Search Results (161)

Perchlorate is a highly mobile and persistent toxic contaminant, with the potassium perchlorate manufacturing industry being a significant anthropogenic source. This study addresses the Energy Conservation and Perchlorate Discharge Reduction (ECPDR) challenges in China’s potassium perchlorate manufacturing industry through a multi-objective optimization model under uncertainty. The objectives encompass energy conservation, perchlorate discharge reduction, and economic cost control, with uncertainty parameters simulated via Latin Hypercube Sampling (LHS). The optimization was performed using both the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Generalized Differential Evolution 3 (GDE3) algorithm, enabling a comparative analysis. Three types of decision-maker preferences were then evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to generate optimal decision strategies. Results revealed: (1) The comprehensive perchlorate discharge intensity in China’s potassium perchlorate industry is approximately 23.86 kg/t KClO₄. (2) Compared to NSGA-II, GDE3 offers a more robust and efficient approach to finding optimal solutions within a limited number of iterations. (3) Implementing the optimal solution under PERP can reduce perchlorate discharge intensity to 0.0032 kg/t. (4) Processes lacking primary electrolysis should be phased out, while those with MVR technology should be promoted. This study provides critical policy recommendations for controlling perchlorate pollution and guiding the industry toward cleaner and more sustainable production practices. Full article

Given that most cities in Sichuan Province, China, are located in mountainous areas and are frequently affected by earthquakes, this study selected 16 mountainous cities in Sichuan Province. Based on the “exposure–sensitivity–coping capacity” framework, we constructed a social vulnerability assessment index system for earthquake disasters that aligns with the characteristics of mountainous regions. Weights were determined using the entropy weight–CRITIC method, and the improved TOPSIS method was used to calculate the social vulnerability index (SoVI) of each city for comparative analysis. Additionally, the social vulnerability maps were created using ArcGIS software to explore the spatial distribution characteristics. The study found that among the 16 mountainous cities, there is a noticeable spatial clustering of social vulnerability. Yajiang, Daofu, and Luhuo are identified as high–high clustering areas, while Jiulong, Luding, Shimian, and Hanyuan also exhibit high–high clustering. Kangding, Baoxing, and Wenchuan fall into low–low clustering areas. Additionally, coping capacity is the most significant factor influencing the social vulnerability of mountainous cities. After experiencing high-magnitude earthquakes, most mountainous cities have not improved their coping abilities and continue to exhibit high vulnerability, primarily due to high illiteracy rates, significant altitude variations, and poor economic conditions. This study provides a scientific basis for local governments to formulate disaster prevention and mitigation strategies, which help enhance the disaster resilience of mountainous cities and promote their sustainable development. Full article

With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China’s 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable. Full article

The complex nature of the steel manufacturing environment, characterized by different types of hazards from materials and large machinery, makes the need for objective and automated monitoring very critical to replace the traditional methods, which are manual and subjective. This study explores the feasibility of implementing computer vision for safety management in steel manufacturing, with a case study implementation for automated hard hat detection. The research combines hazard characterization, technology assessment, and a pilot case study. First, a comprehensive review of steel manufacturing hazards was conducted, followed by the application of TOPSIS, a multi-criteria decision analysis method, to select a candidate computer vision system from eight commercially available systems. This pilot study evaluated YOLOv5m, YOLOv8m, and YOLOv9c models on 703 grayscale images from a steel mini-mill, assessing performance through precision, recall, F1-score, mAP, specificity, and AUC metrics. Results showed high overall accuracy in hard hat detection, with YOLOv9c slightly outperforming others, particularly in detecting safety violations. Challenges emerged in handling class imbalance and accurately identifying absent hard hats, especially given grayscale imagery limitations. Despite these challenges, this study affirms the feasibility of computer vision-based safety management in steel manufacturing, providing a foundation for future automated safety monitoring systems. Findings underscore the need for larger, diverse datasets and advanced techniques to address industry-specific complexities, paving the way for enhanced workplace safety in challenging industrial environments. Full article

The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency. Full article

Buses and subways are crucial modes of transportation for residents, yet frequent disturbances pose serious challenges to their daily commutes. To tackle these disruptions and boost the stability of the transportation network, it is vital to accurately measure the resilience of a bus–subway composite network under such events. Therefore, this study utilizes the generalized cost between stations as weights with which to construct a bus–subway weighted composite network. Subsequently, three indicators, namely reachability, path importance, and weighted coreness, are proposed to evaluate the significance of the nodes, thereby combining the improved CRITIC-TOPSIS method to identify the critical nodes. Then, deliberate attacks and preferential restorations are conducted on the nodes, considering their importance and the critical nodes sequences, respectively. Finally, network resilience changes are characterized by the network connectivity coefficient and global accessibility, and the network resilience is compared under different attack and recovery strategies. The research results indicate that resilience is lowest when using reachability sequences to attack and recover the network. The network’s recovery is most significant when using the critical nodes sequences. When 70% of the nodes are restored, the network’s performance is essentially fully recovered. Additionally, the resilience of a bus–subway network is higher than that of a single bus network. This study applies the generalized cost to weight the transportation network, and considers the impact of multiple factors on the ease of connectivity between the nodes, which facilitates the accurate measurement of the resilience of a bus–subway network and enhances the ability to cope with disruptions. Full article

This study introduces a spatial layout framework for the multi-objective optimization of low-impact development (LID) measures at an urban watershed scale, targeting the mitigation of urban flooding and water pollution exacerbated by urbanization. The framework, tailored for the Dahongmen area within Beijing’s Liangshui River Watershed, integrates the storm water management model (SWMM) with the nondominated sorting genetic algorithm II (NSGA-II). It optimizes LID deployment by balancing annual costs, volume capture ratio of rainfall, runoff pollution control rate, and the reduction in heat island potential (HIPR). High-resolution comprehensive runoff and land use data calibrate the model, ensuring the realism of the optimization approach. The selection of optimal solutions from the Pareto front is guided by weights determined through both the entropy weight method and subjective weight method, employing the TOPSIS method. The research highlights the positive, nonlinear correlation between cost and environmental benefits, particularly in reducing heat island effects, offering vital decision-making insights. It also identifies a critical weight range in specific decision-making scenarios, providing a scientific basis for rational weight assignment in practical engineering. This study exemplifies the benefits of comprehensive multi-objective optimization, with expectations of markedly improving the efficacy of large-scale LID implementations. Full article

In the rapidly evolving field of sustainable construction, this study aims to address the critical need for advancement in the building industry, focusing on vital indicators like energy efficiency and cost-effectiveness, as well as improving occupant comfort. This research introduces a novel approach to support the choice of suitable structural systems for mass housing projects, with a case study on Iran’s national housing scheme. This methodology involves a four-phase process, beginning with compiling a database from existing studies to outline primary and secondary indicators affecting structural system selection. It utilizes the fuzzy AHP method for criteria prioritization and the fuzzy TOPSIS technique for alternatives (LSF, 3DP, ICF, TRC, and RCCF). The study identified the light steel framing (LSF) system as the optimal choice for Iran’s housing needs based on various criteria. Then, in the final phase, the study evaluates the seismic performance of cold-formed steel (CFS) frames with various sheathing panel types (OSB, DFP, CSP, and GWB) under monotonic loads, examining key seismic parameters across 38 frame setups. The findings reveal that LSF structures can effectively withstand seismic events within the elastic behavior range, suggesting that this construction approach is viable for enhancing mass housing production in Iran’s construction sector. Full article

This paper introduces an innovative model for the organization and management of municipal bio-waste collection networks in major cities, particularly relevant in the context of the COVID-19 pandemic. Embracing circular economy principles and sustainable city practices, the proposed model addresses the urgent need for sustainable urban bio-waste management systems. Delving into the dynamic urban landscape, with a focus on the city of Athens, the study highlights the necessity of a robust decision-making methodology, the implementation of resilient processes, and the evaluation of their efficacy, especially during challenging times. The model centers on the effective collection, transportation, and monitoring of bio-waste, with a strategic aim to moderate environmental impacts, limit greenhouse gas emissions, and advance sustainable development goals. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, this paper thoroughly examines critical components of an innovative bio-waste collection network, including infrastructure, technology, and human resources. By merging best practices from global urban centers and accounting for the unique characteristics of Athens, the model envisions a transition toward a circular economy. Notably, the proposed municipal bio-waste collection network at the source anticipates substantial contributions to achieving Sustainable Development Goals in major cities. The study concludes by showcasing the successful application of these methodologies in the Municipality of Athens, providing tangible evidence of their positive impact. Full article

Following the Belt and Road, the Air Silk Road has also been proposed. The coordinated development of multiple transportation modes, including air, land, and water, will create a strong transportation force in node cities. However, the current insufficient supply of cargo in various regions and the lack of integration among different transportation modes result in low transportation efficiency, which in turn affects the further advancement of the Belt and Road. To investigate these issues and attempt to find a solution, we selected 44 candidate cities from the prefecture-level cities in China as nodes based on relevant government policies, and constructed an integrated transportation network. For each node city, we first calculated the values of six classical indicators and then used the CRITIC to assign weights to each indicator. Subsequently, we employed the TOPSIS method combined with Grey Relational Analysis (GRA) to compute the comprehensive score for each node city. Based on the spatial layout and government policies under the BRI, eight cities, including Wuhan, Chongqing, Tianjin, Shanghai, Guangzhou, Lianyungang, Hefei, and Dalian, were finally recommended as the consolidation centers of the integrated transportation network. It is hoped that the results of this analysis can provide some insights for the government to outline and build the consolidation centers of the integrated transportation network composed of railway, air, highway, and water routes, which in turn can offer insights for elevating the Belt and Road Initiative (BRI) to a new level. Full article

With the rapid development of cloud computing, artificial intelligence, etc., data centers have become a flexible load due to their adjustable capabilities and are able to take on power service. However, the existing communication architecture is monolithic and poorly adapted between services and communications, which poses a challenge for data centers to participate in power service. Firstly, this article constructs a multi-domain communication architecture for data center management systems that includes local and remote communication, and analyzes the key technologies supporting this architecture. Secondly, based on differentiated service types and communication requirements, an evaluation system for the adaptability between service and communication technology was constructed, and a communication mode adaptation method based on fuzzy analytic hierarchy process (FAHP)-improved CRITIC combined weighting model and grey Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed to achieve the analysis of the adaptability between differentiated service and multi-domain communication technology. According to the analysis of examples, the architecture and adaptation algorithm proposed in this article provide an effective theoretical basis and solution for the selection of multi-domain communication technologies for data center management system service. Full article

According to the strategic plan for rural revitalization and the consolidation of poverty alleviation achievements, this research has developed an evaluation indicator system encompassing three dimensions: environment, social support, and economic resilience, viewed through a sustainable development lens. This system is designed to gauge the capacity to forestall a relapse into poverty in ecologically fragile regions and can also serve as a foundation for the government to establish a comprehensive early-warning and monitoring system. An integrated approach, combining the TOPSIS and entropy methods, was employed to assess the capability to prevent a recurrence of poverty based on data from Enshi Tujia and Miao Autonomous Prefecture spanning 2016 to 2022. Subsequently, the obstacle degree model was utilized to pinpoint critical barriers to enhancing its capability to mitigate the risk of reverting to poverty. The findings clearly indicated that, compared to other regions, Enshi City and Lichuan City maintained the most robust comprehensive capabilities to avert poverty recurrence between 2016 and 2022. Furthermore, the evaluation of capabilities across various dimensions revealed that, with the exception of Enshi City, other counties and cities demonstrated lower capacities in the environmental, social support, and economic resilience dimensions. Moreover, in 2020, the capabilities of all counties and cities deteriorated, and the capabilities under the dimensions of social support and economic resilience had not returned to their former levels by 2022, suggesting that the social and economic systems are susceptible to emergency public crises. A spatiotemporal analysis of the factors impeding the enhancement of capabilities in the counties and cities of Enshi Prefecture showed that the inhibiting factors varied by region, with the most prevalent obstacles stemming from economic resilience. In terms of environmental dimensions, the total regional water supply played a pivotal role in Enshi Prefecture. There was a pronounced regional disparity in the development of capabilities to prevent the recurrence of poverty, and the evolution of systems, such as the environment, social support, and economic resilience, was markedly uncoordinated. Finally, strategic recommendations and measures were formulated to bolster the capabilities to avert returning to poverty in ecologically fragile areas across these three dimensions. Full article

The multi-criteria decision-making (MCDM) method was applied in a novel way in this study to the multi-objective optimization problem (MOOP) of designing a two-stage helical gearbox with double gear-sets in the second stage. Finding the best fundamental components to increase gearbox efficiency and decrease gearbox cross-section area was the aim of this study. Three main design factors were chosen for investigation in this work: the first stage gear ratio and the first and second stage coefficients of wheel face width (CWFW). Phase 1 solves the single-objective optimization problem to reduce the gap between variable levels, and phase 2 solves the MOOP to determine the optimal critical design factors. This additionally splits the MOOP into two phases. Additionally, the TOPSIS method was used as an MCDM approach to address the multi-objective optimization issue, and the entropy approach was used to compute the weight criteria. In this study, gearbox efficiency is calculated by considering power losses during idle motion. The multi-objective optimization of a helical gearbox with second stage double gear-sets is addressed using the TOPSIS technique for the first time. Full article

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman’s correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato’s ‘government of the wise’ principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. Full article

For an in-depth analysis of the ecosystems of the Jianghuai Valley, this study utilized municipal data from 2017 to 2021. In addition, this study established an index scale evaluation system for the quality of the ecological environment in the Jianghuai Valley. This system encompasses five critical dimensions: drivers, pressures, states, impacts, and responses, in accordance with the DPSIR model. The entropy-weighted TOPSIS method combined with the gray correlation method was used to assess the ecological status of each region of the Jianghuai Valley at different time periods and the driving factors affecting the ecological quality of the Jianghuai Valley. Our study yields several key conclusions. First, it was observed that the ecological environment within the Jianghuai Valley showed a continuous upward bias in inter-annual variability. Second, there exists variation in ecological environment quality among the eleven urban areas within the Jianghuai Valley, highlighting regional disparities. Third, among the eleven urban areas in the Jianghuai Valley, Anqing has the best ecological quality, and Huainan has the worst ecological performance. Fourth, the ecological environment quality within the Jianghuai Valley demonstrates an aggregated pattern. From west to east, this pattern is delineated by distinct areas: one marked by excellent ecological environment quality, another exhibiting average ecological environment quality, followed by a zone characterized by good ecological environment quality, and finally, an area with poor ecological environment. Fifth, our analysis reveals that Q9 (indicating the percentage of excellent air days) and Q13 (denoting the annual average temperature) have a pronounced correlation with the Jianghuai Valley’s ecological quality. Conversely, Q3, which pertains to the rate of natural population growth, had the lowest relevance to the ecological quality of the Jianghuai Valley. Full article