ABSTRACT
This paper critically analyzes the challenges of psychological adjustment faced by international students and explores coping mechanisms and support services that can help them overcome these challenges. The essay first introduces the background information on international students and highlights the importance of psychological adjustment for their well-being and academic success. The challenges of psychological adjustment, including cultural, academic, social adjustment, and language barrier, are discussed in detail. The essay then explores coping mechanisms, including problem-focused coping, emotion-focused coping, seeking social support, and cultural adjustment programs, and the support services, including counseling services and international student services, that can help international students adjust to their new environment. Finally, the essay evaluates the effectiveness of these coping mechanisms and support services, emphasizing the importance of cultural competence in providing effective support services. This essay has practical implications for higher education institutions in providing tailored support to international students and highlights the need for future research to explore the effectiveness of coping mechanisms and support services for different groups of international students and the impact of the COVID-19 pandemic on their psychological adjustment. © 2023 NSP Natural Sciences Publishing Cor.
ABSTRACT
Background: Infectious diseases have been a constant threat to people's health and survival, at least thirty re-emerging and emerging diseases (Parks, 2009) are known to be of public health importance posing a burden to the health system;in addition, emergence of COVID-19 further tested the resilience of the health system to respond to public health emergencies (NCDC, 2020). This study assessed the effect of COVID-19 on use of maternal and child health (MNCH) services with objectives being the impact on family planning use, antenatal care visits, facility-based delivery and child related services such as immunization, child nutrition and outpatient clinic in FMC Gusau. Data from units offering MNCH services for six months, three months pre-covid-19 index case (January to March) and three months post covid-19 index case (April to June), corresponding to the period of lockdown, in addition same periods in the previous year (2019) was retrieved and entered into Statically package for social sciences (SPSS) now IBM statistic, comparison was made using comparable period of the year as well as a pre and post Covid index case. Over the six-months period, aggregate data shows that hospital visit for all categories of maternal newborn and child health reduced three months (April, May and June) post covid index case in the facility and subsequent proclamation of lockdown in the State, as compared to three-months pre-covid (January, February and first three weeks of March) Despite the additional burden imposed by the emergence of COVID-19 in FMC Gusau and the Attendant stretched on health system, the resilience of the health system was brought to fore, however with support from Federal ministry of health, National Centre for disease control and other agencies, FMC Gusau was able to use the COVID-19 emergency to strengthen service provision.
ABSTRACT
Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.