Emerging maritime cyber threats put Indonesia's marine technology-based systems at risk This study aims to determine the dimensions and analysis of risk assessment in maritime cyber security based on 3D models in the Indonesian sea area. A statistical descriptive qualitative method approach supported by the Analytical Hierarchy Process (AHP) and Technique for Order by Similarity to Ideal Solution (TOPSIS) methods were used in this study. Risk analysis in maritime cybersecurity has 3 (three) main criteria: Threat, Vulnerability, and consequence. Based on the results of 3D risk analysis, the six dimensions of MCS are identified as having a level of risk at Very Low and Low Risk. The highest risk value is obtained by the dimension of Cyber security-related company procedures (D2) (0.368) and the lowest risk value is Ship's systems readiness (D3) (0.048).

In a decision-making environment related to risk, there are four basic circumstances, namely certainty, risk, uncertainty and conflict. The dynamics of the strategic environment in Southeast Asia cannot be separated from the movement of the development of the Islamic State (IS). The terror threat in Southeast Asia is currently divided into different generations of terror, namely the threat of the Al-Qaeda terror network and the threat of the ISIS terror network. This study aims to analyze and identify the risk value of the development of the Islamic State network in Southeast Asia using the Borda and TOPSIS methods. The Borda method is used to give weight to the criteria related to risk analysis. The TOPSIS method is used to provide a criteria-based risk score. This research is limited to the Southeast Asia region with 4 (four) major countries, namely Indonesia, Malaysia, Thailand, and the Philippines. This research is expected to contribute to control the development of Islamic state networks in the Southeast Asian region. Based on the results of the overall risk analysis, it was found that the Philippines has the highest risk factor value for Islamic State (IS) with a value of 0.550 at level 4 in the High category. Indonesia maintains a risk factor value of 0.307. Thailand has a risk factor value of 0.427. Indonesia and Thailand are at level 3 with the Medium category. Meanwhile, Malaysia has a risk factor value of 0.203 at level 2 in the Low category.

The implementation of major projects is complicated by the multiplicity of beneficiaries, owners, and all participants in the project as well as the technical overlap between the various engineering, financial and administrative works, while the specific features of the construction activity have a clear influence in shaping the nature of construction projects because the implementation processes were associated with a deep and long-term intervention in the natural environment, where construction is a burden on the environment, both in the construction phase and during the maintenance and liquidation phases: it requires depreciation of a large number of material resources. Through that, this study focused on clarifying the most important concepts of risk management and modern strategies in risk analysis and how to respond to them and monitor projects. The study then presented a questionnaire for the risks facing major projects in Egypt. Through analyzing the results of the questionnaire, a qualitative risk analysis was conducted that can be used to prioritize response to risks, in addition to conducting a Monte Carlo simulation based on theoretical foundations and providing a new process for prioritizing project risks related to sustainability, where the (Primavera Risk Analysis) program was used to clarify the impact of risks on project time and cost. All analyses are based on the theoretical background regarding risk, risk management process, and project life cycle approach in the sustainable construction sector. with the help of this study, it is possible to address ways of mitigating the harmful effects on the environment through the implementation of sustainable management in the planning of future projects and better management of current projects.

This study aims to identify and mitigate supply chain risks associated with organic rice in Thailand, based on the principle of supply chain risk management (SCRM). The risk measurement is performed using Best-Worst method (BWM) for ranking the criticality of different factors in order to find the appropriate ways for improving and developing new ideas for supply risk chain management. The study identifies 26 risk factors associated with the organic rice supply chain based on the literature and interviews with four experts. The order of risk priority in the organic rice supply chain in descending order (the top 5) is as follows: Lack of efficient equipment or machinery, Lack of organic rice mill, Lack of labor, Transportation cost, and Production cost. The SCRM guidelines of organic rice in Thailand include cost reduction and investment in infrastructure.

Education is the future. Education is the only way for a country to start developing and reducing poverty. In countries with medium incomes like Peru, the resources to spend on education is not unlimited. Therefore, it is necessary to have quality in investment. However, risks and uncertainty can make a project surpass its initial budget. Therefore, statistic based methods like Monte Carlo simulation is a powerful tool to forecast possible events that might endanger the profitability and sustainability of a project. Although there is not plenty of academic literature about Monte Carlo empirical usage, many projects employ this method to manage the possible risks the project could have. In consequence, the current research analyzed both risk and sensitivity of an elementary school building project. Both analyses showed that this project had huge probabilities to surpass the current profit and return estimations. However, the sensitivity analysis portrayed that the project could be endangered because of infrastructure overspending. Moreover, it indicated that students’ attendance is also a critical factor to ensure the sustainability of the project.

In the era of Industry 4.0, advanced manufacturing systems are increasingly integrating cyber and physical components, making them susceptible to sophisticated cyber-attacks. Addressing these vulnerabilities is crucial for maintaining the integrity and efficiency of manufacturing processes. This study introduces a comprehensive game-theoretic model to tackle cybersecurity challenges in such systems. The interaction between cyber attackers and defenders is modeled as a strategic game, incorporating a cost function that includes production losses, recovery from attacks, and maintaining of defense strategies. Both deterministic and probabilistic approaches are employed: linear programming identifies optimal strategies, achieving Nash equilibrium under ideal conditions, while the Quantal Response Equilibrium (QRE) method captures player behavior under uncertainty. The optimization problem is solved using the CPLEX library in Python, ensuring robust and efficient computation of optimal mixed strategies. The methodology is demonstrated through a numerical example, highlighting the identification of potential vulnerabilities and optimal defense strategies. The analysis reveals that the defender's learning curve is longer and more complex than the attacker's, emphasizing the necessity for advanced and adaptive defense strategies. This comprehensive approach not only predicts attacker behavior but also suggests effective defense mechanisms tailored to specific threats. The findings underscore the importance of strategic decision-making in enhancing the cybersecurity resilience of cyber-physical manufacturing systems, offering valuable insights for mitigating cybersecurity risks effectively. The most significant result indicates the critical need for timely and adaptive defense mechanisms to counter sophisticated cyber threats, ensuring the sustained operation and security of modern manufacturing environments.

The numerous studies on comparing and ranking fuzzy numbers clear that this task is still young. However, the observation that many papers do not hesitate to use some incorrect ranking methods in risk analysis problems, encouraged the author to point out the shortcoming of some of these methods. In this note, we review briefly the methods for ranking fuzzy number in risk analysis.

Safety is a relative quantity that is a function of the situation in which it is measured. This research has been conducted in a well established pharmaceutical company in Bangladesh practicing GMP (Good Manufacturing Practice), which has a tablet manufacturing facility. Tablet manufacturing comprises a number of manufacturing activities and machineries. Each of these activities, requiring amalgamated interaction of man, machine, equipment and environment has the potential of producing unforeseen hazards. Therefore, analysis of risk to assure worker safety is not only necessary here, it is also important to use an appropriate tool that can incorporate all the risk- producing parameters. The research work conducted here using a very effective tool named Failure Mode and Effect Analysis (FMEA), consisted of observing the works at different sections, breaking them down into sequential steps, noting the surrounding variables of each task, and using these data to perform risk analysis to find out the most hazardous works for the workers.

Road construction projects are considered as the most important governmental issues since there are normally heavy investments required in such projects. There is also shortage of financial resources in governmental budget, which makes the asset allocation more challenging. One primary step in reducing the cost is to determine different risks associated with execution of such project activities. In this study, we present some important risk factors associated with road construction in two levels for a real-world case study of rail-road industry located between two cities of Esfahan and Deligan. The first group of risk factors includes the probability and the effects for various attributes including cost, time, quality and performance. The second group of risk factors includes socio-economical factors as well as political and managerial aspects. The study finds 21 main risk factors as well as 193 sub risk factors. The factors are ranked using groups decision-making method called linear assignment. The preliminary results indicate that the road construction projects could finish faster with better outcome should we carefully consider risk factors and attempt to reduce their impacts.