Impact of Mobile Technology and Use of Big Data in Physics Education During Coronavirus Lockdown

The speed of spread of Coronavirus Disease 2019 led to global lockdowns and disruptions in the academic sector. The study examined the impact of mobile technology on physics education during lockdowns. Data were collected through an online survey and later evaluated using regression tools, frequency, and an analysis of variance (ANOVA). The findings revealed that the usage of mobile technology had statistically significant effects on physics instructors' and students' academics during the coronavirus lockdown. Most of the participants admitted that the use of mobile technologies such as smartphones, laptops, PDAs, Zoom, mobile apps, etc. were very useful and helpful for continued education amid the pandemic restrictions. Online teaching is very effective during lock-down with smartphones and laptops on different platforms. The paper brings the limelight to the growing power of mobile technology solutions in physics education. © 2018 Tsinghua University Press.

An Analysis of COVID-19's Effects on Online Payments Using ANOVA

Adoption of digital payment methods rises during the COVID-19 pandemic. The coronavirus outbreak is affecting how consumers make payments. The majority of customers want to continue using digital payments once the virus has been contained. Global consumers were using them more frequently than they had before the epidemic. The main payment options gaining from this shift are e-Wallets and contactless cards as people use less cash and make more online purchases. Prior to COVID 19, fewer people used digital payment methods worldwide. When Covid-19 spread and physical transactions were on the verge of collapse, digital payments became a reality. This study's primary goals are to evaluate the effectiveness ofonline payment apps used by respondents during Covid 19 and consumer perceptions of the uptake of these methods. Both primary and secondary methods are applied in the process. Structured questionnaires were given to the residents of the Chennai area using the primary data approach. Articles, journals, and various forms of Internet have been utilized in secondary data. The collected data is analysed through Analysis of Variance method. The conclusion of this study shows that people were satisfied that online payment app is more convenient, time saving and easy to adopt. Thoughthere are many barriers in online payment app there were some preventive measures and security. © 2022 IEEE.

STUDIES ON THE LEVEL OF SOUND POLLUTION IN BRAILA, ROMANIA

This paper presents the degree of noise pollution in Braila. Braila is a city located in southeastern Romania. For this purpose, sound level measurements were performed in various important locations in this city. The monitoring took place between November 2019 and June 2020. The measurements were performed using a professional digital acoustic sound level meter that can record sound values between 30dB-130dB. The sound level meter works with two frequency filters: "A” and "C”. "A” filter responds in the same way as the human ear to the increase and decrease of sound amplitude along the spectrum. "C” filter is suitable for uniform measurements without amplitude increase or amplitude decrease. "C” filter can measure the sound level for cars and engines. Both filters operate in the 31.3 Hz and 8 kHz range. The series measured with Noise Logger Communication Tool sound level meter in the two frequency domains were represented as a boxplot. In all monitored areas, the average values recorded in A frequency range are significantly lower than those recorded in C frequency range. This is due to the presence in the environment of some sources of noise from road traffic which includes light and high-speed cars. The data distribution is generally asymmetric to the left, with higher scores. After the establishment of the state of emergency caused by the spread of SARS-CoV-2 coronavirus, it is observed that the data distribution becomes asymmetric to the right, predominating the low values of the sound intensity level. Using Anova program we analyzed the similarity between the noise series measured in the A and C frequency range. For this purpose, Pearson coefficients also were calculated. © 2022 International Multidisciplinary Scientific Geoconference. All rights reserved.

Analysis and Incidents in the Professional Practices of IT University Students in the Context of the COVID-19 Pandemic

The objective of this research is to analyze the level of satisfaction and effectiveness of pre-professional practices in university students, before and during the pandemic. The analyzed data was collected through surveys applied to 67 students. The data was analyzed with a descriptive approach, using tables to summarize the results, while in the analysis of the difference in means in the effectiveness and satisfaction scales, the ANOVA method was used, obtaining a p- value= 0.1134, the same as indicated that there is no variation in the means of the scales. For the correlation analysis, the Pearson coefficient was calculated, whose value indicated a strong correlation between the satisfaction scale of the place where the practice was carried out and the level of satisfaction (0.637). Finally, a web page model is proposed that is capable of better guiding the choice of the place where the students will carry out the pre-professional practices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Statistical Analysis of University Academic Performance in the Area of Exact Sciences, Before and During the Covid-19 Pandemic

The pandemic caused by Covid-19 at the end of 2019 affected the development of academic activities in educational institutions at all levels. This article focuses its interest on analyzing the academic performance, failure and dropout of the students of the Universidad de las Fuerzas Armadas ESPE, Santo Domingo, before and during the pandemic. The analyzed data was collected from the academic results matrices of the Departamento de Ciencias Exactas of the ordinary academic periods: 201950, 201951, 202050, 202051, 202150 and 202151. The information was analyzed with a descriptive approach, using bar charts, which indicate the evolution of the areas of knowledge in the indicated periods. For the variation of academic performance, the ANOVA method was used, obtaining a p − value = 0.126, which indicated that there is no significant variation between the means of the analyzed data. In addition, it was determined that between academic periods the assumptions of normality and homoscedasty with values p − value > 0.05 are met. In the linear correlation analysis, the Pearson coefficient was calculated, whose value indicated a strong negative correlation between the academic performance and the percentage of failure (values of 0.887, 0.796), while between the academic performance and the percentage of abandonment there was a moderate negative correlation (values of 0.428, 0.636). Finally, models based on linear regression are proposed in the areas of knowledge analyzed, to predict the academic performance of the new academic period 202250. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2nd International Conference on Recent Advances in Mechanical Engineering Research and Development, ICRAMERD 2021

The proceedings contain 79 papers presendted at a virtual meeting. The special focus in this conference is on Recent Advances in Mechanical Engineering Research and Development. The topics include: Firmware of Indigenous and Custom-Built Flexible Robots for Indoor Assistance;Automation of AM Via IoT Towards Implementation of e-logistics in Supply Chain for Industry 4.0;Evaluation and Optimization of Process Parameter for Surface Roughness of 3D-Printed PETG Specimens Using Taguchi Method at Constant Printing Temperature;Evaluation of Preventive Activities of COVID-19 Using Multi-criteria Decision Making Method;mechanical Characterization of Concrete with Rice Husk-Based Biochar as Sustainable Cementitious Admixture;Ranking of Barriers for SSCM Implementation in Indian Textile Industries;Framework to Monitor Vehicular GHG Footprint;solution to Real-Time Problem in Shifter Knob Assembly at Automobile Manufacturing Industry;performance of Chemical Route-Synthesized SnO2 Nanoparticles;a Numerical Study to Choose the Best Model for a Bladeless Wind Turbine;Effect of Tissue Properties on the Efficacy of MA on Lungs;effect of Process Parameters and Coolant Application on Cutting Performance of Centrifugal Cast Single Point Cutting Tools;Study and Analysis of Thermal Barrier Application of Lanthanum Oxide Coated SS-304 Steel;recovery of Iron Values from Blast Furnace Gas Cleaning Process Sludge by Medium Intensity Magnetic Separation Method;fatigue Analysis of Rectangular Plate with a Circular Cut-Out;protection of Vital Facilities from the Threat of External Explosion Using D3o Material;investigation on Coefficient of Heat Transfer Through Impact of Engine Vibration;electrical Modulus and Conductivity Study of Styrene-Butadiene Rubber/Barium Hexaferrite Flexible Polymer Dielectrics;preface.

Digital solutions and Moroccan companies during the Covid-19 pandemic

The novel Covid-19 pandemic has massively caused considerable damages to various industries and sectors in the whole world. As is the case in the rest of the other countries, Moroccan supply chains (SCs) have been seriously disrupted. To adapt to this new condition, Moroccan companies need to quickly adjust their processes through the use of digital technologies. This research seeks to analyse how Moroccan companies responded to the Covid-19 pandemic through the adoption of digital solutions. The present data were collected from 196 companies sampled across different regions in Morocco and analysed using the Chi-square test, the algorithmic K-means method, and the analysis of variance (ANOVA). The findings revealed that the sector has impacted the behavior of the supply chain (SC) in the adoption of technological solutions. Findings also revealed that the companies operating in the first and second sector have mobilized expensive technological tools to overcome the devastating effects of the Covid-19 pandemic. Also, companies with a simple SC have mobilized low-cost digital solutions to maintain their resilience. © 2022 IEEE.

Optimising Casting Film Parameters for LPDE Material Assessment

The growing demand for disposable gloves, especially from the healthcare industry amidst the ongoing Covid-19 pandemic and rising awareness about Healthcare-Associated Infections (HAIs). One of the ways to produce disposable gloves is using cast LDPE film machine. The quality of the products depends on material resin used, machine casting film design, part design and the selection of process parameters. However, the part design and casting film design are done at the initial stage of product development, it cannot be change easily. To manufacture a better quality of cast LDPE gloves, the best LDPE casting film parameters have to be identified. This research aims to identify the best LDPE casting film parameters in producing disposable gloves in terms of strong sealed but edges failed defect rate in production line. The three LDPE casting film parameters such as tensile strength, melt flow index (MFI) and load weight of resin were chosen to study their effect on the defect rate. In this research, the Taguchi method is used to optimize the best process parameters. On the other hand, an orthogonal array (OA), signal-to-noise (S/N) ratio, and ANOVA were employed to investigate the strong sealed but edges failed defect rate. According to the results obtained, the tensile strength of 34 MPa, melt flow index of 3 g/10 min and load weight of 2 kg were found to be the best combination of LDPE casting film parameters to fabricate the better performance of LDPE disposable gloves which give the lowest strong sealed but edges failed defect rate with 2%. Based on the statistical ANOVA analysis results, the most significant parameter affecting the strong sealed but edges failed defect rate of LDPE disposable gloves is tensile strength, which is indicated by the percentage contribution of P = 55.56%, followed by melt flow index with 38.89%. The load weight of LDPE resin is the least significant parameter with 5.55%. To conclude, Taguchi and ANOVA method show that tensile strength is the most significant parameter to get the least strong sealed but edges failed defect rate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Teaching AutoCAD in E-learning and Face-to-Face Styles for Undergraduate Engineering Technology Students During and after COVID-19 Pandemic

Online teaching has been used in most schools in the world during the Covid-19 pandemic. The world has started going back to normal in teaching face-to-face (F2F) in the classroom since Fall 2021. The switch from F2F teaching to online teaching has changed the way of interaction between the instructors and students. This research is aimed mainly to compare the learning styles in the traditional F2F to e-learning formats in teaching software-based Engineering Graphics course after going back to F2F mode of teaching. This study addresses the main features of each learning mode and its impact on the academic performance of the Engineering Technology students in a public school in Texas. This paper involves samples of Engineering graphics grades during the pandemic (fall 2020 and spring 2021) and after the end of the pandemic (fall 2021). Two instructors have taught this course during and after the pandemic, while one instructor has only taught the course after the pandemic. Analysis of Variance (ANOVA) is used in the data analysis. It is noticed that the performance of the students in classes delivered in the F2F mode is better than that in online mode, even with the change in the instructors and the change of the grade distribution in a specific semester. © American Society for Engineering Education, 2022.

Teacher Impact on Student Learning Using LC-DLM Implementations in the Classroom

Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an F-test using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for hands-on, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students. © American Society for Engineering Education, 2022.

Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

Purpose: Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction. Design/methodology/approach: In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results. Findings: The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task. Originality/value: The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector. © 2022, Emerald Publishing Limited.

The Influence of Online Teaching Interactive Behaviors on Sustained Learning Results of Learners

Along with the increasing maturity of mobile information technology, mobile-based online learning has become one of the main teaching methods that all kinds of schools should adapt themselves to during the COVID-19 pandemic. However, online education physically isolates teachers from their students, hence weakening their interactions. In the context of this spatial isolation, the frequency of interactive behaviors and quality of communication in online education should be strengthened to improve the sustained learning results of learners. After reviewing the previous literature, a questionnaire investigating the influence of online instructional interaction level on sustained learning results was designed. By considering self-efficacy as a mediating variable, the mediating effect of self-efficacy on sustained learning results at the interactive level of online teaching was analyzed and the difference in sustained learning results that can be attributed to years of familiarity with online learning was measured. Results show that teacher-student interaction has significant positive effects on sustained learning results, whereas student-student interaction has significant positive effects on sustained learning results. Self-efficacy completely mediates the role of teacher-student interaction and student-student interaction in effectively and significantly improving sustained learning results. The duration of online learning has a significant effect on sustained learning results. Conclusions provide an important reference for enriching the learning activity design principles of instructional interaction level.

Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively. © 2022 IEEE.

A Zone-wise Analysis of Covid-19 Related Data in India

In this paper, the statistical analysis of the Covid -19 related data collected from various government portals has been performed in a state-wise and zone-wise manner. R and excel have been used for performing the analysis. Analysis based on Correlation, Chi square test and ANOVA has been performed for various parameters based on the data collected. © 2021 IEEE.

Face-to-Face and E-learning Styles for Undergraduate Engineering Technology Students During COVID-19 Pandemic

Education has changed dramatically with the surging growth of online learning since the COVID-19 pandemic began, and the entire globe has turned away from the classrooms. Teaching has moved to be via digital platforms. With this abrupt shift to digital learning, a concern has been raised about how this shift will affect worldwide learning. This research aims to study the impact of the COVID-19 switch on student performance and whether online learning can deliver the same academic student performance as face-to-face. The data of this study was compiled from three engineering courses taught at the engineering technology department at a public university in Texas. The complexity of these courses ranges from low, average, and high levels for courses A, B, and C, respectively. An analysis of variance (ANOVA) was used to compare the differences in student performance outcomes of three exams and other graded assignments. The impact of four learning modalities, involving face-to-face (F2F), synchronous, asynchronous, and mixed (F2F and synchronous), was explored. The results of the overall mean scores show that, for courses B and C, the student performance outcomes are higher in the mixed (F2F and synchronous) and online groups (synchronous and asynchronous groups) than in the F2F group. For course A, there is a significant difference in the overall academic performance of online learning modes compared to F2F. Whereby, in general, the F2F mode deliver a higher level of student performance outcomes than that delivered by mixed and asynchronous groups for these kinds of courses. © American Society for Engineering Education, 2021

Entrepreneurial alertness in different generations

Under COVID 19 environment it is important to analyse if there are differences between generations (X, Y, Z) within the context of entrepreneurial alertness (EA), and its influence in the creation of a new business. This study used a quantitative methodology trough a survey by questionnaire based on a sample of 978 people organized by age groups. We used, an exploratory factor analysis with principal components and varimax rotation, a one-way analysis of variance (ANOVA) by Tamhane Test and a linear regression model. An exploratory factor analysis is presented, to assess the dimensions of the entrepreneurial alertness from which two factors were obtained: the competence of processing information and establish connections to assure a profitable business (F1) and the capability of searching information and acknowledging opportunities (F2). Then, were applied one-way ANOVA and a linear regression model to compare different generations in relation with EA, and its relation to create a new business. The results demonstrate that generation Z has less propensity than generation Y in respect to F2. Besides F1 has the same importance for all generations. We found either, that the X generation has lower propension to start a new business. Testing the effects of different dimensions of EA, through a linear regression, with the propensity to develop a new business, we confirm that only F1 is significative while F2 is partial rejected. This research contributes to the field by demonstrating how different generations assign distinct relevance to entrepreneurial alertness dimensions and its importance to promote a new activity. © 2021, Academic Conferences and Publishing International Limited. All rights reserved.