This study investigates the critical skills required for new entrants to succeed in today’s workforce, focusing on both soft and hard skills. Through a comprehensive systematic review of existing literature using the PRISMA method, we analyzed 12 selected journals from an initial pool of 870, sourced from major databases such as Scopus, Science Direct, and Emerald Insight. Our research uncovers four key insights. First, we provide a clear and precise definition of employability skills, establishing the foundation for what competencies are essential for workforce readiness. Second, our analysis identifies a distinct separation between soft and hard skills, with soft skills such as communication, problem-solving, teamwork, ethics, and leadership being universally critical across all industries. Third, while soft skills have broad applicability, hard skills are highly specialized, varying significantly depending on industry and job role. To simplify their understanding and application, we categorized these hard skills into specific groups. Finally, the study highlights the urgent need for further empirical research to validate these findings in real-world settings, as the current conclusions are drawn solely from literature. This potential gap between academic preparation and industry expectations underscores the necessity for ongoing collaboration between educational institutions and employers, which will be a primary focus of our future research.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

Under the background of economic globalization and the rapid development of science and technology, the development of higher education (HE) has undergone profound changes. Nowadays, in order to increase the international competitiveness, training international talents has become the primary task of universities and HE institutions. Therefore, taking Shenzhen as an example, the research takes quantitative method to study how the educational resources in the society affect the school from a macro perspective, and the micro perspective of students, teachers and schools, studying the impact on the development of universities. Through in-depth analysis of the integration of educational resources, the results show that multilingual library resource, and other three factors followed, are critical factors in the development of HE. And then, this study puts forward corresponding countermeasures and suggestions after discussion, aiming to provide strategic insights to enhance the quality and international competitiveness of HE in the GBA, especially in the construction of multilingual library resources (MLR), international exchange platform (IEP), sufficient and diverse laboratory facilities (SDLF), and rich academic resources (RAR). Thus, the research narrows the gap in this field to some extent.

The current study aims to determine the post COVID-19 adoption rates, the variation of the adoption by regions, and the effects of communication technologies on higher education with focus on students’ engagement and faculty satisfaction. The present research uses the convergent parallel design which is a form of mixed-methods research design. First, the study searched for 18 relevant articles using key search terms including “post-COVID-19 education”, “e-learning tools”, “communication technologies” and “higher education”. The qualitative analysis, however, shows that the technological strategies have to be in line with the preparedness of the people, the need to address challenges such as the lack of face-to-face contact and how technologies such as augmented reality and simulation-based learning can be used. Quantitative analysis shows that teleconferencing tools (β = 0.45, p < 0.001) and cloud computing (β = 0.38, p < 0.003) have positive impact on engagement and satisfaction. The one-way ANOVA results show that there is a difference in the adoption rates across the regions while the MCAs score for communication challenges is 60%. From the descriptive statistics it can be seen that there is a very high adoption rate of cloud computing (Mean = 89.7%, Standard Deviation = 3.1%) and teleconferencing tools (Mean = 84.9%, Standard Deviation = 4.5%). The Structural Equation Modeling (SEM) shows the domino effect of teleconferencing on engagement (β = 0.60, p < 0.001), satisfaction (β = 0.75, p < 0.002) and collaboration efficiency (β = 0.55, p < 0.001). Thus, the current study establishes the fact that there is a need to provide equal opportunities and technology which is adaptable to improve the students’ engagement and satisfaction in various learning institutions.

This study fills a significant need in the literature by exploring the efficacy of wearable technologies as helpful aids for special needs students in Saudi Arabia. This 12-month quantitative study used a purposive sample of 150 kids representing a range of disability classifications. This study examines the effects of wearable technology, such as smartwatches and augmented reality goggles, on students’ concentration and performance in the classroom. Wearable technology offers great promise, as descriptive statistics show that the experimental group had better involvement and academic achievement. The experimental and control groups vary significantly in terms of academic performance and engagement, as shown by independent samples t-tests. Wearable technology’s distinct benefits are further shown by regression analysis, which shows a favorable correlation with academic achievement after the intervention. According to the results, wearable tech has great promise for inclusive education in Saudi Arabia. Strategic integration, teacher professional development, ongoing research, better accessibility, and wearable gadget customization are some of the suggestions. Stakeholders may use these recommendations as a road map to build a welcoming and technologically sophisticated classroom. This study adds to the growing body of knowledge on assistive technology, especially in Saudi Arabia, and has important implications for academics, politicians, and educators.

Regions rich in natural resources often exhibit a high dependency on revenue from Revenue Sharing Funds (DBH). This dependency can pose long-term challenges, especially when commodity prices experience significant fluctuations. This study examines the role of Revenue Sharing Funds from Natural Resources (DBH SDA) on economic growth in 491 regencies/cities in Indonesia during the 2010–2012 period. The analysis employs panel data regression. The selection of this period was based on the occurrence of a resource boom characterized by a surge in global demand for natural resource commodities, accompanied by an increase in commodity prices. This condition positively impacted the revenues of both the nation and resource-rich regions. The results of the study show that economic growth is not influenced by DBH SDA but rather by General Allocation Funds (DAU). This indicates that the central government still plays a significant role in determining economic growth at the regency/city level in Indonesia. Regions need to prioritize economic diversification to reduce reliance on DBH SDA and DAU. Investment in productive sectors, such as infrastructure, education, and technology, can be a strategic approach to accelerating regional economic growth.

Over the last few decades, countries in the South have been undergoing rapid urbanization, as if to make up for lost time. Sub-Saharan Africa is characterized by a very low urbanization rate compared to0 the rest of the world. Although the African continent reached its urban transition in 2015, Niger remains by far the least urbanized country, with a rate of 17%. The city of Niamey is the main urban center, with an estimated population of 1,449,801 hbts in 2023, spread over an area of around 33,100 ha. The aim of this study is to analyze the spatial expansion of the city of Niamey from 1984 to 2023. The main data used in this study are raster images from the United States Geological Survey (USGS), vector data from Open Sources Map (OSM) and GoogleEarth, secondary data from the National Institute of Statistics (INS) and field observation. This study enabled us to conclude that between 1984 and 2023, the city of Niamey underwent very strong spatial expansion. The city grew from 4,690 ha to 33,100 ha, i.e. 28,410 ha absorbed in 39 years, with exceptional growth between 2014 and 2023, when the urban area doubled. Its population has risen from 397,437 at the time of the 1988 general population and housing census to an estimated 1,449,801 in 2023 (INS), an increase of 1,052,364 in 35 years. Between these two dates, population density fell from 87.7 to 43.8 inhabitants/km², i.e. half that of 1984. This spatial expansion has resulted in unprecedented peri-urbanization.

Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.