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In the study of expatriation and expatriate adaptation, there are limited studies that focus on issues faced by expatriates working in foreign countries with very distinct cultures. This study aims to explore this idea through the experiences of western expatriates working in the Kingdom of Saudi Arabia (KSA). Two research questions were posed to examine the cross-cultural issues and challenges faced by expatriates in the KSA, as well as the role of cross-cultural training in expatriate adjustment.

The study was guided by an interpretivism paradigm through a qualitative method by using a semi-structured in-depth interview approach. Interviews were conducted among 12 expatriates from the USA and UK who are currently working in KSA.

A coding technique and theoretical thematic analysis was conducted to analyze the data. The results of this study highlighted three key themes that had a considerable influence on expatriates’ adjustment, in particular: culture shock, lack of pre-departure training and the demand for an extensive cross-cultural training.

It is acknowledged that the existence of sub-cultures within the KSA would expose the respondents to varying cultural values within the community. Thus, future studies within a similar context should consider the influence of intra-cultural variations.

The findings of the study emphasized on the importance understanding the cultural gap between home and host country and the individual cultural awareness of the expatriate. It calls attention to the need for a tailored and extensive pre-departure, cross-cultural training and a collaborative effort between employees’ and managers to improve expatriates’ motivation and retention.

Research consistently shows that non-scientific bias, equity, and diversity trainings do not work, and often make bias and diversity problems worse. Despite these widespread failures, there is considerable reason for hope that effective, meaningful DEI efforts can be developed. One approach in particular, the bias habit-breaking training, has 15 years of experimental evidence demonstrating its widespread effectiveness and efficacy.

This article discusses bias, diversity, equity, and inclusion (DEI) efforts from the author’s perspective as a scientist–practitioner – the author draws primarily on the scientific literature, but also integrates insights from practical experiences working in DEI. The author provides a roadmap for adapting effective, evidence-based approaches from other disciplines (e.g. cognitive-behavioral therapy) into the DEI context and review evidence related to the bias habit-breaking training, as one prominent demonstration of a scientifically-validated approach that effects lasting, meaningful improvements on DEI issues within both individuals and institutions.

DEI trainings fail due to widespread adoption of the information deficit model, which is well-known as a highly ineffective approach. Empowerment-based approaches, in contrast, are highly promising for making meaningful, lasting changes in the DEI realm. Evidence indicates that the bias habit-breaking training is effective at empowering individuals as agents of change to reduce bias, create inclusion, and promote equity, both within themselves and the social contexts they inhabit.

In contrast to the considerable despair and pessimism around DEI efforts, the present analysis provides hope and optimism, and an empirically-validated path forward, to develop and test DEI approaches that empower individuals as agents of change.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train, and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence, the study analyzes aerodynamic parameters with multi-objective optimization design.

The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics. Then the modified vehicle modeling function (VMF) parameterization method and surface discretization method are adopted for the parametric design of the nose. For the 12 key design parameters extracted, combined with computational fluid dynamics (CFD), support vector machine (SVR) model and multi-objective particle swarm optimization (MPSO) algorithm, the multi-objective aerodynamic optimization design of high-speed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint. The engineering improvement and wind tunnel test verification of the optimized shape are done.

Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train. The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.

Compared with the original shape, the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%, and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%, respectively, after adopting the optimized shape modified according to engineering design requirements.

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.

This paper examines the changes suggested by maritime stakeholders to achieve gender equality in seafaring, a male-dominated profession.

Adopting a four-stage career cycle framework, this paper analyzes changes proposed by 423 industry stakeholders to promote gender equality in seafaring. These proposed changes were posted on the Day of Seafarers 2019 virtual wall set up by the International Maritime Organization, which served as a forum for industry stakeholders from all over the world to voice their opinions and suggestions.

The data analysis shows that the suggested changes reflect many challenges and barriers women seafarers face. While stakeholders from Organisation for Economic Co-operation and Development (OECD) countries are more likely to call for changes to remove barriers in the retention and development stage, gender equality in seafaring in non-OECD countries is still seriously hindered by barriers in the recruitment stage. The paper also reveals that comparatively male stakeholders are less likely to appreciate the problems women seafarers face.

This paper takes a comparative approach, comparing the changes proposed by seafarers and other industry stakeholders from different parts of the world. This approach provides a nuanced understanding of issues related to gender equality in seafaring by showing that stakeholders from different backgrounds have different priorities.

Drawing on social feminist theory, this paper aims to close gaps between knowledge about gender-related barriers to information, communication and technology (ICT) adoption and the provision of entrepreneurship education and training (EET) programs.

Empirical findings are drawn from 21 semi-structured interviews (22 informants) possessing differing training expertise regarding digital technology among women entrepreneurs. An open-coding technique was adopted where descriptive codes were first assigned to meaningful statements. Interpretive and pattern codes were then assigned to indicate common themes and patterns, which were reduced to higher-order categories to inform the research questions.

The findings specify and validate further gender influences in the digital economy. Digital skills are identified, and strategies to close gender barriers to ICT adoption with EET are described. The findings are discussed in reference to a large-scale, Canadian ICT adoption program.

Perceptual data may be idiosyncratic to the sample. The work did not control for type of technology. Gender influences may differ by type of technology.

Findings can be used to construct gender-inclusive ICT supports and inform ICT adoption policies. This includes program eligibility and evaluation criteria to measure the socio-economic impacts.

The study is among the first to examine the intersection between knowledge about gender-related barriers to ICT adoption and EET. The findings can be adopted to ICT support programs targeted at small business owners and entrepreneurs.