Search

In order to rapidly evaluate the working performance of an Electric Power Steering System (EPS) and clarify the key indicators that affect its working condition, targeted maintenance measures can be taken to improve the vehicle's handling, stability, and safety. This paper took an EPS system as the research object and decomposed it into five working indicators: steering lightness, steering returnability, steering wheel mid-zone performance, steering wheel vibration, and power assist characteristics. On this basis, the subjective exponential extension Analytic Hierarchy Process (AHP) was combined with the objective improved entropy weight method, and then the combination weighting method of game theory was employed to ensure that the results were reliable. At the same time, a cloud model was introduced to understand the actual situation of each indicator by using cloud parameters and multi-level feedback results. According to the principle of maximum membership and the comprehensive evaluation method, the EPS performance evaluation result was obtained. The results showed that the comprehensive cloud parameter of EPS was U (74.31, 6.08, 0.50), which indicated that its working performance was in good condition, with some minor damage that did not affect its normal use; however, attention should be paid to the maintenance and repair of vulnerable components to ensure the handling, stability, and safety of the vehicle. [ABSTRACT FROM AUTHOR]

Properly designed and installed interior stairs provide safe and convenient access to all residents of the house, including children and elderly people. While safety considerations are paramount for stair design and placement decisions in a two-storey single-family house, sustainability becomes another important factor in these deliberations. Nevertheless, safety and sustainability of the interior stairs can be seen as conflicting objectives in decision-making processes (i.e., while a larger staircase area positively affect residents' climbing ability, it also causes higher energy consumption and material usage). Therefore, increased awareness of stair-project selection processes is required when multi-objective tasks are involved. Seven criteria affecting stair safety and sustainability in a single-family house are identified in this paper. The integrated AHP-MEREC criteria-weighting approach is proposed, to determine their weights in decision-making processes. Four different multi-criteria decision-making (MCDM) approaches, ARAS, SAW, TOPSIS and PROMETHEE, were used to determine alternative evaluations. Since the selection of the most suitable MCDM method is full of uncertainties, the novel combined-decision-making approach formally called CORST (COmbining MCDM appRoaches using method STability coefficient) is proposed in this paper. The new method stability coefficient is used to determine the stability and reliability of the specific MCDM approach. Nine U-shaped inside-staircase projects dedicated to a single-family house were evaluated, to test the effectiveness of the CORST approach. [ABSTRACT FROM AUTHOR]

This research aims to bridge a critical gap within the realm of logistics coordination, specifically targeted at bolstering the coordination of flows in omni-channels. The outcome of this study culminates in the creation of a comprehensive tool for evaluating logistics operators, discerning those who exhibit supreme proficiency in orchestrating network dynamics within omni-channel contexts. To fulfil the objectives of this paper and elucidate pertinent research inquiries, an exhaustive literature review is coupled with meticulous scrutiny of the SCOPUS database via the advanced VOSviewer 1.6.20 software. The research delineates an array of coordination mechanisms accessible to logistics operators, which can be judiciously tailored as a bespoke fusion of market-driven, social, hierarchical, and logistical coordination tactics. These mechanisms expand upon antecedent investigations, encompassing both network coordination paradigms and the pivotal role of logistics operators within omni-channel frameworks. Experts assessed that forecasting network flows is the most significant element in logistical coordination, receiving a weight of 0.1312, while managing network participants' resources from the logistics operator level received a weight of 0.1148. A tangible contribution to the academic discourse transpires as we introduce a pioneering tool meticulously designed for stakeholders entrenched in omni-channel distribution networks. Termed the 'Multicriteria Assessment Sheet for Evaluating the Coordination Competence of Logistics Operators within Omni-channel Systems,' this instrument augments the scholarly landscape. [ABSTRACT FROM AUTHOR]

The purpose of this research paper is to present a framework for selecting a biogas plant for the Indian rural community, considering performability factors such as reliability, quality, maintainability, safety, and sustainability. This will ensure that the plant operates reliably, efficiently, and safely over its entire life cycle and can play a significant role as a decision-support tool for decision-makers (e.g., managers, engineers, stakeholders). The proposed framework integrates the Analytic Hierarchy Process (AHP), and the Weighted Aggregated Sum Product Assessment (WASPAS) to optimally evaluate and prioritize the best alternative based on performability factors. The findings show that the suitable biogas plant in the context of the Indian rural population is a fixed-dome-type plant. The decision-making process in selecting the best biogas plant can be effectively aided by using this suggested tool. Currently, there are no proper tools or methods for selecting biogas plants for rural areas due to a lack of data or relevant literature on operational issues. The proposed method uses performability factors for the selection, which has not been researched so far. Moreover, the AHP–WASPAS approach offers a robust method for selecting biogas plants, ensuring efficient and sustainable energy production. The proposed method will help policymakers and stakeholders to choose the best biogas plant in the context of Indian rural application. [ABSTRACT FROM AUTHOR]

Freeze–thaw (FT) erosion intensity may exhibit a future increasing trend with climate warming, humidification, and permafrost degradation in the Qinghai–Tibet Plateau (QTP). The present study provides a reference for the prevention and control of FT erosion in the QTP, as well as for the protection and restoration of the regional ecological environment. FT erosion is the third major type of soil erosion after water and wind erosion. Although FT erosion is one of the major soil erosion types in cold regions, it has been studied relatively little in the past because of the complexity of several influencing factors and the involvement of shallow surface layers at certain depths. The QTP is an important ecological barrier area in China. However, this area is characterized by harsh climatic and fragile environmental conditions, as well as by frequent FT erosion events, making it necessary to conduct research on FT erosion. In this paper, a total of 11 meteorological, vegetation, topographic, geomorphological, and geological factors were selected and assigned analytic hierarchy process (AHP)-based weights to evaluate the FT erosion intensity in the QTP using a comprehensive evaluation index method. In addition, the single effects of the selected influencing factors on the FT erosion intensity were further evaluated in this study. According to the obtained results, the total FT erosion area covered 1.61 × 106 km2, accounting for 61.33% of the total area of the QTP. The moderate and strong FT erosion intensity classes covered 6.19 × 105 km2, accounting for 38.37% of the total FT erosion area in the QTP. The results revealed substantial variations in the spatial distribution of the FT erosion intensity in the QTP. Indeed, the moderate and strong erosion areas were mainly located in the high mountain areas and the hilly part of the Hoh Xil frozen soil region. [ABSTRACT FROM AUTHOR]

Coal mines' water inrush is one of the five major disasters that affect the safety of coal mine production. The assessment of coal mines' water inrush is a prerequisite for preventing and controlling coal mines' water inrush. To objectively and effectively evaluate the risk of water inrush in the coal seam roof and overcome the shortage of single assignment methods, two methods, the analytic hierarchy process and the entropy method, are used in this paper to determine each evaluation factor's subjective and objective consequences. Game theory is applied to obtain the combined weights of each influencing factor to make up for the lack of a single assignment method. Taking the roof of Coal Seam No. 7 in mining Areas I and II of Dananhu Coal Mine No. 7 as an example, six primary evaluation indexes are created to control water inrush in the coal seam roof. The comprehensive weights of each index is determined; a vulnerability index evaluation model is established; and the results of the water inrush risk zone in the coal seam roof of Dananhu Coal Mine No. 7 are obtained using the GIS spatial analysis function. The results show that the discriminatory effects of the zoning model have a high accuracy and can provide a reference basis for future coal seam mining control work in this mine. [ABSTRACT FROM AUTHOR]

Biological retention facilities are widely used in the construction of new urban infrastructure because of the increasingly serious problems of urban waterlogging and rainwater pollution. At present, a common method to resolve these problems is to improve the treatment performance of these facilities to improve the soil filling layer. How to evaluate the comprehensive benefits of the improved soil filling layer is a problem that cannot be ignored. In this paper, data from seven groups of soil improvement schemes were collected through laboratory tests, and an evaluation method based on AHP-CRITIC was proposed. Firstly, the evaluation indexes of soil improvement in biological retention facilities were determined, and their subjective and objective weights were determined by the AHP method and CRITIC method, respectively, and the comprehensive weights were further calculated. A comprehensive evaluation model was established by the TOPSISI method. Finally, the AHP-CRITIC evaluation model determined that 10% zeolite-amended soil had the best comprehensive evaluation effect, followed by 20% zeolite-amended soil and 10% gravel-amended soil. The results show that the evaluation model can comprehensively assess the benefits of soil improvement in bioretention facilities from the levels of infiltration, purification, and economy and can then select the optimal program for use in actual engineering practice. [ABSTRACT FROM AUTHOR]

The concrete-filled steel tubular (CFST) arch bridge has achieved significant development in recent years due to its unique mechanical performance and technical advantages. However, due to the lagging theoretical research compared to engineering practice, many problems have been exposed in the existing bridges, resulting in adverse social impacts and enormous economic losses. With the increasing prominence of safety issues in CFST arch bridges, it is necessary to assess their safety condition in service. This paper establishes a safety assessment index system for CFST arch bridges using the fuzzy analytic hierarchy process (AHP) based on an exponential scale. The assessment method proposed includes the following main points: (1) Bridge safety assessment is closely related to the load-bearing capacity of components. This study proposes an assessment index that comprehensively considers both the defect conditions and the design load-bearing capacity of components for the safety assessment. (2) The exponential scale method is introduced to safety assessment for the first time, and the AHP based on an exponential scale is applied to calculate the component weights. (3) Considering the specific structural characteristics of CFST arch bridges, this study provides a detailed division of component types and calculates the component weights. By combining the component assessment indexes, a comprehensive safety assessment index system is established, and a safety assessment method for CFST arch bridges is proposed. (4) Taking the Jiantiao Bridge in Zhejiang Province as an engineering case, the load-bearing capacity of components is calculated using finite element software ANSYS 19.1. Based on the established safety assessment index system, the safety of the bridge is assessed by integrating the inspection results. (5) Software for the safety assessment of a CFST arch bridge is developed using Visual Basic, and the assessment results align well with the actual condition of the bridge. [ABSTRACT FROM AUTHOR]

This study aims to optimize the Environmental Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) of NiZn batteries using Pareto Optimization (PO) and Multi-objective Particle Swarm Optimization (MOPSO), which combine Pareto optimization and genetic algorithms (GA). The optimization focuses on the raw material acquisition phase and the end-of-life phase of NiZn batteries to improve their sustainability Key Performance Indicators (KPIs). The optimization methodology, programmed in MATLAB, is based on a formulation model of LCC and the environmental LCA, using data available from the Ecoinvent database, the OpenLCA software (V1.11.0), and other public databases. Results provide insights about the best combination of countries for acquiring raw materials to manufacture NiZn and for disposing of the waste of NiZn batteries that cannot be recycled. These results were automatically linked to some sustainability KPIs, such as global warming and capital costs, being replicable in case of data updates or changes in production or recycling locations, which were initially considered at Paris (France) and Krefeld (Germany), respectively. These results provided by an AI model were validated by using a sensitivity analysis and the Analytical Hierarchy Process (AHP) through an expert panel. The sensitivity analysis ensures the robustness of mathematical parameters and future variations in the market; on the other hand, the AHP validates the Artificial Intelligence (AI) results with interactions of human factors. Further developments should also consider the manufacturing and use phases in the optimization model. [ABSTRACT FROM AUTHOR]

Mining activities in coal mining areas have exacerbated ecological and geological environmental risks. To explore the impact of mineral resources on the ecological and geological environment risk (EGER) in coal mining areas, we developed a novel ecological and geological risk assessment framework. This framework first quantifies the impact of mining activities on the surface of coal mining areas using remote sensing interpretation and Differential Interferometric Synthetic Aperture Radar (DInSAR) technology. Then, this framework selected six indicators, including subsidence, surface occupation and damage, FVC, RSEI, precipitation, and temperatures. The weights of the evaluation indicators were calculated using a coupled weighting model combining the Analytic Hierarchy Process (AHP) and the Entropy Method (EM). This approach was applied to the Wangwa mining area to assess its ecological and geological risks. The results show that the surface subsidence increase year by year. The EGER in the study area was medium and the change rate of the EGER index in Wangwa mining area from 2017 to 2022 was −0.460 to 0.598. The EGER index increased southwest of the study area but reduced in the pre-investigation area and north of the investigation area. This study can support decision-making to reduce the adverse environmental impact of coal mining activities. [ABSTRACT FROM AUTHOR]

This study presents the development and application of a prototype decision support system (DSS) for tree selection specifically for Punjab, India, a region facing challenges of low forest cover and an increasing demand for sustainable land use practices. The DSS developed using the R Shiny framework integrates ecological, social, and agro-commercial criteria to facilitate scientific knowledge decision making in tree plantation. The modules in this DSS include a tree selection tool based on comprehensive species attributes, a GIS-based tree suitability map module utilizing an Analytical Hierarchical Process (AHP), and a silviculture practice information module sourced from authoritative databases. Combining sophisticated statistical and spatial analysis, such as NMDS and AHP-GIS, this DSS mitigates data redundancy in SDM while incorporating extensive bibliographic research in dataset processing. The study highlights the necessity of fundamental niche-based suitability in comparison to realized niche suitability. It emphasizes on the importance of addressing ecosystem services, agro-commercial aspects, and enhancing silvicultural knowledge. Additionally, the study underscores the significance of local stakeholder engagement in tree selection, particularly involving farmers and other growers, to ensure community involvement and support. The DSS supports agroforestry initiatives and finds applications in urban tree management and governmental programs, emphasizing the use of scientific literature at each step, in contrast to relying solely on local knowledge. [ABSTRACT FROM AUTHOR]

The Changzhou ship lock is approaching its capacity limit. In order to quantitatively analyze the influencing factors that restrict the capacity of the Changzhou ship lock, this study proposes an influencing factor analysis method based on principal component analysis (PCA). This method estimates the confidence interval of ship passing time by fitting a lognormal distribution curve, eliminates redundancy in navigability data by combining the hydrological data and cargo load data, and quantitatively analyzes the influencing factors of ship lock capacity under saturated operating conditions. The results show that the influencing factors of Changzhou ship lock capacity are classified according to their influence contribution rate as minimum water depth above the lock sill, operation direction, ship dimensions, draft, loading capacity, and actual load. The research results can provide a theoretical basis for improving the ship lock capacity and have application value for lock scheduling management. [ABSTRACT FROM AUTHOR]

Consumer decision-making behaviors play a pivotal role in the realm of purchasing sustainable products. It is crucial for businesses to understand the key factors that influence consumers' choices in this context, especially if they aim to align with eco-friendly trends. Conventional methods are inadequate for accurately and successfully identifying the importance of factors that influence consumers' decision-making behaviors in purchasing sustainable products and stem from a lack of holistic consideration. Conventional methods, like AHP, surveys, questionnaires, interviews, and focus groups, often do not fully consider the many aspects of consumer behavior related to sustainability. To address this gap, our study aims to (1) employ a hybrid approach, integrating conventional methods with cutting-edge machine-learning technology for predicting consumer's decision-making behaviors in purchasing sustainable products; (2) demonstrate the practical application of this hybrid approach through the example of green furniture; and (3) provide a practical guide for identifying the importance of factors influencing consumers' decision-making behaviors in purchasing sustainable products. This study will map out implications for the future of consumer decision-making behaviors in purchasing sustainable products. The hybrid approach to studying consumer decision making in sustainable product purchases, combining quantitative and AI methods. This methodology provides a comprehensive analysis of factors influencing environmentally friendly choices, fostering awareness and informed decision making. Businesses can use these insights to tailor strategies, enhance offerings, and meet the rising demand for sustainable products, contributing to environmentally responsible consumer behaviors and promoting economies of scale for sustainable products and innovation. This holistic understanding is crucial for creating a sustainable and socially responsible marketplace. [ABSTRACT FROM AUTHOR]

Selecting a vehicle to monitor navigational safety is an important goal, especially in search and rescue operations. It depends on the criteria set and the type of equipment in use. The study aimed to select the optimal vehicle according to the optimal criterion. In the decision-making, the AHP method was used to analyze and rank the selection criteria and vehicle types. As the most important criterion, the results point to reliability in different weather conditions and the SAR vessel as the first choice for interventions and monitoring navigational safety in Croatia. In the selection process, the AHP methodology pointed directly to the significant inconsistency of the expert group and indirectly to the need for more careful selection of members, additional training, and a broader selection of criteria and equipment. [ABSTRACT FROM AUTHOR]

Surface mining is one of the hazardous industries that have several risky operations, including transportation, treatment, and mineral extraction. To avoid the risk of disaster, it is important to evaluate safety procedures and determine expected hazards. The aim of this study is to develop a thorough safety evaluation model for the surface mining industry based on the analytic hierarchy process (AHP), one important multi-criteria decision-making approach. A total of 11 criteria and 36 sub-criteria that are both independent and homogeneous were involved in the decision problem. Further, a deep sensitivity analysis was conducted to assess the stability of the ranking preference. The findings indicate that four out of the eleven criteria are particularly significant. To test the model's applicability and effectiveness, a case study was conducted involving three surface mining companies located in the north of Jordan. The results demonstrate that the model is reliable, applicable, and effective in addressing real-world problems. [ABSTRACT FROM AUTHOR]

Engineering activities along the Sichuan–Tibet Railway (STR) could cause land degradation and threaten the surrounding ecological security. It is crucial to evaluate the integrated land ecology during and after the construction of this project. This study assesses the land ecology along the STR corridor from 2000 to 2022 using a transfer matrix, the analytic hierarchy process (AHP), and the PSR-TOPSIS model. The main results are as follows: (1) The novel comprehensive ecological assessment process including nine indicators is feasible. (2) The high-quality land ecological, surface vegetation, and environmental regions were concentrated in Ya'an and Nyingchi, whereas the low-quality regions were situated in Qamdo and Garze Tibetan Autonomous Prefecture. (3) There was an overall decline in the integrated land ecological quality along the STR from 2000 to 2022. While it steadily improved in the Ya'an and Nyingchi regions from 2010 to 2022, it continued to decline around the Qamdo region. (4) The most degraded land-use type during the 22 years was grassland, and farmland was the most secure land-use type. Overall, spatial analyses and examinations of residue disposal sites suggested that these locations have negatively impacted integrated land ecology since the beginning of the STR construction project. Our findings have implications for preserving the ecological ecosystem and ensuring the sustainability of the STR construction project. [ABSTRACT FROM AUTHOR]

This study aims to assess the operational capability of disaster and emergency management resources (DEMRs), which is not only critical for effective loss reduction and resilience, but also facilitates the sharing and optimal use of resources for the more effective achievement of sustainable development. This study constructs an evaluation index system of the operational capability of DEMRs, encompassing four key aspects: resource planning, organizational management capability, resource support capability, and information processing capability. It focuses on identifying the factors that influence the operational capability of DEMRs in China and Korea, comparing and analyzing the relative importance and priority of each evaluation domain and indicator within these countries. The results show that the organizational management capability is most significant in China, whereas the resource support capability is prioritized in Korea. A comparative analysis of the local weight of indicators within each domain revealed the largest discrepancy between China and Korea in the information processing capability domain. This study concludes by calculating global weights, identifying the fast response capability and resource allocation capability as the most impactful factors on the operational capability of DEMRs, and highlighting their critical role in disaster and emergency management. [ABSTRACT FROM AUTHOR]

Machine safety is not only a prerequisite for successful production but also the foundation for the sustainability and growth of any manufacturing organization. The latest approaches in this rapidly developing field integrate effective risk management tools and strategies into occupational health and safety (OHS) management systems. The study, through a real example from practice, describes the use of the analytic hierarchy process (AHP) method for machine safety improvement, considering the possible types of losses. Classification and Regression Tree Analysis (CART) was applied to assess the efficiency, cost-effectiveness, and, therefore, the overall sustainability level of the relevant safety measures. These were proposed risk reduction measures that typically raised uncertainty among managers regarding the estimation of cost-effectiveness. The advantage of the application decision tree approach is the possibility to identify and establish relatively homogeneous groups of undesirable events and their impact on the organization's objectives. A comprehensive model has been developed to support management decision making in manufacturing organizations towards implementing and improving safety measures in line with manufacturing sustainability goals. [ABSTRACT FROM AUTHOR]

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p < 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended. [ABSTRACT FROM AUTHOR]

In recent years, large-scale flood events have occurred more frequently, and the concept of resilience has become a prevalent approach to managing flood risk in many regions. This has led to an increased interest in how to effectively measure a city's flood resilience levels. This study proposes a novel modeling approach to quantify urban flood resilience by developing D-number theory and analytical hierarchy process (AHP) models, which are applied to three cities in China using the VIse Kriterijumski Optimizacioni Racun (VIKOR) method. The findings reveal that Hefei City has the most effective level of flood resilience, Hangzhou City was ranked second, while Zhengzhou City has the least effective level of flood resilience. This study provides a new scientific basis on how to quantify flood resilience at the city scale and provides a useful reference for these three specific cities. The methods and approaches developed in this study have the potential to be applied to other cities and in the related aspects of disaster prevention, recovery, and reconstruction. [ABSTRACT FROM AUTHOR]

The increase in extreme weather events causes secondary hazards that can influence people and the environment enormously. The Ca Mau Peninsula is known as one of the areas most severely affected by drought, and excessive groundwater exploitation is one of the reasons leading to a higher risk of land subsidence. This study uses the Delphi method and the KAMET rule table to analyze and select indicators that affect subsidence. The study uses the analytic hierarchy process (AHP) analytical hierarchy method to evaluate the weights of influencing factors, combined with geographic information system (GIS) technology to overlay the map layers of the main influencing factors and build a subsidence risk warning zoning map of the study area. The influencing factors selected to evaluate the impact on land subsidence in the study area during the drought period included geological structure, soil characteristics, groundwater flow exploitation, water flow in the dry season, current land use status, and evaporation in the dry season. The weights of these factors were evaluated based on the synthesis of relevant documents as well as consultation with experts. The results indicate that nearly two-thirds of the Ca Mau Peninsula area is currently at very low or low risk of subsidence. Meanwhile, 23% of the area is at medium risk, nearly 9% is at high risk, and 0.1% of the study area is at very high risk. Subsidence risk warning zoning maps can provide a visual and general overview of areas with high subsidence risk, supporting managers in making reference plans for socio-economic development in the Ca Mau Peninsula. [ABSTRACT FROM AUTHOR]

This study aims to define factors that affect Artificial Intelligence (AI) technology introduction to network operations and analyze the relative importance of such factors. Based on this analysis of critical factors, a rational decision-making framework is suggested to promote network operations with AI technology. As affecting factors were derived based on related previous studies, the study model was designed to consist of 22 attribute factors under 6 key factors: relative advantage, compatibility, top management support, organizational readiness, competitive pressure, and cooperative relation. The questionnaire was designed and analyzed using the Delphi method and Analytics Hierarchy Process (AHP) method based on the Technology–Organization–Environment (TOE) framework. To collect data, a survey was conducted among 30 experts in network operations and AI. The importance of attribute factors was in the order of 'goals and strategies', 'commitment of resources', 'leadership competency', 'financial readiness', and 'technology readiness'. As the importance of factors was analyzed comparatively between the demander group and provider group, organizational factors were important in the demander group. In contrast, technological factors were important in the provider group. In conclusion, there was a difference in perspectives between demanders and providers regarding adopting AI technology to network operations. [ABSTRACT FROM AUTHOR]

The safety condition assessment of prestressed concrete bridges is currently subject to great uncertainty due to the subjectivity of data collection and data types. This study proposes an improved evidence fusion method, improving the conventional Dempster–Shafer fusion method to reduce assessment inaccuracies caused by data uncertainty. Firstly, the uncertain analytic hierarchy process was applied to construct a three-level safety assessment model for 15 different indicators with their initial weights. Secondly, the fuzzy matter element theory was proposed to obtain basic probability assignments required for the evidence fusion. Finally, an improved evidence fusion method was proposed based on the evidence credibility and preprocessing corrections for highly conflicting evidence. In this study, a prestressed concrete bridge in eastern China was used as a case study to perform a comprehensive safety assessment and verify the effectiveness and practicality of the proposed method. The assessment results demonstrate that the improved fusion method in this study can deal with conflicting evidence better than existing fusion methods. Compared with conventional fuzzy AHP method, it has greater sensitivity to certain indicators with severe damages, which prevents those indicators from being overshadowed by other well-performing ones in the overall assessment. [ABSTRACT FROM AUTHOR]

On a global scale, traffic incidents are a leading cause of mortality and material damage. Romania exhibits the highest rate of road traffic fatalities both in the European Union and worldwide, requiring a comprehensive examination of its overall influence on a national level. The current study uses an extensive approach centering on a spatial analysis model based on the Analytic Hierarchy Process (AHP). Employing a series of spatial databases, this model delineates the geographical distribution and characteristics of road accidents to establish both their cumulative national impact and the identification of high-risk areas. The spatial database, containing traffic incident data, is constructed using geolocation techniques and integrated through network analysis to evaluate the impact in relation to distance. The AHP framework is applied in analyzing the impact across five key dimensions: accident severity, occurrence mode, prevailing weather conditions, traffic restrictions, and road markings. This multi-level AHP analysis not only identifies high-risk hotspots but also confirms the effectiveness of the proposed spatial model. [ABSTRACT FROM AUTHOR]

Environmental pollution caused by microplastics has evolved into a global concern; however, limited knowledge exists about microplastics in soils due to the absence of standardized extraction methods. This research aimed to develop an inexpensive, rapid method with user-friendly and environmentally sustainable outcomes for microplastics retrieval. Three salt solutions (Sodium Chloride, Magnesium Sulfate, Sodium Hexametaphosphate) and an oil solution (canola oil) underwent evaluation for microplastics extraction through the flotation process due to the density and oleophilic properties of plastics. Four widely used plastic types, obtained through fragmentation using a grinding mill from clean new plastic containers or membranes, were subjected to analysis. The experimental procedures for microplastics retrieval varied among the evaluated solutions. Through a comprehensive multicriteria analysis, the saturated Sodium Chloride solution emerged as the optimal scenario for microplastics extraction, followed closely by the canola oil scenario. The recovery method utilizing Sodium Chloride demonstrated economic feasibility, safety, and reliability. This study provides valuable insights into an effective and sustainable approach for mitigating microplastic pollution in soil, offering a promising avenue for future environmental conservation efforts. [ABSTRACT FROM AUTHOR]

Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially increased vulnerability of coastal urban areas flooding. This study addresses the pressing concern of SLR and flood vulnerability in the East Coast of North Sumatra (ECNS) and Medan City. We employ a data-driven approach integrating multicriteria analysis, analytical hierarchy process (AHP)-based weighting, and spatial modeling within a geographic information system framework. The analysis considers crucial factors such as slope, land use, soil type, SLR, and land deformation. The study expands the existing framework by incorporating SLR and land subsidence, acknowledging their significant roles in exacerbating flood vulnerability. Future flood-intensity scenarios are simulated based on SLR projections. Data for spatial analysis primarily originated from multisensor satellite imagery, secondary sources from published literature, and field surveys. We validated the consistency of the variable weightings assigned for vulnerability analysis using a consistency ratio threshold (<0.1). Finally, the established flood vulnerability model was validated by comparing its predictions with recorded flood events in the ECNS and Medan City. The ECNS and Medan City areas were classified as very high and highly vulnerable to flooding, respectively. [ABSTRACT FROM AUTHOR]

Groundwater management in the Jinan Spring basin is hampered by its complex topography, overexploitation, and excessive urbanisation. This has led to springs drying up during dry seasons and a decrease in discharge in recent years. GIS and the AHP were employed to delineate groundwater potential zones using eight thematic layers: slope, geology, lineament density, topographic wetness index (TWI), rainfall, soil, drainage density, and land use/land cover (LULC). The model's accuracy was assessed by comparing the findings to basin groundwater observation well data. We found that 74% of the observations matched the projected zoning. Further validation utilising the receiver operating characteristic (ROC) curve gave an AUC of 0.736. According to the study, 67.31% of the land has a good GWPZ, 5.60% has a very good one, 27.07% is medium, and 0.03% is low. Heavy rains throughout the rainy season raise water levels. Dry weather lowers water levels. This study's conclusions will protect groundwater from climate change. Integrating hydrogeological data, GIS, remote sensing, and AHP approaches maximises data use, improves groundwater potential zone delineation, and promotes sustainable groundwater resource management decision making. This integrated method can help land use planners, hydrologists, and policymakers find optimal locations for water supply projects, establish groundwater management techniques, and reduce groundwater risks. [ABSTRACT FROM AUTHOR]

Lean Manufacturing has become, in recent years, one of the most important philosophies for improving production and organizational systems. The literature shows that Hesitant Fuzzy Linguistic Terms Sets (HFLTSs) are highly capable of manipulating the uncertainty that the judgments made by evaluators carry and that they are subject to their perception, especially when used in combination with multicriteria decision making (MCDM) for the measurement of indicators in this type of system, as well as their general performance. However, it is still of interest to researchers to develop techniques and instruments that facilitate the measurement of the results obtained after applying this philosophy in organizations. This article proposes a model for the evaluation of the Lean Manufacturing performance through the Analytic Hierarchy Process (AHP) and Dimensional Analysis (DA) using HFLTSs. The results obtained show that the proposed model is a solid tool for the evaluation of Lean Manufacturing systems from a different perspective and that it can be integrated into the issuance of evaluations in a better way by considering human subjectivity. At the same time, it offers a strategy to create priorities in the action plans that Lean system managers propose after evaluating. However, it is important to apply the proposed model to multiple organizations and analyze the results obtained to maximize its benefits. [ABSTRACT FROM AUTHOR]

Solid waste disposal is an essential component of any waste management system, but finding suitable solid waste disposal sites is seen as the main challenge. The goal of this study was to locate solid waste disposal facilities in Lilongwe that would not present logistical or operational difficulties. We used a hybrid approach involving qualitative and quantitative methods. Primary and secondary data were collected, through interviews, questionnaires, and GPS for coordinates. The study considered 10 suitability factors of slopes, rivers, soil types, built-up areas, airports, forests, wetlands, current dumpsites, roads, and railways weighted using the analytic hierarchy process (AHP). We analyzed site selection techniques, evaluated the current dumpsite, and created a multi-factor geographic information system (GIS) model. This study found that the current dumpsite is dangerous for human health and is close to homes. Additionally, this research reveals that the most popular techniques for choosing the location of a solid waste disposal facility are the AHP and weighted linear combination (WLC) coupled with GIS. Out of 46,283 Ha in Lilongwe, 84.07% are unsuitable, 14.97% are suitable, and 0.96% are highly suitable for solid waste disposal sites. Six sites with capacities ranging from 28 Ha to 94 Ha were identified as optimal to reduce operational costs in areas 55, 44, 58, and 61. Further waste recycling initiatives are recommended to reduce solid waste volume and promote sustainable waste management practices. [ABSTRACT FROM AUTHOR]

Decisions around distributing available funds among potential land consolidation projects require a thorough analysis in order to maximize the effects of land consolidation. In order to avoid choosing the wrong land consolidation projects, different methods can be used. Generally, there are two possible groups of methods: one based on a qualitative approach (DELPHI; SWOT) and one based on a quantitative approach (AHP, VIKOR, SAW, TOPSIS, etc.). In this research, the focus was on the sensitivity of the resulting rankings affected by varying the input data in multi-criteria analysis methods, with an emphasis on the variation in the weight and the choice of criteria. This research was motivated by the subjective character of the choice of criteria and their weighting before applying the multi-criteria analysis methods. Four methods were included (AHP, TOPSIS, VIKOR, and SAW) for the multi-criteria analysis, with three ways of defining weights (consistent, modified, or quasi-consistent and freely determined without taking consistency into account), in order to determine the influence of the different methods on the final ranking. The weights were defined only by an acceptable interval of values. The sensitivity of the methods was investigated using the differences in the obtained rankings between each method. A case study is provided on real data, and the results are discussed. The results showed a relatively small variance and possible equal rankings of projects by means of statistical analyses. This finding opens up the possibility of the valuation of projects instead of simple rankings. [ABSTRACT FROM AUTHOR]

Australia has abundant volumes of forest residues that are a potential feedstock for supplying biomass as a renewable carbon carrier to the market. However, there remains an underutilization of this resource, even in mature bioeconomy markets. Several existing or perceived barriers can be attributed to the underdeveloped, forest-based bioeconomy in Australia. One of these is the limited understanding of feedstock supply costs. In this study, two ranking approaches were applied to identify the optimal biomass feedstock supply chain from field to conversion plant gate. A panel of experts embedded in the Australian bioeconomy were employed to first assign ranks to biomass supply chain items by cost intensity. Then, a layer of analytic hierarchical process (AHP) was used to weigh and rank various biomass supply pathways by efficiency. The results reveal that biomass extraction ranks the highest and biomass feedstock storage ranks the lowest, relative to other supply chain costs. Extracting and chipping material in the field attracted the most support from the experts in terms of efficiency, followed by transporting and chipping at the roadside and, finally, transporting and chipping at the conversion plant. This study provides insights for designers of the forest-based bioeconomy in Australia into relative cost drivers that may be applied to investment and industry decisions. It also provides a framework to support further investigations into forest biomass development and the management of biomass as a renewable carbon carrier at a time when Australia is transitioning from an energy policy focused on fossil fuels to a renewable energy strategy. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) startups possess four key attributes; being small enterprises, adopting AI technology, undergoing digital transformation, and using big data systems to enhance their competitiveness. This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era. To achieve this, the hierarchy concept framework was built with four evaluation areas based on the mechanism-based view theory, and the 16 evaluation factors that can influence were identified through existing literature, combining factors related to the digital transformation, technological application, and business competitiveness of the startups. These factors were analyzed using the Analytic Hierarchy Process (AHP) by the survey, targeting experts in South Korea. The analysis results indicate that the subject area was the most crucial for the business competitiveness of AI startups. It was also revealed that the subject's strategic mind is the most significant factor to AI startups' success. In the case of two control groups, categorized as 'AI experts' and 'startup experts', AI experts chose the subject as the most important area, whereas startup experts selected the environment, and significant differences were observed in all other factors. The results of this study will provide implications for strengthening the business competitiveness of AI startups and factors important for the growth of AI startups in this era. [ABSTRACT FROM AUTHOR]