Learning while Competing -- 3D Modeling & Design

The e-Yantra project at IIT Bombay conducts an online competition, e-Yantra Robotics Competition (eYRC) which uses a Project Based Learning (PBL) methodology to train students to implement a robotics project in a step-by-step manner over a five-month period. Participation is absolutely free. The competition provides all resources - robot, accessories, and a problem statement - to a participating team. If selected for the finals, e-Yantra pays for them to come to the finals at IIT Bombay. This makes the competition accessible to resource-poor student teams. In this paper, we describe the methodology used in the 6th edition of eYRC, eYRC-2017 where we experimented with a Theme (projects abstracted into rulebooks) involving an advanced topic - 3D Designing and interfacing with sensors and actuators. We demonstrate that the learning outcomes are consistent with our previous studies [1]. We infer that even 3D designing to create a working model can be effectively learned in a competition mode through PBL

Hierarchical Parallelism in Finite Difference Analysis of Heat Conduction

Based on the concept of hierarchical parallelism, this research effort resulted in highly efficient parallel solution strategies for very large scale heat conduction problems. Overall, the method of hierarchical parallelism involves the partitioning of thermal models into several substructured levels wherein an optimal balance into various associated bandwidths is achieved. The details are described in this report. Overall, the report is organized into two parts. Part 1 describes the parallel modelling methodology and associated multilevel direct, iterative and mixed solution schemes. Part 2 establishes both the formal and computational properties of the scheme

Economic Performance of Ohio’s 88 Counties

Author Institution: Janson Industries, Canton, OH; Dept of Theoretical and Applied Mathematics; Dept of Statistics, University of AkronThe value added by the work force varies greatly among Ohio’s 88 counties. In the aggregate, the value added equals the gross domestic products (GDP) of the county. With an adjustment for depreciation, the value added by the county production system is equivalent to the aggregated real income (Y) of the county, the best measure of county economic performance. Measuring GDP or Y by aggregating all production of a region is a labor-intensive procedure. The purpose of this paper was to see if data on investment in real capital resources within the county and investment in human resources within the county (education) could be used to estimate domestic income without requiring a production census. Aggregated county income in Ohio was predicted reliably using county-specific data on the current value of taxable real property (investment in non-human resources), and the estimated value of the investment in educational attainment by the non-degreed work force of the county (human resources). A data vector for investment in the degreed work force was also used in the analysis. All vectors include values for the exhaustive set of Ohio’s 88 counties. A total of 9 regressions were computed using various combinations of the data. Using established statistical criteria the regression equation that uses investment in real capital and investment in the non-degreed work force was selected as the best method. These criteria included an R-square in excess of 0.99 and a mean square error that was smallest among the alternative regressions

The Effects on Energy Markets Subjected to Regulatory Changes Using Neural Net Methodology

Author Institution: Department of Geography, Kent State University, Department of Mechanical Engineering, Department of Mathematical Sciences, University of AkronNeural net methodology has been used to model alternative scenarios of fuel utilization. Regulation and legislation to address the problems of energy related pollution such as acid rain, nuclear waste, greenhouse gases, and tailpipe pollution, will alter fuel input ratios with consequential effects in the energy using sectors. Also, alternative input scenarios using clean coal technology, natural gas, and nuclear power have been modeled. Results indicate that large relative increases of coal or nuclear fuel inputs will cause similar substantial increases in electricity generation, and substitution effects will cause a shift of petroleum uses in final consumption from the commercial and residential sectors to the transport sector. Increasing the gas fuel input relative to other fuels causes little disturbance in using sectors. Incremental increases in fuel consumption maintaining constant relative fuel input shares causes little disturbance. On the other hand, massive increases in fuel consumption inputs maintaining constant input shares is likely to be disastrous public policy

Neutral Net Methodology in the Context of Evolving Economic Systmes

Author Institution: Department of Geography, Kent State University ; Department of Mechanical Engineering, University of Akron ; Department of Mathematical Science, University of AkronFive neural nets relate macro-economic input variables to macro-economic output variables. Three nets for the United States (US) and two nets for the Japanese economy were computed to model the production systems of the two most advanced economies in the world. When the Japanese input vector was used through a US net, gross domestic product (GDP), and GDP per capita, and GDP per person employed are reduced in the same order, -0.38, -0.37, and -0.39% per year. Similarly, when the US input vector is passed through the Japanese neural net each of the three measures of gross domestic product drops in the same order -0.22, -0.22 and -0.23% per year. All of the 20 output measurements used in the analysis have similar results when an alien input vector is used. The model presumes that the determinants of growth are implicit in the neural net (black box), and that the determinants of growth have been culturally shaped through adaptation to the norms and values reflected in the input vectors. A neural net could not be obtained using inputs from all G7 nations as a single group. Convergence of predicted outputs with observed outputs required the use of same-nation data in the iterations