Pre-college Characteristics and Online Homework Learning: Factors Associated with First Year Engineering Students’ Academic Success

The purpose of the study was to develop a working model to predict at risk students in an Introduction to Engineering course. The model considers both students’ pre-college characteristics, psychological traits, and online homework learning behavior. The study assisted the course instructor in the creation of an early warning system and the development of targeted interventions for students at risk. A reliable and valid instrument to measure engineering students’ pre-college characteristics was initially developed. The study also applied data mining to analyze the student online homework logs in order to observe engineering students’ homework learning process. A decision tree model containing all of the pre-college characteristics and online homework learning features was also developed, and it identified four key factors related to students’ risk to fail the first module exam: Correctness, Preparedness, Self-efficacy, and percentage of homework attempts after deadline (Plate). The results of the decision tree model helped identify students-at-risk at early stage of the course. Students at risk were grouped into multiple groups. The author also proposed customized interventions to help students in different at risk groups. The findings of the study helped engineering students and educators to build up a comprehensive student profile to better understand students’ academic status and learning needs in the course. Thus this study suggests ways for both the engineering educators and students to improve the learning process in a more efficient manner

Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories

Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

 Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel digital core reconstruction method. First, a convolutional neural network is used as a generative network to learn the distribution of real shale samples, and then a convolutional neural network is constructed as a discriminative network to distinguish reconstructed shale samples from real ones. Through this confrontation training method, realistic digital core samples of shale can be reconstructed. The paper uses two-point covariance function, Frechet Inception Distance and Kernel Inception Distance, to evaluate the quality of digital core samples of shale reconstructed by GANs. The results show that the covariance function can test the similarity between generated and real shale samples, and that GANs can efficiently reconstruct digital core samples of shale with high-quality. Compared with multiple point statistics, the new method does not require prior inference of the probability distribution of the training data, and directly uses noise vector to generate digital core samples of shale without using constraints of "hard data" in advance. It is easy to produce an unlimited number of new samples. Furthermore, the training time is also shorter, only 4 hours in this paper. Therefore, the new method has some good points compared with current methods.Cited as: Zha, W., Li, X., Xing, Y., He, L., Li, D. Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4(1): 107-114, doi: 10.26804/ager.2020.01.1

A study of correlation between permeability and pore space based on dilation operation

CO2 and fracturing liquid injection into tight and shale gas reservoirs induces reactivity between minerals and injected materials, which results in porosity change and thus permeability change. In this paper, the dilation operation is used to simulate the change of the porosity and the corresponding change of permeability based on Lattice-Boltzmann is studied. Firstly we obtain digital images of a real core from CT experiment. Secondly the pore space of digital cores is expanded by dilation operation which is one of basic mathematical morphologies. Thirdly, the distribution of pore bodies and pore throats is obtained from the pore network modeling extracted by maximal ball method. Finally, the correlation between network modeling parameters and permeabilities is analyzed. The result is that the throat change leads to exponential change of permeability and that the big throats significantly influence permeability.Cited as: Zha, W., Yan, S., Li, D., et al. A study of correlation between permeability and pore space based on dilation operation. Advances in Geo-Energy Research, 2017, 1(2): 93-99, doi: 10.26804/ager.2017.02.0

Engage Engineering Students In Homework: Attribution Of Low Completion And Suggestions For Interventions

Homework is an important out-of-class activity, crucial to student success in engineering courses. However, in a first-semester freshman engineering course, approximately one-fourth of students were completing less than 80% of the homework. The purpose of this study was to examine students\u27 attribution of their low completion of homework and suggest corresponding interventions to help students with different attribution types. A qualitative approach was applied using semi-structured interviews for data collection. The interviewees were students who were on track to complete less than 80% of the homework. Students in the study attributed their low rates of completion to multiple factors. We coded and summarized students\u27 attributions of homework incompletion according to Weiner\u27s attribution theory and suggested corresponding interventions for students with different attribution types. Results show that most students attributed their failure to complete their homework to external reasons rather than internal reasons. A large portion of student\u27s attributions for low homework completion was due to poor time management skills. Some students attributed low homework completion to unstable factors such as illness, transition, or adjustment problems. A small portion attributed low homework completion to uncontrollable reasons, such as sickness and homework difficulty. Students\u27 reasons for homework incompletion varied across the three dimensions of Weiner\u27s attribution theory suggesting that a variety of intervention techniques is required. In addition to use of widely adopted interventions such as first-year seminars, tutoring, and tutorial sessions, intervention techniques based on attribution theory may be necessary to employ, to help students avoid negative emotional and behavioral consequences of homework incompletion

Engage Engineering Students In Homework: Attribution Of Low Completion And Suggestions For Interventions

Homework is an important out-of-class activity, crucial to student success in engineering courses. However, in a first-semester freshman engineering course, approximately one-fourth of students were completing less than 80% of the homework.  The purpose of this study was to examine students’ attribution of their low completion of homework and suggest corresponding interventions to help students with different attribution types. A qualitative approach was applied using semi-structured interviews for data collection. The interviewees were students who were on track to complete less than 80% of the homework. Students in the study attributed their low rates of completion to multiple factors. We coded and summarized students’ attributions of homework incompletion according to Weiner’s attribution theory and suggested corresponding interventions for students with different attribution types. Results show that most students attributed their failure to complete their homework to external reasons rather than internal reasons. A large portion of student’s attributions for low homework completion was due to poor time management skills.  Some students attributed low homework completion to unstable factors such as illness, transition, or adjustment problems. A small portion attributed low homework completion to uncontrollable reasons, such as sickness and homework difficulty. Students’ reasons for homework incompletion varied across the three dimensions of Weiner’s attribution theory suggesting that a variety of intervention techniques is required.  In addition to use of widely adopted interventions such as first year seminars, tutoring, and tutorial sessions, intervention techniques based on attribution theory may be necessary to employ, to help students avoid negative emotional and behavioral consequences of homework incompletion

Effects of dietary oxidized fish oil on the growth performance, intestinal health, and antioxidant capacity of zebrafish

This study aimed to investigate the effects of oxidized fish oil (OFO) on growth performance, intestinal health, and antioxidant function and to determine the minimum concentration of oxidized fish oil to cause irreversible damage to the intestinal tissue structure of zebrafish. A 30-day feeding trial on zebrafish (average weight 0.054 g) was conducted in triplicate groups of fish fed four test diets containing different concentrations of OFO: 0% OFO (OFF, blank control), 2% OFO (OF1), 4% OFO (OF2), and 6% OFO (OF3). The body weight gain (WG), specific growth rates (SGR), feed conversion ratio (FCR), survival rate (SR), and antioxidant function {glutathione peroxidase (GSH-PX), total superoxide dismutase (T-SOD), catalase (CAT), and malondialdehyde (MDA)} were recorded. The intestinal structure was observed at the end of the trial. After the 14-day experimental period, Final body weight (FBW), WG, and SGR decreased significantly with the increase in the concentration of feed OFO (P < 0.05), while FCR showed a downward trend. The activity of T-SOD decreased significantly, the activities of GSH-PX and CAT, and the MDA content increased significantly with the increase in the concentration of feed OFO (P < 0.05). The intestinal morphological damage score showed an upward trend with the increase in the concentration of OFO, and it was significantly higher in group OF2 and OF3 than in group OF1 (P < 0.05). After the 28-day test period, the experimental indexes and intestinal antioxidant function trends were the same as those on 14 days. The increased OFO concentration significantly increased the intestinal morphological injury score (P < 0.05). These results demonstrated that adding 4% OFO to the feed for 14 days could induce irreversible damage to the intestinal tissue structure, weaken the antioxidant function, and decrease the growth performance of zebrafish

Effect of Asafoetida Extract on Growth and Quality of Pleurotus ferulic

Different concentrations of asafoetida extract were added to the medium of Pleurotus ferulic and the effects of the extract on growth of P. ferulic mycelium and fruiting bodies was observed. As the amount of asafoetida extract additive was increased, the growth of Pleurotus mycelium was faster, the time formation of buds was shorter and that yield of fruiting bodies was stimulated. However, overdosing of asafoetida extract hampered the growth of Pleurotus ferulic. The amino acid composition and volatile components in three kinds of pleurotus’ were contrasted, including wild pleurotus (WP), cultivated pleurotus with asafoetida extract (CPAE) and cultivated pleurotus without asafoetida extract (CP). CPAE with 2.3 g/100 g asafoetida extract addition had the highest content of total amino acids, as well as essential amino acids. WP had a higher content of total amino acids and essential amino acids than CP. In addition, CPAE with 2.3 g/100 g had the highest score of protein content of pleurotus fruiting bodies, while WP had a higher score than CP. In the score of essential amino acid components of pleurotus fruiting bodies, CP had the highest score, while CPAE was higher than WP. Asafoetida extract influenced the volatile components of Pleurotus ferulic greatly, making the volatile components of cultivated pleurotus more similar to those of wild pleurotus (WP)

An equivalent single-phase flow for oil-water two-phase flow and its potential application in well test

In this work an equivalent single-phase flow model is proposed based on the oil-water two-phase flow equation with saturation-dependent parameters such as equivalent viscosity and equivalent formation volume factor. The equivalent viscosity is calculated from the oil-water relative permeability curves and oil-water viscosity. The equivalent formation volume factor is obtained by the fractional flow of the water phase. In the equivalent single-phase flow model, the equivalent viscosity and phase saturation are interdependent when the relative permeability curves are known. Four numerical experiments based on PEBI grids show that equivalent single-phase flow has a good agreement with the oil-water two-phase flow, which shows that the equivalent single-phase flow model can be used to interpret oil-water two-phase pressure data measured in the wellbore during the buildup period. Because numerical solution of single-phase flow model is several times faster than that of the two-phase flow model, whether the new model interprets the pressure data directly or offers good initial values for the true oil-water two-phase pressure data interpretation, it will obviously improve the efficiency of the interpretation of oil-water pressure data and decrease the burden of engineers.Cited as: Zha, W., Li, D., Lu, Z., Jia, B. An equivalent single-phase flow for oil-water two-phase flow and its potential application in well test. Advances in Geo-Energy Research, 2018, 2(2): 218-227, doi: 10.26804/ager.2018.02.0

Unsupervised Segmentation Method for Diseases of Soybean Color Image Based on Fuzzy Clustering

The method of color image segmentation based on Fuzzy C-Means (FCM) clustering is simple, intuitive and is to be implemented. However, the clustering performance is affected by the center point of initialization and high computation and other issues. In this research, we propose a new color image unsupervised segmentation method based on fuzzy clustering. This method combines advantages of the fuzzy C-means algorithm and unsupervised clustering algorithm. Firstly, by gradually changing clusters c, and according to validity measurement, it can unsupervised search for optimal clusters c; then in order to achieve higher accuracy of clustering effect, the distance measurement scale was improved. In our experiments, this method was applied to color image segmentation for three kinds of soybean diseases. The results show that this method can more accurately segment the lesion area from the color image, and the segmentation processing of soybean disease is ideal, robustness, and have a high accuracy