Objective Weights Research Articles

Construction 4.0 technology evaluation using fuzzy TOPSIS: comparison between sustainability and resiliency, well-being, productivity, safety, and integrity.

This study aims to compare the impact of Construction 4.0 technologies on different organizational core values, focusing on sustainability and resiliency, well-being, productivity, safety, and integrity. To achieve that aim, the study objectives are the following: (i) identify the critical Construction 4.0 technologies between core values; (ii) appraise overlapping critical Construction 4.0 technologies between core values; (iii) examine the ranking performance of Construction 4.0 technologies between core values; and (iv) analyze the interrelationships between Construction 4.0 technologies and core values. First, twelve Construction 4.0 technologies were identified from a national strategic plan. Then, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that incorporates subjective and objective weights was used to evaluate the impact of the Construction 4.0 technologies on the five core values. Finally, the collected data was analyzed using the following techniques: fuzzy TOPSIS, normalization, overlap analysis, agreement analysis, sensitivity analysis, ranking comparison, and Spearman correlation. The study findings reveal four critical Construction 4.0 technologies that enhance all five core values: building information modeling (BIM), Internet of Things (IoT), big data and predictive analytics, and autonomous construction. Also, there is a high agreement on the Construction 4.0 technologies that enhance well-being and productivity. Lastly, artificial intelligence (AI) has the highest number of very strong relationships among the core values. The originality of this paper lies in its comprehensive comparison of the impact of Construction 4.0 technologies on multiple organizational core values. The study findings provide valuable insights in making strategic decisions in adopting Construction 4.0 technologies.

Integrated MCDM techniques for the selection of hybrid MWCNT/Ni–Zn–Fe nanocomposites for X-band radar absorption applications

In the 21st century, microwave-absorbing materials are used in a wide range of applications, such as in anechoic chambers and as electromagnetic interference shielding materials in radar and wireless communications. The design and development of new microwave-absorbing materials require substantial resources such as time, raw materials, and energy, and need to be cost-effective. Judicious selection of radar-absorbing materials from available alternatives often becomes difficult because it involves conflicting and non-commensurate attributes, such as bandwidth, reduced reflection loss, and thickness. This study aims to design and select novel double-layered multi-walled carbon nanotubes (MWCNT)/ Ni–Zn–Fe (NZF) microwave-absorbing materials using multi-criteria decision-making (MCDM) techniques such as TOPSIS, VIKOR, and PROMETHEE-II. Single-layered microwave-absorbing materials with various compositions of MWCNT and NZF were prepared using a low-cost and facilein-situpolymerization process. The multi-layered radar-absorbing materials reflection loss was determined using transmission line theory. Subjective and objective criteria weights from the analytical hierarchy process, entropy methods, and their calculated combined weights were utilized to implement the MCDM techniques. The combination of MWCNT/NZF radar-absorbing materials was chosen using integrated MCDM methods as the preferred microwave-absorbing material with a reflection loss of −15 dB for a 2.6 GHz bandwidth and a maximum reflection loss of –22 dB at 9.6 GHz in the X-band.

Comparison of trunk muscle activity while lifting objects of expected and unexpected weight with and without low back pain

BACKGROUND: Lifting heavy objects can induce postural stress and low back pain. OBJECTIVE: This study aimed to examine the effect of object weight expectations on trunk muscle activity and assess trunk muscle activity in people with chronic low back pain. METHODS: Twenty-two male college students (11 healthy and 11 participants with chronic low back pain) were recruited. The procedure was performed in three settings: lifting an expected 5-kg object, lifting an unexpected 10-kg object, and lifting an expected 10-kg object. Lifting was divided into five phases, and the muscle activity in each phase was compared between groups (chronic low back pain/control), object weight predictions, and phases. RESULTS: Compared to the control group, the chronic low back pain group had higher erector spinae muscle activity, regardless of weight or prediction, and significantly higher rectus femoris muscle activity in the early lifting phase of the expected 10-kg object (p= 0.043). Compared to when lifting the expected 10-kg object, erector spinae muscle activity was higher in the early lifting phase of the control group when lifting the unexpected 10-kg object (p= 0.016). CONCLUSIONS: Healthy individuals and individuals with chronic low back pain had different recruitment strategies for lifting objects heavier than predicted.

Enhancing logistics performance measurement: an effectiveness-based hierarchical data envelopment analysis approach

The logistics performance index (LPI) is a comprehensive and comparable composite index developed by the World Bank to assess a country’s logistics and trade facilitation environment. However, the existing LPI relies on externally assigned weights. To enhance LPI scores, this study adopts an effectiveness-based hierarchical data envelopment analysis method that internally allocates objective weights based on the dataset. Such endogenous weight information can provide additional valuable insights for countries to prioritize and strategize efforts to enhance their performance in the future. The results of this study indicate that focusing on improving the policy category yields greater benefits than improving the service category in terms of ranking national logistics performance. Furthermore, this study finds that logistics performance is influenced by income levels and geographical area. Income levels impact the regulatory and trade facilitation environment, with varying income levels leading to different priority policy areas. Geographical location also plays a crucial role in regional economic integration and trade facilitation. A favorable geographical location reduces costs and time while enhancing supply chain predictability and reliability. It is hoped that this study serves as a valuable resource for countries in identifying optimization strategies to improve their logistics performance.

An Analytical Hierarchy Process Approach for Prioritization of Objectives and Parameters for an Integrated Urban Water Management

For sustainable development of urban areas, it is essential to address urban water management integrated with the urban water systems and spatial planning of the cities and the subsequent uncertainties. An integrated urban water management and planning model will help ensure water security, mitigate floods and droughts, reduce the negative environmental impact of urbanization and climate change, and bring coherence in the water supply management with the cities' spatial planning and relevant policies. In the present research, the Analytical Hierarchy Process (AHP), a multi-criteria decision analysis, has been implemented to calculate the weightage of different parameters associated with urban water management and planning. AHP uses linguistic and quantitative variables based on experts' opinions for selecting and prioritizing the objectives and parameters for an integrated approach to urban water management and spatial planning under uncertainties. The local and global weights of objectives, parameters, key indicators, and sub-indicators were calculated after pairwise comparison by 32 experts from academia and industry in urban planning and civil and environmental engineering for the case of Ranchi, a city in eastern India. The study establishes the financial factors (36.97%) as one of the important attributes, followed by environmental factors (26.71%) for consideration in integrated urban water management. Further, Alternative D (45.256%) is framed by collaborating different objectives of IUWM, such as ensuring water security through alternative water sources, mitigating floods and droughts, and water supply planning considering land use, management policies, and climate change. The research findings can assist decision-makers in prioritizing the various objectives and parameters for integrated urban water management in cities with similar population sizes and organic growth.

Combining subjective and objective weights considerations to solve the emergency location selection problems under spherical fuzzy environments

The development of an emergency will cause difficulties in the selection of emergency locations. The COVID-19 pandemic has caused the establishment of emergency medical facilities to become more important and urgent. However, the selection of emergency locations usually involves unclear assessment information, uncertain information, and unacquirable partial information. Due to incomplete evaluation information, traditional fuzzy set and intuitionistic fuzzy set methods cannot effectively deal with the selection problem of emergency locations. In order to effectively solve the selection problem of emergency locations during an emergency, this paper integrated the spherical fuzzy set and subjective and objective weight considerations to provide correct and reasonable evaluation results for the selection of emergency locations. For the numerical verification, this paper applied the selected location for an emergency temporary hospital during the COVID-19 pandemic location in Istanbul, Turkey as an illustrative example and compared the differences in the calculation results among the proposed flexible emergency location selection method, the spherical weighted arithmetic mean method and the spherical aggregation operator method. The numerical verification assessment results demonstrated that the proposed flexible emergency location selection method not only can deal with unclear, uncertain, and even unacquirable partial information at the same time, but also can consider the subjective and objective weights of assessment data, which could provide effective and correct emergency location selections under spherical fuzzy environments.

Confidence relative off-targets distance-based multi-dimensional transparency evaluation of distribution station area

With the large-scale integration of distributed renewable energy into low-voltage distribution station areas, rapidly growing services put forward new requirements on transparent monitoring. In order to accurately and objectively quantify the transparency of various distribution station areas and measure the impact of different dimensional indexes on the transparency evaluation of distribution station areas, this paper proposes a confidence relative off-target distance-based multi-dimensional transparency evaluation method. First, a multi-dimensional transparency evaluation index system with electrical and communication integration is constructed. Second, an improved gray target model combining both positive and negative target vectors is established to realize bi-directional quantitative analysis. Then, the relative off-target distance is calculated based on the endowment coefficient by leveraging both subjective and objective weights. Finally, the confidence of the relative off-target distance is calculated based on fuzzy entropy to improve the reliability of transparency evaluation. The simulation results show that this method can effectively distinguish the transparency gap between different distribution station areas, identify the single dimensional index with the greatest contribution or potential, and verify the effectiveness of the proposed method applied to the transparency evaluation of distribution station areas.

Geological Disaster Susceptibility Evaluation Using a Random Forest Empowerment Information Quantity Model

Geological hazard susceptibility assessment (GSCA) is a crucial tool widely utilized by scholars worldwide for predicting the likelihood of geological disasters. The traditional information quantity model in geological disaster susceptibility evaluation, which superimposes the information quantity of each evaluation factor without considering their weights, often negatively impacts susceptibility zoning results. This paper introduces a method employing random forest (RF) empowerment information quantity to address this issue. The method involves calculating objective weights based on a parameter-optimized random forest model, assigning these weights to each evaluation factor, and then conducting a weighted superimposition of the information. Utilizing the natural discontinuity method, the resulting comprehensive information volume map was segmented. The proposed method was applied in Kang County, Gansu Province, and its performance was compared with that of traditional methods in terms of geological disaster susceptibility zoning maps, zoning of statistical disaster point density, and receiver operating characteristic (ROC) curve accuracy. The experimental findings indicate the superior accuracy and reliability of the proposed method over the traditional approach.

Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period

Purpose: This study aims to examine the innovation performance of G20 countries in 2018-2022 with multi criteria decision making methods. When the 5-year performance was analyzed, it was also revealed whether the COVID-19 outbreak has an impact on the innovation performance of the countries.
 Methodology: An integrated LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) - MAIRCA (Multi Attribute Ideal-Real Comparative Analysis) method was applied in the study. First, the indicators representing innovation performance (institutions, human capital, and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs) was objectively weighted by the LOPCOW method. Then, the innovation performance of G20 countries was calculated with the MAIRCA method. Finally, a comparative analysis was also presented to support the findings.
 Findings: As a result of the innovation performance analysis using multi criteria decision making methods, human capital, and research were found to be the most important indicators, and the United States was found to be the country with the best innovation performance. In the sensitivity and comparative analysis, it was concluded that the integrated LOPCOW-MAIRCA method provides robust outputs.
 Originality: This study makes original contributions by analyzing the impact of the COVID-19 pandemic on the innovation performance of countries considering the 2018-2022 period and the integrated multi criteria decision making methods it uses that have not yet been applied in the literature.

A new approach to risk assessment in failure mode and effect analysis based on engineering textual data

Failure Mode and Effect Analysis (FMEA) is a valuable tool for improving the quality of products and service systems. However, traditional FMEA methods require examining various engineering textual materials and attending multiple meetings, which can be time-intensive. Additionally, accurately evaluating the severity (S), occurrence (O), and detection (D) of failure modes is essential to ensure the accuracy of FMEA results. Despite efforts by researchers to improve the efficiency of FMEA, the evaluation of these risk factors (S, O, and D) still relies too heavily on a manual and inefficient process. To address these issues, this paper proposes a machine learning-enabled FMEA approach. This new approach combines the strengths of the BERT (Bidirectional Encoder Representations from Transformers) model in transforming textual failure descriptions into word vectors, the advantages of the VSM (Vector Space Model) in determining semantic similarity between different failure modes, and the merits of TOPSIS based on objective weights in handling multicriteria risk assessment. The proposed approach was applied to an actual case study for failure mode and effect analysis in auto parts processing. Comparisons between the proposed method and conventional FMEA were conducted to demonstrate the effectiveness of the new approach.

An integrated group decision making framework for business performance evaluation of performing arts enterprises with probabilistic hesitant fuzzy IDOCRIW-MOOSRA method

There is a lack of domestic and foreign research on the evaluation and improvement strategies of business performance of performing arts enterprises, especially in the context of the “restructuring” of cultural groups in China. Most of the existing studies are distributed in bulk, not only lacking in theoretical depth, but also lacking in systematization to a certain extent, which shows that the existing studies have not fully formed a mature and valuable theoretical system. The business performance evaluation of performing arts enterprises is a multiple attributes group decision making (MAGDM). This paper constructs a novel probabilistic hesitant fuzzy Multi-Objective Optimization Simple Ratio Analysis (PHF-MOOSRA) model based on the integrated determination of objective criteria weights (IDOCRIW) under the probabilistic hesitant fuzzy sets (PHFSs) for this issue. The PHFSs provides an evaluation circumstance containing more information which make the final decision-making results more accurately. Additionally, the IDOCRIW method separately and the MOOSRA method based on the MOORA method is proposed in PHFSs circumstance in this model. In the end, this model is then applied in a numerical case study for business performance evaluation of performing arts enterprises and compare this model with other existing methods.

Integrated triangular fuzzy KE-GRA-TOPSIS method for dynamic ranking of products of customers’ fuzzy Kansei preferences

As society evolves, companies produce more homogeneous products, shifting customers’ needs from functionality to emotions. Therefore, how quickly customers select products that meet their Kansei preferences has become a key concern. However, customer Kansei preferences vary from person to person and are ambiguous and uncertain, posing a challenge. To address this problem, this paper proposes a TF-KE-GRA-TOPSIS method that integrates triangular fuzzy Kansei engineering (TF-KE) with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, a Kansei evaluation system is constructed based on KE and fuzzy theory. A dynamic triangular fuzzy Kansei preference similarity decision matrix (TF-KPSDM) is defined to quantify customer satisfaction with fuzzy Kansei preferences. Secondly, dynamic objective weights are derived using Criteria Importance Though Intercrieria Correlation (CRITIC) and entropy, optimized through game theory to achieve superior combined weights. Thirdly, the GRA-TOPSIS method utilizes the TF-KPSDM and combined weights to rank products. Finally, taking the case of Kansei preference selection for electric bicycles, results indicate that the proposed method robustly avoids rank reversal and achieves greater accuracy than comparative models. This study can help companies dynamically recommend products to customers based on their Kansei preferences, increasing customer satisfaction and sales.

Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector

The present paper introduces a new multi-criteria decision making (MCDM) model such as Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS). The proposed method provides a number of unique features such as use of non-linear standardization combined with a new definition for calculating utility values of the alternatives and design flexibility. The new method is applied to compare the firm performances using Economic, Environment, Social and Governance (EESG) framework. Over the last few decades alongside economic gain, the firms have been equally emphasizing on environmental protection, social values and good governance. This paper aims to bring in the economic aspect in terms of market capitalization and enterprise value while uses rating scores for environment, social and governance to put forth an extended performance measurement framework. A set of eight leading firms belonging to the energy sector listed to NSE, India has been considered for the period FY 2022-23 as a case study. The criteria weights are calculated using the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method. It is observed that ERUNS provides a stable and reliable ranking result free from the rank reversal issue. This paper shall be of interest to both analysts and practitioners in terms of its robust method and practical performance assessment framework.

Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes

Abstract. Identifying dynamic objects in dynamic scenes remains a challenge for traditional simultaneous localization and mapping (SLAM) algorithms. Additionally, these algorithms are not able to adequately inpaint the culling regions that result from excluding dynamic objects. In light of these challenges, this study proposes a novel visual SLAM (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes (VTD-SLAM), which leverages an improved Vision Transformer semantic segmentation technique to address these limitations. Specifically, VTD-SLAM utilizes a residual dual-pyramid backbone network to extract dynamic object region features and a multiclass feature transformer segmentation module to enhance the pixel weight of potential dynamic objects and to improve global semantic information for precise identification of potential dynamic objects. The method of multi-view geometry is applied to judge and remove the dynamic objects. Meanwhile, according to static information in the adjacent frames, the optimal nearest-neighbor pixel-matching method is applied to restore the static background, where the feature points are extracted for pose estimation. With validation in the public dataset TUM (The Entrepreneurial University Dataset) and real scenarios, the experimental results show that the root-mean-square error of the algorithm is reduced by 17.1 % compared with dynamic SLAM (DynaSLAM), which shows better map composition capability.

A comprehensive health diagnosis method for expansive soil slope protection engineering based on supervised and unsupervised learning

ABSTRACT There is a complex multifactorial coupling effect among the damages of various protection structures on slopes. Existing research focused on the health assessment of individual structures is often insufficient in representing the overall health status of the protection engineering system. Considering the characteristics of expansive soil slope protection engineering, this study proposes a health diagnosis method using combined weights and binary K-means clustering algorithm. The method quantifies the damage data of protection structures based on subjective and objective weights, and clusters the data by combining the binary K-means method and target vector layer to obtain the diagnosis results. Furthermore, an XGBoost-based surrogate diagnosis model is constructed to omit the repetitive modelling process in practical applications to achieve dynamic diagnosis. The proposed method is validated to an expansive soil slope in Gaochun district, Nanjing. The results show that the proposed method can accurately evaluate protection engineering with different degrees of damage; the surrogate model follows the same weight assignment process as the diagnostic method to establish reliable prediction. Based on the proposed method, damage coupling effects between individual protection structures are captured, and targeted maintenance and repair can be implemented. The proposed method can be further extended to other slope engineering.

A safety evaluation model of open-pit mine dust based on subjective and objective weighting method

Abstract By analyzing the dust safety of open-pit mines, five primary indicators and 20 secondary indicators are determined for evaluation, and the subjective weights determined by the hierarchical analysis method and the objective weights determined by the entropy weighting method are combined to determine the subjective and objective weights; the scores of experts are integrated using fuzzy numbers to determine the final scores, and the combination of the two is used to construct the evaluation model of the dust safety of open-pit mines. Taking three mines as an example, the results show that the open pit mine dust safety of Mine is A 1 significantly better than that of the remaining two mines. The study shows that the model is suitable for open pit mine dust safety evaluation, considering the typical characteristics of subjectivity and objectivity, better reflecting the intention of the decision makers and the actual importance of the indicators, and making the results more scientific and objective. The proposed method provides a reference for determining dust safety in open pit mines.

Integration of the rural revitalization strategy in the construction of rural elderly services in the information age

Abstract Aiming at the uncertainty of decision-making factors and the difficulty of quantifying various subjective and objective weights in the process of rural pension service quality evaluation, this paper proposes a rural pension service quality evaluation method combining improved interval AHP and CRITIC method based on indicator correlation at the level of rural revitalization strategy. The method is based on 5 guidelines and 11 common factors, comprehensively considers the influence of subjective and objective weights on the design scheme, introduces the interval number and triangular fuzzy number, and establishes the evaluation index system of rural elderly service. By utilizing the improved interval AHP and CRITIC method, the subjective and objective weights of each evaluation index can be calculated. The analysis is done by combining examples to explore the ecological niche of the senior care industry, the rural suitability for elderly people, and their expectations and perceptions of the service. From the market ecological niche, it can be seen that the result of the senior care manufacturing industry measure is 0.85, which contributes the most to the rural economy in rural revitalization. The travel, ecological landscape, living services, and socio-economic environment in rural areas are improved under the rural revitalization strategy, and the weighted scores of the four are 0.5528, 1.1524, 1.299, and 0.6031, respectively. According to the comprehensive results, the rural revitalization strategy has resulted in a noticeable increase in the rural aged-care industry and services.

Analysis of the Utility of Chinese Traditional Culture in Contemporary Digital Media Communication Based on a Big Data Platform

Abstract Chinese traditional culture is profound and has important historical and cultural values, and in the digital information era, the dissemination of Chinese traditional culture is facing new challenges and opportunities. This paper begins with the utility mechanism of Chinese traditional culture dissemination and develops a path for Chinese traditional culture dissemination using digital media. On this basis, a digital platform for Chinese traditional culture is established through the modular division of digital platforms and joint visual and interaction design. Then, the hierarchical analysis method (AHP) is combined with (PCA) to complete the weighting. Then, the subjective and objective weights are balanced by the information entropy method (IE). Finally, the comprehensive evaluation model of the utility of digital communication in Chinese traditional culture is constructed by combining with the method of the distance between the superiority and inferiority solutions (TOPSIS), and the evaluation index system of the utility of cultural communication is established. Among them, the benefit value has the highest weight of 0.2007. The other indicators in order of weight are cultural, emotional experience (0.1735), cultural content experience (0.1687), interactive experience (0.1533), social impact (0.1531), and digital function experience (0.1507). Finally, the communication effect of traditional Chinese culture is analyzed using the digital platform of this paper. The average score is 8.75, with excellent communication effects. Based on the utility analysis of digital media communication, this paper provides a digital path for the communication of Chinese traditional culture and establishes a comprehensive evaluation method of communication utility, which provides an effective research method for the utility of digital media communication of traditional culture.

Online and offline mixed teaching quality assessment method for physical education class based on value assignment method

Abstract The blended teaching mode combines the advantages of online and offline teaching, which can improve the effect of university physical education teaching. The article takes the acquisition path of students’ multidimensional learning behavior data as the entry point, introduces the data preprocessing method after acquiring online learning behavior, and then establishes the online-offline blended teaching quality evaluation index system for physical education classes based on the CIPP model. The subjective and objective weights of the evaluation indexes are solved by using the AHP method and the improved pull-apart grade method, and the comprehensive, integrated assignment method is introduced to weigh the subjective and objective weights in order to obtain the comprehensive weights. Based on the weights of the indicators obtained by AHP-ISD, a teaching quality assessment model for physical education courses was established by combining the SVM model, and the SMO algorithm was selected to optimize the training of the model. To assess the application effectiveness of the model, a comprehensive assessment of its teaching quality was carried out, and the teaching effect of the blended teaching mode was also explored. The weight of the learning outcome evaluation index is 0.438 at most, the accuracy of the SMO algorithm training model is up to 99.99%, and the range of error scores between the model evaluation value and the actual value is between [0.01,0.03] points. The teaching quality assessment model based on the assignment method can obtain more accurate teaching quality assessment results for physical education courses and provide a new direction for optimizing the hybrid teaching assessment method of physical education courses.

Research on Promoting the Micro-Governance of Sports in China’s Urban Communities under the Concept of Common Construction, Common Governance and Sharing

Abstract The study first constructed a system of evaluation indicators, including five secondary and 21 tertiary indicators such as financial resources, site facilities and human resources. The weights were calculated using the combination assignment method, combining subjective weights based on the G1 method and objective weights from the projection seeking model algorithm, emphasizing the importance of human resources. The cloud model was applied to grade governance effectiveness, and most indicators were in the medium and high levels. The complexity and diversity of governance were revealed through quantitative and qualitative comparative Analysis (QCA) of a sample of 10 communities in H city. The results of the study showed that the similarity of community sports facilities as a percentage of expenditures in the 10 community samples in City H reached the high performance level (0.8601), while the similarity of the indicators of sports training and instruction, percentage of human resources in sports, public demand, and management of social organizations was at a lower level. To improve the effectiveness, it is recommended to strengthen the introduction and training of human resources, the construction of hardware facilities and publicity and promotion, and to optimize the organizational security. At the same time, modern technologies such as big data are utilized to establish a more efficient governance system to fully implement the concept.