Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course

Engineering dynamics is a fundamental sophomore-level course that is required for nearly all engineering students. As one of the most challenging courses for undergraduates, many students perform poorly or even fail because the dynamics course requires students to have not only solid mathematical skills but also a good understanding of fundamental concepts and principles in the field. A valid model for predicting student academic performance in engineering dynamics is helpful in designing and implementing pedagogical and instructional interventions to enhance teaching and learning in this critical course. The goal of this study was to develop a validated set of mathematical models to predict student academic performance in engineering dynamics. Data were collected from a total of 323 students enrolled in ENGR 2030 Engineering Dynamics at Utah State University for a period of four semesters. Six combinations of predictor variables that represent students’ prior achievement, prior domain knowledge, and learning progression were employed in modeling efforts. The predictor variables include X1 (cumulative GPA), X2~ X5 (three prerequisite courses), X6~ X8 (scores of three dynamics mid-term exams). Four mathematical modeling techniques, including multiple linear regression (MLR), multilayer perceptron (MLP) network, radial basis function (RBF) network, and support vector machine (SVM), were employed to develop 24 predictive models. The average prediction accuracy and the percentage of accurate predictions were employed as two criteria to evaluate and compare the prediction accuracy of the 24 models. The results from this study show that no matter which modeling techniques are used, those using X1 ~X6, X1 ~X7, and X1 ~X8 as predictor variables are always ranked as the top three best-performing models. However, the models using X1 ~X6 as predictor variables are the most useful because they not only yield accurate prediction accuracy, but also leave sufficient time for the instructor to implement educational interventions. The results from this study also show that RBF network models and support vector machine models have better generalizability than MLR models and MLP network models. The implications of the research findings, the limitation of this research, and the future work are discussed at the end of this dissertation

Learning From Student Projects in Logic Design

As an introductory course, Logic Design is geared towards familiarizing students with concepts, design, and practical use of digital circuits and systems. Part of the course requirement is for students to form teams and work together to conceptualize and design a digital system that meets an identified need for existing conditions or anticipated futuristic technology. This paper presents student approach to the process of need identification, conceptualization, design, and optimization of a digital system in a term project setting. In conclusion, we discuss lessons learned from student logic design, creativity, and aspirations

Exploring Mean Annual Precipitation Values (2003–2012) in a Specific Area (36°N–43°N, 113°E–120°E) Using Meteorological, Elevational, and the Nearest Distance to Coastline Variables

Gathering very accurate spatially explicit data related to the distribution of mean annual precipitation is required when laying the groundwork for the prevention and mitigation of water-related disasters. In this study, four Bayesian maximum entropy (BME) models were compared to estimate the spatial distribution of mean annual precipitation of the selected areas. Meteorological data from 48 meteorological stations were used, and spatial correlations between three meteorological factors and two topological factors were analyzed to improve the mapping results including annual precipitation, average temperature, average water vapor pressure, elevation, and distance to coastline. Some missing annual precipitation data were estimated based on their historical probability distribution and were assimilated as soft data in the BME method. Based on this, the univariate BME, multivariate BME, univariate BME with soft data, and multivariate BME with soft data analysis methods were compared. The estimation accuracy was assessed by cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results showed that multivariate BME with soft data outperformed the other methods, indicating that adding the spatial correlations between multivariate factors and soft data can help improve the estimation performance

Predicted T-XY (X≠\neqY=P, As and Sb) monolayer with intrinsic persistent spin helix and large piezoelectric response

The persistent spin helix (PSH) is robust against spin-independent scattering and renders an extremely long spin lifetime, which can improve the performance of potential spintronic devices. To achieve the PSH, a unidirectional spin configuration is required in the momentum space. Here, T-XY (X$\neq$Y=P, As and Sb) monolayers with dynamical, mechanical and thermal stabilities are predicted to intrinsically possess PSH. Due to the $C_{2\upsilon}$ point-group symmetry, a unidirectional spin configuration is preserved in the out-of-plane direction for both conduction and valence bands around the high-symmetry $\Gamma$ point. That is, the expectation value of the spin $S$ only has the out-of-plane component $S_z$. The application of an out-of-plane external electric field can induce in-plane components $S_x$ and $S_y$, thus offering a promising platform for the on-off logical functionality of spin devices. T-XY (X$\neq$Y=P, As and Sb) monolayers are determined to be excellent two-dimensional (2D) piezoelectric materials. The in-plane piezoelectric coefficient $d_{11}$ (absolute value) of T-SbP is 226.15 pm/V, which is larger than that reported for most 2D materials, providing possibility of tuning spin-splitting of PSH by in-plane electric field induced with a uniaxial in-plane strain through piezoelectric effect. Our work reveals a new family of T-phase 2D materials, which could provide promising applications in spintronic and piezoelectric devices.Comment: 8 pages, 9 figure

A Study of Wolf Pack Algorithm for Test Suite Reduction

Modern smart meter programs are iterating at an ever-increasing rate, placing higher demands on the software testing of smart meters. How to reduce the cost of software testing has become a focus of current research. The reduction of test overhead is the most intuitive way to reduce the cost of software testing. Test suite reduction is one of the necessary means to reduce test overhead. This paper proposes a smart meter test suite reduction technique based on Wolf Pack Algorithm. First, the algorithm uses the binary optimization set coverage problem to represent the test suite reduction of the smart meter program; then, the Wolf Pack Algorithm is improved by converting the positions of individual wolves into a 0/1 matrix; finally, the optimal test case subset is obtained by iteration. By simulating different smart meter programs and different size test suites, the experimental result shows that the Wolf Pack Algorithm achieves better results compared to similar algorithms in terms of the percentage of obtaining both the optimal solution and the optimal subset of test overhead

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Mobile CrowdSensing (MCS), through employing considerable workers to sense and collect data in a participatory manner, has been recognized as a promising paradigm for building many large-scale applications in a cost-effective way, such as combating COVID-19. The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies assume that the qualities of workers are known in advance, or the platform knows the qualities of workers once it receives their collected data. In reality, to reduce their costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform. So, it is very hard for the platform to evaluate the authenticity of the received data. In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem, and design an UCB-based algorithm to separate the exploration and exploitation, considering the Sensing Rates (SRs) of recruited workers as the gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We prove that our SCMABA achieves truthfulness and individual rationality. Additionally, we exhibit outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.Comment: 18 pages, 14 figure

Biochar to improve soil fertility. A review

International audienceAbstractSoil mineral depletion is a major issue due mainly to soil erosion and nutrient leaching. The addition of biochar is a solution because biochar has been shown to improve soil fertility, to promote plant growth, to increase crop yield, and to reduce contaminations. We review here biochar potential to improve soil fertility. The main properties of biochar are the following: high surface area with many functional groups, high nutrient content, and slow-release fertilizer. We discuss the influence of feedstock, pyrolysis temperature, pH, application rates, and soil types. We review the mechanisms ruling the adsorption of nutrients by biochar