212 research outputs found
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
Effect of Tremella fuciformis Polysaccharides on Fatigue Induced by Oxidative Damage in Exhaustive Exercise Mice
Objective: To investigate the effect of Tremella fuciformis polysaccharides (TFP) on oxidative damage induced by exhaustive exercise in mice and analyze its mechanism. Methods: L6 cells were treated with TFP for 48 hours, and the activity of L6 was detected by CCK-8 method. L6 cells were divided into three groups, including the control, H2O2, and H2O2+TFP groups, and incubated for 48 hours. LA levels were measured by a biochemical analyzer, and Nrf2, NQO1, and HO-1 protein levels were detected by Western blot assay. C57BL/6 mice were randomly divided into four groups (n=10 for each group), including model group and model+TFP group (50, 100, 200 mg/kg). The model+TFP groups were continuously gavaged with TFP, while the model group was gavaged with the same dose of distilled water once a day for two consecutive weeks. Exhaustive exercise was performed 30 minutes after the last administration, the duration of exhaustive swimming exercise was recorded, and enzyme-linked immunosorbent assay (ELISA) was used to detect liver glycogen (LG) and skeletal muscle glycogen (MG). ELISA was used to detect blood urea nitrogen (BUN), lactic acid (LA), ROS, malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), TNF-α, IL-6 and NF-κBp65 levels. Western blot assay was used to detect the levels of Nrf2, NQO1, and HO-1 proteins. Results: The LA levels of L6 cells in the H2O2 group were significantly (P<0.01) higher than those in the control group, while the protein levels of Nrf2, NQO1, and HO-1 were significantly (P<0.01) lower than those in the control group. TFP could significantly (P<0.01) reduce the LA levels in the culture medium and upregulate the expression of Nrf2, NQO1, HO-1 in L6 cells. TFP could significantly prolong exhaustive swimming time (P<0.01), reduce LA (P<0.01) and BUN (P<0.05, P<0.01) levels, increase liver and muscle glycogen levels (P<0.05, P<0.01), upregulate SOD (P<0.01) and GSH-Px (P<0.01), downregulate MDA (P<0.05, P<0.01) and ROS (P<0.01) levels, and reduce TNF-α (P<0.01), IL-6 (P<0.01) and NF-κBp65 (P<0.01). Compared with the model group, TFP could significantly increase Nrf2 (P<0.05, P<0.01), HO-1 (P<0.05, P<0.01) and NQO-1 (P<0.05, P<0.01) protein expression levels. Conclusion: TFP improved oxidative damage caused by exhaustive swimming by inhibiting oxidative stress and inflammation, and its mechanism was related to regulating the Nrf2/HO-1 signaling pathway
Predicted T-XY (XY=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 (XY=P, As and
Sb) monolayers with dynamical, mechanical and thermal stabilities are predicted
to intrinsically possess PSH. Due to the 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 point.
That is, the expectation value of the spin only has the out-of-plane
component . The application of an out-of-plane external electric field can
induce in-plane components and , thus offering a promising platform
for the on-off logical functionality of spin devices. T-XY (XY=P, As and
Sb) monolayers are determined to be excellent two-dimensional (2D)
piezoelectric materials. The in-plane piezoelectric coefficient
(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
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