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30,185 Article Results

Soft fuzzy partial metric and some results on fixed point theory under soft set

10.11591/ijaas.v15.i1.pp427-436
Rohini R. Gore , Renu P. Pathak
This research paper establishes a new concept of soft fuzzy partial metric spaces, combining soft sets, partial metric spaces, and fuzzy sets to handle uncertainty and imprecision. This paper's primary goal is to use soft fuzzy partial metric spaces to examine various fixed-point theory conclusions. A few fixed-point results are defined under the 𝛹 −contraction mapping on soft fuzzy partial metric space and the soft fuzzy contraction mapping. Also, illustrate the related example of fixed-point theorem. Soft fuzzy partial metric spaces have applications in various fields, including image processing, decision-making analysis.
Volume: 15
Issue: 1
Page: 427-436
Publish at: 2026-03-01

Designing framework for standardization and testing requirements of rain radar in Indonesia

10.11591/ijaas.v15.i1.pp123-132
Hogan Eighfansyah Susilo , Iqbal Vernando , Amy Reimessa
Indonesia’s tropical environment requires advanced rainfall monitoring systems to strengthen disaster early warning capabilities. However, the absence of a dedicated national standard for rain radar has limited domestic technology growth and interoperability. This study develops a framework for the Indonesian National Standard (SNI) for rain radar by integrating the framework for analysis, comparison, and testing of standards (FACTS) with structural equation modeling (SEM). Stakeholder requirements were systematically analyzed and translated into technical specifications, benchmarked against International Organization for Standardization (ISO) and World Meteorological Organization (WMO) standards, and statistically validated. SEM results indicate that performance parameters (β =0.70) and testing methods (β =0.76) are the most influential components of the framework. The validated model establishes five essential domains—system specifications, testing procedures, calibration and maintenance, installation criteria, and system control. The resulting FACTS-SEM framework provides a robust, evidence-based foundation for developing and validating meteorological instrumentation standards suited to Indonesia’s tropical context.
Volume: 15
Issue: 1
Page: 123-132
Publish at: 2026-03-01

Technical proposal for the design of a helical conveyor for solid waste handling

10.11591/ijaas.v15.i1.pp333-342
Javier Sinche Ccahuana , Jorge Augusto Sánchez Ayte , Margarita F. Murillo Manrique , Richard Flores-Cáceres
The novelty of this work lies in the design of a helical conveyor for solid waste from the chocolate industry, materials that can be cohesive, with variable density, and potentially corrosive. The objective is to present a validated and replicable technical model that optimizes the transport of 5 metric tons per hour of these wastes at Peru's National Chocolate Company. The goal is to minimize human contact, improve ergonomic safety, and transform waste into exploitable resources under circular economy principles. The methodology employed is an applied type with a quantitative approach, supported by the selection of components through specialized technical catalogs from KWS manufacturing and Martin engineering, which implement ANSI/CEMA 350 standards. Results indicate a total required power of 1.5 HP, with a helicoid diameter of 9", a helical tube of 2", a pitch of 6", and operation at 60 RPM. It is concluded that this design constitutes an efficient and replicable technical solution to improve working conditions in industrial environments, significantly reducing occupational injuries while mitigating environmental impact.
Volume: 15
Issue: 1
Page: 333-342
Publish at: 2026-03-01

Extension of Hermite-Hadamard type inequalities to Katugampola fractional integrals

10.11591/ijaas.v15.i1.pp1-18
Dipak Kr Das , Shashi Kant Mishra , Pankaj Kumar , Abdelouahed Hamdi
In this study, we introduce several new Hermite-Hadamard type general integral inequalities for exponentially (s,m)-convex functions via Katugampola fractional integral. The Katugampola fractional integral is a broader form of the Riemann–Liouville and Hadamard fractional integrals. We utilized the power mean integral inequality, the H¨older inequality and a few additional generalizations to derive these inequalities. Numerous limiting results are derived from the main results presented in the remarks. Furthermore, we provide an example illustrating our theoretical findings, supported by a graphical representation.
Volume: 15
Issue: 1
Page: 1-18
Publish at: 2026-03-01

Corporate social responsibility by listed commercial banks in Vietnam: practice and financial performance

10.11591/ijaas.v15.i1.pp261-271
Viet Ha Nguyen , Thi Minh Nguyet Dang
This study examines the impacts of financial performance (FP) on corporate social responsibility (CSR). The article investigates whether FP, as measured by return on assets (ROA) and net interest margin (NIM), influences the likelihood of CSR disclosure, drawing on stakeholder theory and legitimacy theory. The analysis employs binary logistic regression models and an unbalanced panel dataset comprising 26 listed banks between 2014 and 2024. If the bank discloses its CSR practices in its annual or sustainability report, the code for CSR disclosure is 1; otherwise, it is 0. The results show that, while NIM shows a negative relationship, ROA significantly improves CSR. Furthermore, there is a positive correlation between bank size (TA), equity to asset (EA), and CSR; a negative relationship of loan to deposit ratio (LDR) with CSR, and no significant statistical correlation was observed between debt to equity (DTE) and CSR. The study adds to the body of knowledge on CSR in developing nations and offers recommendations for sustainability and bank governance.
Volume: 15
Issue: 1
Page: 261-271
Publish at: 2026-03-01

Induction motor simultaneous fault diagnosis based on Takagi-Sugeno models

10.11591/ijape.v15.i1.pp195-210
Samira Souri , Mohamed Lakhdar Louazene , Abdelghani Djeddi , Youcef Soufi
This article proposes a model-based approach to the concurrent diagnosis of stator and rotor faults in induction motors (IMs) using Takagi-Sugeno (TS) fuzzy models. Fault-free detection is essential to prevent unexpected downtime and economic loss in industrial applications. The study first develops a dynamic model of the IM in the synchronized reference frame with the rotor under healthy and faulty operations. Different fault conditions like stator inter-turn short circuits, defective rotor bars, and combination thereof are considered. A TS model for every case is developed based on the precise nonlinear model. Simulation outcomes prove the validity of the new models in simulating the dynamic response of the motor under faulty operating modes. The residual signals are used to compare the performance of the model in fault isolation. The proposed method offers a classification that inherently separates between fault types. Such a contribution presents an efficient real-time fault detection and predictive maintenance facility, which renders it suitable for hardware-in-the-loop application in intelligent drive systems.
Volume: 15
Issue: 1
Page: 195-210
Publish at: 2026-03-01

Impact of ferrite materials on wireless power transfer efficiency for electric vehicles battery chargers

10.11591/ijape.v15.i1.pp361-372
Wan Muhamad Hakimi Wan Bunyamin , Rahimi Baharom
This paper investigates the impact of ferrite materials on the efficiency of wireless power transfer (WPT) systems designed for electric vehicle (EV) and E-bike battery chargers. The study employs 3D full-wave electromagnetic simulations in CST Studio Suite 2024 to evaluate how Laird Performance Materials 33P2098-0M0 ferrite influences magnetic coupling, field confinement, and overall transfer efficiency. Two configurations were analyzed: coil-only and coil-with-ferrite plates, under a fixed 20 mm air gap and an operating range of 30–50 kHz. The inclusion of ferrite materials significantly improved magnetic-flux directivity and coupling strength, resulting in a peak efficiency of 99.21% at 41.3 kHz, compared to 99.09% at 38.1 kHz for the coil-only design. The enhanced configuration also reduced magnetic leakage and improved resonance stability, as verified through mesh-independent simulations and analytical validation with less than 2% error. The proposed model correlates ferrite permeability with mutual inductance and resonant-frequency tuning, confirming the theoretical basis of the efficiency gain. This work bridges a gap in small-scale EV and E-bike WPT research by quantifying the measurable benefits of ferrite integration and providing design guidelines for compact, thermally stable, and high-efficiency wireless charging systems.
Volume: 15
Issue: 1
Page: 361-372
Publish at: 2026-03-01

Implementation of the soil health monitoring system to achieve better yield

10.11591/ijape.v15.i1.pp308-318
S. R. Bhagyashree , Guddappa Halligudra , Anupama Sindagi , Madhu Nagaraj , C. Shyamala , Shaista Tarannum , R. Thailagavathy , T. R. Yashavantha Kumar
Agriculture is a fundamental pillar of the economy, particularly in developing countries where a significant proportion of the population depends on farming for their livelihood. Crop productivity is influenced by soil type and its changing chemical properties. A lack of understanding of soil health, crop-specific nutrient requirements, and the interaction between water and the environment often leads to inappropriate irrigation and fertilizer use. As a result, crops receive either excessive or insufficient nutrients, leading to reduced yields and the waste of water, energy, and other valuable resources. To address these issues, this paper presents an IoT-based soil health monitoring system that supports sustainable crop management. The proposed system integrates sensors to monitor key soil parameters such as temperature, humidity, soil moisture, and pH levels in real time. Based on the collected data, the system autonomously adjusts irrigation and environmental conditions to maintain soil health. This approach improves crop productivity, optimizes resource utilization, and promotes energy conservation in agricultural systems.
Volume: 15
Issue: 1
Page: 308-318
Publish at: 2026-03-01

ANFIS-MPPT based PMSG-wind turbine interfaced with water pumping and battery management systems for optimal power flow and energy management

10.11591/ijape.v15.i1.pp141-152
Saritha Kandukuri , Ram Dulare Nirala , Sivaprasad Kollati , Tata Himaja , Durga Bhavani Adireddy
This paper presents the adaptive neuro-fuzzy inference system-maximum power point tracking (ANFIS-MPPT) approach for optimizing power flow in a water system powered by a permanent magnet synchronous generator (PMSG)-wind turbine. The system uses a PMSG-based wind energy conversion system (WECS) with an ANFIS for MPPT, enabling efficient power extraction under variable wind conditions. A bidirectional SEPIC-Zeta converter interfaces a battery energy storage system (BESS) to regulate the DC-bus voltage and maintain continuous power supply to a three-phase induction motor driving the water pump. An artificial neural network (ANN)-based controller is used to manage the charging and discharging of the battery based on real-time voltage deviation. The entire system, including wind turbine, PMSG, converters, and intelligent control algorithms, is modeled and simulated in MATLAB/Simulink. Comparative analysis with conventional MPPT techniques highlights the superior performance of the proposed hybrid ANFIS-based control in terms of power flow regulation, voltage stability, and operational reliability. The results confirm that the proposed approach significantly enhances energy management and system resilience, making it suitable for standalone or remote water pumping applications powered by renewable energy sources.
Volume: 15
Issue: 1
Page: 141-152
Publish at: 2026-03-01

Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems

10.11591/ijape.v15.i1.pp257-274
Bodapati Venkata Rajanna , Aaluri Seenu , Kondragunta Rama Krishnaiah , Anantha Sravanthi Peddinti , Nelaturi Nanda Prakash , Bandreddi Venkata Seshukumari , Giriprasad Ambati , Shaik Hasane Ahammad , Chakrapani Srivardhan Kumar , Allamraju Shubhangi Rao
In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
Volume: 15
Issue: 1
Page: 257-274
Publish at: 2026-03-01

Performance evaluation of a trapezoidal solar pond using magnesium sulphate (MgSO₄)

10.11591/ijape.v15.i1.pp403-411
P. Dineshkumar , M. Raja , M. Venkatesan , M. Dineshkumar
Emerging global demand for clean and sustainable energy has intensified research into efficient methods of solar energy capture and storage. Among various renewable energy storage technologies, salt gradient solar ponds (SGSPs) have emerged as a reliable and cost-effective solution. This study presents an advanced experimental evaluation of a trapezoidal SGSP using magnesium sulphate (MgSO₄) as the salinity medium to enhance heat storage performance and system stability. A laboratory-scale trapezoidal pond with a depth of 30 cm was constructed using 18 mm thick plywood and an optimized 16% MgSO₄ concentration (SGSP-M16) was employed to maintain thermal stratification. Experiments conducted over a four-month period in Salem, Tamil Nadu, India, involved detailed energy and temperature analysis across upper convective zone (UCZ), non-convective zone (NCZ), and lower convective zone (LCZ). Results revealed maximum temperature difference of 28 °C among UCZ and LCZ, with LCZ achieving peak energy efficiencies of 25.24%, 26.80%, 28%, and 32.09% from January to April, respectively. These findings confirm the effectiveness of the trapezoidal MgSO₄ based SGSP as a sustainable and scalable system for renewable energy storage and efficient thermal management, suitable for applications such as desalination, greenhouse heating, and industrial preheating.
Volume: 15
Issue: 1
Page: 403-411
Publish at: 2026-03-01

Machine learning-based real-time power stability optimization for photovoltaic systems using hybrid inductor-capacitor patterns

10.11591/ijape.v15.i1.pp248-256
Jayashree Kathirvel , S. Pushpa , P. Kavitha , Sathya Sureshkumar , Kannan Andi , Prabakaran Pramasivam
Photovoltaic (PV) systems often face real-time power stability challenges due to rapid fluctuations in solar irradiance and varying load conditions, which conventional control strategies struggle to manage effectively. Addressing this limitation, the present study proposes a novel machine learning-based control framework integrated with a hybrid inductor-capacitor (LC) network to enhance dynamic power regulation. The proposed system employs predictive algorithms to adjust LC parameters in real time, enabling adaptive voltage and current stabilization during transient conditions. Simulation results validate the model's effectiveness, showing a 58% reduction in power fluctuation (from 12% to 5%) and consistent improvement in voltage stability index (VSI), maintaining values above 0.95 compared to 0.88-0.93 in traditional systems. Moreover, the approach reduces computation time by 66% (150 ms versus 450 ms for PID-based systems), supporting faster and more efficient control actions. These outcomes demonstrate that the proposed intelligent control strategy significantly improves energy efficiency, voltage stability, and responsiveness in PV systems, offering a scalable solution for reliable grid integration of renewable energy sources.
Volume: 15
Issue: 1
Page: 248-256
Publish at: 2026-03-01

Hydrothermal synthesis and defect-driven optical characterization of CdS nanoparticles for semiconductor and solar applications

10.11591/ijape.v15.i1.pp440-448
Deepti Bhargava , R. K. N. R. Manepalli , M. C. Rao , P. Venkata Ramana Rao , N. S. Subba Rao , A. Narendra Babu , P. Sree Brahmanandam
Nanoparticles (NPs) play a crucial role in advancing technology, particularly by enhancing the performance of energy storage in semiconductor applications. The synthesis of NPs with reduced particle size and increased surface area, along with a higher number of active sites, facilitates improved ion diffusion, making them highly suitable for such applications. Various methods have been employed to reduce the size of NPs, depending on factors such as purity and controlled composition. The present study focuses on controlling both the size and composition of cadmium sulfide (CdS) NPs, aiming to achieve a high surface-to-volume ratio. These NPs were synthesized using a hydrothermal method in a high-pressure autoclave. The evaluation of the synthesized inorganic CdS-NPs for technological applications requires experimental validation of their characteristics, including particle size, energy band gap, thermal stability, temperature response, as well as optical and electronic properties. The results obtained using the proposed methods reveal a bandgap of 2.28 eV, a hexagonal wurtzite structure with an average crystallite size of 10.26 nm, reduced effective mass, and an intense absorption peak at a higher wavelength. These characteristics indicate that the synthesized CdS nanoparticles are suitable for various applications, including high-power semiconductors, solar energy harvesting, optoelectronic devices, and materials for energy and electrical engineering.
Volume: 15
Issue: 1
Page: 440-448
Publish at: 2026-03-01

The current status of the hydrogen value chain in India: a critical review

10.11591/ijape.v15.i1.pp110-119
Shyamsing Thakur , Lalitrao Amrutsagar , Dipankar Kakati , Vijaykumar Kisan Javanjal , Kuldeep A. Mahajan , Dipali B. Tawar
The Bharat is the largest economy with a humongous population that has increasing energy demands day by day. Clean energy sources like green hydrogen are necessary to balance climate change and meet energy demand, which also reduce carbon footprints in related energy sectors. This paper critically reviews the need of green hydrogen, production, storage and transportation strategies, the role of government schemes, and prominent private corporations working in the Indian green hydrogen sector. Efforts are made to analyze available data and current advisory regulations pertaining to the green hydrogen ecosystem in India. Based on this, suggestions are made for a research and development roadmap for establishing a green hydrogen value chain. This research paper suggests salt caverns as potential geological structures for hydrogen storage chains and also sheds light on potential collaborative initiatives and pilot projects for improving the efficiency and sustainability of the green hydrogen value chain across developing countries like India.
Volume: 15
Issue: 1
Page: 110-119
Publish at: 2026-03-01

Modulation and performance analysis of two-wheeler electric vehicle

10.11591/ijape.v15.i1.pp186-194
Debani Prasad Mishra , Rudranarayan Senapati , Pavan Kumar , Lakshay Bhardwaj , Surender Reddy Salkuti
When compared to traditional cars, electric vehicles (EVs) have less pollution, better fuel efficiency, and are better for the environment. This essay explores the evolution of EVs in great detail, emphasizing their vital role in lowering CO2 emissions and promoting sustainability. It builds a dynamic model for EVs using MATLAB/Simulink, which explains the state of charge (SOC) and range prediction. The study emphasizes the importance of EVs in promoting a sustainable future by thoroughly covering design details, modeling, and a scientific methodology. Through the use of modeling to clarify technical aspects and highlight the significance of EV adoption, this study highlights the vital role that EVs play in reducing environmental impact and advancing environmentally friendly transportation. It highlights EVs' potential to revolutionize the automobile sector while promoting cleaner modes of transportation. It offers a thorough overview of EV production and usage and fervently promotes their wider acceptance as a means of laying the groundwork for a more sustainable and clean future.
Volume: 15
Issue: 1
Page: 186-194
Publish at: 2026-03-01
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