Trauma adapted family connections: reducing developmental and complex trauma symptomatology to prevent child abuse and neglect

Families living in urban poverty, enduring chronic and complex traumatic stress, and having difficulty meeting their children's basic needs have significant child maltreatment risk factors. There is a paucity of family focused, trauma-informed evidence-based interventions aimed to alleviate trauma symptomatology, strengthen family functioning, and prevent child abuse and neglect. Trauma Adapted Family Connections (TA-FC) is a manualized trauma-focused practice rooted in the principles of Family Connections (FC), an evidence supported preventive intervention developed to address the glaring gap in services for this specific, growing, and underserved population. This paper describes the science based development of TA-FC, its phases and essential components, which are based on theories of attachment, neglect, trauma, and family interaction within a comprehensive community-based family focused intervention framework.     

Estimating operational costs in an electric utility

Generation expansion planning in the electric utility industry requires consideration of uncertainties in both the demand and supply of electric power. The expected demand is usually expressed via a load-duration curve, while, on the supply side, each generating unit has a given nameplate capacity and a predicted reliability. This paper focuses on considerations of the supply-side uncertainties and their effects on estimating operating costs in electric utility planning. However, the methods and analysis developed in this paper may be applicable to a wider class of production planning problems which deal with any nonstorable product with time varying demand. Two methods for estimating the energy generation from each generating unit are compared. The first is the method of probabilistic simulation, while the second involves a heuristic technique usually denoted the derating method. A bias inherent in the derating method is examined by comparing it with a probabilistic simulation method. The bias is examined for various load curve shapes. In certain cases, a closed form expression for the bias is obtained. However, a closed form expression of the bias for an arbitrary load curve is difficult to achieve. In these situations some examples are studied in which the trend of the relative bias among plants in the loading order is examined. Finally, the bias is examined using actual 1977 load and supply data for some New England utilities.