This paper presents a system-wide approach for contingency severity screening intended as a practical and scalable precursor to full dynamic security assessment (DSA). DSA is the standard practice used by Transmission System Operators (TSOs) to ensure that power systems remain stable following disturbances, with key variables—such as voltage, frequency, and thermal loading—kept within defined dynamic performance limits. However, applying DSA comprehensively across all credible contingencies and operating points is prohibitive, making a pre-screening stage indispensable. Commonly used static-security filters offer limited insight to transient dynamics, motivating the development of alternative screening strategies. To support early-stage analysis and prioritization, three static indicators are devised as proxies for dynamic behaviour, grounded in the power–angle characteristics determining transient stability. For each n−1 contingency, these indicators provide a severity estimate and serve as inputs to a supervised learning model trained on labels derived from time-domain simulation. This approach aims to reflect how each disturbance might impact system stability, consistent with the objectives of DSA. Contingency rankings derived from these estimates serve to guide further analysis, enabling TSOs to focus dynamic simulations on the most critical scenarios.
Consensus-building can be considered a cornerstone of democratic societies and effective governance. Recently, it has been shown that social contagions can shape some forms of consensus-based collective decision-making (Horsevad et al., Nature Communications, (2022)13:1442). The topology of the network underpinning such processes plays a key role in promoting or hampering simple contagions—based on pairwise interactions—and complex contagions, which require social influence and reinforcement. However, considering the ubiquitous scalefreeness of most social networks, it becomes imperative to delve into how this particular characteristic of networks impacts the dynamics of social contagions and the subsequent group consensus. Two specific aspects are worth analyzing: (i) understanding the interplay between scalefreeness and the transition from a simple to a complex contagion, and (ii) exploring the specificities associated with highly clustered networks. Here, we consider two distinct collective decision-making processes: (1) the classical linear threshold model, and (2) the leader-follower consensus model—a paradigmatic approach to collective decision-making—to systematically explore the transition from simple to complex contagions in the presence of a tunable family of synthetic scale-free networks. In contrast to previous findings, our results show that scale-free networks can, under certain conditions, support the spread of complex contagions. These findings carry profound implications for the development of innovative strategies aimed at fostering consensus within social groups.
This systematic literature review covers the extant literature on health and clinical governance. Using a sample of 103 studies published in 2014-2022, we categorize those papers based on the methods employed, the theoretical framework, the thematology and others. We underline the interdisciplinary of the clinical governance, that mostly examined in developed countries through qualitative methodologies (case studies, surveys and others). Particularly, we give prominence to the need for conducting more research that employs a worldwide sample, quantitative methods that prove empirical evidence on the balance that needs to be achieved on, most of the time, conflicting matters that revolve around clinical and health governance.
