Emerging Science Journal (ESJ)
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Cycloartobiloxanthone, a Flavonoid with Antidiabetic, Antibacterial and Anticancer Activities from Artocarpus kemando Miq.
Get PDFIn this present work, a cycloartobiloxanthone compound was isolated from the stem wood and root bark of the Pudau plant (Artocarpus kemando Miq.). The purity of the compound was determined using thin-layer chromatography with three eluent systems and melting point tests. The sample was then analyzed using UV-Vis, IR, and NMR spectroscopy, ensuring that the compound is cycloartobilox-anthone. The cycloartobiloxanthone compound was obtained in a yellow crystalline form with a melting point of 285.1-294°C. The compound was then investigated for antidiabetic, anticancer, and antibacterial properties, showing that the compound has an anti-diabetic effect by reducing the activity of the α-amylase enzyme, with the highest percentage of inhibition of 48.53 ± 1.84% achieved with the use of 1000 ppm of the compound. Cycloartobiloxanthone isolated has an IC50 value of 9.21 µg/mL for anticancer activity against MCF-7 cells, indicating that the compound shows active cytotoxic actions. Staphylococcus aureus was very strongly inhibited by the compound in the antibacterial test at all doses, whereas for Salmonellasp., the activity was categorized as moderate at concentrations of 0.4 and 0.3 mg/disc and strong at 0.5 mg/disc. The anti-diabetic, anti-cancer, and antibacterial bioactivity studies indicated that the cycloartobiloxanthone compound isolated has a broad spectrum of pharmacological actions, indicating that the compound has promising potential. Doi: 10.28991/ESJ-2024-08-01-04 Full Text: PD
A Third-order Two Stage Numerical Scheme and Neural Network Simulations for SEIR Epidemic Model: A Numerical Study
Get PDFThis study focuses on the cutting-edge field of epidemic modeling, providing a comprehensive investigation of a third-order two-stage numerical approach combined with neural network simulations for the SEIR (Susceptible-Exposed-Infectious-Removed) epidemic model. An explicit numerical scheme is proposed in this work for dealing with both linear and nonlinear boundary value problems. The scheme is built on two grid points, or two time levels, and is third-order. The main advantage of the scheme is its order of accuracy in two stages. Third-order precision is not only not provided by most existing explicit numerical approaches in two phases, but it also necessitates the computation of an additional derivative of the dependent variable. The proposed scheme's consistency and stability are also examined and presented. Nonlinear SEIR (susceptible-exposed-infected-recovered) models are used to implement the scheme. The scheme is compared with the non-standard finite difference and forward Euler methods that are already in use. The graph shows that the plan is more accurate than non-standard finite difference and forward Euler methods that are already in use. The solution obtained is then looked at through the lens of the neural network. The neural network is trained using an optimization approach known as the Levenberg-Marquardt backpropagation (LMB) algorithm. The mean square error across the total number of iterations, error histograms, and regression plots are the various graphs that can be created from this process. This work conducts thorough evaluations to not only identify the strengths and weaknesses of the suggested approach but also to examine its implications for public health intervention. The results of this study make a valuable contribution to the continuously developing field of epidemic modeling. They emphasize the importance of employing modern numerical techniques and machine learning algorithms to enhance our capacity to predict and effectively control infectious diseases. Doi: 10.28991/ESJ-2024-08-01-023 Full Text: PD
Marketing Communication Strategy in the Retail Sector: Examining Repurchase Intention
Get PDFIn the face of intensifying market competition, the significance of retail marketing strategies and tactics cannot be understated, as they are pivotal in enhancing customers' satisfaction, fostering loyalty in customers, and ultimately elevating the likelihood of repurchase intentions. This research strives to discern the distinct attributes of customers and gauge their perceptions of the marketing strategies employed by Hypermarket Companies. Additionally, it examines the interplay between customer satisfaction, loyalty, and the consequent cultivation of repurchase intentions. Furthermore, the study explores the foundational retail marketing strategies that underpin the establishment of customer loyalty. The analytical approach encompasses Confirmatory Factor Analysis, SEM-PLS, and Biplot techniques. The findings underscore a customer profile primarily comprising males within the 18–29 age bracket, married, childless, holding undergraduate degrees, enjoying middle-income status, and engaged as private sector employees. Notably, the paramount driver shaping loyalty is the quality of retail service. It is intriguing to note that while the retail mix strategy within Bandung's hypermarkets is predicted to wield no direct influence on loyalty, marketing communication emerges as a potent determinant significantly impacting repurchase intentions. This study contributes to understanding customer behavior and loyalty in hypermarkets. It sheds light on the critical role of service quality in building loyalty. It highlights the importance of effective marketing communication for encouraging repurchase intentions and offers valuable insights for retail marketing strategies. Doi: 10.28991/ESJ-2024-08-01-08 Full Text: PD
Extracting Explicit and Implicit Aspects Using Deep Learning
Get PDFThe proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people's attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis. Doi: 10.28991/ESJ-2024-08-01-05 Full Text: PD
The Nexus of Covid-19 and Behavioral Intentions of University Students Towards Online Education
Get PDFThe onset of the COVID-19 pandemic has significantly disrupted the traditional pedagogical educational system worldwide. The story of Pakistan is also not different from that of the rest of the world. Pakistan's higher education institutes were closed for classes due to the outbreak. Some universities started the virtual education system, and it is critically important to assess the behavioral intentions of university students toward online education during the COVID-19 pandemic in Pakistan. This is the first study investigating students' responses to online education during the COVID-19 pandemic. For this purpose, an online survey was conducted to obtain the students' responses from the higher institutions providing online classes during COVID-19. The results were evaluated using multivariate analysis and descriptive statistics. It was observed that there is a significant difference between male and female students concerning the positive consequences of COVID-19 on students. According to the findings, the students' intentions for online education are more concerned with saving time to complete the degree program. Higher education institutions should also provide online educational opportunities to students besides traditional physical modes. Online educational interventions will be helpful for students during unavoidable circumstances like political instabilities, natural disasters, viral disease outbreaks, etc. to complete degrees and diplomas. Doi: 10.28991/ESJ-2024-SIED1-02 Full Text: PD
Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms
Get PDFThe localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PD
An Integrated Framework for Addressing the Challenges and Strategies of Technology Adoption: A Systematic Review
Get PDFWhile the rapid growth of information technology (IT) adoption has not lessened, insufficient attention has been directed towards the major themes and sub-themes of IT adoption challenges. Consequently, this study consists of a systematic literature review of the challenges of technology adoption based on a total of 235 peer-reviewed articles from the business and management literature between 2012-2022. Our longitudinal study provides an integrated framework for matching IT challenges to organizational strategies for transforming IT practices and processes. The results of the review broaden scholarly understanding of the importance of strategic IT agility, the need to keep pace with competitive information systems and IT environments. The findings enhance understanding of the pre-change and post-change processes of IT adoption, expanding knowledge on adoption success and organizational strategies for achieving IT strategic agility. Three key contributions include closing gaps not explored in comparative studies, adopting a unified approach with an integrated research model, and strategies to enhance an organization's absorptive IT capacity and agility. Doi: 10.28991/ESJ-2024-08-03-025 Full Text: PD
The Internal Work Environment and Job Alienation: The Case of Faculty Members
Get PDFThe objective of this study is to examine the nature of the relationship between the internal work environment including its all-encompassing dimensions (organizational structure, participation in decision-making, incentives) and job alienation in Higher education institutions through focusing on faculty members at Jijel University in Algeria. That being the case, the study aims to fill a research gap by investigating the underexplored relationship between the Internal Work Environment and Job Alienation, hence while there is ample literature on work alienation, studies specifically focusing on work alienation within higher education institutions through the prism of internal work environment are notably scarce. The study utilized a descriptive approach, employing a survey sampling method to collect data from the target population with a specifically designed questionnaire for this purpose. The questionnaire consisting of 60 items was administered to a randomly selected sample of 167 faculty members at Jijel University. The collected data were analyzed through the Statistical Package for Social Sciences (SPSS), version 22. The study's findings illustrate that faculty members perceive their work environment as inadequate for carrying out their activities, coupled with a notably high level of job alienation. Additionally, the research underscores a significant correlation between the internal work environment, encompassing its various dimensions, and the prevalence of job alienation among faculty members at Jijel University. Doi: 10.28991/ESJ-2024-SIED1-07 Full Text: PD
A New Concept of Specialized Standards to Improve the Quality of Higher Legal Education
Get PDFThis study analyzes specialized national and international legal acts and standards that enjoy national and international recognition and are relevant to ensuring the appropriate quality of higher legal education in Kazakhstan. Applying methods of logical and legal analysis, a global approach to comparing the practices of legal education within the country and abroad, the authors offer specific recommendations for improving specific standards of Kazakh legislation on education, namely, the adoption of the draft law "On Higher Education” in the republic; the introduction of the Socratic method in the lecture and educational process, creative vacations as a special type of professional development at law faculties of universities; the use of digital artificial intelligence tools at all stages of law students' education; providing more practical training; and significant restructuring of the knowledge verification system in exams. It is concluded that improving the quality of legal education is necessary for training highly qualified lawyers for legal practice, and lawyers with an anti-corruption legal consciousness. The novelty of this study is determined by its unique thematic focus, since it concerns an unexplored domain of specialized legal norms in the legal education sector of Kazakhstan. In addition, this study is novel as it is the first study in the history of the Republic in terms of improving higher legal education with the help of legislative and international legal norms. The practical significance of this study lies in the regulations and practices proposed for implementation. Doi: 10.28991/ESJ-2024-08-04-09 Full Text: PD
A New Concept of Consumer Behavior in the Circular Economy
Get PDFThe increasing volumes of produced and consumed wastes and the very low level of waste disposal weakly correlate with the key provisions of the concept of sustainable development, environmental protection, and socioeconomic development of society. This study aims to substantiate the provisions of a new concept of consumer behavior in a circular economy based on responsible and environmentally oriented consumer behavior. Online participants (N=400 suburban eco-conscious shoppers) were randomly assigned packaging with either collective, generational, or no sustainability messaging before rating eco-friendliness, intentions, and conducting discrete choice tasks. Results showed that collective impact messaging drove the highest perceived eco-alignment (M=5.81 vs. 4.22 control) and willingness-to-pay thresholds. Moderation analysis indicated that collective appeals strengthened eco-perception relationships with premiums. Findings provide empirical evidence that targeted circular economy communications can increase sustainable packaging appeal and economic adoption tradeoffs to overcome attitudinal barriers around consumer recycling behavior. Doi: 10.28991/ESJ-2024-08-04-018 Full Text: PD