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Variable Length Multi-Objective Whale Optimization for Trust Aware Data Gathering in Wireless Sensor Network

IEEE Access 2025年 13卷 32100-32121页

作者： Saad, Mohammed Ayad Jaafar, Rosmina Chellappan, Kalaivani Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance. © 2015 The Authors.

关键词： Data collection

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Nanoparticle formulation of ZnO: TiO2 as sunscreen cream for Cutaneous Lupus Erythematosus (CLE) patients 12

Nanoparticle formulation of ZnO: TiO2 as sunscreen cream for...

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12th International Conference on Physics and Its Applications, ICOPIA 2024

作者： Siswanto Qulub, Fitriyatul Junico, Vallery Ofelia Viannco Physics Program Study Faculty Science and Technology Universitas Airlangga Surabaya Indonesia Biomedical Engineering Program Study Faculty Science and Technology Universitas Airlangga Surabaya Indonesia

Cutaneous Lupus Erythematosus (CLE) is a form of lupus that affects the skin and is highly sensitive to ultraviolet (UV) light. UV-B. in particular. can trigger inflammation and apoptosis in CLE patients. making a sunscreen with effective UVB protection crucial. This study evaluates the effectiveness of zinc oxide (ZnO) and titanium dioxide (TiO2) nanoparticles in sunscreen. ZnO and TiO2 nanoparticles were prepared using the High Energy Milling method. resulting in particle sizes of 7.77 nm and 4.44 nm. respectively. The creams containing these nanoparticles were tested for physical stability and UV protection. Results showed that the creams were physically stable and provided good UV protection. with ZnO and TiO2 nanoparticles making up 12.60% of the total cream ingredients. resulting in the highest SPF (Sun Protection Factor) value of 62.0 ± 1.37. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： TiO2 nanoparticles

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN Computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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The Influence of Silicon Dioxide as a Fuel Additive in a Diesel Engine Fuelled by Pure Palm Oil 4

The Influence of Silicon Dioxide as a Fuel Additive in a Die...

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4th International Symposium on Advances and Innovation in Mechanical engineering, ISAIME 2023

作者： Prahmana, Rico Aditia Darmanto, Prihadi Setyo Reksowardojo, Iman Kartolaksono Prakoso, Tirto Juangsa, Firman Bagja Doctoral Program of Mechanical Engineering Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung40132 Indonesia Program Study of Mechanical Engineering Faculty of Industrial Technology Institut Teknologi Sumatera South Lampung35365 Indonesia Thermal Science and Engineering Research Group Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Department of Chemical Engineering Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia Program Study of Mechanical Engineering Faculty of Industrial Technology Universitas Pertamina Jakarta12220 Indonesia

In the last decade, the Indonesian government has implemented a program to use biodiesel as a substitute for fossil diesel to reduce dependence on fossil fuels. On the other hand, the development of biodiesel as an alternative fuel to replace fossil diesel has several challenges, one of which is the production of biodiesel, which is more complicated and requires a long process. However, the direct use of crude palm oil (CPO) in diesel engines is not recommended because it has many drawbacks. Research recently shows that fuel additives in diesel engines have promising prospects. Therefore, to solve the problem, this study used pure palm oil (PPaO) as the primary fuel, with the addition of silicon oxide (SiO2) as a fuel additive in diesel engines. The results showed that using SiO2 additive in PPaO can increase thermal efficiency by 18.8% at 60% load compared to diesel fuel (DF). Nevertheless, using this fuel increased specific fuel consumption (SFC) by 3.9% on average and increased the opacity by 153% compared to diesel fuel. These results indicate that SiO2 has the potential to be developed as an effective additive to PPaO fuel that can improve the efficiency and performance of diesel engines. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Biodiesel

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Combustion Profile of Bio-pellet Produced from Sawdust Using Wet Torrefaction Method with Various Concentrations of Acetic Acid 4

Combustion Profile of Bio-pellet Produced from Sawdust Using...

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4th International Symposium on Advances and Innovation in Mechanical engineering, ISAIME 2023

作者： Pertiwi, Indah Ayu Muhammad Abubakar Setiawan, Adi Iskandar, Rizky Magister Program in Renewable Energy Engineering Faculty of Engineering Universitas Malikusssaleh Jalan Batam Bukit Indah Lhokseumawe24352 Indonesia Chemical Engineering Department Faculty of Engineering Universitas Malikussaleh Kampus Bukit Indah Muara Satu Lhokseumawe24352 Indonesia Mechanical Engineering Vocational Education Study Program Faculty of Teaching and Educational Science Universitas Malikussaleh Cot Teungku Nie Reuleut Muara Batu Aceh Utara24355 Indonesia Mechanical Engineering Department Faculty of Engineering Universitas Malikusssaleh Jalan Batam Bukit Indah Lhokseumawe24352 Indonesia

Sawdust is one of the industrial wastes which naturally contains low energy density. An effective method is needed to utilize this biomass as a solid fuel product. This work aimed to investigate the physical and combustion properties of bio-pellets produced from sawdust under the wet torrefaction method. To prepare sawdust bio-pellet a number of steps were carried out including the wet torrefaction process, dough preparation, pelletizing, and drying. Wet torrefaction was performed by hydrothermally treating the sample in an autoclave at a temperature of 130°C and pressure of 2 atm (g) for 80 minutes. For the hydrothermal process, the used acetic acid solution was varied with variations of 1.3 M, 1.8 M, and 2.3 M. Properties of the product were analyzed under proximate and bomb calorimetry analyses. To evaluate the combustion properties a purposely built test apparatus was developed equipped with a data logger system. Based on the results it can be observed that increases in torrefaction temperature and acetic acid concentration resulted in an increase in the calorific value as well as decreases in the ash content. The highest calorific value was 4745 kcal/kg obtained from sawdust torrefied in acetic acid with a concentration of 2.3 M at a temperature of 130?. This sample also exhibits the lowest ash content which is only 1.97%. The combustion test disclosed a maximum combustion temperature of 589? with a burning rate of 0.711 g/min. Overall, the wet torrefaction applied in this investigation was beneficially enhanced the properties of bio-pellet products. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Sawdust

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Nafion edge passivation and nitrogen annealing treatment to improve solar cell reliability under light and elevated temperature induced degradation (LeTID) investigation

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Current Applied Physics 2025年 75卷 1-7页

作者： Jony, Jaljalalul Abedin Yousuf, Hasnain Khokhar, Muhammad Quddamah Zahid, Muhammad Aleem Madara, Polgampola Chamani Rahman, Rafi Ur Alamgeer Chu, Mengmeng Sanyal, Simpy Wang, Fucheng Kim, Youngkuk Kim, Kyesoo Dhungel, Suresh Kumar Park, Sangheon Yi, Junsin Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of University Tamilnadu 600124 India Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Faculty of Technology Nepal Academy of Science and Technology 3323 Khumaltar Lalitpur Nepal

This research examines the effectiveness of nitrogen annealing and Nafion treatment in mitigating Light and Elevated Temperature Induced Degradation (LeTID) in Passivated Emitter and Rear Contact (PERC) solar cells, employing a 30-kW photovoltaic (PV) system for evaluation. As a photosensitive device, solar cells are susceptible to damage from laser irradiation, leading to reduced photoelectric conversion efficiency, structural damage, and functional loss. To minimize this loss, nitrogen annealing and Nafion treatments were applied, followed by the analysis of LeTID effects on untreated, Nafion-treated, and combined nitrogen-annealed and Nafion-treated PERC cells. Untreated cells showed an 8 % fall in open-circuit voltage (Voc) and a 7 % fill factor (FF) loss. The combined treatment (Combination of Nitrogen & Nafion Treatment) improved stability, reducing losses to 6 % in Voc and 5 % in FF. The Nafion treatment yielded the best results, limiting deterioration to 4 % in Voc and 3 % in FF. PVsyst simulations, incorporating real-world data, confirmed enhanced stability and efficiency, aiding in optimizing PV system design and maintenance. © 2025 Korean Physical Society

关键词： Annealing

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Smart Decision-Making with GIS: Advanced Multi-Criteria Analysis for Solar–Wind Hybrid Renewable Energy Systems

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Arabian Journal for science and engineering 2025年 1-26页

作者： Abdullah, Ade Gafar Aziz, Muhammad Chairulloh, Agil Electrical Engineering Study Program Universitas Pendidikan Indonesia Bandung Indonesia Engineering and Vocational Pedagogy Laboratory Universitas Pendidikan Indonesia Bandung Indonesia Institute of Industrial Science The University of Tokyo Tokyo Japan

Addressing climate change and fossil fuel depletion necessitates the optimal selection of sites for solar–wind hybrid renewable energy systems (SWHRES) to ensure sustainable clean energy. This study identifies the most suitable location for installing an SWHRES in Lampung Province, Indonesia, utilizing a hybrid geographical information system (GIS) model that integrates the technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighting (SAW) methods. The analysis is conducted using GIS-based multi-criteria decision-making (MCDM), overlaying nine criteria: solar radiation, wind speed, slope, distance to roads, distance to transmission lines, distance to settlements, elevation, land use, and temperature. Data sources include Solargis, the Global Wind Atlas, the Indonesian Geospatial Information Agency, OpenStreetMap, and regional geoportals. The study identifies five potential sites, with the highest-ranked location (A3) having an average solar radiation of 4.58 kWh/m2/day, a wind speed of 4.02 m/s, and a 320 m distance from the nearest road, making it the most favorable site for SWHRES implementation. Sensitivity analysis with weight variations of ± 20% and ± 40% confirms the robustness of the decision, as the ranking order remains stable. The findings demonstrate that integrating GIS with TOPSIS and SAW enhances decision-making accuracy in site selection by effectively processing diverse spatial and technical data. This study provides crucial insights for policymakers in optimizing Indonesia’s renewable energy infrastructure.

关键词： Solar-wind hybrid renewable energy systems TOPSIS Simple additive weighting Geographic information systems

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Interconnected Through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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A Performance study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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