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1.
Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.  相似文献   
2.
Abusive supervision in the workplace has been shown to have important direct consequence in work and work relationship, and also indirect consequences to workers’ well-being and relationships outside work. Consequences of abusive supervision have not been studied among migrant workers whose status in the host country of work is dependent on maintaining the work contract. This study investigates abusive supervision in 247 Filipino migrant workers in Macau, who hold temporary work contracts and work visas to engage in various low-skilled work (e.g., domestic helper, security guard, etc.). The study tests a model representing the indirect consequences of abusive supervision on the self-esteem and acculturation orientation of migrant workers, in particular, on the tendency to reject their heritage culture in their attempt to acculturate in the host country. Mediation analysis indicated that abusive supervisory perceptions led to lower self-esteem (b = ?.19), which in turn relates to tendency to reject their heritage culture as part of acculturation (b = ?.45) [indirect effect = .08, 90 % CI .04, .15]. The rejection of heritage culture is interpreted as a coping response to the negative indirect consequences of abusive supervision perceptions that may be partly attributed to being a migrant Filipino worker. The results are discussed in terms of how the acculturation of migrant workers reflects aspects of their well-being that may be adversely affected by vocational-related stress in the host country.  相似文献   
3.
A dynamic network introduced by Ford and Fulkerson is a directed graph with capacities and transit times on its arcs. The quickest transshipment problem is one of the most fundamental problems in dynamic networks. In this problem, we are given sources and sinks. Then the goal of this problem is to find a minimum time limit such that we can send the right amount of flow from sources to sinks. In this paper, we introduce a variant of this problem called the mixed evacuation problem. This problem models an emergent situation in which people can evacuate on foot or by car. The goal is to organize such a mixed evacuation so that an efficient evacuation can be achieved. In this paper, we study this problem from the theoretical and practical viewpoints. In the first part, we prove the polynomial-time solvability of this problem in the case where the number of sources and sinks is not large, and also prove the polynomial-time solvability and computational hardness of its variants with integer constraints. In the second part, we apply our model to the case study of Minabe town in Wakayama prefecture, Japan.  相似文献   
4.
To analyse the role of self-efficacy in goal setting in public administrations, this study combines goal-setting theory, public service motivation literature and cognitive theory. Using partial least squares structural equation modelling (PLS-SEM), survey data from 105 German civil servants are analysed. The results underline the role of goal setting (represented by goal difficulty and specificity) in determining the self-efficacy of public servants. Public service motivation and goal specificity both increase public servants’ work performance; however, the effects of goal difficulty are negatively mediated by the employee’s self-efficacy. That finding reflects the central role of self-efficacy, which should not be ignored in the goal-setting processes of public administrations.  相似文献   
5.
Feminist and empowerment theories are especially important to the understanding of individual and sociopolitical levels of social work assessment and intervention. Incorporating feminist and empowerment approaches in practice will provide social workers with the knowledge, values and skills most likely to promote human rights and social justice. In this paper, we present an overview of both theories and illustrate them with a case example.  相似文献   
6.
Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...  相似文献   
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Urban Ecosystems - The development of urban areas imposes challenges that wildlife must adapt to in order to persist in these new habitats. One of the greatest changes brought by urbanization has...  相似文献   
10.
Social Indicators Research - Debates about the appropriate role of markets and governments are often shaped by sharply contrasting opinions. Based on individual data from the World Values Survey...  相似文献   
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