Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

The technology known as Internet of Things, or IoT, has started to permeate many facets of our lives and offers a plethora of options and empowerment, expanding the potentials of integrating it in education. Considering how new IoT is and how it may affect education, it is now essential to investigate its potential in order to choose where to begin using it in the classroom. Examining the possible applications of IoT in education may be strongly aided by the knowledge and perspectives of professionals and experts. As a result, the present research concentrated on looking at and evaluating the viewpoints that relevant experts shared on platform (X) via a variety of tweets. The present study takes a qualitative approach, analyzing a collection of expert tweets on IoT in education on platform X using qualitative content analysis. The primary themes of the study findings, the software-based and material-based enablers of IoT in education, indicate the key potentials of IoT in education. These consist of data, sensors, interactive devices, e-learning tools, network accessibility and communications, integrating developing technologies, and system administration. The enormous individual enablers of IoT in education also include sustainability, professional growth, planning, preparing the next generation, and upholding the safety of the learning environment. The study suggested that in order to handle the IoT, classrooms and the educational environment needed to be restructured. Additionally, human resources needed to be developed in order to keep up with the educational environment’s progress.

Despite the efforts of public institutions and government spending, progress on the SDGs is mixed at the midpoint of the 2030 timeframe-some targets are off track and some have even regressed. ICT-related indicators, on the other hand, stand out for their strong progress. The author notes this progress, but questions its relationship to the implementation of the 2030 Agenda. He argues that the growth in internet and mobile network penetration is due to the economic characteristics of communications development. The objectives of the article are to review the impact of the ICT sector on economic growth, to consider the role of government spending in the development of this sector in the context of fostering the achievement of the Sustainable Development Goals, and to identify the prerequisites for significant progress towards SDG targets in communications. Achievement of these objectives will make it possible to determine whether this progress is a consequence of targeted efforts to achieve the SDGs, or whether, in accordance with the author’s hypothesis, it is based on the specifics of the ICT sector’s development, allowing for the accelerated spread of mobile communications and the Internet, which is reflected in the SDG indicators.

Despite having a strategic position in supporting the Indonesian economy, the productivity of SME’s is still suboptimal. The increase in the number of SME’s has not been followed by increased competitiveness due to various limitations experienced by this sector. In an effort to provide a comprehensive picture in improving the performance of food processing SME’s in developing countries such as Indonesia, the purpose of this study was to examine the function of product innovation, internet marketing, and brand identity in shaping competitive advantage having an impact on business performance. This research is focused on food processing SME’s in the city of Bogor. The number of samples used was 100 SME’s. The sampling method used the non-probability sampling method with a snowball sampling technique. The data obtained were analyzed using the Structural Equation Model (SEM). Based on the age characteristic of business actors, the majority of business actors were 40–50 years old, of which 52% had their final formal education at high school level. As many as 61% of respondents had attended business training. Based on the results of the Partially Least Square (PLS) SEM analysis, it was found that product innovation, internet marketing and brand identity all had a significant positive effect on competitive advantage and business performance. The influence of brand identity on competitive advantage had the greatest effect, with a value of 0.451. This study contributes to existing research by examining the determinants of the business performance of processed food SME’s through the holistic model offered. This research is innovative because the business raises new issues related to internet marketing by SME’s and investigates them empirically.

Improving the practical skills of Science, Technology, Engineering and Mathematics (STEM) students at a historically black college and university (HBCU) was done by implementing a transformative teaching model. The model was implemented on undergraduate students of different educational levels in the Electrical Engineering (EE) Department at HBCU. The model was also extended to carefully chosen high and middle schools. These middle and high school students serve as a pipeline to the university, with a particular emphasis on fostering growth within the EE Department. The model aligns well with the core mission of the EE Department, aiming to enhance the theoretical knowledge and practical skills of students, ensuring that they are qualified to work in industry or to pursue graduate studies. The implemented model prepares students for outstanding STEM careers. It also increases enrolment, student retention, and the number of underrepresented minority graduates in a technology-based workforce.

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.