Today, the spread of the Coronavirus 2019 (COVID-19) pandemic continues to impact on world public health and bring about considerable human suffering partly due to government policies on reducing the spread. COVID-19 has significantly affected human health and it has impacted on the occupation of vulnerable groups such as tour guides, drivers and shop assistants. Of these, the present study aims to investigate the impact of the COVID-19 self-isolation policy on the occupation of vulnerable groups in Semarang City, Indonesia. To achieve this objective, this study uses a qualitative method with an ethnography approach considering a rational or non-rational thinking model. The binary opposition thinking pattern pioneered by Lévi-Strauss was used in the interview process with 25 informants in Semarang City, Indonesia. The data analyzed the response pattern of informants through the taxonomy analysis. Three levels of vulnerability among groups relating to occupation were identified; jobs lost, income decreased, and delayed salary. The result of the analysis found that the group who obeyed self-isolation was categorized as a rational thinking; these groups stay at home, do not go to work, and have no income. Besides that, the group who ignored self-isolation is categorized as non-rational thinking; they work, as usual, get their salary, and believe that the COVID-19 pandemic is a disaster and they pray for their safety to God. In conclusion, COVID-19 brings a significant impact on occupation in the forms of postponing, declining, and missing income besides the health effects among vulnerable groups in Semarang city, Indonesia. In avoiding COVID-19 infection, the circumstances of vulnerable groups are worse when self-isolation is required. Thus, this study suggests that the government needs to assist vulnerable groups by focusing on strategic policies, such as strategies for survival, providing access to basic needs, including health, and offering livelihood plans by providing access to medical services and other source of income.
Nowadays, the issue of teachers' psychological well-being causes serious concern, especially in Malaysia. Many studies related to psychological well-being have focused on students rather than on the health and well-being of teachers. Thus, the current study investigated the determinants of psychological well-being (depression, anxiety and stress) from the psychosocial work environment (job control, job demands and social support), and examined the moderating role of job control and social support in the relationship between job demands and psychological well-being among teachers. The design of this study was quantitative research through a survey questionnaire. The sample consisted of 335 high school teachers (23.3%-male; 76.7%-female) who responded to measuring scales of job demands, job control, social support, depression, anxiety and stress, and socio-demographic profile. The data were analyzed using two statistical methods, namely descriptive and inferential statistics. The hierarchical linear regression model was used to analyze the data by assisting the statistical software, i.e., SPSS-23. The results showed that job demands, job control and social support significantly predicted teachers' psychological well-being. Furthermore, the effect of job demands on teachers' depression and anxiety was partially moderated by job control and social support. In conclusion, this study has successfully identified the significant predictors of teachers' psychological well-being and the role of job control and social support as a moderating variable to teachers' psychological well-being in Malaysia. The result provides insights and contributes to the literature of teachers' psychological well-being determinants and involves Malaysian respondents with a collectivistic eastern culture.
In this paper, we introduce a category of Novel Jerk Chaotic (NJC) oscillators featuring symmetrical attractors. The proposed jerk chaotic system has three equilibrium points. We show that these equilibrium points are saddle-foci points and unstable. We have used traditional methods such as bifurcation diagrams, phase portraits, and Lyapunov exponents to analyze the dynamic properties of the proposed novel jerk chaotic system. Moreover, simulation results using Multisim, based on an appropriate electronic implementation, align with the theoretical investigations. Additionally, the NJC system is solved numerically using the Dormand Prince algorithm. Subsequently, the Jerk Chaotic System is modeled using a multilayer Feed-Forward Neural Network (FFNN), leveraging its nonlinear mapping capability. This involved utilizing 20,000 values of x1, x2, and x3 for training (70%), validation (15%), and testing (15%) processes, with the target values being their iterative values. Various network structures were experimented with, and the most suitable structure was identified. Lastly, a chaos-based image encryption algorithm is introduced, incorporating scrambling technique derived from a dynamic DNA coding and an improved Hilbert curve. Experimental simulations confirm the algorithm's efficacy in enduring numerous attacks, guaranteeing strong resiliency and robustness.