Emerging Science Journal (ESJ)
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A New Concept of Techno-Economic Institutions within Institutional Economics: Integrating Technologies and Institutional Frameworks
Get PDFThis study investigates the concept of techno-economic institutions within institutional economics, focusing on the integration of technologies into economic frameworks to foster development. The primary objective is to introduce and advocate for the novel concept of "techno-economic institutions,” which is essential for embedding technologies into the socio-economic environment. This research employs a comprehensive methodological approach, including theoretical analysis, literature review, comparative studies, and case studies, to develop a new analytical model and provide fresh insights. The key findings include a comparative analysis of the interplay between institutions and technologies, a variational model detailing the life cycles of General-Purpose Technologies (GPTs), and an in-depth examination of institutional roles. The econometric models developed in this study demonstrate the significant impact of ICT patents and SCM systems on government efficiency, empirically validating the proposed theoretical framework. This paper contributes to the academic discourse by offering a methodologically robust and empirically substantiated examination of technological advancements in institutional frameworks, highlighting the importance of flexible institutional structures capable of adapting to technological change. These insights provide actionable recommendations for policymakers and suggest strategic investments in technological infrastructure to enhance government performance. Future research should explore the generalizability of these findings in different institutional contexts and examine variability in technology-institution interactions across diverse geopolitical landscapes. Doi: 10.28991/ESJ-2024-08-05-022 Full Text: PD
SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition
Get PDFVisual Speech Recognition (VSR), commonly referred to as automated lip-reading, is an emerging technology that interprets speech by visually analyzing lip movements. A challenge in VSR where visually distinct words produce similar lip movements is known as homopheme problem. Visemes are the basic visual units of speech that are produced by the lip movements and positions. Furthermore, visemes are typically having shorter durations than words. Consequently, there is less temporal information for distinguishing between different viseme classes, leading to increased visual ambiguity during classification. To address this challenge, viseme classification must not only extract lip image spatial features, but also to handle visemes of varying durations and temporal features. Therefore, this study proposed a new deep learning approach SlowFast-TCN. SlowFast network is used as the frontend architecture to extract the spatio-temporal features of the slow and fast pathways. Temporal Convolutional Network (TCN) is used as the backend architecture to learn the features from the frontend to perform the classification. A comparative ablation analysis to dissect each component of the proposed SlowFast-TCN is performed to evaluate the impact of each component. This study utilizes a benchmark dataset, Lip Reading in Wild (LRW), that focuses on English language. Two subsets of the LRW dataset, comprising of homopheme words and unique words, represent the homophemic and non-homophemic dataset, respectively. The proposed approach is evaluated on varying lighting conditions to assess its performance in real-world scenarios. It was found that illumination can significantly affect the visual data. Key performance metrics, such as accuracy and loss are used to evaluate the effectiveness of the proposed approach. The proposed approach outperforms traditional baseline models in accuracy while maintaining competitive execution time. Its dual-pathway architecture effectively captures both long-term dependencies and short-term motions, leading to better performance in both homophemic and non-homophemic datasets. However, it is less robust when dealing with non-ideal lighting scenarios, indicating the need for further enhancements to handle diverse lighting scenarios. Doi: 10.28991/ESJ-2024-08-06-024 Full Text: PD
A New Concept of Transforming Service: Impact of Generative Voice Chatbots on Customer Satisfaction and Banking Industry Productivity
Get PDFThis study examines the impact of implementing generative AI voice chatbots on customer expectations and satisfaction in the banking sectors of Kazakhstan, Russia, and Italy. To achieve this objective, this study conducted a survey of 253 customers from 35 commercial banks in Kazakhstan, Russia, and Italy from November 2023 to early April 2024. This study employed partial least squares structural equation modelling (PLS-SEM) to assess and validate the validity and reliability of the research model. The study integrates the Expectation Confirmation Model with AI components to analyze factors influencing customer satisfaction with AI-enabled digital banking services. Key findings indicate that expectation confirmation, perceived performance, visual attractiveness, problem-solving capabilities, and communication quality significantly affect customer satisfaction with AI chatbots. However, trendiness and customization features showed minimal impact. The research also explores how customer satisfaction and corporate reputation influence chatbot adoption. Additionally, the study investigates the relationship between chatbot adoption and productivity performance in banking operations. The study provides several policy recommendations, including enhancing perceived performance, expectation confirmation, communication quality, visual attractiveness, and corporate reputation, which can improve customer satisfaction and increase confidence in generative AI voice chatbots in the digital banking industry. Doi: 10.28991/ESJ-2024-08-06-09 Full Text: PD
Factors Affecting Population Density and Mound Distribution of Mud Lobsters, Thalassina spp.
Get PDFThis study is the first to investigate factors affecting population density and mound distribution of mud lobsters, Thalassina spp., in Southern Thailand. Mud lobsters are essential for nutrient cycling and maintaining mangrove ecosystems through their bioturbation activities. This study was conducted by establishing three transect lines in a 5í—350 m2area beginning 100 m from the edge of the river towards inland and composed of six subplots with 50-m intervals (i.e., 100, 150, 200, 250, 300, and 350-m subplots). Numbers of mounds were recorded, and mound height and diameter basal area in each subplot were measured. Soil samples were collected, and moisture, grain size distribution, and pH were measured. The results showed that soil grain size was mostly less than 250 μm with an average soil pH of 4.48. The mound density and mound height increased with increased distance from the river (i.e., 267 mounds per hectare at 100 m increased to 1,734 mounds per hectare at 350 m from the river edge) and with decreased soil moisture (72.6% to 65.9%). This indicated that the mud lobsters preferred to build more and higher mounds farther away from the river edge, where they were less affected by the tide and the soil was drier. Findings also indicated that mud lobsters used resource partitioning to reduce intraspecific competition. This study is the first to show that mounds associated with prop roots had greater heights than mounds without prop roots nearby. Doi: 10.28991/ESJ-2024-08-01-012 Full Text: PD
Design and Study the Performance of a CMOS-Based Ring Oscillator Architecture for 5G Mobile Communication
Get PDFOscillator circuits are used to make accurate and reliable clock signals for systems as simple as a wristwatch and as complicated as satellites, which are important for long-distance communication. There are many ways to build an oscillator circuit, using either passive or active parts. Each option has pros and cons, but at the current level of mobile communication development, the most important things are interoperability and low power use. This need has driven the development of compact, battery-operated electronics, and Very Large-Scale Integration (VLSI)-based ring oscillators provide the ideal solution. These oscillators ought to dissipate less power, have a large tuning range, and be compact. The paper presents a novel Complementary Metal Oxide Silicon (CMOS) ring oscillator that serves as a Voltage Controlled Oscillator. The suggested architecture utilizes the advantages of both a current-starved ring oscillator and a negative-skewed delay by combining their constituent parts. The proposed architecture has a control voltage of 1.15 V and a supply voltage of 2 V, generating a 9.35 GHz dominant frequency with a 13.82% harmonic distortion between the inputs and outputs. The proposed architecture can implement 5G-based applications that require high frequency and low power by carefully selecting the passive components within the design. Doi: 10.28991/ESJ-2024-08-01-020 Full Text: PD
Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease
Get PDFMango leaf diseases represent a serious threat to world agriculture, necessitating prompt and accurate detection to avert catastrophic effects. In response, this study suggests a light-weight, deep learning-based method for automatically classifying mango leaf diseases. The model is based on the original DenseNet architecture, which is well known for its effectiveness in image classification tasks. Custom layers have been added over the existing layer of the original DenseNet model. The proposed model has been compared with other existing pre-trained models. Based on comparisons, the proposed model, DenseNet78, proved to be efficient even on a relatively small dataset, where the conventional model failed. The proposed model ensured generalization across regions, disease variants, and diverse datasets of mango leaves. The results demonstrate that the fine-tuned DenseNet architecture (DenseNet78), along with an ideal growth rate, modifying block size, and a number of layers, provides optimum accuracy, with 99.47% accuracy in identifying healthy mango leaves and 99.44% accuracy in detecting various mango leaf diseases. The results also demonstrate that the model is effective in accelerating the training process because of careful comparative analysis of all the available alternatives, including the most effective combination of optimizers, learning rate schedulers, and loss functions. The study's conclusion is an automated approach for diagnosing mango leaf disease using an improved and optimized DenseNet architecture (DenseNet78). Doi: 10.28991/ESJ-2024-08-01-03 Full Text: PD
Agriculture 5.0 and Explainable AI for Smart Agriculture: A Scoping Review
Get PDFThe visionary paradigm of Agriculture 5.0 integrates Industry 4.0 principles into agricultural practices. Our scoping review explores the landscape of Agriculture 5.0, emphasizing the pivotal role of Explainable AI (XAI) in shaping this domain. Guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Scoping Review, we rigorously analyzed 84 articles published from 2018 to September 2023. Our findings highlight XAI's potential within Agriculture 5.0, recognizing its influence on intelligent farming. We propose a conceptual framework for integrating XAI, emphasizing its impact on model transparency and user trust. Despite transformative applications, existing literature often lacks XAI discussions. Our objective is to bridge this gap and provide a reference for academics, practitioners, policymakers, and educators in the field of smart agriculture that is both environmentally friendly and technologically advanced. Doi: 10.28991/ESJ-2024-08-02-024 Full Text: PD
The Impact of Motivation on MOOC Retention Rates: A Systematic Review
Get PDFThis systematic review investigates the effectiveness of motivational strategies on learner engagement and retention rates in Massive Open Online Courses (MOOCs). Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 140 studies published between 2014 and 2023 from key academic databases. The objective was to identify and evaluate motivational strategies that significantly reduce MOOC dropout rates. Our findings reveal that personalized learning, interactive content, and peer collaboration are strongly correlated with increased learner engagement and persistence. These strategies align well with learners' intrinsic goals, enhancing their educational experience and adherence to courses. The review also identifies gaps, such as the need for longitudinal studies and culturally tailored motivational strategies, offering a refined agenda for future research in MOOC education. This study contributes to the field by systematically synthesizing existing research, providing new insights into effective educational strategies, and highlighting areas for improvement in MOOC design and implementation. Doi: 10.28991/ESJ-2024-SIED1-08 Full Text: PD
Gender Differences and Stereotypes in Teacher Resilience Research
Get PDFIn the present study, the issues of teacher resilience and the persistent gender stereotypes in the field are discussed. The main objective of the conducted research study was to examine the presence of gender-stereotype-confirming behavior in coping with adversity in vocational school teachers. The Connor-Davidson Resilience Scale CDRISC-25SLOVAKwas selected as the most suitable research instrument, by means of which gender differences in the participants' (N=474) responses in its subscales were studied. The results obtained confirmed the hypothesis presuming the existence of gender differences in the achieved scores in five of the seven dimensions of the scale, and also stereotype-confirming behaviors”according to which men are rational problem solvers while women tend to apply emotion-focused coping strategies”were reported. This knowledge can be the first step towards introducing measures with the aim to provide individuals of all genders with opportunities to broaden their scale of coping strategies and promote resilience in them. Since vocational school teachers are on the periphery of researchers' interest and no available extensive study has been focused on gender differences in teacher resilience, the research findings aim to fill the gap in the existing knowledge, provide unique data for policymakers, and create a basis for further resilience research. Doi: 10.28991/ESJ-2024-SIED1-011 Full Text: PD
Exploring Factors Influencing Gen Z's Acceptance and Adoption of AI and Cloud-Based Applications and Tools in Academic Attainment
Get PDFGeneration Z faces diverse challenges in education amidst the swift evolution of technology. This study investigates the factors shaping Generation Z's acceptance and adoption of AI and Cloud-based applications in Oman's higher education sector. Despite limited attention to this area in Oman and the Gulf Cooperative Council countries (GCC), this research addresses the gap by employing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, recognized for its effectiveness in understanding technology adoption. Through a quantitative approach, Generation Z students in Omani higher education institutions were surveyed, and SmartPLS was utilized for analysis. Results indicate a significant positive relationship between all UTAUT antecedent factors, with Performance Expectancy being non-significant. This study offers novel insights into global understandings of Generation Z's learning trends with AI and Cloud-based applications in higher education, aiming to enhance pedagogical approaches. Notably, it pioneers such efforts within the GCC context. Recommendations for similar research in other GCC countries are provided to enrich regional perspectives. Limitations and future directions are addressed, emphasizing the importance of comprehending Generation Z's interaction with technology to advance educational practices in the digital age. Doi: 10.28991/ESJ-2024-08-03-02 Full Text: PD