IDENTIFYING LEVERAGE POINTS IN U.S.RESIDENTIAL WATER HEATER STAKEHOLDER SYSTEM AND DECISION-MAKING PROCESS:AN INTEGRATIVE REVIEW

The system of stakeholders impacting water heater selection decisions in the U.S.is complex,with numerous actors and key players engaging with varying degrees of information asymmetry.The limited availability of decision-making tools could lead to unintended consequences of water and energy saving decisions on public health,such as the growth of opportunistic pathogens in water systems.We use a qualitative meta-synthesis to identify key stakeholders,map interactions among these stakeholders,identify decisions,roles,and influences,and inventory potential interventions.This study identifies and characterizes the important attributes of the residential water heater stakeholder system(leverage points)that influence the selec-tion of water heating technologies.The ultimate desired outcome of the work is to facilitate the selection of water heating components and design configurations that meet occupant objectives and constraints while limiting the potential for health and safety risks due to scalding or opportunistic pathogens.This effort identifies a clear need for decision-making support tools for selecting residential water heaters,one that takes a whole-systems perspective in the water heater stakeholder system and takes advantage of key leverage points for effective intervention.

On Identifying Influential Observations in the Presence of Multicollinearity

Influential observation is one which either individually or together with several other observations has a demonstrably large impact on the values of various estimates of regression coefficient. It has been suggested by some authors that multicollinearity should be controlled before attempting to measure influence of data point. In using ridge regression to mitigate the effect of multicollinearity, there arises a problem of choosing possible of ridge parameter that guarantees stable regression coefficients in the regression model. This paper seeks to check whether the choice of ridge parameter estimator influences the identified influential data points.

A mixed-integer linear programming approach for robust state estimation

In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is proposed.By using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmixed integer nonlinear programming formulation for RSE is converted to a MILP problem.The proposed approach not only guarantees to find the global optimum,but also does not have convergence problems.Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.

Outlier detection by means of robust regression estimators for use in engineering science

This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.