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International Journal of Advances in Applied Sciences
Published by Institute of Advanced Engineering and Science
ISSN : 22528814     EISSN : 27222594     DOI : http://doi.org/10.11591/ijaas
Core Subject : Science, Education, Social,
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics
International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.
Arjuna Subject : -
Articles 680 Documents
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The impact of fast charging technology on battery longevity in electric vehicles Nagabushanam, Perattur; Chenchireddy, Kalagotla; Dora, Radhika; Babu, Thanikanti Sudhakar; Jagan, Vadthya; Manohar, Varikuppala
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp936-944

Abstract

Fast charging technology has revolutionized the electric vehicle (EV) industry by addressing range anxiety and significantly reducing charging times. However, this convenience introduces challenges concerning battery longevity, as high charging currents and elevated temperatures accelerate battery degradation. This paper investigates the mechanisms through which fast charging impacts lithium-ion batteries, including thermal stress, lithium plating, and mechanical wear. It synthesizes findings from various studies, highlighting how fast charging can shorten battery lifespan by up to 20-30% compared to standard charging methods. Strategies to mitigate these effects, such as advanced materials, adaptive charging protocols, and efficient thermal management systems, are discussed. Furthermore, the paper emphasizes the importance of standards and policies to promote sustainable fast charging practices. By balancing charging speed with long-term battery health, the EV industry can achieve widespread adoption while ensuring sustainability. This work aims to provide a comprehensive understanding of the trade-offs associated with fast charging and offers actionable insights for improving EV battery durability.
A bibliometric review of lean principles in highway pavement for productivity improvement Gohil, Pooja P.; Malek, MohammedShakil S.; Upadhyaya, Deep Shaileshkumar
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp639-649

Abstract

A past study of 25 years reveals the positive impact of lean principles on highway pavement productivity. This bibliometric review extracted 389 papers from the Scopus database that revolved around three terms, “lean principles,” “highway pavement,” and “productivity improvement,” and used VOSviewer for scientometric analysis and scientific mapping. Study reveals that addressing this topic on a global scale is of chief significance, given the potential variations in indices of the issue across different countries or provinces. This research undertakes a comprehensive qualitative analysis that highlights diverse indicators that exert influence on the productivity of pavements. Additionally, this analysis also seeks to propose potential avenues for future research within lean construction. An intensive investigation provides four unique clusters of words that have been formed through the process of keyword science mapping within the context of the lean principles, which are road segment, techniques, productivity improvement, and lean. Last but not least, 4 pointers are recommended that will help stakeholders and policymakers assess pavement performance practices, identify areas for improvement, and execute targeted interventions to improve productivity.
Large language models and retrieval-augmented generation-based chatbot for adolescent mental health Riansyah, Andi; Subroto, Imam Much Ibnu; Nur'aini, Intan; Supradewi, Ratna; Suyanto, Suyanto
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp849-858

Abstract

Access to fast and efficient information is crucial in today's digital era, especially for teenagers in obtaining mental health services. The manual method used by Youth Information and Counselling Centre (PIK R) to provide mental health information requires significant time and effort. This research presents an AI-based solution by developing a chatbot system using retrieval-augmented generation (RAG) and large language models (LLM). This chatbot is designed to provide accurate and effective mental health information for teenagers throughout the day. An analysis of a dataset consisting of articles on teenage mental health and data from the Alodokter website was used as the basis for the development of this chatbot. The research results show that the chatbot is capable of providing relevant and accurate information, with evaluations using the recall-oriented understudy for gisting evaluation (ROUGE) score method yielding an average of ROUGE-1 with a precision of 87.8%, recall of 83.0%, and F1-measure of 84.0%; ROUGE-2 with a precision of 82.8%, recall of 76.8%, and F1-measure of 78.2%; and ROUGE-L with a precision of 88.0%, recall of 82.6%, and F1-measure of 83.4%. These findings indicate the potential use of chatbots as an effective tool to support the mental health of adolescents.
Autonomous navigation system for a rover with robotic arm using convolutional neural networks El mrabet, Aziz; Hihi, Hicham; Laghraib, Mohammed Khalil; Chahboun, Mbarek; Amalaoui, Aymane
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp724-739

Abstract

The aim of this project is to design and develop an autonomous rover equipped with a KUKA robotic arm. This mobile vehicle will be able to move autonomously thanks to the use of machine learning techniques. It will also be able to detect and retrieve objects using the KUKA arm. The rover will feature Mecanum wheels for improved maneuverability and will be controlled by a Raspberry Pi 3 board, with machine learning algorithms implemented using TensorFlow and Python. The development process will follow the V-methodology. The use of such an autonomous rover and its manipulative capabilities opens the way to many practical applications, including sampling in dangerous or difficult-to-access environments, search and rescue operations in the event of natural disasters or industrial accidents, and inspection and maintenance of industrial or construction sites. The rover could also be used for educational purposes, enabling students to explore the concepts of robotics and artificial intelligence.
A method classifying the domestic tourist destination base similarity measuring Hoi, Nguyen Thi; Nhung, Tran Thi; Truong, Bui Quang; Trung, Nguyen Quang
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp740-750

Abstract

The classification problem is crucial in business, providing an effective method for supporting search activities in areas such as e-commerce, education, and marketing. This has become especially important in the wake of the COVID-19 pandemic, which has increased the need to promote and stimulate domestic tourism. This research focuses on recommending tourist destinations based on historical search data related to domestic tourism. The study uses techniques like term frequency-inverse document frequency (TF-IDF) weight vector analysis and similarity measures to calculate recommendation scores. Data was collected from various tourism websites, covering destinations across all 63 provinces and cities in Vietnam. Experiments were conducted using three approaches: cosine similarity, the brute force algorithm, and long short-term memory (LSTM) for long-text processing. The results indicate that similarity-based methods produce recommendations that closely match user preferences. For full-sentence queries, the brute force algorithm delivers more accurate results, while LSTM provides faster processing times. These findings offer businesses multiple strategies for improving recommender systems in practical applications.
Numerical study of non-linear twisted blades for tidal turbines improvement Arini, Nu Rhahida; Atmojo, Philips Ade Putera; Saputra, Deni; Satrio, Dendy
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp894-906

Abstract

Despite the growing demand for renewable energy, the utilization of tidal energy remains underdeveloped due to efficiency limitations in turbine design. Addressing this gap, this study investigates the performance of horizontal-axis tidal turbines (HATT) by comparing two foil designs, National Advisory Committee for Aeronautics (NACA) 2415 and OptA, to optimize energy extraction efficiency. The research employs computational fluid dynamics (CFD) simulations using OpenFOAM to evaluate the effects of foil modifications and non-linear twist distributions on turbine performance across varying tip speed ratios (TSR). The results indicate that the OptA foil significantly improves turbine performance, achieving a 41.4% increase in torque and a 40.2% increase in power coefficient (CP) at TSR 5, which was identified as the optimal operating condition. The OptA foil enhances velocity distribution, reduces flow separation, and improves vortex behavior, leading to greater efficiency and stability. These findings confirm that foil selection and blade design modifications play a critical role in HATT optimization.
Searchable encryption based on a chaotic system and AES algorithm Sherali, Fairouz; Sarhan, Falah
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp975-984

Abstract

Cloud computing provides on-demand access to computing resources, such as storage and processing power. This technology allows businesses to scale efficiently while reducing infrastructure costs. However, protecting the security and privacy of data has grown to be a top priority. This is where enhancing cloud security with searchable encryption (SE) is crucial. SE effectively secures users’ sensitive data while preserving searchability on the cloud server side. It enables the cloud server to search via encrypted data without disclosing information in plaintext data. SE uses different encryption methods to encrypt data before uploading it to servers. The advanced encryption standard (AES) is a common algorithm for encrypting this data. In this paper, a novel SE method has been presented. The technique exploits the properties of the chaotic map to generate an AES key, which makes the AES algorithm more secure for encrypting the searchable index and uploaded files. We implement and test our method with real data from files. The experimental results show that the proposed method can significantly satisfy a higher level of security as compared to other schemes.
Redesign the layout of the raw material warehouse from randomized storage to class-based storage Iftitah, Nur; Qurtubi, Qurtubi; Setiawan, Danang; Helia, Vembri Noor
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp773-783

Abstract

The company has a problem of ineffectiveness in the layout of the raw material warehouse due to the use of storage methods that ignore factors such as the type, dimensions, and condition of the goods. This reduces the optimal function of the warehouse and increases the time to retrieve goods. This research aims to redesign the suitable and practical layout of the raw material warehouse by considering its form and function, as well as filling methodological gaps from previous research. The method used is class-based storage. Based on ABC analysis, the category with the highest value is class C goods, with 73 units. Meanwhile, from the fast, slow, non-moving (FSN) analysis, class F (fast-moving) goods have the highest frequency of movement, with a movement percentage of 63% for 10 units of goods. The warehouse slotting analysis shows an increase in the number of shelves from nine to 15 shelves with five different shelf models and layout changes in raw material warehouses 1 and 2. The class-based storage method results in a more organized layout, efficient movement of goods, and faster picking time to optimize warehouse functions.
Optimizing retail systems: using big data and power business intelligence for performance insights Quoc, Huu Dang; Viet, Ha Le
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp945-954

Abstract

In the rapid development of information technology, using enterprise data to support timely management decisions is crucial in helping businesses operate effectively and improve competitiveness. This study uses Microsoft power business intelligence (MPBI) to analyze data in retail systems, allowing managers to grasp the business situation in real time, track advanced sales, optimize inventory control, and analyze customer behavior and supply chain visibility. From the data generated by the business, the study uses the streaming extract transform load (ETL) model to support real-time data aggregation, then converts to the MPBI data visualization system to convert data into visual charts, helping businesses easily monitor, track, analyze, and make decisions to promote business activities. The study proposes a data structure to organize retail information storage. It proposes a system of calculation formulas and data synthesis, making integrate and convert tabular data into visual charts. Through analysis of real data from the LH83 retail system, the study shows the feasibility of implementing a data visualization system and the difficulties encountered when businesses want to deploy this model.
Comprehensive structured analysis of machine learning in safety models Wahab, Mohd Shukri Abdul; Shazali, Syed Tarmizi Syed; Mohamed, Noor Hisyam Noor; Abdullah, Abdul Rani Achmed
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp627-638

Abstract

Machine learning (ML) integration into various industries has revolutionized operations recently, enhancing efficiency and predictive capabilities. However, the rapid adoption of ML models also presents significant safety concerns that are highly demanded. To achieve this, scholarly articles from reputable databases such as Scopus and Web of Science (WoS) focus on studies published between 2022 and 2024, which were extensively searched. The study's flow is based on the PRISMA framework. The database found (n=40) that the final primary data was analyzed. The findings were divided into three themes: i) safety and risk management, ii) ML and artificial intelligence (AI) applications in safety, and iii) smart technology for safety. The conclusion highlights the need for continuous monitoring and updating of the safety protocols to keep in step with the growing ML landscape. This review contributes to the understanding of ML safety. It offers global lessons that can guide future research and policy-making efforts to ensure ML technologies' safe and ethical use.

Page 62 of 68 | Total Record : 680

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