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This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).

The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.

Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.

The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

The study aims to propose a comprehensive overview of the Omnichannel phenomenon by identifying its theoretical foundations as well as future research directions.

In order to systematize Omnichannel-centered contributions and identify future research directions for post-Covid-19, this study adopted a mixed-method study, combining a systematic literature review, a bibliometric co-citation analysis and a panel discussion by field experts.

In Study 1, the authors traced extant literature on Omnichannel back to its theoretical foundations, which led to the identification of four research areas in which the concept of Omnichannel is rooted. Contributions pertaining to the aforesaid research areas were discussed and submitted to a panel of experts (Study 2) after the lockdown periods. The experts gave various insights into both the past and future of Omnichannel research. Finally, a framework synthesizing theoretical foundations of Omnichannel, literature gaps and opportunities for future research is provided.

To our knowledge, this is the first attempt to combine mixed methods study in Omnichannel research and to involve a panel of experts in order to discuss the findings of a literature review and evaluate future research directions. This choice allowed us to investigate both incumbent academic and managerial challenges raised by Omnichannel and to provide guidance for the post-pandemic recovery.

This study aims to facilitate consistency of information in building information modelling (BIM) and address the current BIM gaps through the perspectives of the productization concept and product structure (PS).

The study follows a conceptual research approach in conjunction with a single case study. First, the previous studies on BIM implementation, productization and PS are reviewed. Further, a case study is used to analyse the current state of productization in the construction sector and develop a functional PS for construction.

A Part-Phase-Elements Matrix is proposed as a construction-specific PS to facilitate consistency in information and to enhance BIM. The proposed matrix provides new avenues to facilitate consistent information exchange through the interconnection between conceptual PS and standard building objects library, and encourage collaborative communication between stakeholders.

This study explores the core of the productization concept and PS as means to facilitate consistency of information and thus address the current gaps in BIM. This as building projects progressively move towards systematic modular and prefabricated construction where the flow of reliable information about product and construction offerings becomes increasingly important.

The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost.

A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search.

Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space.

This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured.

The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases.

The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

To study the situation of alcohol relapse and to investigate the relationship between psychological factors and alcohol relapse in persons with alcohol dependence in Thai Nguyen hospitals, Vietnam.

A correlation study was conducted among 110 patients. Data were collected through structured interviews and were analyzed using descriptive statistics and Spearman's correlation coefficient (r_s).

More than two-thirds of the participants were found to relapse more than once (X¯ = 2.04, SD = 0.86). Positive outcome expectancies, cravings, negative emotional states, and maladaptive coping were positively associated with relapse (r_s = 0.550, 0.522, 0.497; p = 0.000 and r_s = 0.217, p < 0.05, respectively). While, motivation to change with three subscales had a negative correlation to relapse including recognition (r_s = −0.199, p < 0.05), ambivalence (r_s = −0.331, p = 0.000), and taking steps (r_s = −0.606, p = 0.000). Adaptive coping, self-efficacy, and social support were also found to be negatively correlated to relapse (r_s = −0.535, −0.499, −0.338; p = 0.000, respectively). However, negative outcome expectancies (r_s = −0.024, p = 0.805) and positive emotional states (r_s = 0.081, p = 0.399) were not significantly related to relapse.

The findings of this study are significant implications for relapse prevention strategies. It suggests that the essential parts of relapse prevention are through: changing alcohol expectations, increase drinking refusal self-efficacy, coping skills training, enhancing motivation to change, managing alcohol craving and expanding social support.

This is the first study in Vietnam which investigated the relationship between psychological factors and alcohol relapse in individuals with alcohol dependence.

This paper aims to analyze the impact of Covid-19 on the stock market volatility and uncertainty during the first and second waves.

This study has applied event study and autoregressive integrated moving average models using daily data of confirmed and death cases of Covid-19, US S&P 500, volatility index, economic policy uncertainty and S&P 500 of Bombay Stock Exchange to attain the purpose.

It is observed that, during the first wave, the confirmed cases and the fiscal measure have a significant impact, while the vaccination initiative and the abnormal hike of confirmed cases have a significant impact on the US stock returns during the second wave. It is further observed that the volatility of Indian and US stock markets spillovers during the sample period. Moreover, a perpetual correlation between the Covid-19 and the stock market variables has been noticed.

At present, the world is experiencing the third wave of Covid-19. This paper has considered the first and second waves.

It is expected that business leaders, stock market regulators and the policymakers will be highly benefitted from the research outcomes of this study.

This paper briefly highlights the drawbacks of existing policies and suggests appropriate guidelines to successfully implement the forthcoming initiatives to reduce the catastrophic impact of Covid-19 on the stock market volatility and uncertainty.