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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 30 Documents
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Search results for , issue "Vol. 9 No. 6 (2025): December" : 30 Documents clear
DC Motor Angular Speed Controller Using an Embedded Microcontroller-Based PID Controller Ma'arif, Alfian; Nugraha, Ikhwan; Maghfiroh, Hari; Furizal; Suwarno, Iswanto
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-03

Abstract

This research presents the implementation of a Proportional Integral Derivative (PID) controller to control the angular speed of a Direct Current (DC) motor using an embedded system (microcontroller). The system’s hardware consists of an Arduino microcontroller, a DC motor with an encoder sensor, a driver motor, and a power supply. Proportional control regulates the response proportionally to the calculated error, while integral control manages the cumulative error over time, and derivative control responds to the rate of change of the error, preventing overshoot. With a proper combination, PID control achieves stability, speeds up response, and reduces overshoot, improving overall system performance. Based on experimental data, the DC motor angular speed control system using PID control achieves the best results, in which the parameter values are Kp=1; Ki=0.3; and Kd=0.6. The augmented system responded with 0.0890 seconds of the rise time, 11.772 seconds of settling time, and 0.12 seconds of the peak time, with an overshoot of less than 10% (7%).
Enhanced Optimization Strategy to Maximize Achievable Rate of Millimeter-Wave Full-Duplex UAV on Multiple User Harinitha, Dwi; Zakia, Irma; Iskandar
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-07

Abstract

This study proposes an enhanced optimization strategy to maximize the achievable data rate of millimeter-wave (mmWave) full-duplex (FD) unmanned aerial vehicles (UAVs) in multi-user scenarios. The objective is to address signal degradation from high-frequency path loss and self-interference while ensuring efficient resource allocation across multiple user equipment (UEs). A joint optimization framework is introduced, integrating UAV positioning, beamforming vector design at both the gateway and UAV, and power allocation. Initially, the Alternating Interference Suppression (AIS) algorithm is adapted for multiple UEs, but due to emerging non-convexity, the problem is reformulated using a first-order approximation approach. The solution is decomposed into two iterative sub-problems—optimizing UAV location and then solving for beamforming and power distribution. MATLAB-based simulations validate the proposed approach, revealing a threefold increase in achievable data rate and a 40.85% improvement in power efficiency compared to non-optimized systems. The novelty of this work lies in its scalable multi-user adaptation and its integrated, power-aware optimization algorithm, outperforming conventional FD and half-duplex strategies. This contribution significantly advances the design of efficient, high-throughput UAV communication systems for next-generation wireless networks, especially in environments with frequent line-of-sight obstructions.
Small Dual Polarized UWB Antenna and Its Array Analysis for 5G/6G Applications Dastkhosh, Amir Reza; Mehbodniya, Abolfazl; Webber, Julian; Naseh, Mehdi; Dadras Jedi, M.; Lin, Fujiang
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-04

Abstract

In this work, a novel low-profile tunable ultra-wideband (UWB) K/Ka-band (14–40 GHz) dual circularly polarized magneto-electric antenna element has been designed, analyzed, and validated through circuit modeling, simulations, fabrication, and experimental testing for application in 5G/6G phased-array antennas. The antenna has compact dimensions of 0.5λ₀× 0.5λ₀ × 0.06λ₀/0.09λ₀, which can be further reduced to 0.25λ₀× 0.25λ₀ × 0.05λ₀ when metal–insulator–metal (MIM) and/or gap capacitors are employed. The proposed antenna exhibits a high gain of 9 dB, a wide scanning angle of ±75°, and an efficiency exceeding 85% across the entire operating frequency band. In addition, it demonstrates high isolation between ports and between co-polarized and cross-polarized radiation patterns, reaching 25 dB. The resonant frequency of the antenna is tunable, with a variation of up to 97% over the K/Ka-band frequency range. This tuning capability is achieved using MIM capacitors connected to the vias of the circular patch and/or gap capacitors, which collectively function as split-ring resonators (SRRs). Fabrication and experimental testing of the antenna confirm good agreement with the simulated results. The antenna is easily fabricated using glass substrates and standard epoxy/glass processes with only two layers, making it highly suitable for antenna-in-package applications based on glass technology. Since the antenna element is specifically designed for phased-array applications, array configurations were also investigated. Analysis of 512-element arrays shows that the Sunflower layout provides enhanced gain and overall performance while utilizing more than 50% fewer antenna elements compared to a conventional rectangular array.
Multi-Objective Optimization of Injection Molding Using Taguchi, Fuzzy Methods, and GA Moayyedian, Mehdi; Chalak Qazani, Mohammad Reza; Hedayati-Dezfooli, M.; Mussin, Askhat; Bissekenova, Zhanel
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-028

Abstract

The objective of this research is to optimize the injection molding process of an automotive window regulator bracket by improving the moldability index while minimizing key defects. To achieve this, a multi-objective framework is developed that combines the Taguchi method with Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy-TOPSIS. Five critical processing parameters—melt temperature, mold temperature, filling time, holding pressure time, and cooling time—were investigated, with polypropylene as the base material. A Taguchi L25 orthogonal array was employed to reduce the number of experimental trials from 3,125 to just 25, thereby saving resources while maintaining reliability. The evaluation considered warpage, residual stress, and shear stress, which are the most influential defects affecting part performance. Finite Element Analysis (FEA) was incorporated to validate the accuracy of the results, while a hybrid ANFIS-GA predictive model was applied to forecast the moldability index, demonstrating an improvement of about 1% over conventional optimization methods. The optimized settings resulted in minimized warpage (1.8122 mm), residual stress (43.03 MPa), and shear stress (0.08 MPa). The novelty of this work lies in integrating Taguchi with FAHP and Fuzzy-TOPSIS for a single-objective transformation, offering a systematic and efficient approach for multi-objective optimization in injection molding applications.
YOLOv10-MsA: Attention-Augmented Real-Time Insulator Defect Detection from UAV Imagery Yang, Junbiao; Mat Isa, Nor Ashidi
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-02

Abstract

The reliable operation of transmission systems depends on early detection of defects within high-voltage insulators because it helps stop power outages. Scalable inspection remains a challenge since UAV-based imagery and deep learning generate recent promising solutions. The goal of this study is to build an accurate and time-efficient insulator defect detection system through the YOLOv10 architecture integration of Manhattan Self-Attention (MsA), which strengthens spatial feature detection and increases robustness during evaluations in complex aerial inspection scenarios. The designers implemented the MsA modules into the backbone and neck sections of YOLOv10 to develop YOLOv10-MsA as their novel detection model. The model relied on 5,000 annotated insulator images acquired by unmanned aerial vehicles throughout various defect classes during training and evaluation. Standard object detection metrics consisting of mAP@0.5, mAP@0.5:0.95, precision, recall, F1-score, and inference speed evaluated the performance of the model. The YOLOv10-MsA system reached an mAP@0.5 performance of 93.1% and an F1-score of 91.9% at an inference speed of 79 FPS, which surpasses YOLOv8, YOLOv9, and baseline YOLOv10. The model demonstrated its best performance at detecting various small and hard-to-detect defects such as chipping and contamination. The application of MsA in detection systems resulted in better accuracy while preserving real-time operation, according to related model assessment. The proposed YOLOv10-MsA serves as a powerful deployable system for UAV-based insulator inspection because it achieves both high accuracy and fast operation. The method establishes conditions for real-time smart infrastructure observation with attention-augmented frameworks for object detection.
LH-Moments Parameter Estimation of Weibull Distribution Guayjarernpanishk, Pannarat; Chiangpradit, Monchaya
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-014

Abstract

Natural disasters such as sudden floods, storms, severe snowfall, and droughts are major problems in the world. Generally the distributions of extreme values are heavy-tailed distributions, and an important heavy-tailed distribution is the Weibull distribution, especially for non-linear behaviors. Therefore, accurately estimation of the occurrence of disasters is required to deal with such situations in a timely and efficient manner. Several methods can be used to estimate the parameters, for example, moments estimate, maximum likelihood estimate, linear of moment, and high-order L-moments. The objectives of this article are to estimate the parameters of the four-parameter Weibull distribution with weak non-linear effects (W4DN) based on the LH-moments method, and to propose a new parameter estimation formula. The proposed formula is classified into two cases based on the coefficient of the second-order term (δ): Case 1, where the coefficient is positive (δ > 0) and Case 2, where the coefficient is negative (δ < 0). In both cases, the corresponding estimation formulas are derived βr and λrp for p=1, 2, ... and r=1, 2, ..., respectively. The parameter estimations (γ ̂,α ̂,δ ̂,ϕ ̂ and κ ̂) are then optimized using the augmented Lagrangian adaptive barrier minimization algorithm. These formulas provide a practical approach for parameter estimation that is essential for forecasting extreme events in various disciplines, including hydrology, meteorology, insurance, finance, and engineering.
The Influence of Work Motivation on Job Performance: Engagement and Burnout as Mediators Zeng, Jing; Pathak, Shubham; Zhaowen, Shuai
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-018

Abstract

Based on self-determination theory, a conceptual model is proposed in which work motivation operates as the antecedent variable, with work engagement and occupational exhaustion acting as dual mediators. To test this framework, data were collected through a structured questionnaire from 469 academic staff members across 24 private higher education colleges in Jiangxi Province and analyzed using structural equation modeling (SEM). The results demonstrate that greater levels of educators' work motivation are significantly correlated with improved job performance and that this effect is channeled through increased work engagement and reduced burnout. By elucidating these mediatory pathways, the findings deepen theoretical comprehension of how motivation drives performance and yield practical guidance for devising effective motivation and performance-management strategies within private higher education institutions.
A Novel Statistical Process Control Approach for PM2.5 Monitoring Using Time Series Modeling Supharakonsakun, Yadpirun; Areepong, Yupaporn
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-05

Abstract

This research seeks to create a novel control chart capable of managing autocorrelated time series data by proposing a modified Exponentially Weighted Moving Average (EWMA) approach tailored to processes following the ARMA(p,q) model, which also makes use of exponential white noise. The key methodological contribution involves an explicit formula to compute the Average Run Length (ARL), while the Numerical Integral Equation (NIE) approach is utilized for verification purposes. The proposed formula not only demonstrated 100% agreement with NIE results but also significantly reduced computational time, requiring less than 0.001 seconds per run, compared to the 3–4 seconds typically needed by NIE. To assess the performance, simulation experiments and real-world case studies on PM2.5 air pollution data from Nakhon Phanom, Nan, and Nonthaburi provinces in Thailand were conducted. Our modified control chart was better at identifying minimal changes than a standard EWMA chart, as shown by lower ARL1, SDRL1, AEQL, and optimal PCI values. The one-sided chart structure, designed to monitor upward shifts in pollutant levels, further supports its application in environmental surveillance. Overall, the study introduces a fast, accurate, and practical tool for quality control in autocorrelated environments, offering both analytical and computational advantages over existing methods.
Human-Centered Organizational Culture in the Global Workplace: Strategic Approaches, Trends, and Practical Models Barabanova, Yelena; Tyulyupergeneva, Raushan; Nazyrova, Larissa; Ladzina, Natalya; Bekbayeva, Malika
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-027

Abstract

Amid accelerating global transformations, the need to reconsider human resource management strategies through the lens of human-centricity is becoming increasingly urgent. This study aims to examine the systemic implementation of human-centered organizational culture within international labor contexts, with a focus on enhancing employee well-being, adaptability, and organizational resilience. A mixed-methods approach was employed, combining comparative policy analysis, content analysis of regulatory documents, and empirical case studies. The empirical sample included 320 employees from multinational companies across four sectors (education, IT, healthcare, and manufacturing). The findings revealed statistically significant improvements following the implementation of the proposed model: autonomy increased from 5.48 to 5.86 (p = 0.012), competence from 5.33 to 5.61 (p = 0.038), and relatedness from 5.07 to 5.58 (p = 0.004). Positive emotion expression scores rose from 3.98 to 4.42 (p = 0.009), while the Human-Centeredness Index increased from 4.18 to 4.71 (p = 0.002). These results underscore the limitations of hierarchical management models and highlight the value of flexible, emotionally supportive systems. The scientific contribution of the study lies in the typologization of human-centric management models and the empirical validation of a scalable integration framework that combines emotional intelligence development, inclusive feedback cycles, and leadership support. This model provides a strategic foundation for building sustainable, inclusive, and ethically grounded organizational environments.
Feature Transformation on Big Data for Species Classification in Machine Learning Yow, Li Wen; Ong, Lee Yeng; Tan, Joon Liang
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-09

Abstract

Classification of bacterial species, particularly for closely related taxa, remains a major challenge in many areas, e.g., public health, food industries, and many others. The issues are mainly caused by overlapping genetic features of organisms and data complexities. In this study, a bacterial taxonomic identification framework that integrates genome-derived motif sequences with machine learning was introduced. Two hundred and forty genome sequences from Salmonella enterica, representing six subspecies and ten serovars, were used for modelling. Sequence motifs were predicted from single-copy orthologous core genes of the downloaded genomes. Single nucleotide polymorphisms (SNPs) within these motifs were extracted and numerically encoded as machine learning features. The 20 top-most informative predictors from feature selections were used for model training in Random Forest and Support Vector Machine. Comparing the output from multiple analyses, the Random Forest model achieved the highest accuracy of 97.92%, demonstrating reliable differentiation of Salmonella at both subspecies and serovar levels. This research presents two key innovations: i) the use of sequence motifs as molecular signatures for bacterial classification; ii) a novel feature engineering method that transforms genome-derived data into machine learning-readable features. The proposed framework offers a practical and scalable solution for fine-level bacterial classification and has high potential to be applied for other microbial taxa.

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