Nowadays one of the issues interesting for researchers of political, social, and economic sciences is to investigate immigration from different angles. Accordingly, they use a diverse range of instruments, different types of graphs, descriptive indices and complex mathematical models. Immigration has always been one of the paths helping humans in their own efforts for getting compatible with the environment and coping with difficulties. Nevertheless, if this issue has been considered as an instrument regulating resources and species automatically, in the present era, its negative aspects have been evident and socioeconomic consequences of immigration in the framework of development economy and security has attracted scholars’ attentions. One of the countries attracting for immigrants particularly in the continent of Africa is South Africa. Statistics indicates that this country is the most attractive one for refugees. Somalis are one of the most important and largest groups seeking refuge in South Africa because they are usually merchants who earn money in the poor places. However, business in the poor places endangers their lives and properties because of high rate of crimes and xenophobia. As a result, the target countries face a lot of socioeconomic and security problems. The present study tries to investigate the issue of Somali immigration using network models.

This article's goal is to investigate the structural model that explains how awareness of consequences, subjective norms, attitude and behavior control variables influence plastic wastes behavioral intention in Jeddah's beaches, Saudi Arabia. A quantitative approach was employed. For data analysis, structural equation modeling was used. Based on the literature analysis, the study used a structured questionnaire. Multiple statistical analyses were used to examine the hypotheses. This study conducts a survey on a sample of 390 local tourists to investigate their intention to reduce plastic wastes. To increase the explaining power for behavioral intention, this study extended the TPB by adding awareness of consequences. The study's participants were selected randomly. 340 of them agreed and answered the questionnaire, yielding a percentage response rate 87%. This produced 271 valid questionnaires for data analysis after closely examining the survey. The results demonstrate a positive influence of subjective norms, perceived behavioral control, and consequences awareness on environmental behavioral intention. On the other hand, attitude does not significantly contribute to predict EBI. Governments, legislators, and researchers may use the work's findings to create and execute efficient waste management programs.

The phenomenon of vaccine hesitancy is a growing threat to public health with far-reaching implications. The widening gap between the vaccinated and the proportion needed for herd immunity raises two critical research questions that are of interest to practitioners, researchers, and policymakers: (1) What determines one’s decision to be vaccinated? and (2) What is the implication of COVID-19 vaccine hesitancy on economic recovery? In this study, we use empirical data in the context of South Africa to investigate factors affecting COVID-19 vaccine hesitancy and their implications for economic recovery. Findings reveal key socio-demographic and institutional drivers of COVD-9 vaccine hesitancy, which include age (the youth are more hesitant), inadequate information on the vaccine (those who perceive they have adequate information are vaccinated), trust issues in government institutions, conspiracy beliefs, vaccine-related factors, and perceived side effects associated with the vaccine. Additionally, an individual’s decision to remain hesitant about COVID-19 vaccination has implications for businesses and the economy by limiting movement and trade, increasing unemployment, and causing a resurgence of new variants. Based on the findings, action plans such as information dissemination, convenience vaccination centers, consistency communications, and targeted campaign strategies are recommended for improving vaccine intakes and a positive economic recovery.

The brick manufacturing sector in Bangladesh is experiencing rapid growth, leading to notable environmental and health concerns. The primary objective of this research is to examine the Savar Upazila, a region of significant economic significance owing to its favorable brick manufacturing conditions and proximity to Dhaka city. Brick kilns, among other sources, constitute significant contributors to air pollution. The study utilized primary and secondary data to examine brick kiln emissions' effects on the surrounding area's environment and public health. The collection of primary data was conducted in multiple zones within Savar Upazila, which is home to a significant number of brick kilns. The utilization of secondary data provided valuable insights regarding the toxic pollutants discharged by these kilns and the corresponding health risks they pose. The participants provided accounts of various health ailments, such as dermatological conditions, optical difficulty, and respiratory complications, further aggravated by brick kilns' incidents. The prevalence of crop losses, soil degradation, and reduced agricultural yields was confirmed by 74% of the respondents. The trees and crops in the surrounding area experienced adverse effects due to the deposition of dust particles. During periods of inactivity, the concentrations of sulfur oxide (SOx) were between 6 and 9 times lower compared to periods of operation. Similarly, nitrogen oxide (NOx) levels were observed to be 4 to 6 times lower during non-operational phases. The degradation of water quality in adjacent bodies has been attributed to the deposition of dust and ash originating from brickfields, resulting in substantial repercussions on aquaculture. A significant proportion of participants indicated experiencing diverse health issues, notwithstanding the employment prospects generated by brick kilns, thereby underscoring the adverse consequences on both the environment and the local economy.

In this empirical article, we argue that, while emergency remote teaching (ERT) may have achieved its goal of saving the academic years during the COVID-19 pandemic, it also constructed unintended pedagogical consequences that were possibly overlooked at the time of advocating it. The research question that the article attempted to answer is, what unintended pedagogical consequences did students and lecturers suffer because of the move to ERT at rural-based universities (RBUs) in South Africa? Drawing on students' and lecturers’ lived experiences of ERT, this article foregrounds unintended pedagogical consequences that arose at one RBU in South Africa during the transition from face-to-face teaching to ERT. Underpinned by the tenets of critical realism philosophy, as well as student integration theory, in-depth interviews with three lecturers and six students were conducted. The findings of the study indicate that home conditions, individual characteristics, pre-COVID-19 blended learning experiences, university training and support, and teaching, learning, and assessment practices and policies altogether contributed to the construction of unintended pedagogical consequences of ERT presented in this article. These consequences include (1) exclusion of low-income students in active teaching and learning, (2) equipping middle-class students with better chances of success than working-class students. (3) distressing female students and lecturers more than their male counterparts, and (4) unproductive assessment practices. This study may be beneficial to academics and policymakers from similar contexts in their plight to continue with remote teaching and assessment (RTA) beyond the pandemic.

Background: Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and life-threatening highly infectious disease. The people of Bangladesh are at high risk of COVID-19 and have already experienced various socio-economic, health and psychological (mental) consequences. Particularly, mental health problems are dominantly reported in the literature and should be controlled. The main objective of this epidemiological study is to assess the mental distress and identify its determinants using online-based survey. Such information is urgently needed to develop feasible strategies for Bangladesh. Methods: An online survey was conducted for this study from May 01 to May 05, 2020. A total of 240 respondents provided self-reported online responses. Respondent’s mental distress was measured by the General Health Questionnaire 12 (GHQ-12) and by the self-rated mental health (SRMH) question. Various kinds of statistical analyses ranging from simple to multivariable logistic recession were performed using SPSS 23.0. Results: About 31.3% and 48.3% of respondents were mentally distressed by GHQ-12 and SRMH question, respectively. Logistic regression analysis revealed that mental distress was significantly higher among those respondents, whose usual activity was affected by the coronavirus (OR = 6.40, 95% CI: 1.87 - 21.90, p<0.001) and whose financial stress was increased due to lockdown (OR = 2.12, 95% CI: 1.01 – 4.46, p<0.05) on GHQ-12. Female sex (OR = 1.97, 95% CI: 1.03 – 3.75, p<0.05) and respondents with poor mental health before the outbreak (OR = 3.38, 95% CI: 1.18 – 9.72, p<0.05) were also significantly affected by mental distress on SRMH. Conclusions: At least thirty percent of the respondents were found to be mentally distressed. Some of the study findings, particularly significant determinants, should be considered while developing strategies to reduce the burden of mental distress among study respondents or similar group in Bangladesh.

This study provides a detailed investigation into the pathways through which microplastics from urban sources are transported to marine ecosystems, their distribution across different environments, and their impacts on marine life. Utilizing a combination of water and sediment sampling across urban, riverine, and marine environments, the research quantitatively assesses the concentration and types of microplastics present, and evaluates the ecological consequences of their accumulation in marine ecosystems. The findings reveal a significant concentration of microplastics in all examined environments, with the highest levels detected in urban areas, closely linked to industrial activities and inadequate waste management practices. The study documents a variety of microplastic polymers, with polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) being the most prevalent. A substantial impact on marine organisms was observed, including evidence of microplastic ingestion across a wide range of species and indications of stress and physical damage to coral reefs. Seasonal variations indicate that microplastic concentrations peak during periods of high rainfall, suggesting urban runoff as a critical pathway for microplastic transport. The study concludes with recommendations for improving waste management practices, enhancing public awareness and engagement in plastic recycling efforts, and implementing policies aimed at reducing the production and use of single-use plastics. This research underscores the urgent need for coordinated actions to mitigate the pervasive issue of microplastic pollution, highlighting the role of urban management and policy interventions in safeguarding marine ecosystems.

The determinants of enterprises providing ICT training in Europe are analyzed in this article. Data are collected from the European Innovation Scoreboard-EIS of the European Commission for 36 European countries in the period 2000-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. Results show that the number of enterprises providing ICT training in Europe is positively associate with “Innovation Index”, “Innovators”, “New Doctorate Graduates”, “Tertiary Education” and negatively associated with “Government Procurement of Advanced Technology Products”, “Human Resources”, and “Marketing or Organisational Innovators”. In adjunct a cluster analysis is performed by using k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of four clusters. Finally, we use eight different machine learning algorithms to predict the value of the enterprises providing ICT training in Europe. We found that the Simple Tree Regression is the best predictor and that the number of enterprises providing ICT training in Europe is expected to growth of the 5,02%.