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Family risks are known to be detrimental to children’s attachment development. This study investigated whether parental sensitivity plays different roles in early attachment development in the context of risk: Sensitivity was hypothesized to mediate risk effects on attachment, as well as a moderator that shapes the relation between risk and attachment. Multiple family risks, parental sensitivity (defined as responsivity and supportive presence), and children’s attachment security of 197 infants and toddlers (Mage = 15.25 months) and their caregivers were assessed in a prospective study with a cohort-sequential-design in Germany. Caregivers’ sensitivity served as a mediator of risk effects on attachment as well as a moderator that buffers adverse consequences of risk. Early sensitivity might be relevant in setting the stage for attachment development supporting resilience.
Integrated Risk and Opportunity Management (IROM) goes far beyond what is found in organizations today. However, it offers the best opportunity not only to keep pace with the VUCA world, but to actually profit from it. Accordingly, the introduction of opportunity-based thinking in addition to risk-based thinking is part of the design specification for ISO 9000 and ISO 9001. The prerequisite for the successful design of an IROM is the individual definition, control and integration of risk and opportunity management processes, considering eight success factors, the "8 C". Top management benefits directly from the result: better, coordinated decision memos enable faster and more appropriate decisions.
Renewable energy production is one of the strongest rising markets and further extreme growth can be anticipated due to desire of increased sustainability in many parts of the world. With the rising adoption of renewable power production, such facilities are increasingly attractive targets for cyber attacks. At the same time higher requirements on a reliable production are raised. In this paper we propose a concept that improves monitoring of renewable power plants by detecting anomalous behavior. The system does not only detect an anomaly, it also provides reasoning for the anomaly based on a specific mathematical model of the expected behavior by giving detailed information about various influential factors causing the alert. The set of influential factors can be configured into the system before learning normal behaviour. The concept is based on multidimensional analysis and has been implemented and successfully evaluated on actual data from different providers of wind power plants.