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Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

As a consequence of global changes, the landscape of immigration is changing. This brings opportunities for researching more nuanced aspects related to immigrant entrepreneurship in new contexts. The purpose of this paper is to establish the extent to which Africa-to-African immigrants leverage their social capital and human capital towards improving the success of their entrepreneurial ventures.

First-generation immigrant entrepreneurs within the Johannesburg area in South Africa were surveyed (n = 230). Instrument validity and reliability was first established, and then the hypotheses were tested using multiple regression analyses.

Hypotheses are supported insofar African immigrant entrepreneurs in South Africa rely on their structural and resource-related dimensions of social capital to achieve entrepreneurial success. Furthermore, human capital in terms of both work experience and entrepreneurial experience was found to be a significant predictor of entrepreneurial success.

There is value in developing policies that promote African immigrant entrepreneurs with higher levels of human and social capital. These African immigrants have the potential to increase the national skills base and knowledge required for successful entrepreneurship development in South Africa.

While both human capital and social capital have been associated significantly with the generic entrepreneurship literature, this paper provides an empirical contribution by focusing on the relevance of these constructs in the context of immigrant entrepreneurship from an African emerging market perspective.

The study aims to delve into the influence of tourism on the happiness of city residents, grounded in the overarching concept of livability. It posits that prioritizing residents’ happiness is crucial for effectively addressing cities’ challenges in balancing development and distinctiveness. The study pursues three primary objectives: first, establishing a potential correlation between city tourism and residents’ happiness; second, examining the contributing factors to this correlation and third, identifying potential mediators that influence the connection between tourism development and residents’ happiness.

Using a quantitative single-case design and partial least square analysis, the study underscores the intricate nature of various tourism development components. It specifically explores the roles of cognitive flexibility and social comparison in shaping the relationship between city tourism and happiness.

The findings make a distinctive contribution by revealing that not all tourism domains contribute positively to happiness. Furthermore, it sheds light on how tourism development impacts the emotional and cognitive dimensions of happiness, emphasizing the adverse effects of inequality and feelings of insecurity.

The study acknowledges certain constraints, including its employment of a cross-sectional design, the issue of generalizability, potential sampling bias and the reliance on subjective measurements when evaluating constructs like happiness and satisfaction with life. Using self-reported data introduces susceptibility to social desirability bias and individual perceptual differences, potentially resulting in measurement inaccuracies. Nevertheless, despite these limitations, the study’s findings offer valuable insights that contribute to both theoretical advancement and the realm of urban management.

The findings elucidated through the mediation analyses conducted for hypotheses three to seven shed light on the significant roles played by mental adaptation and social comparison mechanisms in shaping individuals’ happiness. These insights substantially enhance our understanding of this field. Particularly, the dimensions of social and environmental impact within tourism appear to counterbalance the positive effects stemming from the economic and cultural aspects. This suggests a scenario where an excessive focus on tourism development could potentially undermine the overall livability of the city. These outcomes further indicate the necessity for proactive interventions by destination managers. Their efforts should be directed toward enhancing the environmental and social domains, aiming to reinvigorate the sense of community among residents, which appears to be gradually waning.

The outcomes of this study emphasize the utmost significance of prioritizing residents’ happiness above mere considerations of economic growth when formulating efficacious strategies for tourism. By concentrating on the happiness of the local population, a harmonious resonance is established with Sustainable Development Goal 11, which advocates for the creation of habitable cities founded upon the principle that “a city that is not good for its citizens is not good for tourists.” This alignment underscores the interconnected nature of residents’ happiness and the sustainable development of tourism. Moreover, residents’ happiness plays a pivotal role in addressing the challenge that cities face in harmonizing growth and their uniqueness, ensuring competitiveness and sustainability.

The research underscores the need for a people-oriented perspective in urban planning and tourism development initiatives. The study identifies diverse factors impacting residents’ happiness in city tourism, highlighting the complex interplay of environmental, cultural and socioeconomic elements. It emphasizes income’s role but underscores nonmaterial factors and individual preferences. Overall, the study offers timely and valuable insights into the intricate connection between tourism development, residents’ happiness, living conditions and human perception, guiding urban planners and stakeholders.

The paper aims to assess the impact of weather-induced shocks on household food consumption in the rural Vietnamese Mekong Delta (VMD) through the case of Long An province and evaluate the effectiveness of widely used coping strategies in mitigating weather-related shock impacts.

The system generalized method of moments (GMM) estimation method is applied to explore information on shock incidence, recovery, and time occurrences. The paper uses a sample of 272 repeated farming households from 5-wave survey data from 2008 to 2016, resulting in 1,360 observations.

The paper confirms the robust negative effect of a natural shock on food consumption. Additionally, using savings proves to be the most potent measure to smooth food consumption. Other favorable coping strategies are “getting assistance from relatives, friends” or “getting assistance from the Government, and non-government organizations (NGOs).” The mitigating effects are also traced in the current analysis.

Using caution when generalizing the results from Long An to the whole VMD is reasonable. The rather limited observations of coping strategies do not allow the authors to analyze any specific strategy.

The proposed approach employs the GMM technique and controls for endogenous coping strategies and thus provides accurate estimates of the effects of weather-related shocks and the mitigation effectiveness in the rural VMD.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

During the past few years, social media has faced the challenge of maintaining its user base. Reports show that the social media giants such as Facebook and Twitter experienced a decline in their users. Taking WhatsApp's recent change of its terms of use as the case of this study and using the push-pull-mooring model and a configurational perspective, this study aims to identify pathways for switching intentions.

Data were collected from 624 WhatsApp users recruited from Amazon Mechanical Turk and analyzed using fuzzy set qualitative comparative analysis (fsQCA).

The findings identify seven configurations for high switching intentions and four configurations for low intentions to switch. Firm reputation and critical mass increase intention to switch, while low firm reputation and absence of attractive alternatives hinder switching.

This study extends extant literature on social media migration by identifying configurations that result in high and low switching intention among messaging applications.

The study identifies factors the technology service providers should consider to attract new users and retain existing users.

This study complements the extant literature on switching intention that explains the phenomenon based on a net-effect approach by offering an alternative view that focuses on the existence of multiple pathways to social media switching. It further advances the authors’ understanding of the relevant importance of switching factors.

This study examines the impact of inclusive leadership on two important work outcomes, i.e., employee performance and well-being. In order to better understand the above relationships, this study theorizes that employee psychological capital is a mediating mechanism and family motivation is a moderating mechanism.

The authors collected 370 responses in three different time waves with an interval of one week. All the constructs of the study were rated by employees except for the supervisor’s family motivation, which was rated by their supervisors. Given the predictive nature of the study, partial least squares structural equation modeling (PLS-SEM) was used for data analysis.

The authors' findings confirm the mediating role of employee psychological capital in the relationship between inclusive leadership and employee performance and in the relationship between inclusive leadership and employee well-being. The moderating effects of supervisor family motivation in the relationship between inclusive leadership and employee performance were also significant; however, the authors did not find empirical support for the moderating effects of family motivation in the relationship between inclusive leadership and employee well-being.

Drawing on the conservation of resources (COR) theory, the present study extends the authors' understanding of the unique ways in which inclusive leadership improves employee performance and benefits their well-being.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.