials, pipelines and powerlines, and proximity to airports. Image, graphical abstract [ABSTRACT FROM AUTHOR]

To stimulate the pro-environmental behavior of college students and cultivate the public consciousness of protecting the ecological environment, based on referring to the current research results, this article summarizes five dimensions and 17 major variables that may impact college students’ environmental behaviors and uses the decision making trial and evaluation laboratory based analytic network process method (DEMATEL-based ANP) combining decision making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP). The regulation mechanism of influencing elements of college students’ pro-environment behavior decision making is quantitatively studied, including impacting degree and mutual relations among factors, and key variables are identified. The results show that the emotion cognition, governmental and social dimensions of college students are the reason factors, while the subjective dimension of college students and the dimension of universities are both outcome factors and key factors; knowledge of the environment, geographical features, relational network, construction of environmental protection in colleges and universities, and national development idea are the most influential elements among the five dimensions respectively; The emotion cognition dimension of college students, campus environmental protection propaganda, environmental protection construction in colleges and universities, environmental protection course offering, model effect, personal responsibility, and values are the seven key factors. The study results not only confirm that DANP can effectively recognize the mechanism of influencing factors of college students’ pro-environment behavior, but also offer conductive decision-making reference for standardizing college students’ environmental behavior. [ABSTRACT FROM AUTHOR]

This paper presents an integrated Fuzzy Analytical Hierarchy Process (f AHP) and Weighted Fuzzy Axiomatic Design (wf AD) methodology for a strategic level problem of hydrogen energy storage (HES) method selection for Turkey. Considering alternatives of tank, metal hydride and chemical storage, we design our decision problem with respect to five assessment criteria as weight, capacity, storage loss and leak, reliability, and total system cost. In the solution methodology, we first implement f AHP to determine criteria weights by incorporating linguistic judgement and vague information. Next, we define functional requirements and design parameters for the selection problem and use wf AD to compare alternatives. After implementing the proposed integrated approach, we perform additional sensitivity analysis experiments to test the robustness of the solution with respect to changes in functional requirements. • We consider a hydrogen energy storage method selection problem for Turkey. • We incorporate three alternatives as tank, metal hydride, and chemical. • We use fuzzy analytical hierarchy process to determine criteria weights. • We use weighted fuzzy axiomatic design to select the most appropriate alternative. • Our results reveal that metal hydride is the most appropriate solution. [ABSTRACT FROM AUTHOR]

• Three factors are ascertained to measure the effects of disruptions. • Real-time prediction requirements are particularly considered in the model. • The model shows high accuracy in predicting the effects of disruptions. • The model shows strong generalizability on two different high-speed railway lines. Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L , N , and T , respectively. [ABSTRACT FROM AUTHOR]

With the exponentially growing earth observation data of specific sensor-determined resolutions and update frequencies, earth observation has irreversibly arrived in the Big Data era, enabling new insights in science and engineering. With great opportunity comes great challenges regarding efficient and effective data management because earth observation data is of different scales and characterized by complexity in spatial relationships related to the real world. To overcome the challenges is crucial for, for instance, data mining, land surveying, and especially emergency mapping for disaster response. To improve the querying efficiency of big earth observation data, we proposed a novel data management approach: Global Multi-scale Grid Integer Coding and Spatial Indexing. Among our contributions are: (1) proposing Global Multi-scale Grid Integer Coding Model (GMGICM), which presents clustering property in both the scale dimension and spatial dimension, and theoretically facilitates an efficient querying; (2) deliberately applying GMGICM on multi-scale earth observation data spatial indexing, which results in one-dimensional data index, which can be queried using simple B-tree, inversion, and other one-dimensional indexes; (3) designing a strategy to assure the completeness of spatial querying, which is not well solved by existing grid-based coding models. The advantages of our proposed approach have been demonstrated with both simulated and real remote sensing data, with spatial operation 20 times as fast as Geohash and spatial querying 10 times as fast as Oracle Spatial on average. The proposed approach can be easily adapted for three or higher-dimensional earth observation data and bring potential benefit to all big earth observation data analytic projects. [ABSTRACT FROM AUTHOR]

Owing to the intricate nature of the ecological environment and spatial complexity, effectively mitigating over-exploitation and ecological harm poses a challenge during the development and construction of mountain scenic areas. Ttraditional ecological sensitivity assessment employed in mountain scenic areas mainly focuses on ecological protection, neglecting the dynamic interplay between ecology and the evolution of construction land.Taking Xiandu mountain scenic area, a representative mountainous development zone in Jinyun County, Lishui City, Zhejiang Province, China, as a study area, this study employs the minimum cumulative resistance model (MCR) to simulate the resistance encountered by ecological sources and construction activities during the expansion process. The study establishes an evaluation framework for development in mountain scenic areas, considering ecological processes both vertically and horizontally. Quantitative analyses are performed through ArcGIS and the analytic network process (ANP) to delineate development adaptability zones in the mountain scenic area. The study utilizes grid mutation points to determine the zoning threshold for development suitability in mountain scenic areas, resulting in the categorization of these areas into four distinct types of development zones: most appropriate for construction, suitable for construction, not suitable for construction, and least suitable for construction.The partition result shows that the most appropriate construction area constitutes 6.68% of the primary scenic area, while the suitable construction zone occupies 40.64% of the main scenic expanse.The study can expand the application area of the MCR model, and the evaluation framework can augment the accuracy and breadth for future mountain scenic area development researches. [ABSTRACT FROM AUTHOR]

Partially observable Markov decision processes (POMDPs) is a rich mathematical framework that embraces a large class of complex sequential decision-making problems under uncertainty with limited observations. However, the complexity of POMDPs poses various computational challenges, motivating the need for an efficient algorithm that rapidly finds a good enough suboptimal solution. In this paper, we propose a novel accelerated offline POMDP algorithm exploiting Anderson acceleration (AA) that is capable of efficiently solving fixed-point problems using previous solution estimates. Our algorithm is based on the Q-function approximation (QMDP) method to alleviate the scalability issue inherent in POMDPs. Inspired by the quasi-Newton interpretation of AA, we propose a maximum entropy variant of QMDP, which we call soft QMDP , to fully benefit from AA. We prove that the overall algorithm converges to the suboptimal solution obtained by soft QMDP. Our algorithm can also be implemented in a model-free manner using simulation data. Provable error bounds on the residual and the solution are provided to examine how the simulation errors are propagated through the proposed algorithm. Finally, the performance of our algorithm is tested on several benchmark problems. According to the results of our experiments, the proposed algorithm converges significantly faster without degrading the solution quality compared to its standard counterparts. [ABSTRACT FROM AUTHOR]

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia. [ABSTRACT FROM AUTHOR]

The high dependence of single and dual objective optimization algorithms and commercial energy optimization tools on economic criteria has a negative impact on the sub-goals of energy systems. This is due to the typical inverse relationship between economic affairs and other objectives, such as reducing emissions, minimizing energy waste, and enhancing energy security. Optimizing energy systems with high sustainability indexes becomes even more challenging, particularly for off-grid applications that cater to essential human needs like food and water. This need for a practical global decision-making framework based on objective/subjective prioritization has led to a knowledge gap in maximizing sustainability indexes through optimization methods. In this study, for the first time, the 169 targets of Sustainable Development Goals (SDG17) developed by the United Nations are applied to determine the influence of criteria in the relation map using the DEMATEL (Decision Making Trial and Evaluation Laboratory) method. The developed method is applicable to all countries; however, Iran was selected as a case study due to the significant challenge of providing a sustainable power supply for agricultural demands in this country. The resulting relation matrix is then imported into the Analytic Network Process (ANP) to enhance the reliability of criteria weighting. Finally, the weighted multi-criteria decision-making (MCDM) method is employed to address the real greenhouse demand, wherein potential power supply solutions are determined using the predictive dispatch strategy in the HOMER tool. The hybrid solutions include conventional PV, agri-photovoltaic (APV) units, wind turbines, a diesel generator, and a battery bank. The results of the SDG17-DEMATEL-ANP method indicate the prioritization of environmental, economic, technical, energy security, and social objectives, with total decision influence weights of 22.6%, 22.1%, 21.2%, 18.4%, and 15.7% respectively. While the commercial optimization tool shows a higher affordability of conventional PV compared to APV units, the proposed method demonstrates that implementing APV units, despite a slight increase in energy cost (reaching 0.137 $/kWh), can result in a higher sustainability index of about 75.5%, over 48% renewable fraction, <1% unmet load, and >7% improvement in CO 2 emission reduction. These advantages are achieved while significantly enhancing the land usage index and without any negative effects on excess electricity levels, capacity factor, and total energy system life cycle emissions. Therefore, the developed framework can be utilized by investors and agricultural demand owners to select energy systems with higher sustainability indexes. • The 169 targets of SDG17 were applied to determine the DEMATEL-ANP influence matrix. • Environmental and economic objectives show the highest effectiveness with >21% weights. • Applying agri-photovoltaic improved the sustainability indexes of the off-grid energy system. • Optimum energy cost of <0.14 $/kWh and renewable fraction of >48% were achieved. • The proposed weighted MCDM method led to lower CO 2 emissions and higher land utilization. [ABSTRACT FROM AUTHOR]

The sustainable design of infrastructure involves assessing economic, environmental, and social impacts. While significant progress has been made in evaluating economic and environmental life cycle impacts since the Paris Agreement, there's a notable gap in techniques for assessing social aspects in infrastructure design. This study introduces social indicators tailored for evaluating the lifecycle of railway infrastructures. The indicators are applied to assess the social impacts of three common railway track substructure solutions: conventional ballasted track, embedded slab track (BBEST solution), and sleeper-based, ballastless (RHEDA2000) substructure solutions. Using the Analytic Network Process (ANP), the social performance of each alternative is synthesized into a single indicator for comparison. Results indicate that the conventional ballasted track outperforms, scoring 12% higher than BBEST and 61% better than RHEDA in social terms. This is attributed to its reliable capacity for generating high-quality employment and fostering economic activities in the defined product system regions. • Life cycle assessment of three alternative railway track substructures. • Quantitative impact indicators proposed for Social Life Cycle Assessment. • Analytic Hierarchy Process-based sustainability scoring of alternatives. • Conventional ballasted railway tracks perform best from a social point of view. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of reliable and green routing in a road-rail multimodal transportation network. To enhance network reliability, types of disruptions occurring simultaneously in each node and arc are identified. Additionally, carbon emission is considered a risk caused by transportation process disruptions for achieving green routing. A combination of rating methods such as DEMATEL-based Analytic Network Process (DANP) and the Combined Compromise Solution (COCOSO), along with Failure mode and effects analysis (FMEA) Criticality reliability assignment method, is used to evaluate disruptions and select a safe route from origins to destinations. The DANP method produces a ranking of sub-criteria, and the COCOSO method produces a ranking of criteria in the network. The FMEA Criticality reliability assignment method is used to assign reliability to each option. Next, the route with the highest reliability from each origin to each destination is selected. The presented model investigates a cement road-rail transportation network in Iran. After analyzing the sensitivity of criteria and sub-criteria, the results provide best routes with increased reliability and reduced emissions risk. • Identifying risks in each node and arc in the transportation network. • Prioritizing criteria, sub-criteria, and options using a mix of DANP and COCOSO ranking methods. • Assessing the reliability of each option using the FMEA Criticality method. • Selecting the best path with the highest reliability and the less carbon emission risk in each O-D pair. • Considering a case study in the cement road-rail transportation network in Iran. [ABSTRACT FROM AUTHOR]

Resilient regional development can be favoured employing suitable decision-aiding techniques. Multicriteria Sorting Analysis tools are increasingly being used to deal with complex issues. The Group Analytic Network Process Sorting II method (GANPSort II), which builds on the previous ANPSort II and I, is here developed for the territorial resilience assessment of the Champagne-Ardenne in France, a broad-level Socio-Ecological System. This method can sort a large number of alternatives from the most to the least resilient by reducing the number of pairwise comparisons. In this way, an adaptive response can be provided to system perturbations. The evaluation is supported by cognitive and spatial analyses and allowed the definition of a set of resilience indicators as components of the evaluation model. A pool of multidisciplinary experts joined the evaluation to sort the alternatives into specific resilience classes. GIS completion facilitates the outcomes visualization of both solo and grouped expert sorting. The results demonstrate that the GANPSort II enriches the existing sorting methods scenario in handling planning problems, such as territorial resilience assessment in the regional planning of broad systems, proving its decision-making utility for spatial planners and public bodies engaged in the co-design of resilient strategies. • A decision-aiding model is developed for territorial resilience assessment of SESs. • GANP Sort II can solve problems with interdependencies and numerous alternatives. • The application sorts the territorial resilience of the Champagne-Ardenne, France. • The results support both public bodies and urban planners to design strategies. [ABSTRACT FROM AUTHOR]

The increasing demand for oil, natural gas, and green energy has led to massive construction of storage and transportation infrastructure, and the resulting corrosion problems have attracted increasing attention. The complexity of the factors influencing corrosion and the uniqueness of infrastructure pose great challenges to the inspection, assessment, and prediction of corrosion. This challenge is reflected in the large number of influencing factors and the intricate interrelationships between them, which often require the involvement of experts in the assessment. However, due to reasons such as incomplete data and the complexity of corrosion mechanisms, there is uncertainty in the expert's judgment. To address this problem, this work proposes a multilevel, multi-index framework for corrosion evaluation. This framework integrates both analytic network process and comprehensive evaluation method under an intuitionistic fuzzy environment. It uses the triangular intuitionistic fuzzy numbers-based analytic network process (TIFANP) to establish and analyze the weights of the interactive and employed triangular intuitionistic fuzzy comprehensive evaluation (TIFCE) to address the subjective judgment and uncertainty issues in corrosion evaluation. The introduction of the intuitionistic fuzzy concept effectively handles the uncertainty in corrosion assessment. To verify the feasibility and practicability of the framework, it was applied to external corrosion assessment of the floor of a crude oil storage tank. Soil corrosion, cathodic protection, coating protection, and stray current interference were considered the main factors influencing the external corrosion of the storage tank floor. The assessment results were consistent with the open-tank inspection. This work can provide valuable information for storage tank operation, corrosion protection, and maintenance to reduce economic losses and safety accidents caused by the corrosion of crude oil tank floors. [ABSTRACT FROM AUTHOR]

This study explores the recent use of the Analytic Network Process (ANP) in the decision process in the areas of economics, finance and management to identify common contingency factors, current trends, representative studies and directions for further research applications in the target areas. A systematic literature review of 434 ANP studies for a 10-year period (2012–2021) within the Scopus database was conducted using the keyword "Analytic Network Process" in articles indexed in the following two categories: (1) Business, Management and Accounting, and (2) Economics, Econometrics and Finance. Further analysis using a citation-based graph and contingency analysis approaches was performed to identify usage trends. Our findings indicate that the most common ANP applications are with sustainable supply chain management and business evaluation frameworks. There is also a trend of applications engaging stakeholders in the decision-making process. Finally, it was found that the ANP is most commonly used as part of a multicriteria multi-method (a method followed by others) or integrated decision-making (hybridization of methods) approach rather than alone. The most common use (>80%) of the ANP is as part of a multi-method or integrated method with other tools such as DEMATEL, which suggests these approaches, in particular integrated ones (>50%), are becoming the preferred method of analysis to simplify the ANP process. From a practical point of view, it was found that the ANP is particularly utilized in sustainable projects to facilitate the participation of various stakeholders. This is the first focused review of the use of the ANP in the areas of economics, finance and management with an emphasis on its application as well as its contingent factors. Also, representative studies have been highlighted in each area. Traditional reviews have not delved deeply into the areas and contingent factors that this study explores. [ABSTRACT FROM AUTHOR]