Development of Economic Water Usage Sensor and Cyber-Physical Systems Co-Simulation Platform for Home Energy Saving

In this thesis, two Cyber-Physical Systems (CPS) approaches were considered to reduce residential building energy consumption. First, a flow sensor was developed for residential gas and electric storage water heaters. The sensor utilizes unique temperature changes of tank inlet and outlet pipes upon water draw to provide occupant hot water usage. Post processing of measured pipe temperature data was able to detect water draw events. Conservation of energy was applied to heater pipes to determine relative internal water flow rate based on transient temperature measurements. Correlations between calculated flow and actual flow were significant at a 95% confidence level. Using this methodology, a CPS water heater controller can activate existing residential storage water heaters according to occupant hot water demand. The second CPS approach integrated an open-source building simulation tool, EnergyPlus, into a CPS simulation platform developed by the National Institute of Standards and Technology (NIST). The NIST platform utilizes the High Level Architecture (HLA) co-simulation protocol for logical timing control and data communication. By modifying existing EnergyPlus co-simulation capabilities, NIST’s open-source platform was able to execute an uninterrupted simulation between a residential house in EnergyPlus and an externally connected thermostat controller. The developed EnergyPlus wrapper for HLA co-simulation can allow active replacement of traditional real-time data collection for building CPS development. As such, occupant sensors and simple home CPS product can allow greater residential participation in energy saving practices, saving up to 33% on home energy consumption nationally

An Empirical Examination of Managerial Competencies Among Black Women Entrepreneurs and Black Women Corporate Executives*

The new economic reality of the 1990' s is clearly the M table entrepreneurial pursuits of women business owners in general and black women entrepreneurs in particular. This paper reports on the managerial competencies and perceived skill development needs of black women entrepreneurs and contrasts their profile with black women corporate managers. Managerial competencies were assessed through the use of the "Leadership Competency Inventory" (LCI). The LCI provides feedback on 55 competencies arranged in four categories: (1) Socio-Economic Environment of Business, (2) Technical and Operational Methods, (3) Human Resource and Interpersonal Communication Skills, and (4) Vision and Environmental Co-Alignment Scanning Abilities. Upon testing for significance, the conclusions indicate that a relatively different and distinct competency profile exists and illustrates the developmental needs for  black women managers anticipating stepping off the corporate track in favor of the entrepreneurial alternative. As for black women entrepreneurs, they continue 10 display mixed perceptions of their skills and abilities with some areas identified as very weak and others very positively viewed

3D ab initio modeling in cryo-EM by autocorrelation analysis

Single-Particle Reconstruction (SPR) in Cryo-Electron Microscopy (cryo-EM) is the task of estimating the 3D structure of a molecule from a set of noisy 2D projections, taken from unknown viewing directions. Many algorithms for SPR start from an initial reference molecule, and alternate between refining the estimated viewing angles given the molecule, and refining the molecule given the viewing angles. This scheme is called iterative refinement. Reliance on an initial, user-chosen reference introduces model bias, and poor initialization can lead to slow convergence. Furthermore, since no ground truth is available for an unsolved molecule, it is difficult to validate the obtained results. This creates the need for high quality ab initio models that can be quickly obtained from experimental data with minimal priors, and which can also be used for validation. We propose a procedure to obtain such an ab initio model directly from raw data using Kam's autocorrelation method. Kam's method has been known since 1980, but it leads to an underdetermined system, with missing orthogonal matrices. Until now, this system has been solved only for special cases, such as highly symmetric molecules or molecules for which a homologous structure was already available. In this paper, we show that knowledge of just two clean projections is sufficient to guarantee a unique solution to the system. This system is solved by an optimization-based heuristic. For the first time, we are then able to obtain a low-resolution ab initio model of an asymmetric molecule directly from raw data, without 2D class averaging and without tilting. Numerical results are presented on both synthetic and experimental data

Mexico Beckons Mid-American Small Business: Prospect for University Centered Export Trade Assistance*

Midwestern small business firms are being aggressively beckoned to enter the rapidly growing Mexican economy. With the recent passage of the North American Free Trade Agreement (NAFTA), Mexico is ready to start spending to upgrade its quality of life. Small business firms with products that can help with these improvements are being urged to scour Mexico for trade leads. This study reports on a survey of 1,104 midwest-central small and medium size manufacturing firms who find it difficult to take advantage of the export opportunity that Mexico may offer them. Among the respondents, 287firms are in industries classified by the U.S. Department of Commerce as having the greatest potential for rapid growth in export to Mexico. Characteristics of survey firms are presented along with their managerial and technical assistance needs. The prospect for a university-based export assistance center is explored along with the operational dimensions for such an agency partnership

Smart Control for Home Water Heater Saving

Existing gas or electric water heaters can become inefficient through the overheating of water and through parasitic heat loss. These inefficiencies are able to be solved by monitoring when a home uses hot water. AquAdapt is a smart sensor which is capable of attaching to any existing residential gas or electric water heater. By constantly monitoring the temperature change of a home water heater, the first law of thermodynamics can be used to relate temperature change to the amount of hot water leaving the water heater. Utilizing this information, a schedule can be generated to optimize the heating of home hot water. Regulating the on-off state of a water heater based on the household’s learned usage pattern, allows AquAdapt to reduce residential water heating energy consumption by up to 33%. With a final product cost of $60, the return on investment of AquAdapt is estimated to be 8 months

Lessons for Small Business: Incentive Health Care and Risk Rating Practices

As the health care system reform debate  continues, the central challenge of bringing nearly 38 million uninsured American workers under a quality health care plan remains the goal. Another objective that remains clear is that the plan to be implemented will follow the employer-based model which Americans' employers have crafted for over fifty  years.  Small business owners will be mandated to provide health insurance  to all  workers,  including  those who work part-time. This paper examines the health care incentive measures and risk rating practices perceived to be effective cost  control mechanisms for small business firms. Conclusions concerning a  well-designed  incentive program  are offered  within  the context  of the  Americans   With Disabilities  Act

Earthmover-based manifold learning for analyzing molecular conformation spaces

In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction. We demonstrate the potential benefits of this approach for learning shape spaces of proteins and other flexible macromolecules using a simulated dataset of 3-D density maps that mimic the non-uniform rotary motion of ATP synthase. Our results show that EMD-based diffusion maps require far fewer samples to recover the intrinsic geometry than the standard diffusion maps algorithm that is based on the Euclidean distance. To reduce the computational burden of calculating the EMD for all volume pairs, we employ a wavelet-based approximation to the EMD which reduces the computation of the pairwise EMDs to a computation of pairwise weighted-$\ell_1$ distances between wavelet coefficient vectors.Comment: 5 pages, 4 figures, 1 tabl

Ariel - Volume 10 Number 6

Executive Editors Madalyn Schaefgen David Reich Business Manager David Reich News Editors Medical College Edward Zurad CAHS John Guardiani World Mark Zwanger Features Editors Meg Trexler Jim O\u27Brien Editorials Editor Jeffrey Banyas Photography and Sports Editor Stuart Singer Commons Editor Brenda Peterso

Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms

The number of noisy images required for molecular reconstruction in single-particle cryo-electron microscopy (cryo-EM) is governed by the autocorrelations of the observed, randomly-oriented, noisy projection images. In this work, we consider the effect of imposing sparsity priors on the molecule. We use techniques from signal processing, optimization, and applied algebraic geometry to obtain new theoretical and computational contributions for this challenging non-linear inverse problem with sparsity constraints. We prove that molecular structures modeled as sums of Gaussians are uniquely determined by the second-order autocorrelation of their projection images, implying that the sample complexity is proportional to the square of the variance of the noise. This theory improves upon the non-sparse case, where the third-order autocorrelation is required for uniformly-oriented particle images and the sample complexity scales with the cube of the noise variance. Furthermore, we build a computational framework to reconstruct molecular structures which are sparse in the wavelet basis. This method combines the sparse representation for the molecule with projection-based techniques used for phase retrieval in X-ray crystallography.Comment: 31 pages, 5 figures, 1 movi