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A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This paper aims to investigate the effect and mechanism of O atom single doping, Ce and O atoms co-doping on the interfacial microscopic behavior of brazed Ni-Cr/diamond.

Using first-principles calculations, the embedding energy, work of separation, interfacial energy and electronic structures of Ni-Cr-O/diamond and Ni-Cr-O-Ce/diamond interface models were calculated. Then, the effect of Ce and O co-doping was experimentally verified through brazed diamond with CeO₂-added Ni-Cr filler alloy.

The results show that O single-doping reduces the interfacial bonding strength between Ni-Cr filler alloy and diamond but enhances its interfacial stability to some extent. However, the Ce and O co-doping simultaneously enhances the interfacial bonding strength and stability between Ni-Cr filler alloy and diamond. The in-situ formed Ce-O oxide at interface impedes the direct contact between diamond and Ni-Cr filler alloy, which weakens the catalytic effect of Ni element on diamond graphitization. It is experimentally found that the fine rod-shaped Cr₃C₂ and Cr₇C₃ carbides are generated on diamond surface brazed with CeO₂-added Ni-Cr filler alloy. After grinding, the brazed diamond grits, brazed with CeO₂-added Ni-Cr filler alloy, present few fracture and the percentage of intact diamond reaches 67.8%. Compared to pure Ni-Cr filler alloy, the brazed diamond with CeO₂-added Ni-Cr filler alloy exhibit the better wear resistance and the slighter thermal damage.

Using first-principles calculations, the effect of Ce and O atoms co-doping on the brazed diamond with Ni-Cr filler alloy is investigated, and the calculation results are verified experimentally. Through the first-principles calculations, the interface behavior and reaction mechanism between diamond and filler alloy can be well disclosed, and the composition of filler alloy can be optimized, which will be beneficial for synergistically realizing the enhanced interface bonding and reduced thermal damage of brazed diamond.

Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

This research aims to examine which cooperative contract (wholesale-price contract or cost-sharing contract) can more effectively upgrade the green degree of product and promote demand when considering consumer reference price effect under different power structures.

This research investigates a dyadic green supply chain composed of one manufacturer and one retailer. Four Stackelberg game models with a cost-sharing contract or a wholesale-price contract are built in retailer-led and manufacturer-led scenarios, respectively. Using backward induction, the optimal green decision under each model is obtained. In addition, the optimal cooperative contract is proposed by comparing these four models.

It is found that under consumer reference price effect, a cost-sharing contract outperforms a wholesale-price contract in upgrading product greenness and promoting demand. Under any single contract, the retailer-led situation is more conducive to improving product greenness than the manufacturer-led situation. Moreover, consumer reference price effect would reduce the sharing ratio of a cost-sharing contract when the manufacturer dominates, but it could mitigate the problem of double marginalization by reducing wholesale and retail prices under both types of contracts, which would enhance consumer surplus.

It is a new attempt to incorporate consumer reference price effect and power structure into a green supply chain framework and proposes a novel demand function that simultaneously emphasizes consumer reference price effect, consumer environmental awareness and product green attribute. In addition, it provides managerial insights for business managers to choose green cooperative contracts with consumer reference price effect under different power structures.

As climate change impacts residential life, people typically use heating or cooling appliances to deal with varying outside temperatures, bringing extra electricity demand and living costs. Water is more cost-effective than electricity and could provide the same body utility, which may be an alternative choice to smooth electricity consumption fluctuation and provide living cost incentives. Therefore, this study aims to identify the substitute effect of water on the relationship between climate change and residential electricity consumption.

This study identifies the substitute effect of water and potential heterogeneity using panel data from 295 cities in China over the period 2004–2019. The quantile regression and the partially linear functional coefficient model in this study could reduce the risks of model misspecification and enable detailed identification of the substitution mechanism, which is in line with reality and precisely determines the heterogeneity at different consumption levels.

The results indicate that residential water consumption can weaken the impact of cooling demand on residential electricity consumption, especially in low-income regions. Moreover, residents exhibited adaptive asymmetric behaviors. As the electricity consumption level increased, the substitute effects gradually get strong. The substitute effects gradually strengthened when residential water consumption per capita exceeds 16.44 tons as the meeting of the basic life guarantee.

This study identifies the substitution role of water and heterogeneous behaviors in the residential sector in China. These findings augment the existing literature and could aid policymakers, investors and residents regarding climate issues, risk management and budget management.