Relationship Quality of Siblings Attending the Same University

This phenomenological qualitative study explores the relationship quality of siblings who both attend Cedarville University. This study seeks to identify commonalities and key components to close sibling relationships. The desire to attend the same school, or remain close to a sibling was explored, as well. Questions specifically focused on family life growing up, and current family life, while attending Cedarville University. These questions were designed to gain background information, while also gaining insight to current relationship quality and conflict. Some themes that have emerged are similarities in sibling roles based on birth order, and influencing each other in making morally sound decisions. A majority of the conflicts encompassing sibling relationships were linked to family issues and disagreements about morality. Most sibling pairs had maintained a strong relationship with his or her sibling previous to attending the same university, which essentially facilitated the relationship at Cedarville University

"The connection between distortion risk measures and ordered weighted averaging operators"

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and nite random variables is presented. This connection oers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed.Fuzzy systems; Degree of orness; Risk quantification; Discrete random variable JEL classification:C02,C60

An active particle diffusion theory of flame quenching for laminar flames / Dorothy M. Simon and Frank E. Belles

An equation for quenching distance based on the destruction of chain carriers by the surface is derived. The equation expresses the quenching distance in terms of the diffusion coefficients and partial pressures of the chain carriers and gas phase molecules, the efficiency of the surface as a chain breaker, the total pressure of the mixture, and a constant which depends on the geometry of the quenching surface. Quenching distances measured by flashback for propane-air flames are shown to be consistent with the mechanism. The derived equation is used with the lean inflammability limit and a rate constant calculated from burning velocity data to estimate quenching distances for propane-air (hydrocarbon lean) flames satisfactorily

Variation of the pressure limits of flame propagation with tube diameter for propane-air mixtures

An investigation was made of the variation of the pressure limits of flame propagation with tube diameter for quiescent propane with tube diameter for quiescent propane-air mixtures. Pressure limits were measured in glass tubes of six different inside diameters, with a precise apparatus. Critical diameters for flame propagation were calculated and the effect of pressure was determined. The critical diameters depended on the pressure to the -0.97 power for stoichiometric mixtures. The pressure dependence decreased with decreasing propane concentration. Critical diameters were related to quenching distance, flame speeds, and minimum ignition energy

The connection between distortion risk measures and ordered weighted averaging operators [WP]

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and finite random variables is presented. This connection offers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed

Indicators for the characterization of discrete Choquet integrals

Ordered weighted averaging (OWA) operators and their extensions are powerful tools used in numerous decision-making problems. This class of operator belongs to a more general family of aggregation operators, understood as discrete Choquet integrals. Aggregation operators are usually characterized by indicators. In this article four indicators usually associated with the OWA operator are extended to discrete Choquet integrals: namely, the degree of balance, the divergence, the variance indicator and Renyi entropies. All of these indicators are considered from a local and a global perspective. Linearity of indicators for linear combinations of capacities is investigated and, to illustrate the application of results, indicators of the probabilistic ordered weighted averaging -POWA- operator are derived. Finally, an example is provided to show the application to a specific context

The connection between distortion risk measures and ordered weighted averaging operators

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and finite random variables is presented. This connection offers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed