Livskvalitet i familier : nære relasjoners betydning for ”det gode liv”

Life quality in families: close relations and “the good life” The aim of this paper is to explore how aspects and qualities of modern family life influence subjective well-being. Although the patterns of family, work and sexuality have changed dramatically the last four decades, it is here claimed that the family is still an important base of socialization and upbringing. Thus the study of processes within the family can contribute to the understanding of the environment in which many children grow up. In most families, work, partner and family roles are intertwined, and one central hypothesis of this work is that family members influence each other in the individual formation of well-being. The main research question of this study asks: How is life quality affected by the quality of parent-child and the partner relationships and aspects of their shared everyday life? Empirical implications are tested against 2002-data from a representative, Norwegian sample. Analyses conclude that the quality of parent-child and the spousal relationship, and also aspects of families’ everyday life such as work-life balance and time pressure influence the subjective life quality, but mother’s and father’s life quality is affected by differing and partly incompatible conditions.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.