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This paper takes the seven overcapacity industries such as the textile industry, electricity and heat, steel, coal, automobile manufacturing, nonferrous metals and petrochemical industry as research objects and proposes a TOPSIS grey relational projection group decision method with mixed multiattributes, which is used for the ranking of the seven industries with overcapacity and provided relevant departments with a basis for decision-making.

First, an evaluation index system from four aspects is established. Secondly, the attributes of linguistic information are converted into two-dimensional interval numbers and triangular fuzzy numbers, and an evaluation matrix is constructed and normalized. This paper uses the AHP method to determine the subjective weights and uses the coefficient of variation method to determine the objective weights. Moreover, this paper sets up the optimization model with the largest comprehensive evaluation value to determine the combined weights. Finally, the TOPSIS grey relational projection method is proposed to calculate the closeness of grey relational projections and to rank them.

This paper analyzes the problem of overcapacity in seven industries with the TOPSIS grey relational projection method. The results show that the four industries of automobile manufacturing, textile, coal and petrochemical are all in serious overcapacity levels, while the three industries of steel, nonferrous metals and electric power are relatively in weak overcapacity level in the three years of 2016–2018. TOPSIS grey relational projection method ranks the overcapacity degree of the seven major overcapacity industries, making the relative overcapacity degree of each industry more clear and providing a reference for the government to formulate targeted policies and measures for each industry.

By using TOPSIS grey relational projection method to evaluate the overcapacity of the seven major overcapacity industries, on the one hand, it makes the relative overcapacity degree of each industry more clear, on the other hand, it can provides the basis for the government and decision-making departments. This helps them promote better the healthy and orderly economic development of the seven major industries and avoid resource waste caused by overcapacity.

This article solves the single evaluation method caused by the limited indicators in the past, combines TOPSIS and the grey relational projection method and applies it to the overcapacity evaluation of the industry, not only applies it to the evaluation of overcapacity for the first time but also involves novel problems and methods, which expands the scope of application of the model.

This study aims to conduct the aircraft electrical wiring interconnection system (EWIS) safety risk assessment process abundantly and hierarchically and establish the assessment index system considering the weights and interrelationships of different levels of indices.

Due to the failure of EWIS being multifactorial, hidden and diverse, this paper divides the factors influencing the failure of EWIS into 3 primary indices, 13 secondary indices and 38 tertiary indices. Taking open circuit failure (OCF) and short circuit failure (SCF) as examples, calculate the weights of assessment indices based on the triangular fuzzy number analytic hierarchy process (TFNAHP) and triangular fuzzy number decision-making trial and evaluation laboratory (TFNDEMATEL). The cloud model (CM) divides the risk levels and obtains the safety risk assessment results. The comparative analyses of different weight calculation methods, different failure modes and different aircraft EWIS zones verify the effectiveness and practicability of the proposed method.

The results show that the proposed method aligns more with the actual situation than other methods. Also, the results identify key focus objects in EWIS safety risk assessment, such as the surrounding environmental factors among the primary indices having the most significant influence on OCF and SCF, the risk level of SCF being higher than that of OCF, etc.

This paper proposes a safety risk assessment index system for aircraft EWIS based on the cable parameters, surrounding environmental factors, installation and protection methods. The weight assignment is added to the assessment index system, and the safety risk assessment model is constructed by combining TFNAHP, TFNDEMATEL and CM.

This study aims to evaluate enterprise technology innovation capability in prefabricated construction (PC) from an input-output perspective, using six integrated enterprises in China as cases.

An evaluation system for enterprise technology innovation capability in PC was constructed, including total input, technology output (TO) and project output. All the evaluation indexes were quantified, and the subject and object indexes weights were determined using the fuzzy cognitive map and information entropy, respectively. The final scores and ranks were evaluated through gray relational analysis (GRA) based on the combined weights.

It was found that enterprise technology innovation capability in PC was low in China, with its unbalanced development in different dimensions and the poorest performance in TO, currently.

This research has developed an evaluation system for technology innovation capability in PC at the enterprise level and scientifically quantified all the indexes, which is a breakthrough over existing studies. The GRA model based on the combined weights proposed in this study can be applied to other comparable fields and regions, with its easy operation.

Background/Objectives: Protein-based meal replacements (MR) with viscous soluble fibre are known aids for weight loss. This study aims to compare the effects of new whey and vegan MR containing different amounts of PGX (PolyGlycopleX) on weight loss over 12 weeks, along with a calorie-restricted diet.

Subjects/Methods: Sixty-eight healthy adults of both sexes (53 women; 15 men; average age 47.1 years; BMI 31 ± 7.1 kg/m² and weight 85.05 ± 23.3 kg) were recruited. Participants consumed a whey or vegan MR twice/d (5–10 g/day PGX) with a low-energy diet (1,200 kcal/day), over 12 weeks. Weight, height, waist and hip circumference were recorded (four time periods).

Results: Forty-four participants completed the study. Results showed significant reductions in average body weight and at week 12, whey group was [−7.7 kg ± 0.9 (8.3%), p < 0.001] and vegan group was [−4.5 kg ± 0.8 (6.2%), p < 0.001)]. All participants (n = 44; BMI 27 to 33 kg/m²) achieved significant reductions in body measurements from baseline to week 12; p < 0.001. Conclusions: Supplementation of protein-based MR with PGX and a balanced, low-energy diet, appears to be an effective approach for short-term weight loss.

As the authors were evaluating if the MR as a whole (i.e. with PGX) caused weight loss from baseline over the 12 weeks, no comparators, i.e. just the MR without PGX, were used. Formulation of these new MRs resulted in a whey product with 5 g PGX and a vegan product with 2.5 g PGX. Only 2.5 g PGX could be formulated with the vegan protein due to taste and viscosity limitations. Study participants were not randomized and no control groups (e.g. no MR or MR without PGX but with energy restricted diet) were used. Furthermore, it is not clear whether the sort of protein alone or the combination with a higher amount of PGX (whey with 5 g PGX/serving vs vegan with 2.5 g PGX/serving) has contributed to these significant greater weight-loss effects. This was something the authors were testing, i.e. could only 2.5 g PGX/serving have an effect on weight loss for a vegan MR. These limitations would be somethings to evaluate in a subsequent randomized controlled study. Hence, the results of this study may serve as a good starting point for further sophisticated randomized controlled trials that can demonstrate causality – which the authors acknowledge as one of the fundamental limitations of an observational study design. Participants tracked their calories but adherence and compliance were self-assessed and they were encouraged to keep their exercise routine consistent throughout the study. Hence, these are further limitations. No control group was used in this study to observe the effect of the dietary intervention and/or physical activity on weight loss alone. However, a goal of the authors was to keep this study as close to a real-life situation as possible, where people would not be doing any of these measurements, to see if with minimal supervision or intervention, people can still lose weight and alter their body composition. Furthermore, differences in gender and the corresponding weight loss effects in response to MR-protein-based treatments could be evaluated in follow-up studies.

This study indicates that the consumption of protein-based (animal, whey or plant, pea protein) MR incorporating the highly soluble viscous PGX is beneficial for weight loss when combined with a healthy-balanced, calorie-restricted diet. MRs at either 2.5 g or 5 g per serving (RealEasy^TM with PGX) proved to be a highly effective as a short-term solution for weight loss. The observed results are encouraging, however, further long-term studies (i.e. randomized clinical trials RCT) are needed to confirm the clinical relevance. RCTs should focus on the individual effects of PGX and/or the different protein sources used in MRs, on weight loss and the maintenance of the reduced body weight, and should measure detailed blood parameters (lipid profiles, glucose etc.) as well as collect detailed exercise and food consumption diaries.

To the authors’ knowledge, this is the first study comparing a whey versus vegan, (as pea) protein-based MR that is supplemented with fibre PGX; thus, this work adds information to the already existing literature on fibre (such as PGX) and MRs regarding their combined weight loss effects. The purpose of this study was to observe if the novel protein-based (either whey or vegan versions) MR RealEasy^TM with PGX at 2.5 or 5 g in addition to a calorie-restricted diet (total of 1,200 kcal/day) would aid in weight loss in individuals over a 12-weeks period. Adding increasing amounts of whey protein and soluble fibre can help reduce subsequent ad libitum energy intake which could help adherence to energy restricted diets, but whether similar effects are seen with vegan protein is unclear – this study does aim to address this.

This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.

Using the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.

The empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.

This study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.

First, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging market.

Pier scour is one of the main causes of damage to the columns of the river bridges. It is essential to select the best method among various repair methods based on different evaluation indices. However, there is no procedure for ranking these repair methods based on their attributes. The present study seeks to set an approach for this ranking.

In this paper, a multi-attribute decision-making (MADM) model is presented for ranking the repair techniques, in which alternatives are examined using the most important evaluation criteria. In addition, a combination of entropy and eigenvector methods has been proposed for weighting these attributes. A case study is then used to demonstrate the applicability and the validity of the method.

The execution of the model using two multi-criteria methods yielded similar results, which confirms its accuracy and precision. Moreover, the research findings showed the consistency of the objective and subjective weighting methods and the conformity of the weights obtained for the attributes from the combination of these methods to the nature of the problem.

The selection of the proper method for repairing the bridge columns plays an essential role in success of the bridge restoration. The proposed model introduces an approach for ranking repair methods and selecting the best one that has not been presented so far. Also, the weighing method for attributes is an innovative method for ranking restoration methods that has been proven in a case study.

Evaluation of energy and climate policy interactions is a complex issue, whereas stakeholders' preferences incorporation has not been addressed systematically. The purpose of this paper is to present an integrated weighting methodology that has been developed in order to incorporate weighting preferences into an ex ante evaluation of climate and energy policy interactions.

A multi‐criteria analysis (MCA) weighting methodology which combines pair‐wise comparisons and ratio importance weighting methods has been elaborated. It initially introduces the users to the evaluation process through a warming up holistic approach for an initial rank of the criteria and then facilitates them to express their ratio relative importance in pair‐wise comparisons of criteria by providing them an interactive mean with verbal, numerical and visual representation of their preferences. Moreover, it provides a ranking consistency test where users can see the degree of (in)consistency of their preferences.

Stakeholders and experts in the energy policy field who tested the methodology stated their approval and satisfaction for the combination of both ranking and pair‐wise comparison techniques, since it allows the gradual approach to the evaluation problem. In addition, main difficulties in MCA weights elicitation processes were overcome.

The methodology is tested by a small sample of stakeholders, whereas a larger sample, a broader range of stakeholders and applications on different climate policy evaluation cases merit further research.

The novel aspect of the developed methodology consists of the combination of ranking and pair‐wise comparison techniques for the elicitation of stakeholders' preferences.

This study aims to focus on the sustainability of prefabricated medical emergency buildings (PMEBs) renovation after the epidemic, to address the problem that large numbers of PMEBs may be abandoned for losing their original architectural functions. This study develops an evaluation system to identify and measure sustainable factors for PMEBs’ renovation schemes. Qualitative and quantitative analysis of PMEBs’ renovation scheme was conducted based on cloud model evaluation method and selected the renovation scheme in line with sustainable development. The study promotes evaluation methods and decision-making basis for the renovation design of global PMEBs and realizes the use-value of building functions again.

By referring to the existing literature, design standards and expert visiting a set of evaluation index systems which combines the renovation of the PMEBs and the sustainability concept has been established, which calculates the balanced optimal comprehensive weight of each indicator utilizing combination weighting method, and quantifies the qualitative language of different PMEBs’ renovation schemes by experts through characteristics of the cloud model. This paper takes Huoshenshan hospital a representative PMEB during the epidemic period as an example, to verify the feasibility of the cloud model evaluation method.

The research results of this paper are that in the PMEBs’ renovation scheme structural reformative (T11) and corresponding nature with the original building (T13) have the most important influence; the continuity of architectural cultural value (T22) and regional development coherence (T23) are the key factors affecting the social dimension; the profitability of renovated buildings (T34) is the key factor affecting the economic dimension; the environmental impact (T41), resource utilization (T42) and ecological technology (T43) are the key factors in the environmental dimension.

This study contributes to the existing body of knowledge by supplementing a set of scientific evaluation methods to make up for the sustainability measurement of PMEBs’ renovation scheme. The main objective was to make renovated PMEBs meet the needs of urban sustainable development, retain the original cultural value of the buildings, meanwhile enhance their social and economic value and realize the renovation with the least impact on the environment.

This paper improves the evaluation index system of green building operation effect and establishes the evaluation model of green building operation effect. It is expected to promote energy saving and emission reduction and provide a more scientific evaluation method for green building operation effect evaluation.

First, 20 key evaluation indexes are selected to establish the operation effective evaluation index system. Then, the combined weight method is proposed to determine the weight of each evaluation index. Next, the gray clustering-fuzzy comprehensive evaluation method is used to construct the green building operation effective evaluation model. Finally, the feasibility and validity of the selected model were verified by taking Shenzhen Bay One green building in Shenzhen as an example.

This paper establishes the evaluation system of green building operational effect, and evaluates green building from the angle of operational effect. Taking Shenzhen Bay One project as an example, the rationality and applicability of the model are verified.

In this paper, for the first time, relevant indexes of user experience are included in the evaluation system of green building operational effect, which makes the evaluation system more perfect. In addition, a more scientific fuzzy gray clustering method is used to evaluate the operational effect of green building, and a new evaluation model is established.

Statistics show that selecting the best investment program based on both cost and effectiveness can avoid financial losses. However, investment evaluation of a power transformer is full of uncertainty as it is hard to obtain accurate and useful cost-effectiveness results. Therefore, the purpose of the paper is to establish an investment evaluation model.

The cost-effectiveness evaluation model in this study used grey correlation analysis for the power transformer selection based on life cycle cost (LCC). Indices of cost and effectiveness factors were chosen to form a three-level index system including quantitative and qualitative factors. Evidential reasoning was applied to quantify the qualitative indexes. Grey correlation analysis was applied to select the best investment program.

The results from this study show that the proposed approach is effective and offers a new approach to evaluating transformer investment.

The model was applied to an investing decision-making problem of the transformer in a new substation in Wuhan, China.

It is very important to select the best transformer program in the candidate investment programs because the investment program decides almost 70 percent of the LCC of the power transformer.