Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,185 Article Results

Evaluating the performance of TAG-IT for prediabetes detection in Indonesian population

10.11591/ijphs.v15i1.26887
Yaltafit Abror Jeem , Rahma Yuantari , Hajar Admira Widiatninda , Russy Novita Andriani , Siti Solichatul Makkiyyah
Early detection of prediabetes plays a critical role in preventing type 2 diabetes mellitus (T2DM), especially within primary care, where access to laboratory testing may be constrained. Non-laboratory-based risk assessment instruments, including the tool to assess the likelihood of fasting glucose impairment (TAG-IT), can facilitate preliminary risk screening. This study sought to determine the diagnostic accuracy of the TAG-IT questionnaire in detecting prediabetes, using the oral glucose tolerance test (OGTT) as the reference standard. A cross-sectional design was implemented across three community health centers in the Special Region of Yogyakarta, Indonesia. Although 308 individuals were initially enrolled, only 93 participants with complete datasets were eligible for final analysis. The discriminative capacity of TAG-IT was evaluated through receiver operating characteristic (ROC) curve analysis along with a contingency table. Among the participants analyzed, 24.7% (23/93) were classified as having prediabetes. The TAG-IT tool exhibited modest discriminatory performance, yielding an AUC of 0.656 (95% CI 0.525-0.786; p = 0.026). Using the identified optimal threshold, sensitivity reached 52.2% while specificity was 67.1%. The negative predictive value was 81.0%, indicating better performance in excluding low-risk individuals. Overall, TAG-IT demonstrated moderate utility as a preliminary screening instrument in primary healthcare, particularly for identifying individuals unlikely to have prediabetes.
Volume: 15
Issue: 1
Page: 32-42
Publish at: 2026-03-05

Empathy and forgiveness on student victims of toxic relationships

10.11591/ijphs.v15i1.24037
Felani Omie Timpal , Arthur Huwae
A toxic relationship shows a negative impact on the physical and mental condition of individuals who have undergone the subject. Even though the students have experienced unpleasant conditions in life, to continue her life journey, the student needs to make peace and build a concept of forgiveness for the circumstances that are formed through empathy. This research aims to determine the relationship between empathy and forgiveness in students who have been victims of toxic relationships. The method used is quantitative correlational. An equal number of 355 students who had been victims of toxic relationships came to be participants in this study, with the sampling technique used, specifically, incidental sampling. This study uses a scale, namely the Interpersonal Reactivity Index (α = 0.863) and Transgression-Related Interpersonal Motivations -18 (α = 0.843). The research data analysis method uses the product-moment correlation test from Karl Pearson. The results show that there was a significant positive relationship between empathy and forgiveness (r = 0.228 and sig = 0.000 (p<0.01). Empathy contributed 5.2% to forgiveness. It appears that empathy is one of the factors associated with increased forgiveness in students who have been victims of a toxic relationship.
Volume: 15
Issue: 1
Page: 274-282
Publish at: 2026-03-05

Analysis of the implementation of the healthy Indonesia program policy with a family approach

10.11591/ijphs.v15i1.26855
Abdul Haris , M. Rizki Aditya
This study evaluates the implementation of the Healthy Indonesia Program Family Approach to improve public health and identify inhibiting and supporting factors. This qualitative research employs a case study approach to investigate healthcare dynamics at the Tambora Community Health Center in Bima City, Indonesia. Twelve informants were purposively selected. Data collection involved in-depth interviews, direct observation, and document review, allowing for a multifaceted perspective. An interactive analysis model by Miles and Huberman was used for data analysis, incorporating data collection, reduction, and presentation iteratively. Results showed that 85% of implementing staff had been trained, but there was a shortage of field surveyors, and family visit coverage only reached 72%, below the national target of 80%. Although the average healthy family index of 0.65 indicates adequate results, the main challenges lie in clean living behavior and nutritious diets. The program for mothers giving birth in health facilities reached 95%, but awareness of improving the management of chronic diseases, such as hypertension and tuberculosis, remained low, with treatment fulfillment at 65% and 70%, respectively. The conclusions highlight the need for adaptation strategies and continuous evaluation for the Healthy Indonesia Program to be successful across communities.
Volume: 15
Issue: 1
Page: 232-241
Publish at: 2026-03-05

The wellness equation: understanding health practices and behaviors of university students in Southern Philippines

10.11591/ijphs.v15i1.26278
Jomar B. Esto , Jemwell B. Francisco , Ruben L. Tagare Jr. , Cheeze R. Janito , Norge D. Martinez , Eduard S. Sumera , Marichu A. Calixtro , Vinus P. Java , Moreno B. Java Jr. , Gladys Pearl O. Ambrocio , Jessa S. Buisan , Gauvin Adlaon
This study aimed to explore the health practices and behaviors of university students in the Southern Philippines, focusing on key domains such as health perception and management, nutritional practices, physical activity, sleep, cognitive function, and stress tolerance. Using a quantitative, descriptive correlation design, data were gathered from 1,086 students through a simple random sampling technique. The primary instrument used was the health practices and behaviors questionnaire, which assessed students' health behaviors across the various domains. Statistical analysis involved computing composite means to describe health practices and Spearman's rho to examine the interrelationships between these behaviors. Results indicated that students exhibited moderate health practices across most domains, with a significant positive correlation found between health behaviors in areas such as physical activity, stress tolerance, and sleep. The study highlights the interconnected nature of health behaviors and the need for integrated health promotion strategies that address multiple aspects of student well-being simultaneously. The findings suggest that universities should focus on holistic programs that foster healthier lifestyles, improving students’ overall health outcomes and academic performance. Future research should explore additional factors influencing student health behaviors, including socio-economic, environmental, and cultural influences, to create more targeted interventions.
Volume: 15
Issue: 1
Page: 81-91
Publish at: 2026-03-05

Association between risky dietary behaviors and academic achievement among adolescent girls: a cross-sectional study in Surabaya, Indonesia

10.11591/ijphs.v15i1.26877
Tatarini Ika Pipitcahyani , Ervi Husni , Dina Isfentiani , Nina Primasari , Halimatus Sa'diyah , Mohammad Zamroni
Risky dieting is an unbalanced eating pattern increasingly practiced by adolescent girls and has the potential to impact cognitive function and academic achievement. However, previous research has focused more on the impact of risky dieting on nutritional status and physical health, while empirical evidence regarding its relationship with academic achievement in adolescent girls is still limited, particularly in the context of higher education. This study aimed to analyze the relationship between risky dieting and academic achievement in adolescent girls. The study used a quantitative design involving 70 adolescent girls. Data were collected through a peer counselor-based questionnaire to measure risky dietary habits and academic achievement. Data analysis was performed using Fisher's exact test. The results showed p-values of 0.05 and 0.023, respectively, indicating a significant relationship between risky dieting and academic achievement. Adolescent girls who engaged in risky dieting tended to have lower semester GPAs (11%) and cumulative GPAs (4%). The study concluded that risky dieting contributes to decreased academic achievement. This study contributes by expanding empirical evidence regarding the impact of risky dieting on academic achievement and emphasizes the importance of integrating nutrition interventions and health education in strategies to improve academic achievement in adolescent girls.
Volume: 15
Issue: 1
Page: 92-98
Publish at: 2026-03-05

Game on for health: designing gamified campaigns to drive behavioral change

10.11591/ijphs.v15i1.26232
Nurul Hidayah Mat Zain , Anita Mohd Yasin , Zainab Othman , Siti Nuramalina Johari , Norshahidatul Hasana Ishak , Siti Rahayu Abdul Aziz
An awareness campaign aims to reach out to the public, measure the outreach accurately, and motivate the public to act. In other words, awareness campaigns deliver messages on the importance and effects of the promoted event to the audience. However, studies that examine the users’ perception of design for behavior change through gamified health awareness campaigns are limited, especially in combating the coronavirus disease 2019 (COVID-19) outbreak. Subsequently, analysis of such perceptions is crucial in supporting game designers in developing engaging games for health awareness campaigns. Thus, the current study explores users’ perceptions based on elements in the GAMEBC Model: Autonomy, Relatedness, Competence, and Engaging. A total of 180 students from UiTM Cawangan Melaka (UiTMCM), Jasin Campus, participated in the study. The data was evaluated using conventional descriptive statistical methods. The findings revealed users’ perceptions of the design for behavior change in the context of a gamified health awareness campaign. The study offers a valuable understanding of the necessity of creating a comprehensive gamified campaign that promotes behavioral change for improved quality of life.
Volume: 15
Issue: 1
Page: 72-80
Publish at: 2026-03-05

Effects of soy plus zinc supplementation on growth and kidney health in Wistar rats: Implications for childhood stunting prevention

10.11591/ijphs.v15i1.26895
Yuniastini Yuniastini , Purwati Purwati , Antun Rahmadi , Sulastri Sulastri , Wien Wiratmoko , Iradah Lia Prasetio , Hendri Busman , Muhammad Al Hafidz , Fannia Khairani Mz , Mohammad Hafid Hak
Zinc deficiency can cause growth and health problems, whereas protein from soy sources contributes to essential nutritional intake. This study aimed to evaluate the effects of soy plus zinc (SPZ) supplementation on growth and kidney health in Wistar rats. This study used a randomized controlled trial design with 24 rats divided into five treatment groups, including a control group. SPZ supplementation was administered daily for 14 days with varying zinc doses (0.020 mg and 0.035 mg per gram of body weight) and palatability enhancement using vanilla flavoring. Data obtained through measurements of initial and final body weights and kidney weights were analyzed using ANOVA to determine significant differences between groups. The results showed that SPZ supplementation positively contributed to growth, as evidenced by a significant increase in the final weight of rats compared to their initial weight (p < 0.05). Histological analysis of the kidneys indicated no visible structural damage, and the average increase in kidney weight was approximately 26.5%. The combination of soy and zinc in SPZ was shown to have a synergistic effect that benefits the development and kidney health of rats, demonstrating its potential application in the context of animal nutrition.
Volume: 15
Issue: 1
Page: 140-149
Publish at: 2026-03-05

A framework for robust PID controller design: an optimization-based approach for inductive loads

10.11591/ijpeds.v17.i1.pp359-369
Ali Abderrazak Tadjeddine , Miloud Kamline , Latifa Smail , Soumia Djelaila , Hafidha Reriballah
This paper presents a comprehensive comparative study of proportional-integral-derivative (PID) controller tuning methodologies for inductive load applications across three representative scenarios. We systematically evaluate classical methods (Ziegler-Nichols, internal model control) against global optimization algorithms (genetic algorithm (GA), particle swarm optimization (PSO)) applied to resistor-resistor-inductor (RRL) circuit models. Results demonstrate that PSO achieves superior performance for moderate-to-slow systems, reducing settling time by 84% while completely eliminating overshoot compared to Ziegler-Nichols. The algorithm automatically discovers optimal PI controller structures, simplifying implementation. However, for ultra-fast systems (time constants < 1 ms), internal model control proves more reliable, achieving 0.84 ms settling with only 0.16% overshoot. Optimized controllers demonstrate exceptional robustness, maintaining stability under ±50% parameter variations and effectively rejecting disturbances. This research provides engineers with a scenario-based framework for method selection, moving beyond heuristic tuning to achieve previously unattainable performance levels. The findings establish optimization-based tuning as a systematic, reliable approach for high-performance control system design in industrial applications.
Volume: 17
Issue: 1
Page: 359-369
Publish at: 2026-03-01

A novel adaptive constant power optimal efficiency control strategy for bidirectional DS-LCC wireless charger

10.11591/ijpeds.v17.i1.pp653-662
Jiabo Yan , Mohd Junaidi Abdul Aziz , Nik Rumzi Nik Idris , Mohammad Al Takrouri , Tole Sutikno
This paper presents a novel adaptive constant power optimal efficiency control (ACPOEC) strategy that enables efficient constant power (CP) charging in a double-sided inductor-capacitor-capacitor (DS-LCC) wireless charger. The proposed control strategy is built upon triple-phase-shift (TPS) modulation and employs a pre-computed lookup table derived from offline optimization to achieve CP charging with corresponding optimal efficiency. The CP charger with the proposed strategy can eliminate switch-controlled capacitors (SCCs) in the topology. The proposed strategy is validated through simulation studies, achieving an efficiency range of 90.72% to 92.46%, which is also competitive with other advanced CP wireless charging systems. Compared with existing state-of-the-art CP wireless charging techniques, the wireless CP charger with the proposed ACPOEC strategy features a simplified topology, bidirectional power transfer capability, and competitive efficiency performance.
Volume: 17
Issue: 1
Page: 653-662
Publish at: 2026-03-01

High efficient DC-AC inverter for low wireless power transfer applications

10.11591/ijpeds.v17.i1.pp453-464
Kyrillos K. Selim , Hanem Saied Ebrahem Torad , Mostafa R. A. Eltokhy , Hesham F. A. Hamed , Mohamed Elzalik
The inverter's simplicity is an important aspect that must be considered especially for electronic devices, as adding the number of power switches increases the complexity and overall cost of the inverter. This work proposes an inverter design that converts DC into AC power. It receives 12 VDC as an input voltage, and it is composed of a boost converter that converts an input voltage of 5-20 VDC to an output voltage of 4-30 VDC and a pulse width modulation controller to produce a square wave with a frequency of 100 kHz to drive the switching MOSFET. The designed inverter can be operated on different loads ranging from 50 Ω to 1000 Ω, tested in both simulations and experimentally. The design was optimized by the LT Spice simulator. The proposed inverter has operating frequencies ranging from 40 kHz to 110 kHz, taking into account different loads. The obtained results showed that both simulation and experimental results converged, whereas the highest efficiency was 96.96% at 55 kHz at a fixed load of 100 Ω. On the other hand, the maximum achieved efficiency when the load was sweeping was 80% at a load of 50 Ω at a fixed frequency of 100 kHz.
Volume: 17
Issue: 1
Page: 453-464
Publish at: 2026-03-01

Machine learning based models for solar energy

10.11591/ijpeds.v17.i1.pp752-764
Dalila Cherifi , Abdeldjalil Dahbi , Mohamed Lamine Sebbane , Bassem Baali , Ahmed Yassine Kadri , Messaouda Chaib
Photovoltaic (PV) technology is one of the most promising forms of renewable energy. However, power generation from PV technologies is highly dependent on variable weather conditions, which are neither constant nor controllable, which can affect grid stability. Accurate forecasting of PV power production is essential to ensure reliable operation within the power system. The primary challenge of this study is to accurately predict photovoltaic energy production, considering that weather conditions, such as irradiance, temperature, and wind speed, are random variables. The key contribution of this article is developing a machine learning model to predict the energy production of a real PV power plant in Algeria. Using real measurements sourced from the Center of Renewable Energy Development (CDER) in Adrar, Algeria, in 2021. The data are from two PV power plants located in harsh desert climate conditions. The results presented in this study offer a comparison of several predictive methods applied to real-world data from a PV power plant situated in the Saharan Region. Our findings reveal that the artificial neural network (ANN) model yields the most accurate predictions of 94.96%, with the smallest prediction error: root mean square (RMSE) and mean absolute error (MAE) are 7.78% and 3.80%, respectively.
Volume: 17
Issue: 1
Page: 752-764
Publish at: 2026-03-01

Design and improvement of dynamic performance of solar-powered BLDC motor for electric vehicles in agricultural applications

10.11591/ijpeds.v17.i1.pp168-179
Savitri Medegar , M. Sasikala
One of the most pressing environmental problems is the rapid increase in the production of greenhouse gases by transportation vehicles. This paper looks into SPEVs, or solar-powered electric vehicles. The answer to the problems of transportation-related pollution and fuel usage. In an electric vehicle, the power comes from a battery that may be charged by solar panels or any other external power source. By making use of the perturb and observe (P&O) maximum power point tracking (MPPT) controller, one can achieve maximum power. The DC voltage that the photovoltaic module produces is amplified when it is fed into a voltage source inverter (VSI) via this enhanced output. The tool for the job here is a buck-boost converter. To power their wheels, EVs rely on brushless direct current (BLDC) motors and variable speed inverters (VSIs), which transform DC power from solar panels into AC power. We compare the efficiency of electric vehicles (EVs) attained by raising converter voltages and battery state of charge (SoC) using a PI controller, and we look at the performance of photovoltaic (PV) and brushless linear direct current (BLDC) motors. We use MATLAB/Simulink to do the validation.
Volume: 17
Issue: 1
Page: 168-179
Publish at: 2026-03-01

Fuzzy logic direct torque control of induction motors using three-level NPC inverter

10.11591/ijpeds.v17.i1.pp180-194
Jamila Chennane , Lahcen Ouboubker , Mohamed Akhsassi
Induction motor drives are extensively used for their robustness and efficiency, but precise control remains difficult under dynamic conditions. Conventional direct torque control offers a simple structure and fast response, but is limited by torque ripple, flux distortion, and poor low-speed performance. This paper proposes a fuzzy logic-based direct torque control (FDTC) combined with a three-level neutral point clamped (NPC) inverter. A fuzzy inference system (FIS) replaces the hysteresis comparators and switching table, while speed regulation is improved using a PI-fuzzy controller. MATLAB/Simulink simulations under speed variations and load disturbances demonstrate reduced torque and flux ripples, smoother flux trajectories, improved current waveforms, and faster transient response compared with classical DTC. These results confirm that the FDTC–NPC approach provides a robust and efficient solution for advanced applications such as industrial automation, renewable energy, and electric vehicles.
Volume: 17
Issue: 1
Page: 180-194
Publish at: 2026-03-01

Grey wolf optimization approach to optimal backstepping control for buck converter output voltage regulation

10.11591/ijpeds.v17.i1.pp640-652
Sana Mouslim , Belkasem Imodane , Imane Outana , M’hand Oubella , El Mahfoud Boulaoutaq , Mohamed Ajaamoum
DC-DC converters are essential in regulating voltage levels within DC power systems, relying on high-efficiency electronic switching devices such as MOSFETs to ensure effective power conversion. Despite their widespread use, one of the major challenges encountered in practical implementations lies in accurately tuning controller parameters particularly for nonlinear approaches such as the backstepping controller. While recent studies have demonstrated the effectiveness of particle swarm optimization (PSO) in enhancing backstepping control performance, further improvements remain possible. In this work, we propose the grey wolf optimization (GWO) algorithm as an advanced and efficient technique for the optimal tuning of backstepping controller parameters. The goal is to minimize the voltage tracking error between the reference and the output of the DC-DC buck converter, ensuring enhanced dynamic response and stability. Additionally, the proposed control strategy has been experimentally implemented and validated in a photovoltaic context, demonstrating its practical relevance and strong potential for real-world energy conversion applications.
Volume: 17
Issue: 1
Page: 640-652
Publish at: 2026-03-01

Application of machine learning for production optimization and predictive maintenance in an iron processing plant

10.11591/ijpeds.v17.i1.pp765-776
Lakhdari Lahcen , Mohamed Habbab , Alhachemi Moulay Abdellah
The modern metallurgical industry requires advanced solutions for process optimization, cost reduction, and predictive maintenance. This paper proposes a unified simulation-based framework using machine learning (ML) to jointly address production optimization and maintenance prediction in a virtual iron processing environment. Several ML models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and k-nearest neighbors (k-NN), were evaluated on synthetic datasets representing production, maintenance, and transport processes. A reproducible methodology was adopted, including preprocessing, time-aware data splitting, and cross-validation to prevent information leakage. Model performance was assessed using F1-score, area under the receiver operating characteristic curve (AUC), and regression metrics. Tree-based models achieved near-perfect classification performance (AUC ≈ 1, precision and recall > 0.99), while light gradient boosting machine (LightGBM) and CatBoost provided the best regression accuracy. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified vibration and temperature as key maintenance indicators. Although based on simulation, the framework is designed for integration with supervisory control and data acquisition (SCADA) and the Industrial Internet of Things (IIoT), supporting real-time industrial deployment and alignment with operational key performance indicators.
Volume: 17
Issue: 1
Page: 765-776
Publish at: 2026-03-01
1 2 3
...
2012 2013
Show 38 of 2013

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration