A Computational Framework for Systemic Seismic Risk Analysis of Civil Infrastructural Systems

The paradigm shift in the pharmaceutical industry to continuous manufacturing, which has recently progressed from conceptual demonstration to pilot production, has stimulated the development and application of process systems engineering (PSE) tools for implementing efficient and robust control strategies. In this study, a systematic framework for process control design and risk analysis for continuous pharmaceutical solid-dosage manufacturing is proposed, consisting of system identification with state-space models; control design and analysis metrics; hierarchical three-layer control structures; risk mapping, assessment and planning (Risk MAP) strategies; and control performance indicators. The framework is applied to a feeding-blending system, wherein the major source of variance in the product quality arises. It can be demonstrated that the variance in the feeding-blending system can be mitigated and managed through the proposed systematic framework for control design and risk analysis. The process analytical technology (PAT) tool for mass fraction measurement of active pharmaceutical ingredient (API) and its relative standard deviation (RSD) were indispensable to achieve an efficient control design at the advanced layers. Specifically, the improvements in control performance by implementing advanced model-based control strategy are found to be limited by model-plant mismatch and the sampling time of the PAT tools.

The extended evolution of systems analysis has created a challenging problem for the modern student, scholar, teacher, and practitioner: an overabundant, highly specialized body of literature that is difficult to grasp in terms of a generalized framework and methodology. The need for a comprehensive synthesis of systems analysis knowledge is apparent, since modern systems analysts apply their skills in a complex arena: policy analysis of large-scale societal systems. The purpose of the paper is to: 1) propose a generalized system analysis framework that is composed of a) steps in the systems analysis process, b) areas of application and problem categories, and c) analytical techniques for accomplishing the steps; 2) describe, in detail, a methodology of fundamental tasks in the systems analysis process; and 3) briefly describe a few techniques that have particular relevance in the proposed systems analysis methodology.

Plurality of beliefs and theories in different knowledge domains calls for modelling multi-viewpoint ontologies and knowledge organization systems (KOS). A generic theoretical approach recently proposed for heterogeneity representation in KOS was linking each ontological statement to a specific validity scope to determine a set of conditions under which the statement is valid. However, the practical applicability of this approach has yet to be empirically assessed. In addition, there is still a need to investigate the types of inconsistencies that might arise in multi-viewpoint ontologies as well as their possible causes. This study proposes a new framework for systematic analysis and classification of inconsistencies in multi-viewpoint ontologies. The framework is based on eight generic logical structures of ontological statements. To test the validity of the proposed framework, two ontologies from different knowledge domains were examined. We found that only three of the eight structures led to inconsistencies in both ontologies, while the other two structures were always present in logically consistent statements. The study has practical implications for building diversified and personalized knowledge systems.

The seismic vulnerability and risk assessment of infrastructure and utility systems are essential to prevent or mitigate sufficiently the negative consequences, implement resilience management strategies, and recover efficiently after a major earthquake. In a complex urban environment, having multiple interacting and interdependent infrastructures becomes even more important. Earthquake hazards not only affect a single asset, but also their impact is much greater because of the inter- and intra-dependences among various infrastructure, utility systems, and lifelines. Therefore, we urgently need efficient tools to quantify and assess the systemic vulnerability and risk of urban infrastructure and utility systems. This is a challenging topic that is nowadays receiving more attention from the research community, the industry domain, and the policymakers. This paper aims to review the available modelling approaches and tools for the seismic risk analysis of interconnected systems, including advantages and limitations. It focuses in particular on the European funded SYNER-G project that encompasses interdependencies, delivers a holistic methodology, and implements a comprehensive framework based on the Object-Oriented Modelling paradigm. The capacities of the SYNER-G framework are illustrated through a selected application regarding the seismic risk analysis of interconnected infrastructure and utility systems in the city of Thessaloniki, Greece. Among other aspects, the paper discusses hazard modelling issues of the two common approaches, the probabilistic and the scenario-based procedure and illustrates in a specific example the impact of mitigation strategies, based on their effect on the performance of the interconnected systems and the overall loss reduction. The integration of interdependencies into the risk analysis and resilience strategies facilitates a better understanding of critical infrastructure operation and enables well-informed proactive and reactive decision-making and efficient disaster risk management, by infrastructure owners and operators, insurance companies, consulting agencies, and local authorities.

In this article, a capacity expansion framework is proposed for failure-prone flow-networks. A systemic risk measure that quantifies the risk of unsatisfied demand due to cascaded edge failures is considered. To minimize the total cost of additional edge capacities, while keeping the risk of unsatisfied demand below a certain threshold, a general stochastic optimization problem is formulated. The distribution of unsatisfied demand is calculated via Monte-Carlo simulations embodied within a grid search algorithm that identifies the feasible region. Thereafter, the cost-optimal edge capacity expansion plan is computed by a differential evolution algorithm. Contributions of this article are: 1) consideration of both immediate investment and future risk costs of capacity expansion plans; 2) a generic flow-network model that can be tuned for different real-life applications; 3) addressing the stochastic nature of both supply and demand simultaneously within a systemic risk framework; 4) use of eigenvector centrality for edge grouping in systemic risk analysis. An extensive numerical study is performed to investigate the effects of different edge grouping methods, characteristics of stochastic components, and cost parameters on the feasible region and optimal solution. The proposed framework is also demonstrated on a case study adapted from ERCOT 13-bus test system.

Time domain intrusive probabilistic seismic risk analysis of nonlinear shear frame structure

Evaluation of socio-economic impact is an emerging theme for publicly-funded academic research. Within this context, the paper suggests that the concept of institutional research capital be expanded to include the capture and evaluation of socio-economic impact. Furthermore, it argues that understanding the typology of impacts and the tracking from research to impact will assist the formulation of institutional strategies for capturing socio-economic impact. A three-stage approach is proposed for capturing and planning activities to enhance the generation of high-quality impact. Stage one outlines the critical role of user engagement that facilitates the tracking of such impact. Stage two employs an analytical framework based on the criteria of ‘depth’ and ‘spread’ to evaluate impacts that have been identified. Stage three utilises the outcomes of the framework to devise strategies, consisting of either further research (to increase depth) or more engagement (to increase spread) that will improve the generation of higher quality impact.

This research provides an in-depth review of the integration of digital literacy and cyber socialization in 21st-century education, positing the importance of these two aspects in preparing students for an ever-evolving world. Using PRISMA's framework for systematic analysis, the study evaluated articles from reputable journals between 2014 and 2024, presenting results that underscore a significant shift in the educational paradigm from traditional to digital. Digital literacy, defined as the ability to use technology efficiently, search and evaluate information, and communicate effectively on digital platforms, has become an essential requirement in modern education. The study shows that digital literacy not only improves technical skills but also supports critical and creative learning among students. In addition, digital literacy education includes teaching ethics and online safety and encouraging responsible internet use. Cyber socialization, with its focus on interaction through social media and digital platforms, has enriched the learning experience through wider collaboration and exchange of resources. However, it also poses challenges such as cyberbullying and security issues, which demand a structured educational response to address related social and psychological issues. The Industrial Revolution 4.0 and the COVID-19 pandemic have accelerated the acceptance of technology in education, emphasizing the importance of character education and the adaptation of innovative teaching methods. Education that is responsive to technological change must integrate digital literacy into all aspects of learning, leading to a more holistic and inclusive approach. Overall, the study underscores the importance of digital literacy and cyber socialization in shaping adaptive and supportive learning environments. 21st-century education must recognize and utilize technology not only as an auxiliary tool but as an integral part of the curriculum, to equip students with the skills needed to succeed in the digital age. By ensuring that digital literacy and cyber socialization are at the heart of education, we can anticipate future needs and challenges, shaping a generation that can not only survive but thrive in an ever-changing digital landscape.

<strong>A new framework for systematic analysis and classification of inconsistencies in multi-viewpoint ontologies</strong>

Uncertainty propagation in risk and resilience analysis of hierarchical systems

This study aims to measure systemic risk in conventional commercial banks in Indonesia with the Conditional Value at Risk (CoVaR) model developed by Adrian and Brunnermeier (2009). The financial crises of 1998 and 2008 have become valuable lessons for Indonesian banks to always maintain financial system stability because the impact caused by systemic risks is very large. Systemic risk is the possibility of instability due to contagion in some or all of the financial system. This study uses a sample of 6 conventional commercial banks in Indonesia from January 2012 to December 2018. The results obtained from this study is the systemic risk is not related to the size of banks. Each bank has a negative externality so that it can cause a bank rush and systemic impact.

Uncertainty is inherent to risk analysis. Therefore, it is extremely important to properly address the issue of uncertainty. In the field of risk analysis for maritime transportation systems, the effect of uncertainty is rarely discussed or quantified. For this reason, this article discusses a case study dealing with risk analysis for a chemical spill in the Gulf of Finland and analyses the related uncertainties by adopting a systematic framework. Risk is assessed in terms of the expected spill frequency and spill volumes caused by collisions between ships and chemical tankers in the Gulf of Finland. This is done by applying a collision consequence with a novel approach-to-collision-speed linkage model and Gulf of Finland–specific causation factors, which are based on reanalysing accident data. This article also presents a metamodel for assessing collision probability with initial vessel speeds for any given scenario where a chemical tanker is about to be struck by another vessel. Even when conducting a risk analysis using state-of-the-art methods, there is still a medium-high degree of uncertainty in the model presented in this article, which only becomes apparent when conducting a systematic uncertainty assessment analysis. However, an uncertainty assessment is an important part of quantitative maritime risk analysis. For this purpose, a qualitative framework for uncertainty assessment analysis is introduced for general use in the field of maritime risk analysis.

Systemic Risk in the Insurance Sector- General Issues and a First Assessment of Large Commercial (Re-)Insurers in Bermuda

Climate change causes precipitation to be increasingly unstable both spatially and temporally and, thus, is a major factor contributing the risk of a water deficit in Taiwan. Because many planned water shortage mitigation projects lack extensive risk evaluation of this problem, this risk might strongly affect the stability of regional water supply. Therefore, the purpose of this study was to consider climate change in a systematic quantitative risk analysis of water shortage mitigation projects. Accordingly, this paper proposes a process for integrating the method for downscaling global climate models, generalized watershed loading functions rainfall-runoff model, system dynamics approach, and Monte Carlo simulation to evaluate water projects practicably and quan- titatively. The proposed methodology was verified using a practical case study of Northern Taiwan and can be used to make decisions regarding project implementation priorities and alternatives. While every alternative of 11 proposed projects can reduce at least 9 % of the shortage risks for both Taoyuan and Banchia-Hsintien regions, five alternatives can reduce the risk to less than 1 % for the Banchia-Hsintien region. And simulation results indicate that expanding water plant is the first priority for the study area. Furthermore, the proposed concepts and procedures are applicable to other areas.

Over the past two decades, seismic hazard modeling has advanced along two complementary frontiers: empirical probabilistic frameworks, which systematically capture uncertainty through statistical inference, and physics-based simulation platforms, which directly compute ground motions from the governing equations of wave propagation. This project seeks to unify these two worlds by developing an end-to-end integration between OpenQuake and CyberShake, thereby creating a new generation of seismic hazard models that are globally extensible, probabilistically complete, and physically consistent. CyberShake has been under active development for more than a decade, demonstrating its robustness and scientific maturity through extensive implementations in California. It performs a physics-based probabilistic seismic hazard analysis (PSHA), replacing traditional empirical Ground Motion Prediction Equations (GMPEs) with full 3D numerical simulations of seismic wave propagation. Built upon the UCERF2/3 Earthquake Rupture Forecasts, CyberShake computes hazard curves directly from synthetic seismograms generated via Strain Green’s Tensors and thousands of stochastic rupture variations. This approach enables non-ergodic, site-specific hazard estimation and has set a global benchmark for high-fidelity hazard computation. However, its application has remained geographically limited: both the ERF and 3D velocity models were designed specifically for California, requiring extensive datasets that are rarely available elsewhere. Conversely, OpenQuake, developed by the Global Earthquake Model (GEM) Foundation, provides a fully open-source, Python-based framework for probabilistic seismic hazard and risk analysis. It serves as the computational backbone of large-scale hazard models such as the European Seismic Hazard Model 2020 (ESHM20), which integrates decades of regional expertise into a unified and statistical representation. OpenQuake provides a complete probabilistic framework to build Earthquake Rupture Forecasts (ERFs) that combine declustered catalogs, background seismicity, and multi-branch logic trees, ensuring a balanced and uncertainty-aware representation of regional tectonics. Furthermore, its ecosystem extends seamlessly to vulnerability and exposure modules, enabling the translation of hazard into actionable risk assessments and resilience planning.This project will establish a direct pipeline from OpenQuake’s event-based results to the generation of an ERF compatible with CyberShake’s simulation framework, ensuring moment–rate consistency. By doing so, it will enable CyberShake simulations to be performed for regions beyond California, extending its use to Europe based on the knowledge contained in the ESHM20. The first pilot region is Istanbul, Turkey, a densely populated metropolis located near the western termination of the North Anatolian Fault. Our initial results show that the workflow is already functioning at the prototype level: we have developed a unified 3D velocity model for the Istanbul region by combining available tomographic models with local datasets; generated preliminary event-based rupture catalogs from ESHM20 using OpenQuake; and demonstrated early convergence behavior in hazard curves, indicating that the rupture sampling strategy is statistically robust. These initial results demonstrate the feasibility of the integration approach and indicate that the essential elements needed for a CyberShake-ready ERF are already in place.