Emerging Science Journal (ESJ)
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Investigation of Ni- and Co-Based Bifunctional Electrocatalysts for Carbon-Free Air Electrodes Designed for Zinc-Air Batteries
Get PDFNi- and Co-oxide materials have promising electrocatalytic properties towards the oxygen evolution reaction (OER) and the oxygen reduction reaction (ORR), and attract with low cost, availability, and environmental friendliness. The stability of these materials in alkaline media has made them the most studied candidates for practical applications such as a gas diffusion electrode (GDE) for rechargeable metal-air batteries. In this work, we propose a novel concept for a carbon-free gas GDE design. A mixture of catalyst (Co3O4, NiCo2O4) and polytetrafluoroethylene was hot pressed onto a stainless-steel mesh as the current collector. To enhance the electrical conductivity and, thus, increase ORR performances, up to 70 wt.% Ni powder was included. The GDEs produced in this way were examined in a half-cell configuration with a 6 M KOH electrolyte, stainless steel counter electrode, and hydrogen reference electrode at room temperature. Electrochemical tests were performed and coupled with microstructural observations to evaluate the properties of the present oxygen electrodes in terms of their bifunctionality and stability enhancement. The electrochemical behavior of the new types of gas-diffusion electrodes, Ni/Co3O4 and Ni/NiCo2O4, shows acceptable overpotentials for OER and ORR. Better mechanical and chemical stability of electrodes consisting of Ni/NiCo2O4 (70:30 wt.%) was registered. Doi: 10.28991/ESJ-2023-07-03-023 Full Text: PD
Behavior of Russian Premium Fashion Consumers and Designers after the COVID-19 Pandemic and International Sanctions
Get PDFThe purpose of this paper was to investigate the emerging changes in Russian premium fashion brand consumers' behavior on the eve of the COVID-19 pandemic and international economic sanctions, the impact on foreign fashion brands' decisions to leave the market, and the willingness of some Russian fashion designers to scale their businesses and occupy vacated market niches. This problem had arisen for the first time; the situation is unexpected and unique. Therefore, the researchers combined multiple methods of data collection: (1) Observation; (2) Netnography to identify emerging changes in Russian consumers' behavior, which increases the objectivity of the data obtained since personal contact was excluded; and (3) Expert in-depth interviews to assess the situation by Russian fashion designers. QDA and qualitative content analysis were used. Fashion designers in Russia percept the situation as an opportunity for business development, similar to the situation that occurred in Iran, but entrepreneurs understand the market risks and expect more serious measures of state support for business. The results may inform state policymakers and stakeholders about the stated changes in consumer behavior and the capabilities of Russian entrepreneurs to scale the business, which will help identify possible growth vectors for domestic fashion designers in the premium sector. Doi: 10.28991/ESJ-2023-SPER-010 Full Text: PD
Stand up Against Bad Intended News: An Approach to Detect Fake News using Machine Learning
Get PDFThe purpose of this approach is to find out the effects and efficiently detect fake news by using a publicly available dataset. However, it is difficult for human beings to judge an article's truthfulness manually, which is why This paper mainly wanted to cure the effect and to found out an automated fake news detection system with benchmark accuracy by using a machine learning classifier, which must be higher than other recent research works. In essence, this work's target is to find out an efficient way to detect fake and real news, and it also the target is to compare with existing work where researchers used machine learning classifiers and deep learning architecture. The proposed approach depended on a systematic literature review and a publicly available dataset where 7796 news data are recorded with 50% real and 50% fake news. The best and benchmark accuracy is 93.61%, achieved by the Support Vector Machine (SVM) among the used Random Forest, Decision Tree, KNN, and Logistics Regression classifiers, and the achieved accuracy is better than the exciting recent research works. Moreover, fake news is detected, people are able to differentiate between fake or real news, and effects are cured when people used SVM. Doi: 10.28991/ESJ-2023-07-04-015 Full Text: PD
Educational Data Mining to Predict Bachelors Students' Success
Get PDFPredicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries' economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor's historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network's best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student's previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PD
Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock
Get PDFSolar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in order to assess the potential of smart grids. This paper presents a study that utilizes various machine learning models to predict solar photovoltaic (PV) power generation in Lubbock, Texas. Mean Squared Error (MSE) and R² metrics are utilized to demonstrate the performance of each model. The results show that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the other models, with a MSE of 2.06% and 2.23% and R² values of 0.977 and 0.975, respectively. In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy. Doi: 10.28991/ESJ-2023-07-04-02 Full Text: PD
A Binary Survivability Prediction Classification Model towards Understanding of Osteosarcoma Prognosis
Get PDFThe objective of this study is to explore effective and innovative machine learning techniques that can assist medical professionals in developing more accurate prognoses that can enhance the survivability of osteosarcoma patients by investigating potential prognostic factors and identifying novel therapeutic approaches. A comprehensive analysis was conducted using a dataset of 128 osteosarcoma patients between 1997 to 2011. The dataset included 52 attributes in total that covered a wide range of demographics, together with information on clinical records, treatment protocols, and survival outcomes. Data was obtained from NOCERAL (National Orthopaedic Centre of Excellence in Research and Learning), Kuala Lumpur. Three distinct binary classification methods (i.e., random forest, support vector machine (SVM), and artificial neural network (ANN)) were employed to identify the prognostic factors that are associated with improved survival efficacy measures. The results of this study revealed that both SVM and ANN outperformed random forests in predicting survivability for both the 2-year and 5-year time frames. These findings indicate the potential of SVM and ANN as effective tools for predicting osteosarcoma survivability. The study signifies a significant step towards integrating machine learning techniques into the existing toolkit available to medical practitioners. This study contributes to the medical field by providing a comparative analysis of three prominent machine learning techniques for predicting osteosarcoma survivability. The superior performance of SVM and ANN over random forests highlights the potential of these methods in generating more accurate survivability predictions. Further development and refinement of these machine learning techniques hold promise for enhancing their effectiveness and instilling greater confidence among medical professionals and patients in the predictive capabilities of machine learning and artificial intelligence models for osteosarcoma survivability. Doi: 10.28991/ESJ-2023-07-04-018 Full Text: PD
Exploring the Utilization of Augmented Reality in Higher Education Perceptions of Media and Communication Students
Get PDFThe present study aims to explore the perceptions and usage of Augmented Reality (AR) technology among media students in Palestinian universities. A quantitative approach was adopted, and data was gathered from a web-based survey of 237 media students. The Technology Acceptance Model (TAM) was utilized to gauge participants' perceptions of AR, and descriptive statistics were used for analysis. The findings reveal a generally positive perception of AR as a beneficial tool for skill enhancement, with mean scores ranging from 3.70 to 4.04 indicating strong agreement. The study also found moderate to high AR usage among participants, particularly for translating texts using Google Translate, but noted that usage patterns were more individual-oriented. Additionally, 91.1% of respondents attributed the COVID-19 pandemic to increased technology usage in higher education. The novelty of this study lies in providing insights into the perception and application of AR in higher education within the Palestinian context, an under-researched area. The study sheds light on the potential for integrating AR more formally into curricula, which could foster a more engaging and immersive educational experience. However, it also highlights the need to address barriers such as lack of technical support and possible discomfort with technology. Doi: 10.28991/ESJ-2023-SIED2-016 Full Text: PD
Impacts of Foreign Direct Investment on Economic Development: Does Institutional Quality Matter?
Get PDFThe linkage between foreign direct investment (FDI) and economic development has been demonstrated in economic literature. In this study, we analyze the impact of FDI on economic development, considering the role of institutional quality in 63 provinces/cities in Vietnam in the period 2005–2022. Applying various regression methods, such as Pooled OLS, FEM, REM, GMM, and PVAR, the results confirm that foreign direct investment and institutional quality have a positive impact on economic development. Findings also provide evidence that institutional quality is an important factor in attracting FDI, determining both the quality and quantity of inflows from other countries into Vietnam. Some policy implications are given to promote the role of institutions and attract foreign direct investment, thereby promoting the economic development of provinces and cities in Vietnam. Doi: 10.28991/ESJ-2023-07-06-05 Full Text: PD
Bridging Sustainable Bank Performance through Fintech and Enacted Norms
Get PDFSince the launch of green banking, the Government Authority still needs to accommodate enacted norms and fintech in measuring sustainable bank performance. Empirically, this study aims to reveal the impact of variables and business drivers on sustainable bank performance. This research uses a quantitative approach through path analysis. By analysing 70 out of 78 bank managers or directors who are members of the National Banking Association as respondents, this study states that business drivers, fintech, and enacted norms encourage sustainable bank performance improvement. In addition, fintech and enacted norms are suitable as moderating and exogenous variables for sustainable bank performance, but the variables are not endogenous variables for business drivers. In addition, fintech and enacted norms can bridge the achievement of sustainable bank performance. The originality of this research is that enacted norms and fintech are the moderating variables in realising bank sustainability. The research suggests that enacted norms should be one of the new dimensions in measuring bank sustainability, and the existence of fintech could be an integral part of realising sustainable bank performance. Doi: 10.28991/ESJ-2023-07-06-017 Full Text: PD
Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing
Get PDFCurrently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone. Doi: 10.28991/ESJ-2023-07-02-09 Full Text: PD