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In the supply chain disruption risk, the issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

Considering the influence of irrational emotions of decision makers, an evaluation model is designed by the regret theory and VIKOR method, which makes the decision-making process closer to reality.

The paper has some innovations in the evaluation index system and evaluation model construction. The method has good stability under the risk of supply chain interruption.

The mixed evaluation information is used to describe the attributes, and the evaluation index system is constructed by the combined method of the social network analysis method and the literature research method to ensure the accuracy and accuracy of the extracted attributes. The issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

The purpose of this study is to effectively optimize vehicle lane-changing behavior and alleviate traffic congestion in ramp area through the study of vehicle lane-changing behaviors in upstream segment of ramp areas.

In the upstream segment of ramp areas under a connected vehicle environment, different strategies of vehicle group lane-changing behaviors are modeled to obtain the best group lane-changing strategy. The traffic capacity of roads can be improved by controlling group lane-changing behavior and continuously optimizing lane-changing strategy through connected vehicle technologies. This paper constructs vehicle group lane-changing strategies in upstream segment of ramp areas under a connected vehicle environment. The proposed strategies are simulated by VISSIM.

The results show that different lane-changing strategies are modeled through vehicle group in the upstream segment of ramp areas, which can greatly reduce the delay of ramp areas.

The simulation results verify the validity and rationality of the corresponding vehicle group lane-changing behavior model strategies, effectively standardize the driver's lane-changing behavior, and improve road safety and capacity.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

The purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort.

An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers.

The collaborative patterns can indeed be leveraged to structure properly every annotator’s behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench.

The various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.

The purpose of this paper is to investigate the impact of native Chinese R&D team directors’ differential leadership on team performance, so as to understand whether and how the directors’ differential leadership impacts team conflict, whether and how team conflict impacts new product development performance of the R&D team and whether team conflict plays full mediation on the relationship between directors’ differential leadership and new product development performance.

A literature review on differential leadership and team conflict provided the model and hypothesis. Two-wave data collected from 103 directors and 344 subordinates from 103 R&D teams of high-tech enterprises from China’s Pearl River Delta Area were used as empirical study samples. Hierarchical multiple regression analysis was conducted to test the model and hypothesis.

First, the team director’s differential leadership would cause significant team relationship conflict and team task conflict in the R&D team. Second, team relationship conflict and team task conflict would produce significantly bad new product development performance in the R&D team. Third, team relationship conflict would significantly mediate the relationship between the team director’s differential leadership and the team’s new product development performance.

To yield broader conclusions and to show to that the results can be replicated in other areas or in other types of organizations, further empirical research should expand the sampling by choosing high-tech enterprises from Beijing and Shanghai that have strong innovative abilities. Moreover, to extend the differential leadership theory, few more related variables of consequences, such as team communication, team cooperation and team knowledge share, should be included in future studies.

In general, the native Chinese R&D department director needs to try their best to avoid the use of differential leadership style. In addition, reasonable incentive measures, promotion mechanisms and fair team work culture are needed so as to reduce the negative impact from the director’s differential leadership.

The paper is original in its investigation on how Chinese indigenous organizational factor – differential leadership – influences the R&D team’s conflict and new product development performance, and provides theoretical contribution and managerial implications for the R&D team management.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Taking the COVID-19 as the background, this study aims to investigate the direct influencing factors regarding knowledge sharing behavior (KSB) on new media platforms and discuss how the characteristics of the users could enhance the KSB through moderation effect, and provide empirical evidences.

Based on the social exchange theory and after the text analysis of the data collected from the Tiktok platform in 2020, this paper uses the quantitative method to evaluate the factors influence KSB on short video social platform during the COVID-19 outbreak.

KSB on new media platform could be enhanced by richer knowledge content of the video posted and the attribute of the platform users directly. Platform users could affect the trustworthiness of the knowledge shared, thus influence the knowledge sharing. On the early stage of the COVID-19, the richer content of the knowledge released by users could effectively enhance the KSB. On the early stage of the emergency events, the official users could play a significant role on KS. During the mitigation stage of COVID-19, the KSB of the knowledge shared by unofficial users with richer content could be enhanced and the moderation effect is relatively stronger.

The research extends the social exchange theory to a disaster management context. The authors provide an effective reference for future governments to effectively cope with the epidemic and spread public knowledge in an emergency response context. By analyzing the influence of knowledge content and influencer characteristics, it could help the social media platform to improve content management and optimize resource allocation.

This research aims to explore how consumers' local brand choices differ between air-polluted days and clean days, and why the difference occurs.

Two studies were conducted. Study 1 used the longitudinal consumption data of various yogurt brands and daily air quality indexes in 2014 and 2015. Study 2 conducted three rounds of surveys on a clean day, a general air-polluted day and a seriously air-polluted day.

The findings indicate that consumers show less tendency of attribution and compensatory consumption during air-polluted days, which in turn decrease their willingness to choose local brands.

Implications are provided for future research and marketing practice, especially for local companies that rely heavily on local consumers, and retailers in heavy air-polluted areas.

This paper is the first to illustrate the influence of air pollution on consumers' local brand choices, and it extends current understanding on air pollution and consumer choices by discovering psychological process underneath to explain the effect.

Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA).

To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies.

The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014’s special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design.

A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.