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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 874 Documents
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Fiber Optic Breakthrough: Terahertz Detection of Illegal Drugs Noor, Khalid Sifulla; Bani, Most. Momtahina; Islam, Md. Safiul; Ferdous, A.H.M. Iftekharul; Hossen, Md. Jakir; Al-Mamun, Abdullah; Badhon, Nasir Uddin
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-019

Abstract

The article presents an illegal drug detector that utilizes photonic crystal fiber (PCF). The fiber structure of the H-PCF comprises a dodecagonal core and circular air gaps in cladding areas. We have analyzed the designed terahertz (THz) frequency range utilizing the Finite Element Method (FEM) and the COMSOL Multiphysics application. The revised design has a high sensitivity in detecting amphetamine (n = 1.518) and cocaine (n = 1.5022) at a frequency of 3 THz, via detection rates of 99.43% and 99.20%, correspondingly. Furthermore, the suggested fiber, which operates at a frequency of 3 THz, has a relatively tiny confinement loss of 4.93×10-08 dB/m and 6.16×10-09 dB/m and a minimal effective material loss of construction of 0.0032 cm-1. In conclusion, it may be stated that drug misuse not only leads to immediate repercussions but also has severe and enduring impacts on human health, potentially resulting in fatality. Hence, it is imperative to accurately and effectively detect illicit substances. H-PCF architecture we offered is well-suited to detect illegal drugs. Doi: 10.28991/ESJ-2024-08-06-019 Full Text: PDF
Extreme Rainfall Trends and Hydrometeorological Disasters in Tropical Regions: Implications for Climate Resilience Yanfatriani, Elsa; Marzuki, Marzuki; Vonnisa, Mutya; Razi, Pakhrur; Hapsoro, Cahyo A.; Ramadhan, Ravidho; Yusnaini, Helmi
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-012

Abstract

Hydrometeorological disasters due to extreme weather events represent a significant threat to the security of life in Jambi Province. In order to develop effective strategies for mitigating this threat, it is essential to gain a comprehensive understanding of the underlying dynamics that give rise to such disasters. Despite the high frequency of these events, more research is needed on the complex relationship between trends in extreme indices and the frequency of hydrometeorological disasters in this region. This study addresses this gap by utilizing rainfall data from 2008 to 2020 from the Integrated Multi-satellite Retrievals for GPM (IMERG) and hydrometeorological disaster data from the National Disaster Management Agency (BNPB). A range of extreme rainfall indices, including PRCPTOT, R85P, R95P, R99P, CWD, CDD, R1mm, R10mm, R20mm, R50mm, RX1Day, RX5Day, and SDII, were subjected to careful analysis concerning hydrometeorological disasters, including floods, landslides, tornadoes, droughts, and forest fires. Notable results indicate a significant increasing trend (p < 0.05) for the CWD index, while decreasing trends are observed for R85P, R95P, R99P, R50mm, RX1Day, RX5Day, and SDII. PRCPTOT and R20mm show decreasing trends, and CDD shows an increasing trend, although it is not statistically significant (p > 0.05). Subsequently, there was a significant increase in landslides and tornadoes, while forest fires and floods showed an insignificant increase (p > 0.05). Drought exhibited a significant decreasing trend in Jambi. Correlation analysis revealed the complex relationship between extreme weather indices and hydrometeorological disasters. The positive correlations observed between most extreme rainfall indices and floods and landslides, except for CDD, indicate that extreme rainfall is the primary cause of these disasters in Jambi. The correlation is particularly pronounced in areas with mountainous topography, where landslides are more prevalent. The positive correlations observed between CDD and droughts and forest fires suggest that periods of reduced rainfall and increased drought contribute to these disasters. This correlation is more robust in districts with extensive peatlands. The results provide valuable insights into the vulnerability of Jambi Province to hydrometeorological disasters and highlight the importance of understanding regional variations in extreme weather events. These findings improve our understanding of the interactions between climate indices and disasters and provide the basis for informed risk reduction and adaptation strategies in changing climatic conditions. Doi: 10.28991/ESJ-2024-08-05-012 Full Text: PDF
Effective Forecasting of Insurer Capital Requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH Approaches Chaiyawat, Thitivadee; Guayjarernpanishk, Pannarat
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-03

Abstract

This research paper presents a comprehensive analysis of three prominent volatility and dependence models for financial time series: ARMA-GARCH, GARCH-EVT, and DCC-GARCH. These models are employed to assess and forecast capital requirements for life and non-life insurer investments. This study evaluates the models' performance in forecasting Value-at-Risk, using daily data on key Thai financial indicators (representing permissible insurer investment assets) from March 2009 to March 2024. Specifically, 1-day and 10-day VaR forecasts are generated using the ARMA-GARCH and DCC-GARCH models, while the ARMA-GARCH-EVT model is employed for 1-day VaR forecasting. Our findings indicate that the ARMA-GARCH model effectively captures time-varying volatility, while the GARCH-EVT approach enhances tail risk estimation, particularly relevant for stress testing. Additionally, the DCC-GARCH model allows for the examination of dynamic conditional correlations between assets, providing insights into portfolio diversification benefits. Rigorous backtesting procedures, employing Kupiec and Christoffersen tests with a rolling window of 1,000 out-of-sample observations, confirm that the majority of models accurately forecast VaR at their respective horizons, with only a very small subset of 10-day VaR models exhibiting limitations. These results highlight that ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH models offer insurers robust tools for estimating minimum capital requirements, forecasting investment risk, and guiding strategic asset allocation decisions. This research underscores the effectiveness of these models for practical application in the insurance industry while also emphasizing the importance of continued model validation, particularly for extended forecasting horizons. Doi: 10.28991/ESJ-2024-08-06-03 Full Text: PDF
Breast Cancer Prediction Using Transfer Learning-Based Classification Model Armoogum, Sheeba; Motean, Kezhilen; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Kijsomporn, Jureerat
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-014

Abstract

Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics. Doi: 10.28991/ESJ-2024-08-06-014 Full Text: PDF
EFL Instructors’ Perspective on Using AI Applications in English as a Foreign Language Teaching and Learning Hazaymeh, Wafa A.; Bouzenoun, Abdeldjalil; Remache, Abdelghani
Emerging Science Journal Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-SIED1-05

Abstract

This study aimed to explore the perspectives of EFL instructors working in a variety of universities in the UAE on the effectiveness of AI applications in the EFL classroom. EFL teachers need to use AI applications in ways that are aligned with instructional goals and support student learning. A quantitative approach was used, and data was gathered from a survey of 46 EFL instructors. The results showed that the instructors strongly relied on AI applications to facilitate tasks, offer data-driven insights to improve instructional strategies and customize the learning process for each student. They also positively valued the benefits that AI applications bring to their classrooms for improving the teaching process. Notably, the results showed that the years of teaching experience had a statistically significant impact on the means of EFL instructors' perspectives regarding the benefits of adopting AI apps in EFL classrooms. The results also showed that, despite teaching experience, there were no significant differences in perceptions regarding the challenges of utilizing AI apps. This is probably because EFL students are accustomed to using technology in their lectures. Due to their benefits in English language instruction, the study suggests incorporating AI applications into the EFL teaching process. Doi: 10.28991/ESJ-2024-SIED1-05 Full Text: PDF
Neural Networks in Optimizing the Performance of the Elliptical-Plasmonic Sensor Ramadhan, Khaikal; Syamsul, Andi M. N. F.; Marwan, Arip; Agustirandi, Beny; Yasir, Mhd; Christian, Hadi
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-07

Abstract

In this work, we report the capability of a PCF-SPR sensor with an elliptical core, which has high sensitivity, and it is explained using a machine learning approach. The sensor component consists of fused silica as the background material, TiO2 as the adhesive material between the dielectric material and the plasmonic material, and Au was chosen as plasmonic material with optimal thicknesses of 35 nm for TiO2and 45 nm for Au. Numerical results show that the sensor component has a high sensitivity of 24,000 nm/RIU for four modes that have consistent shifts, including x-polarized, x-odd, y-polarized, and y-odd. Meanwhile, AS maximums were found of -91.82 1/RIU for x-polarized, -91.88 1/RIU for y-polarized, -90.98 1/RIU for x-odd, and -89.276 1/RIU for y-odd respectively, on the refractive index of the analyte of 1,365 RIU. The ML algorithm was used to optimize the sensor parameters, and it was found that the algorithm had a very low MSE of 0.00083; this result is better than the previous report work. Doi: 10.28991/ESJ-2024-08-05-07 Full Text: PDF
Strategies for Pedagogical Interventions to Develop Emotional Intelligence (EI) of Employees in a Hybrid Work Schedule Matulčíková, Marta; Breveníková, Daniela; Vaľko, Michal; Gawrych, Roman; Procházka, David A.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-023

Abstract

The purpose of the research article is to identify the educational methods suitable for developing employee emotional intelligence. The focus was on the number of hours that small and medium sized enterprises are willing to invest in training their employees in emotional intelligence and on the benefits, i.e., changes in work outcomes as evaluated by respondents. The questionnaire method and interviews were used to obtain data from respondents, line managers, and education managers. Based on correlation coefficient calculations, brainstorming was identified as a frequently used method of active learning, which is related to the physical presence in the learning premises. The analysis of the responses of the respondents and their calculation using the correlation coefficient surprisingly showed that the lecture method gained great support and was considered by the respondents, i.e., managers and education managers, as very important to achieve the cognitive, affective, and psychomotor goals of education. Moreover, it was assessed as a method suitable for remote learning, i.e., for virtual educational spaces. Doi: 10.28991/ESJ-2024-08-05-023 Full Text: PDF
A New Concept of Transforming Service: Impact of Generative Voice Chatbots on Customer Satisfaction and Banking Industry Productivity Kondybayeva, Saltanat; Daribayeva, Meruyert; Fiume, Raffaele; Abilda, Symbat; Staroverova, Olga; Ponkratov, Vadim; Vatutina, Larisa; Shapoval, Galina; Mikhina, Elena; Nikolaeva, Irina
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-09

Abstract

This 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: PDF
Relationship between Emotional Intelligence, Social Skills, and Anxiety: A Quantitative Systematic Review Ramos-Galarza, Carlos; Rodríguez-Naranjo, Brayan; Brito-Mora, Deyaneira
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-025

Abstract

Introduction: Emotional intelligence allows us to manage, regulate, and recognize our emotions and those of others, also allowing us to face and solve problems by choosing to provide an appropriate response to the situation experienced by a subject. Social skills are the behaviors that an individual emits in the interpersonal context through their feelings, rights, and opinions, seeking to resolve conflict situations immediately, minimizing the likelihood of experiencing them in the future. Anxiety appears in the individual when he perceives certain situations as threatening or dangerous, hindering his ability to provide an adequate response, being excessive, uncontrollable, or lasting, and this is classified as a mental disorder. Objective: The objective of this study is to describe the relationship between emotional intelligence, social skills, and anxiety. Methods: A quantitative methodology has been employed, basing the study on a systematic review of previous research using the Scopus, Scielo, Redalyc, and Google Scholar repositories. Findings: An initial sample of 1722 articles was obtained, which passed through inclusion and exclusion criteria, resulting in 73 articles. Novelty:The contribution of this study lies in understanding that low anxiety levels lead to better performance of emotional intelligence and social skills. This situation allows people to resolve conflicts that arise in the daily lives of individuals. Doi: 10.28991/ESJ-2024-08-06-025 Full Text: PDF
Assessing the Impact of Innovation Processes on Electronic Systems Technology Adoption Ouheda, Salem; Murray, Peter A.; Alam, Khorshed; Ali, Omar
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-02

Abstract

Objectives: This study aims to explore the adoption of electronic health records (EHRs) in the Australian private healthcare sector by integrating three prominent innovation models, namely the Technology Acceptance Model (TAM), the Diffusion of Innovation (DOI) model, and the Technology-Organization-Environment (TOE) framework. The objective of the study is to understand how these combined models might better inform the EHR adoption process and identify the key factors influencing successful implementation. Methods/Analysis: An exploratory qualitative research design employing a phenomenological approach was utilized to investigate the research. Data were collected through semi-structured interviews with senior managers at a private hospital in South-East Queensland. Purposive sampling was employed to select participants, ensuring representation from key decision-makers involved in the EHRs planning process. Thematic analysis, guided by the reflexive thematic analysis (RTA) approach of Braun and Clarke, was used to analyze the data and derive insights into the factors influencing EHRs adoption. Findings: Key findings indicate that perceived usefulness and job relevance (from TAM), innovation attributes and communication channels (from DOI), and technological, organizational, and environmental contexts (from TOE) are critical elements for successful EHRs implementation. The study also highlights the importance of user engagement, comprehensive training, leadership support, and financial resources. Novelty/Improvement: This study offers a novel contribution by integrating the TAM, DOI, and TOE models to provide a more holistic understanding of EHRs adoption in the private healthcare sector. It also introduces the concept of time as a critical innovation artefact, highlighting its significance in the adoption process. Doi: 10.28991/ESJ-2024-08-05-02 Full Text: PDF

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