A Brief Survey of the Biblical Integration of Engineering and Missions with Emphasis on Appropriate Technologies

A Biblical foundation for the integration of engineering and missions, an overview of appropriate technology in a Christian context, a survey of projects at other Christian engineering programs, and proposed engineering projects are discussed for remote areas and developing countries. At the core of the Biblical integration of engineering and missions is the desire to use engineering skills and resources to assist others, especially those in the Christian family, improve their lives, help them obtain Biblical information, and encourage personal responsibility. Engineering topics are proposed that will help people gain access to information, improve agricultural methods, improve their health, and grow in the Word. Through engineering efforts, Godly relationships between engineers and others in developing countries can be fostered to help spread the Gospel. The specific categories of topics that are considered are electrical engineering focused and are in the areas of electrical power, instrumentation and data logging, telecommunications, remote sensing, radio, agriculture, medical equipment, and security systems. Emphasis is placed on appropriate technologies that are small-scale, can be fabricated with local inexpensive materials, and can be maintained locally by the people

Hybrid Gauss Pseudospectral and Generalized Polynomial Chaos Algorithm to Solve Stochastic Optimal Control Problems

A numerical algorithm combining the Gauss Pseudospectral Method (GPM) with a Generalized Polynomial Chaos (gPC) method to solve nonlinear stochastic optimal control problems with constraint uncertainties is presented. The GPM and gPC have been shown to be spectrally accurate numerical methods for solving deterministic optimal control problems and stochastic differential equations, respectively. The gPC uses collocation nodes to sample the random space, which are then inserted into the differential equations and solved using standard solvers to generate a set of deterministic solutions used to characterize the distribution of the solution by constructing a polynomial representation of the output as a function of uncertain parameters. The proposed algorithm investigates using GPM optimization software in place of deterministic differential equation solvers traditionally used in the gPC, providing minimum cost deterministic solutions that meet path, control, and boundary constraints. A trajectory optimization problem is considered where the objectives are to find the path through a two-dimensional space that minimizes the probability a vehicle will be ’killed’ by lethal threats whose locations are uncertain and to characterize the effects those uncertainties have on the solution by estimating the statistical properties

Application of a Finite-Volume Time-Domain Technique to Three-Dimensional Objects

Concurrent engineering approaches for the disciplines of computational fluid dynamics (CFD) and electromagnetics (CEM) are necessary for designing future high-performance aircraft. A characteristic-based finite-volume time-domain (FVTD) computational algorithm, used by CFD and now applied toCEM, is implemented to analyze the radar cross section (RCS) of the ogive and cone-sphere. The technique utilizes a scattered-field formulation of the time-dependent Maxwell equations. The FVTD formulation implements a monotone upstream-centered scheme for conservation laws for the flux evaluation and a Runge-Kutta multi-stage scheme for the time integration. The results are obtained from the electromagnetic fields via a Fourier transform and a near-to-far field transformation

AC Power Monitoring System Provides Individual Circuit Energy Consumption Data

Motivated by high energy costs, people and organizations want to cut back on their energy consumption. However, the only feedback consumers typically receive is a monthly bill listing their total electricity usage (in kWh). Some companies have begun developing systems that allow households and organizations to monitor their energy usage for individual circuits. Available systems are expensive so a CU engineering senior design team has designed, fabricated, and tested a system for use at Cedarville University. The AC power monitoring system has the ability to measure energy consumption for each individual circuit in the breaker panel, store the data, and then provide the user with visual feedback on energy usage behavior. The basic system provides the proof of concept for future senior design teams. After more testing is completed, further development of this product will be needed by other senior design teams. Eventually, this energy monitoring system could be expanded to include larger loads such as HVAC systems and refrigeration units. It is also envisioned that future projects might be able to provide the user with suggestions for changing and improving energy usage behavior. Failure prediction of equipment on individual circuits could also stem from this initial project. For this project, it has been clearly shown that the concept is feasible, expandable, and cost-effective

AC Power Monitoring System

Motivated by high energy costs, people and organizations want to cut back on their energy consumption. However, the only feedback consumers typically receive is a monthly bill listing their total electricity usage (in kWh). Some companies have begun developing systems that allow households and organizations to monitor their energy usage for individual circuits. Available systems are expensive so a CU engineering senior design team has designed, fabricated, and tested a system for use at Cedarville University. The AC power monitoring system has the ability to measure energy consumption for each individual circuit in the breaker panel, store the data, and then provide the user with visual feedback on energy usage behavior. The basic system provides the proof of concept for future senior design teams. After more testing is completed, further development of this product will be needed by other senior design teams. Eventually, this energy monitoring system could be expanded to include larger loads such as HVAC systems and refrigeration units. It is also envisioned that future projects might be able to provide the user with suggestions for changing and improving energy usage behavior. Failure prediction of equipment on individual circuits could also stem from this initial project. For this project, it has been clearly shown that the concept is feasible, expandable, and cost-effective

Towards a taxonomy of process quality characteristics for assessment

Previous assessment of process quality have focused on process capability (i.e. the ability of a process to meet its stated goals). This paper proposes a taxonomy of alternative process quality characteristics based on intrinsic and extrinsic quality attributes. The ultimate goal of this taxonomy is to provide a framework to conduct process assessments using different process quality aspects. Such a framework would considerably broaden process quality perspectives beyond the primary measure of process capability. It would also allow practitioners to identify and evaluate relevant quality characteristics for processes based on specific contexts and implications. For the process assessment model developers, it offers a list of process quality characteristics that could be used to develop relevant process measurement frameworks

Enhanced Immunogenicity, Mortality Protection, and Reduced Viral Brain Invasion by Alum Adjuvant with an H5N1 Split-Virion Vaccine in the Ferret

Pre-pandemic development of an inactivated, split-virion avian influenza vaccine is challenged by the lack of pre-existing immunity and the reduced immunogenicity of some H5 hemagglutinins compared to that of seasonal influenza vaccines. Identification of an acceptable effective adjuvant is needed to improve immunogenicity of a split-virion avian influenza vaccine.No serum antibodies were detected after vaccination with unadjuvanted vaccine, whereas alum-adjuvanted vaccination induced a robust antibody response. Survival after unadjuvanted dose regimens of 30 µg, 7.5 µg and 1.9 µg (21-day intervals) was 64%, 43%, and 43%, respectively, yet survivors experienced weight loss, fever and thrombocytopenia. Survival after unadjuvanted dose regimen of 22.5 µg (28-day intervals) was 0%, suggesting important differences in intervals in this model. In contrast to unadjuvanted survivors, either dose of alum-adjuvanted vaccine resulted in 93% survival with minimal morbidity and without fever or weight loss. The rarity of brain inflammation in alum-adjuvanted survivors, compared to high levels in unadjuvanted vaccine survivors, suggested that improved protection associated with the alum adjuvant was due to markedly reduced early viral invasion of the ferret brain.Alum adjuvant significantly improves efficacy of an H5N1 split-virion vaccine in the ferret model as measured by immunogenicity, mortality, morbidity, and brain invasion

Hybrid Solution of Nonlinear Stochastic Optimal Control Problems Using Legendre Pseudospectral and Generalized Polynomial Chaos Algorithms

A novel hybrid technique is discussed to numerically solve nonlinear stochastic optimal control (OC) problems with numerous potential applications. The hybrid technique combines a Legendre Pseudospectral Method (LPM) with a generalized Polynomial Chaos (gPC) method, which are highly accurate numerical methods for solving deterministic OC problems and stochastic differential equations (SDE), respectively. The hybrid algorithm first selects samples from the random space using collocation nodes, inserts those sample values into the differential equations of the OC problem, and then uses a pseudospectral-based deterministic solver to generate solutions for each of the resulting deterministic problems. The set of deterministic solutions (i.e., ensemble of random realizations) are then used to construct a polynomial representation of the solution to the stochastic OC problem as a function of the random inputs using a gPC method (i.e., a high-order stochastic collocation method). The hybrid technique is used to solve a nonlinear stochastic OC problem to demonstrate its utility. The algorithm is a highly accurate technique for solving nonlinear OC problems with uncertain parameters. The hybrid technique will allow the user to analyze the solution to an OC problem and understand how uncertainty on the parameters such as the states, initial conditions, and boundary conditions can affect the solution (i.e., uncertainty quantification). The algorithm may also be useful for near real-time OC since a new trajectory can be followed if the uncertainty can be estimated