The ability to take advantage of new digital solutions and technology will give companies a competitive edge, and operational optimization remains a major concern. A significant area of risk is cyber security because software-based technologies are integral to ship operations. Particular emphasis has been placed on the vulnerabilities of the Global Navigation Satellite System (GNSS), since it is an essential part of many maritime facilities and hence a target for hackers. Presently, research has shown that increased integration of new enabling technologies, like the Internet of Things (IoT) and big data, is driving the dramatic proliferation of cybercrimes. However, most of the attacks are related to ransomware attacks and/or with direct attack to the information technology (IT) and infrastructure. Nevertheless, there is a strong trend toward increased systems integration, which will produce substantial business value by making it easier to operate autonomous vessels, utilizing smart ports more, reducing the need for labour, and improving economic stability and service efficiency. Cybersecurity is becoming more and more important as a result of the quick digital transformation of the offshore and maritime sectors, which has also brought new dangers and laws. The marine sector has started to take cybersecurity seriously in light of the multiple documented instances of cyberattacks that have exposed business or personal data, caused large financial losses, and caused other problems. However, the body of existing research on emerging threats in maritime cyberspace is either inadequate or ignores important variables. Based on the most recent developments in the maritime sector, the article presents a classification of the most serious cyberthreats as well as the risks to cybersecurity in maritime operations and possible mitigation strategies from an educational research perspective.

This study examines factors associated with an increasingly poor perception of the novel coronavirus in Africa using a designed electronic questionnaire to collect perception-based information from participants across Africa from twenty-one African countries (and from all five regions of Africa) between 1 and 25 February 2022. The study received 66.7% of responses from West Africa, 12.7% from Central Africa, 4.6% from Southern Africa, 15% from East Africa, and 1% from North Africa. The majority of the participants are Nigerians (56%), 14.1% are Cameroonians, 8.7% are Ghanaians, 9.3% are Kenyans, 2% are South Africans, 2.1% are DR-Congolese, 1.6% are Tanzanians, 1.2% are Rwandans, 0.4% are Burundians, and others are Botswana’s, Chadians, Comoros, Congolese, Gambians, Malawians, South Sudanese, Sierra Leoneans, Ugandans, Zambians, and Zimbabweans. All responses were coded on a five-point Likert scale. The study adopts descriptive statistics, principal component analysis, and binary logistic regression analysis for the data analysis. The descriptive analysis of the study shows that the level of ignorance or poor “perception” of COVID-19 in Africa is very high (87% of individuals sampled). It leads to skepticism towards complying with preventive measures as advised by the WHO and directed by the national government across Africa. We adopted logistic regression analysis to identify the factors associated with a poor perception of the virus in Africa. The study finds that religion (belief or faith) and media misinformation are the two leading significant causes of ignorance or poor “perception” of COVID-19 in Africa, with log odd of 0.4775 (resulting in 1.6120 odd ratios) and 1.3155 (resulting in 3.7265 odd ratios), respectively. The study concludes that if the poor attitude or perception towards complying with the preventive measures continues, COVID-19 cases in Africa may increase beyond the current spread.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

Assessment of water resources carrying capacity (WRCC) is of great significance for understanding the status of regional water resources, promoting the coordinated development of water resources with environmental, social and economic development, and promoting sustainable development. This study focuses on the Longdong Loess Plateau region and utilized panel data spanning from 2010 to 2020, established a three-dimensional evaluation index system encompassing water resources, economic, and ecological dimensions, uses the entropy-weighted TOPSIS model coupled with global spatial autocorrelation analysis (Global Moran’s I) and the hot spot analysis (Getis-Ord Gi* index) method to comprehensively evaluate the spatial distribution of the WRCC in the study region. It can provide scientific basis and theoretical support for decision-making on sustainable development strategies in the Longdong Loess Plateau region and other regions of the world.From 2010 to 2020, the overall WRCC of the Longdong Loess Plateau area show some fluctuations but maintained overall growth. The WRCC in each county and district predominantly fell within level III (normal) and level IV (good). The spatial distribution of the WRCC in each county and district is featured by clustering pattern, with neighboring counties displaying similar values, resulting in a spatial distribution pattern characterized by high carrying capacity in the south and low carrying capacity in the north. Based on these findings, our study puts forth several recommendations for enhancing the WRCC in the Longdong Loess Plateau area.

COVID-19 is among the tremendous negative pandemics that have been recorded in human history. The study was conducted to give a breakdown of the effect of post-COVID-19 mental health among individuals residing in a developing country. The two scales, namely DASS-21 and IES-R, were employed to collect the essential related data. The findings indicated that anxiety was a typical and common mental issue among the population, including up to 56.75% of the participants having extremely severe anxiety, 13.18% reporting severe anxiety. Notably, no one has anxiety and depression under moderate levels. Additionally, there is 51.92% depression and 43.64% stress ranging from severe to extremely severe levels. Furthermore, there were significant statistical differences among the data on stress, anxiety, and depression according to gender (males and females) and subgroups (students, the elderly, and medical healthcare workers). Besides, the prevalence of post-traumatic stress disorder in the study was relatively high, especially when compared to the figures reported by the World Health Organization. Moreover, stress, anxiety, and depression all displayed positive correlations with post-traumatic stress disorder. This is big data on the mental health of the entire population that helps the country’s government propose policy strategies to support, medical care and social security for the population.