TOPSIS Method Research Articles

Bambusa balcooa bamboo-reinforced concrete beams: experimental and FEM investigation for energy-efficient pavement construction.

This study explores the viability of using Bambusa bambos, sourced from Madhya Pradesh, India, as a reinforcement material in continuously reinforced concrete pavement (CRCP) construction, aiming to assess its potential as a sustainable alternative to traditional steel reinforcement. The research encompasses a comprehensive evaluation of physical and mechanical properties, including tensile, compressive, and bending strengths, and a detailed microstructural analysis using scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) of Bambusa bambos. The study involved finite element analyses that modeled bamboo-reinforced concrete (BRC) beams, exploring the impact of horizontal and vertical placements of bamboo strips on flexural behavior under bending loads. The analysis aided in observing compressive and tensile stresses generated in concrete and bamboo, with specific FEA results indicating that beams with vertically aligned bamboo strips in both the compression (compressive stress of 16.90MPa for beam B1) and tension zones (tensile stress of 7.22MPa for beam B1) withstand flexural stresses effectively. Additionally, the multi-criteria decision-making approach using the TOPSIS method to rank different beam designs. Key findings obtained from FEA indicate that the vertical alignment of bamboo strips in both the compression and tension zones of the beams is optimally effective in handling flexural stresses.

The Effect of Financial Information on Decision Making to Purchase Shares Using the TOPSIS Method (Technique for Order Preference by Similarity to Ideal Solution)

In making stock investment decisions, it is important for investors to perform certain calculations and analyzes in order to be able to assess how well the performance of the issuer (company), with the aim of getting the expected rate of return and being able to minimize investment risk. This study was conducted to find out how the decisions that will be made by investors on buying shares on the Indonesia Stock Exchange using fundamental analysis. The difference between this study and previous research is that it adds a Positive Ideal Solution (PIS) and a Negative Ideal Solution (NIS) from financial information indicators ( Dept to Equity Ratio, Return on Equity, Net Profit Margin, Return On Assets, Earning Per Share, Price Earning Ratio). , and Price Book Value ) using the TOPSIS method. The sample in this study uses securities listed on the Indonesia Stock Exchange as many as 94 securities. The results showed that the Dept to Equity Ratio, Return On Assets, Earning Per Share, Price Earning Ratio, and Price Book Value had a significant effect on stock purchase decisions, while Return on Equity and Net Profit Margin had no effect on stock purchase decisions.

Parametric optimization of Al2O3-ZrO2 (Y2O3) based self-lubricating composite cutting tool materials for turning operations using TOPSIS method

Parametric optimization of Al2O3-ZrO2 (Y2O3) based self-lubricating composite cutting tool materials for turning operations using TOPSIS method

Construction and Application of Regional Carbon Performance Evaluation Index System: The Case of Chinese Provinces

As global warming becomes increasingly severe, reducing carbon emissions and promoting low-carbon development has become an international consensus. Against this backdrop, evaluating regional carbon performance helps better understand the carbon emission status, emission reduction capabilities, and low-carbon development levels, providing a scientific basis for formulating targeted carbon emission reduction policies. This study constructed a “5E” regional carbon performance evaluation index system from five dimensions: economy, effectiveness, efficiency, environmentality, and equity. Then, this study evaluated and analyzed the carbon performance of 30 provinces in China from 2008 to 2021 using the entropy weight TOPSIS method. The research results indicated that (1) during the sample period, China’s carbon performance ranged from 0.416 to 0.504, exhibiting a steady upward trend; the highest score among the first-level indicators was Effectiveness, while the lowest was Economy; (2) in terms of carbon performance among China’s three major regions, it showed a decreasing pattern from east to west, with the growth potential of the central and western regions being greater than that of the eastern region; (3) in 2033, the carbon performance of China in the eastern region, the central region, and the western region will reach 0.602, 0.612, 0.613, and 0.582, respectively. A carbon performance evaluation carries significant practical and strategic implications. Our study can provide a reference for policymakers to assess carbon emission performance and improve carbon management efficiency and decision-making levels.

The coupling coordination relationship between sports industry agglomeration and economic resilience in the Yangtze River Delta region.

With a rapidly growing sports industry worldwide, one may argue that sports industry agglomeration can play a crucial role in the economy of the sports industry. In particularly, the coupling linkage between sports industry agglomeration and economic resilience can be leveraged to promote both economic quality and efficiency. Based on data on three provinces and one city in the Yangtze River Delta region during the 2011-2020 period, this study uses the entropy-weighted TOPSIS method, coupling coordination degree model, and relative development models to explore the coupling coordination relationship between sports industry agglomeration and economic resilience in this region. The results show that: (1) Sports industry agglomeration shows an overall increasing albeit fluctuating trend with inter-provincial differences. (2) Economic resilience has steadily increased, while the economic resilience kernel density curve generally shows a "dual peaks" trend. (3) The coupling coordination between sports industry agglomeration and economic resilience remains in a fluctuating, albeit coordinated state. These findings are relevant for the integration and high-quality development of the sports industry in the Yangtze River Delta region.

Navigating the challenges in the land intervention practices of peri-urban areas in Indian cities using CRITIC-TOPSIS approach

ABSTRACT Peri-urbanisation is a dynamic phenomenon that is perpetually altering the territories of the metropolises and land management regimes for peri-urban areas have left a void in combating the acceleration of metropolitan encroachment into the peripheral areas. Land-related decisions in the conflicted context of peri-urban are required to be strategized, considering the opportunities they present for urban development. The research aims to rank the significance of the challenges in land intervention practices by the planning professionals in the peri-urban context of India. A questionnaire-based survey is used to collect data for analysis from 84 experts from the planning field and academia. The CRITIC method is used to ascertain the weight of each participant’s response, while the TOPSIS method ranks the significance of the challenges (C1–C10). The lack of comprehensive planning standards and benchmarks (C5) is of the the most significance. Uncertain demarcation of peri-urban areas (C1) and the the absence of post-implementation and compliance evaluation studies (C9) are the next most challenging, as per the analysis. The result aids in the understanding of the policymakers to mitigate the challenges faced in peri-urban land management by prioritising actions.

A combination approach for optimization operation of multi-objective cascade reservoir systems (Case study: Karun reservoirs)

ABSTRACT Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due to their uncertainties, non-linearities, dimensions and conflicts. The operation of multi-reservoir systems is crucial to increasing hydropower production. In this study, we have investigated the application and effectiveness of the new optimization algorithm MOAHA in multi-objective cascade reservoirs with conflicting objectives, and it has been investigated on a case-by-case basis on Karun cascade reservoirs (Karun 3, Karun 1, Masjed Soleyman and Gotvand). The suggested method (MOAHA) output with other optimization algorithms, MOALO, MOGWO and NSGA-II, were compared and evaluation criteria were used to select the best performance. Additionally, we employed the powerful TOPSIS method to determine the most suitable algorithm. The considered restrictions have also been observed. The results indicate that MOAHA's proposed method is better than the compared algorithms in solving optimal reservoir utilization problems in multi-reservoir water resource systems. The reduction of evaporation (losses) from the tank surface by 9% is accompanied by a 15% increase in hydropower energy production. MOAHA, scoring 0.90, is deemed the best algorithm in this study, whereas MOGWO, with a score of 0.10, is regarded as the least effective algorithm.

The response rules to maintain social stability facing the climate change in ming dynasty

Studying the mechanisms by which climate change interacted with human societies during the historical period can provide historical insights and cultural roots for climate policy building in the region. In this study, we constructed Stability-Robustness-Resilience Model (SSR model) and used the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) and the entropy weight method to assess the change processes of robustness, resilience, and stability of Ming's society in response to climate threats. We have also compared three extreme droughts that occurred in different periods of social robustness-resilience combinations by using the SRR model. The results are as follows. (1) The stability of the Ming society was high in YongLe - HongZhi period (1402–1505 CE), when both social robustness and resilience were higher than the average level of Ming Dynasty (0.5611 for the former and 0.4215 for the latter), but there was a significant decline in social stability in TianShun period (1457–1464 CE). In ZhengDe - ChongZhen period (1506–1644 CE), the stability of Ming society gradually decreased, and it rebounded shortly in the LongQing-WanLi period (1506–1620 CE). (2) The high stability benefited from higher socio-economic levels, better government finance levels, larger national food reserves, safer social environments (high robustness), and higher levels of ruling class governance and risk response (high resilience); whereas insecure social environment induced by war, declining socio-economic levels and government finance levels were the main reasons for the decline in society's stability. (3)The ChengHua and WanLi droughts both happened at a time with high social robustness, so although their meteorological anomalies were severe, their impact on society was small. While the JiaJing drought happened at a time with low social robustness and resilience, so although the meteorological anomaly was relatively weak, it resulted in a more severe social consequence than the other two events.

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model

As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occurrence, persistence, bioaccumulation and toxicity based on the integrated assignment method. A classification model for ADs was constructed using an improved mixup-transformer deep learning method, and its classification accuracy was compared with those of other models. The accuracy of the proposed model improved by up to 23.25 % compared with the random forest model, and the reliability was 80 % more than that of the TOPSIS method. A priority screening candidate list was proposed to screen 33 high-priority ADs. Finally, SHapley Additive explanation (SHAP) visualization, molecular dynamics, and amino acid analysis were performed to analyze the correlation between AD structure and toxic receptor binding characteristics and reveal the differences in AD risk priority. ADs with more intramolecular hydrogen bonds, higher hydrophobicity, and electronegativity had a more significant risk. Van der Waals and electrostatic interactions were the primary influencing factors, and significant differences in the types and proportions of the main amino acids in the interaction between ADs and receptors were observed. The results of the study provide constructive schemes and insights for AD priority screening and risk management.

Multiattribute group decision-making based on weighted correlation coefficient of linguistic q-rung orthopair fuzzy sets and TOPSIS method

Multiattribute group decision-making based on weighted correlation coefficient of linguistic q-rung orthopair fuzzy sets and TOPSIS method

Multi-objective hierarchical strategy for university dorm renovation in severe cold areas

In China, there are many old buildings whose renovation could enhance building environment quality, reduce energy consumption, lower carbon emissions, and create economic benefits. However, there is currently a lack of efficient architectural renovation optimization methods to provide decision-makers and designers with highly feasible renovation plans. This paper proposes an innovative optimization method for renovating old buildings to address this issue. The research employs a hierarchical optimization framework integrating Artificial Neural Networks (ANN), the NSGA-II algorithm, and the entropy-weighted TOPSIS method. The first layer's three-objective optimization using the trained neural network model significantly improves the algorithm's efficiency in searching for optimal solutions. The second layer's entropy-weighted TOPSIS optimization includes eight objective optimizations, such as energy, economy, and lighting, thus broadening the assessment dimensions. Taking an old multi-story university dormitory in the Shenyang area as an example, the study optimizes nine passive design variables across eight objectives. The hierarchical optimization results show that the high-scoring renovation plans can save 1.52 to 3.17 million yuan in energy costs over the building's remaining lifecycle and reduce carbon dioxide emissions by 644–962 kg per square meter. SRRC results indicate that external windows significantly impact economic efficiency and energy consumption, emphasizing the importance of selecting the right type of windows in actual engineering projects. The new method proposed in this study can offers a comprehensive and highly feasible renovation plan for old buildings.

A vulnerability evaluation method of earthen sites based on entropy weight-TOPSIS and K-means clustering

The degradation of earthen sites due to natural and human factors has become a pressing issue, necessitating urgent protection measures. In this context, accurate assessment of the vulnerability of earthen sites is essential for the development of effective conservation strategies. In this study, a comprehensive evaluation framework that incorporates multiple indicators is proposed. In particular, the entropy weight- (Technique for Order Preference by Similarity to an Ideal Solution) TOPSIS method is employed for quantitative vulnerability assessment and combined with K-means clustering to define vulnerability levels for earthen sites. To validate the proposed approach, the vulnerability of 29 sections of the Ming Great Wall is evaluated. Eventually, the 29 earthen sites are categorized into three levels: high, medium, and low, according to their degree of vulnerability. The results of gray correlation analysis and entropy weight-TOPSIS method are compared using the ontology missing amount in the original data as the validation standard. The results show that the Pearson correlation coefficient value of the entropy weight-TOPSIS method with the ontology missing amount was 0.859, while the Pearson correlation coefficient value of the gray correlation analysis method with the ontology missing amount was 0.691, so that the results of the entropy weight-TOPSIS method can more accurately reflect the actual vulnerability of earthen sites.

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

The White Paper on Peak Carbon and Carbon Neutral Action 2022 states that China is to achieve peak carbon by 2030 and carbon neutrality by 2060. Based on the “3060 dual-carbon” goal, how to improve the efficiency of energy performance is an important prerequisite for building a low-carbon, energy-saving, green, and beautiful China. The office performance building studied in this paper is located in the urban area of Turpan, where the climate is characterized by an extremely hot summer environment and a cold winter environment. At the same time, the building is oriented east–west, with the main façade facing west, and the main façade consists of a large area of single-layer glass curtain wall, which is affected by western sunlight. As a result, there are serious problems with the building’s energy consumption, which in turn leads to excessive carbon emissions and high life cycle costs for the building. To address the above problems, this paper analyzes and optimizes the following four dimensions. First, the article creates a Convolutional Neural Network (CNN) prediction model with Total Energy Use in Buildings (TEUI), Global Warming Potential (GWP), and Life Cycle Costs (LCC) as the performance objectives. After optimization, the R2 of the three are 0.9908, 0.9869, and 0.9969, respectively, thus solving the problem of low accuracy of traditional prediction models. Next, the NSGA-II algorithm is used to optimize the three performance objectives, which are reduced by 41.94%, 40.61%, and 31.29%, respectively. Then, in the program decision stage, this paper uses two empowered Topsis methods to optimize this building performance problem. Finally, the article analyzes the variables using two sensitivity analysis methods. Through the above research, this paper provides a framework of optimization ideas for office buildings in extremely hot and cold regions while focusing on the four major aspects of machine learning, multi-objective optimization, decision analysis, and sensitivity analysis systematically and completely. For the development of office buildings in the region, whether in the early program design or in the later stages, energy-saving measures to optimize the design have laid the foundation of important guidelines.

Research on Student Learning Attitude Evaluation Model Based on TOPSIS Comprehensive Evaluation Algorithm

In order to ensure the quality of teaching in universities, this article uses the TOPSIS comprehensive evaluation algorithm to help analyze the learning attitude of school students, as well as the class atmosphere and academic atmosphere of the college. At the same time, a list of five classes with the best academic atmosphere, three colleges, and sixty students with positive and negative learning attitudes were selected. Firstly, this article conducts a quantitative analysis of the scores and participation in the tests of 9048 students. The article identifies three factors that reflect students' learning attitudes: average test score, standard deviation, and participation rate in the tests. Then, based on student test data, an evaluation model reflecting the academic atmosphere, class atmosphere, and student enthusiasm of the college was established based on student performance information. The model results were ranked to obtain the ranking of model scores for each college, class, and student. By studying student data and quantifying the characteristics that reflect their learning attitudes, three indicators that can effectively reflect student attitudes and enthusiasm were established by analyzing the attached data. When assigning weights to factors, it is necessary to consider the importance of the factor in describing student attitudes, and make judgments and trade-offs based on specific situations. In response to this situation, although the standard deviation can reflect the stability of students' grades, it is greatly limited by the number of tests, while test completion and average score can better reflect students' learning attitude and performance. Therefore, subjective weighting method can be considered to assign weights to these three indicators, which are average score, standard deviation, and test participation rate, respectively; 0.3, 0.2, 0.5. By using the TOPSIS method and combining three indicators, establish an evaluation model for the academic atmosphere of the college, a class atmosphere evaluation model, and a student motivation evaluation model. Finally, evaluate the academic atmosphere of each college, class atmosphere, and the learning enthusiasm of each student. Based on the evaluation results of the model, this article selected a list of five classes with the best academic atmosphere, three colleges, and sixty students with positive and negative learning attitudes.

Integrated Framework for Light Pollution Assessment and Mitigation: A Multifaceted Approach with TOPSIS and Machine Learning

In this study, we propose the development of an evaluation system to quantify the degree of light pollution and offer three strategies for mitigating its negative effects. Our research identifies 4 distinct types of cities and employs a 16 key indicators to assess the level of light pollution risk, forming the basis for the construction of the Light Pollution Assessment (LPA) Model. Utilizing the TOPSIS and EWM methods, we ranked each city and determined a composite score interval, categorizing pollution levels from I to IV. Our evaluation system designates level I with an overall score of 0.7466, establishing it as a benchmark for assessing the risk level of light pollution. Recognizing that certain factors influencing LPA levels may not directly contribute to light pollution, we introduce the ML-based Strategy Evaluation (MLE) method, this approach classifies 11 out of 16 indices based on their relevance to light pollution. Our analysis reveals that the number of streetlights, the range of ULOR (Upward Light Output Ratio), and the presence of natural protected areas are the top three factors affecting light pollution. With an accuracy of 79.1%, the MLE method demonstrates a strong alignment with the data. Based on our findings, we propose three targeted strategies focusing on luminance regulation, greenery implementation, and legal enhancements to address and alleviate light pollution challenges in urban environments. This research contributes a comprehensive assessment framework and actionable strategies for policymakers and urban planners to effectively manage and reduce light pollution.

Examining the complex relationship between Urbanization and ecological environment in ecologically fragile areas: a case study in Southwest China.

The sustainable development of ecologically fragile areas and the implementation of regional coordinated development strategies cannot be separated from the coordinated development and common progress of urbanization and the ecological environment, and this is particularly the case in Southwest China. This study examines the interplay between urbanization and the ecological environment across 26 cities in Southwest China from 2009 to 2019, utilizing 30 statistical indicators to analyze their coupling coordination relationship and its spatiotemporal evolution. The Entropy TOPSIS method, the coupling coordination degree model, and the obstacle factors model were used to calculate the subsystem score, coupling coordination degree, and obstacle factors, respectively. Our findings reveal an upward trajectory in urbanization scores across the 26 cities, juxtaposed with a fluctuating downward trend in ecological environment scores. The coupling coordination degree of urbanization and ecological environment in most cities maintained a rapid upward trend and showed spatial distribution characteristics of "strong core, weak middle, and edge." Moreover, our analysis identified public transport facilities, aggregate purchasing power, and cultural supply service services as primary obstacle factors impeding the development of coupling coordination degrees. These research results offer valuable insights for informing future endeavors in achieving high-quality development and fostering ecological civilization.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Comprehensive Evaluation of Esophageal Cancer Surgical Treatment Quality in Tertiary Hospitals of a Prefecture-level City Using the TOPSIS Method

This study employed the TOPSIS method to conduct a comprehensive evaluation of the esophageal cancer surgical treatment quality in tertiary hospitals of a prefecture-level city in China from January to September 2023. The analysis aims to provide data support for health administrative departments and hospitals, guiding the optimization of future treatment strategies. The results reveal that among the participating hospitals, Hospital 8 had the highest treatment quality, while Hospital 2 had the lowest. Based on these findings, we recommend accelerating the development and accreditation of relevant disciplines in underperforming hospitals to promote the standardization and regulation of esophageal cancer diagnostics and treatment, thereby improving overall treatment outcomes.

An Empirical Study on Offensive and Defensive Ability of College Men's Football Teams Based on TOPSIS-RSR Comprehensive Evaluation Method

This study aimed to explore the characteristics and rationality of ranking offensive and defensive abilities of 32 college football teams and their division into tiers, as well as the application effects in teaching and training experiments. A combination of literature review, questionnaire survey, TOPSIS method, RSR method, and experimental method were primarily employed. The objective was to furnish a more extensive theoretical reference basis for future college football team training. The findings revealed that the TOPSIS-RSR method was capable of assessing each team's outcomes objectively and quantitatively. However, potential errors were identified in the evaluation process stemming from the final match results, necessitating multifaceted considerations between teams and players. Numerous internal and external uncertain factors, such as time, individuals, and coaches, were acknowledged. The application of the TOPSIS-RSR comprehensive evaluation method to football training technology was found to assist college students with specific football skills in enhancing their technical proficiency. It was concluded that for athletes with variable factors such as age, athlete level, and training years at different phases, the rational arrangement of training cycles must be thoroughly considered.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.