A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation.

BACKGROUND: The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system. OBJECTIVE: This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data. METHODS: A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version. RESULTS: The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13). CONCLUSIONS: The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.

High-Speed Network DDoS Attack Detection: A Survey.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber-Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

Prevalence and risk factors for alcohol use disorders, substance use disorders, and depression anxiety and stress among users of sexual health services in Singapore: a cross-sectional survey study.

Syndemics of poor mental health also drive poorer sexual health outcomes. This study used three scales, the Alcohol Use Disorders Identification Test (AUDIT), the Drug Abuse Screening Test (DAST-10), and the Depression Anxiety Stress Scale (DASS-21) among beneficiaries of sexual health services in Singapore (n =975), respectively. We found that a prevalence of 20.4% and 18.6% of hazardous and moderate-severe alcohol use disorders and substance use risks, respectively. About 13.7%, 18.1% and 10.5% of participants reported severe to extremely severe symptoms of depression, anxiety, and stress, respectively. Further investigation and integrated interventions for mental health in sexual health settings are warranted.

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review.

Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

Model for successful development and implementation of Cyber Security Operations Centre (SOC).

Cyberattacks have changed dramatically and have become highly advanced. This latest phenomenon has a massive negative impact on organizations, such as financial losses and shutting-down of operations. Therefore, developing and implementing the Cyber Security Operations Centre (SOC) is imperative and timely. Based on previous research, there are no international guidelines and standards used by organizations that can contribute to the successful implementation and development of SOC. In this regard, this study focuses on highlighting the significant factors that will impact and contribute to the success of SOC. Simultaneously, it will further design a model for the successful development and implementation of SOC for the organization. The study was conducted quantitatively and involved 63 respondents from 25 ministries and agencies in Malaysia. The results of this study will enable the retrieval of ten success factors for SOC, and it specifically focuses on humans, processes, and technology. The descriptive analysis shows that the top management support factor is the most influential factor in the success of the development and implementation of SOC. The study also contributes to the empirical finding that technology and process factors are more significant in the success of SOCs. Based on the regression test, the technology factor has major impact on determining the success of SOC, followed by the process and human factors. Relevant organizations or agencies can use the proposed model to develop and implement SOCs, formulate policies and guidelines, strengthen human models, and enhance cyber security.