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28,296 Article Results

Social media and social capital as risk of voluntary counseling and testing for human immunodeficiency virus

10.11591/ijphs.v14i3.25799
Eny Qurniyawati , Jayanti Dian Eka Sari , Farah Fadhillah , Yeshita Alifia Yuvianti , Prima Kartika , Nayla Mohamed Gomaa Nasr
Teenagers are more susceptible to contracting human immunodeficiency virus (HIV). Of the 34 provinces in Indonesia, East Java continues to have the highest rate of new HIV diagnoses. One tactic for HIV prevention and control is voluntary counseling and testing (VCT). The purpose of this study is to examine the risk factors associated with the uptake of VCT for HIV among adolescents in the general population of East Java. A cross-sectional study design, a quantitative technique, and an observational methodology are all used in this research. 329 individuals in total, selected at random, took part in this study. The prevalence ratio (PR) with a 95% confidence interval was examined in order to determine the level of risk. Out of all the parameters that were found, social media access (PR = 10.133; 95%CI 1.293 - 79.422) and social capital (PR = 10.741; 95% 3.240 - 35.601) were found to have a substantial impact on VCT for HIV. Thus, it's critical to enhance social capital and implement educational initiatives on VCT for HIV using social media in order to improve teenagers' favorable perceptions of the treatment.
Volume: 14
Issue: 3
Page: 1171-1178
Publish at: 2025-09-01

Knowledge and practices of nurses regarding prevention of hepatitis B and C viral infection: findings from a single center cross-sectional study in Bangladesh

10.11591/ijphs.v14i3.25824
Rahima Parvin , Md. Abdul Jabbar , Hafiza Sultana , Mohammad Meshbahur Rahman , Most Rownak Zahan Rimu , Rafaat Choudhury
The study aimed to evaluate the nurses’ levels of knowledge and practices in preventing hepatitis B and C viral infections in tertiary level hospitals. A cross-sectional study was conducted among 119 nurses in tertiary level hospital by simple random sampling technique. Data were collected by face to-face interview with semi-structured questionnaire and analysis involved the frequency distribution tables, bar diagrams, and proportion (z-tests). The analysis revealed that most of the nurses fell within the 25-34 age groups, and predominantly held a diploma in nursing. Analysis indicated that 95.79% demonstrated good knowledge, whereas 70.59% exposed good practices. Proportion tests revealed significant associations between demographic factors and knowledge/practice levels. Higher educated nurses (poor knowledge, good knowledge: 13.0%, 87.0%; p = 0.021) and those in older age groups (poor practice, good practice: 36.8%, 63.2%; p = 0.002) displayed significantly better knowledge and practices. This study highlights good knowledge among nurses concerning the prevention of hepatitis B and C infections; significant variation exists in the application of preventive practices. Training programs are recommended to bridge the gap between knowledge and practice.
Volume: 14
Issue: 3
Page: 1294-1303
Publish at: 2025-09-01

Improved hybrid DTC technology for eCAR 4-wheels drive

10.11591/ijpeds.v16.i3.pp1566-1585
Njock Batake Emmanuel Eric , Nyobe Yome Jean Maurice , Ngoma Jean Pierre , Ndoumbé Matéké Max
This article deals with the design of a hybrid controller (HyC). It combines fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS). It is combined with direct torque control (DTC). This HyC-DTC combination is designed to improve the technical performance of a 04-wheel drive electric vehicle (EV). A stress test is identically applied to the DTC combined with the FL (FDTC) and to the HyC-DTC in order to certify the suitability of this new control following a cross-validation. This is based on dynamic stability criteria (overshoot, rise time, accuracy), analysis of torque and flux oscillations, and the EV's robustness symbol. The EV's magnetic quantities are managed by a master-slave module (VMSC). Simulations are carried out using MATLAB/Simulink software. The HyC-DTC achieves near-zero accuracy like the FDTC, with overshoot around 0.2% less than the FDTC, and torque oscillation amplitude around 4 times less than the FDTC. However, its rise time is 0.045% greater than that of the FDTC. It is therefore slower, but more precise and suitable for EV transmission systems in terms of safety and comfort.
Volume: 16
Issue: 3
Page: 1566-1585
Publish at: 2025-09-01

Modeling and simulation of klystron-modulator for linear accelerators in PRTA

10.11591/ijpeds.v16.i3.pp1822-1831
Wijono Wijono , Dwi Handoko Arthanto , Galih Setiaji , Angga Dwi Saputra , Taufik Taufik , Andang Widi Harto
Approximately 70% of commercial industries worldwide use electron accelerator technology for various irradiation processes. The advantages of irradiation processes compared to thermal and chemical processes are higher output levels, reduced energy consumption, less environmental pollution, and producing superior product quality and having unique characteristics that cannot be imitated by other methods. Research Center for Accelerator Technology (PRTA), BRIN, Indonesia is developing standing wave LINAC (SWL) for food irradiation applications at S-band frequencies (±2856 MHz), electron energy of 6-18 MeV, and an average beam power of 20 kW. This paper aims to model, simulate, and analyze the klystron modulator in the RF linear accelerator (LINAC). The klystron modulator is the main component of the RF LINAC, which functions to supply klystron power with the order of megawatt peak DC, so that the klystron can amplify the low-level RF signal from the RF driver into a high-power RF signal with a power of 2-6 MW peak. The klystron modulator modeling is carried out based on mathematical modeling, then simulated using LTspice to analyze the system performance of the klystron modulator. The results of the klystron modulator modeling simulation show stable system performance and dynamic response. So that it meets the specifications of the 6-18 MeV SWL LINAC being developed by PRTA-BRIN.
Volume: 16
Issue: 3
Page: 1822-1831
Publish at: 2025-09-01

Permanent magnet generator performance comparison under different topologies and capacities

10.11591/ijpeds.v16.i3.pp1516-1527
Ketut Wirtayasa , Muhammad Kasim , Puji Widiyanto , Anwar Muqorobin , Sulistyo Wijanarko , Pudji Irasari
This paper compares the magnetic, electrical, and mechanical characteristics of two permanent magnet generator topologies: single-gap axial flux and single-gap inner rotor radial flux. The study aims to identify how the key parameters fluctuate at each power capacity and investigate the trends in their values as power changes. The power capacities observed are 300 W, 600 W, 900 W, 1200 W, and 1500 W. Simulations used with the help of Ansys Maxwell software to obtain: i) magnetic characteristics without load, including air gap flux density, flux linkage, and induced voltage, ii) electrical performance, consisting of armature current, terminal voltage, voltage regulation, total harmonic distortion, core loss and output power, and iii) mechanical performance, including shaft torque and cogging torque. The last step compares the power density of both topologies. The simulation results show that the axial flux permanent magnet generator (AFPMG) has better air gap flux density, voltage regulation, total harmonic distortion (THD), efficiency, electromagnetic torque, and power density characteristics. Meanwhile, the radial flux permanent magnet generator (RFPMG) is superior in induced voltage and output power. These results conclude that, in general, AFPMG is exceptional from a technical point of view and is more economical when applied to hydro or wind energy systems.
Volume: 16
Issue: 3
Page: 1516-1527
Publish at: 2025-09-01

Optimization of ANN-based DC voltage control using hybrid rain optimization algorithm for a transformerless high-gain boost converter

10.11591/ijpeds.v16.i3.pp1711-1720
Mohcine Byar , Abdelouahed Abounada
This paper introduces an adaptive voltage regulation technique for a transformerless high-gain boost converter (HGBC) integrated within standalone photovoltaic systems. A neural network controller is trained and fine-tuned using the rain optimization algorithm (ROA) to achieve improved dynamic behavior under variable solar conditions. The proposed ROA-ANN framework continuously updates the duty cycle to ensure output voltage stability in real time. Validation was carried out using MATLAB–OrCAD co-simulation under multiple scenarios. Comparative results highlight superior performance of the ROA-ANN controller in terms of convergence speed, overshoot minimization, and steady-state response, outperforming conventional PID and ANN-based methods.
Volume: 16
Issue: 3
Page: 1711-1720
Publish at: 2025-09-01

Improving electrical energy efficiency through hydroelectric power and turbine optimization at the El Oued water demineralization plant in Algeria

10.11591/ijpeds.v16.i3.pp1881-1896
Khaled Miloudi , Ali Medjghou , Ala Eddine Djokhrab , Mosbah Laouamer , Souheib Remha , Yacine Aoun
This paper presents an investigation into the energy potential of the Albian aquifer in the Algerian Sahara at the El Oued water demineralization plant, focusing on its capacity to generate electrical power due to its high-pressure and high-temperature water reserves. We designed and implemented a turbine-generator system to convert hydraulic energy into electricity, achieving an average annual energy output of 1,804,560 kWh, which translates to a financial gain of approximately 345,888,600 DZD per year from energy savings. The selection of a Francis turbine was justified based on its efficiency, which ranges from 90% to 95%, and the system design was simulated using MATLAB-Simulink, demonstrating its robustness and effectiveness in managing the electrical network parameters. Our economic analysis indicates a high return on investment, confirming the feasibility of utilizing the Albian aquifer as a strategic asset for clean and reliable energy production in the region.
Volume: 16
Issue: 3
Page: 1881-1896
Publish at: 2025-09-01

Low voltage fault ride-through operation of a photo-voltaic system connected utility grid by using dynamic voltage support scheme

10.11591/ijpeds.v16.i3.pp1608-1619
Satyanarayana Burada , Kottala Padma
This research suggests a control technique that makes use of a microgrid's energy storage and to enable low voltage ride through (LVRT) process with a flexible dynamic voltage support (DVS) system. First, the requirements for the microgrid's maximum DVS are stated, together with an explanation of how these requirements depend on the characteristics of the analogous network that the microgrid sees. In order to create a flexible DVS regardless of the changing system circumstances, reference signals for currents that are derived from maximum voltage tracking technique are suggested in this research. These signals take into account the challenges involved with real time parameter assessment in the context of transient voltage disruptions. Second, a control scheme is suggested to allow a microgrid's energy storage-based LVRT operation. Thirdly, a novel approach to energy storage sizing for LVRT operation is offered, taking into account the corresponding network characteristics, grid code requirements, and the rated current value of the power electronic converter. Real-time MATLAB simulations for low-voltage symmetrical faults are used to validate the suggested control technique.
Volume: 16
Issue: 3
Page: 1608-1619
Publish at: 2025-09-01

Synchronous generator system identification via dynamic simulation using PSS/E: Malaysian case

10.11591/ijpeds.v16.i3.pp1658-1672
Saleh Baswaimi , Renuga Verayiah , Tan Yi Xu , Nagaraja Rupan Panneerchelvan , Aidil Azwin Zainul Abidin , Marayati Marsadek , Agileswari K. Ramasamy , Izham Zainal Abidin , W. Mohd Suhaimi Wan Jaafar
The synchronous generator (SG) plays a crucial role in power systems by serving as a stable and reliable source of electrical energy. The performance of an SG hinges on its standard parameters, which can be derived through dynamic tests. This study introduces a method for determining the standard parameters of an SG from dynamic tests conducted via power system simulation for engineering (PSS/E). The proposed method entails conducting several key tests on the generator, including a direct-load rejection test, excitation removal test, quadrature-axis load rejection test, arbitrary axis load rejection test, and open-circuit saturation test. The results obtained from these tests are then utilized to calculate the standard parameters of the SG accurately. To validate the effectiveness of the method, simulation data from the SG, as well as the designed initial data, are utilized. Statistical analysis reveals that the maximum relative error is equal to or less than 2.7% of the design values for all standard parameters, emphasizing the robustness and accuracy of the proposed method. The methodology presented in this study can complement field or site measurements, as it enables the verification of system parameters through dynamic simulations.
Volume: 16
Issue: 3
Page: 1658-1672
Publish at: 2025-09-01

Accurate state of health estimation using hybrid algorithm for electric vehicle battery pack performance and efficiency enhancement

10.11591/ijpeds.v16.i3.pp1438-1445
Rajesh Kumar Prakhya , Puvvula Venkata Rama Krishna
Temperature fluctuations, overcharging, and over-discharging are all issues that can cause fast deterioration, capacity loss, and thermal runaway in lithium-ion batteries (LIBs). To overcome these challenges, a hybrid model combining a stacked recurrent neural network (SRNN) and bidirectional long short-term memory (biLSTM) is presented for a reliable state of health (SoH) estimate. This model finds subtle patterns in battery data using SRNN layers to capture sequential dependencies and biLSTM modules to solve long-term temporal correlations while avoiding vanishing gradient concerns. The effectiveness of model is assessed by performance measures such as root mean square error (RMSE), mean absolute error (MAE), and maximum error (MAX), which demonstrate its superiority for precise SoH estimation. The stacked RNN-based SoH estimation achieves superior accuracy, with RMSE, MAE, and MAX error levels of 1.5%, 0.8%, and 4.84%, respectively, compared to GRU’s higher errors (3.8%, 3%, and 5.5%). Stacked RNN hierarchically processes sequential battery data, effectively capturing complex temporal relationships, and ensuring accurate and reliable SoH estimation for LIBs.
Volume: 16
Issue: 3
Page: 1438-1445
Publish at: 2025-09-01

Optimizing slow-charging EV loads with a two-layer strategy to enhance split-phase voltage quality and mitigate issues in PDNs

10.11591/ijpeds.v16.i3.pp1472-1483
Attada Durga Prasad , Manickam Siva , Alla Srinivasa Reddy
Power distribution networks (PDN) were mostly affected by the voltage imbalances created by the slow charging of electric vehicles (EV), were there random load into the PDN system, causing split-phase voltage quality (SPVQ) issues. Hence, to mitigate the problems associated with EVs’ slow charge in distributed phases of the power system, a multi-layer charging strategy is proposed considering the following constraints in the system: voltage deviation (VD) and voltage harmonics (VH) in split phase (SP). Further multi-layer control is associated with an inner layer equipped with hybrid non-dominated sorting genetic algorithm (NSGA-II) to select the optimal phase for charging the EV and send it to the output layer where a SP current algorithm is utilized so that voltage quality can be fed in loop to inner layer so that iterations were performed to satisfy the convergence condition. Simulation results in MATLAB demonstrate a voltage unbalance (VU) reduction of up to 32.81%, a maximum VD reduction of 9.11%, and a VH reduction of 6.25% at key grid nodes. The proposed method significantly enhances PDN efficiency and maintains voltage quality within national standards across 1,000 to 5,000 EV connections. The generated results reflected the optimal improvement in SPVQ, and the harmonics content reduced further; PDN operational efficiency also improved to a greater extent.
Volume: 16
Issue: 3
Page: 1472-1483
Publish at: 2025-09-01

Torque sharing function optimization for switched reluctance motor control using ant colony optimization algorithm

10.11591/ijpeds.v16.i3.pp1537-1551
Dhiyaa Mohammed Ismael , Thamir Hassan Atyia
Switched reluctance motors (SRMs) are gaining popularity in industrial and automotive applications due to their robust design, fault tolerance, and high torque density, particularly in wide-speed-range operations. However, SRM performance is often limited by torque ripple, speed oscillations, and inefficiencies, which can lead to mechanical stress, vibration, and acoustic noise. Addressing these challenges requires the effective optimization of control strategies. This study aims to enhance the performance of SRM drives by employing an ant colony optimization (ACO) algorithm to optimize the torque sharing function (TSF). The proposed method iteratively tunes TSF parameters to minimize torque ripple and improve speed stability under varying operating conditions. Simulation results demonstrate significant improvements: torque ripple is reduced from a range of –20 Nm to 10 Nm without optimization to below 10 Nm with ACO-based optimization. Similarly, current peaks decrease from 60 A to 5.5 A, ensuring smoother motor operation and enhanced efficiency. Comparative analysis confirms that the ACO-based TSF provides superior tracking of speed set points, reduced mechanical stress, and improved reliability, making it well-suited for high performance applications in both industrial and automotive sectors.
Volume: 16
Issue: 3
Page: 1537-1551
Publish at: 2025-09-01

A model predictive control strategy for enhance performance of totem-pole PFC rectifier

10.11591/ijpeds.v16.i3.pp1687-1700
Le Chau Duy , Nguyen Dinh Tuyen
This paper proposed a simple but effective finite control set-based model predictive control (FCS-MPC) method to control a totem-pole bridgeless boost PFC rectifier (TBBR). The control algorithm selects from the possible switching states an appropriate one that fulfills a predefined cost function. This method also successfully eliminates the zero-crossing current distortion so that the grid current can synchronize well with the grid voltage. The theoretical analysis was presented and verified by simulation. Finally, a 3.3 kW/400 Vdc prototype was fabricated and investigated through various working conditions to realize the effectiveness of the proposed control strategy. Both simulation and experimental results show that the proposed control method can ensure accurate control of DC link output voltage and sinusoidal input current with unity power factor.
Volume: 16
Issue: 3
Page: 1687-1700
Publish at: 2025-09-01

Smart energy management in renewable microgrids: integrating IoT with TSK-fuzzy logic controllers

10.11591/ijpeds.v16.i3.pp1620-1627
Moazzam Haidari , Vivek Kumar
Hybrid microgrids powered by renewable energy sources are gaining popularity globally. Photovoltaic (PV) and permanent magnet synchronous generator (PMSG)-based wind energy systems are widely used due to their ease of installation. However, wind and solar energy are unpredictable, leading to fluctuating power generation. Simultaneously, load demand varies randomly, making it necessary to integrate storage devices to maintain a balance between generation and consumption. To enhance system economy, a small battery is combined with a hydrogen-based fuel cell and electrolyzer for efficient energy storage and management. A robust energy management system (EMS) is critical to ensure power quality and reliability across all microgrid components. Maximum power point trackers (MPPTs) are employed to maximize renewable energy utilization. Frequency stability and ensuring power balance is important in autonomous microgrids, especially during rapid load or source variations. This paper presents a novel fuzzy rule-driven Takagi-Sugeno-Kang (TSK) controller for the EMS, ensuring fast, precise responses and improved microgrid reliability.
Volume: 16
Issue: 3
Page: 1620-1627
Publish at: 2025-09-01

Intelligent MPPT system improved with sliding mode control

10.11591/ijpeds.v16.i3.pp1926-1938
Said Dani , Asmaa Drighil , Khadija Abdouni , Khalid Sabhi
The sharp rise in global energy demand over recent decades has necessitated the exploration of alternative energy sources. Solar energy, known for being both pollution- and fuel-free, stands out as a preferred choice. However, its efficiency is sensitive to factors like temperature fluctuations and solar irradiation. To optimize energy extraction, a maximum power point tracking algorithm is crucial for photovoltaic systems. This paper proposes a robust sliding mode control enhanced with an artificial neural network to achieve the Maximum Power Point in a stand-alone PV system. The artificial neural network determines the reference voltage, which is then regulated by the sliding mode control to match the photovoltaic array voltage. The performance of the suggested controller is compared to that of a proportional integral-based neural network controller and the perturb and observe method using MATLAB/Simulink. The results show that the suggested method provides excellent tracking performance and rapid convergence even under quickly changing weather conditions, highlighting its efficiency and robustness.
Volume: 16
Issue: 3
Page: 1926-1938
Publish at: 2025-09-01
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