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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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Machine learning for Internet of Things (IoT) device identification: a comparative study

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PeerJ Computer Science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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Innovative metallization pattern and technique for industrial rear emitter p-type tunnel oxide passivated contact solar cells

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Journal of Industrial and engineering Chemistry 2025年 147卷 628-635页

作者： Khokhar, Muhammad Quddamah Yousuf, Hasnain Rahman, Rafi Ur Aida, Maha Nur Madara, Polgampola Chamani Jony, Jaljalalul Abedin Dao, Vinh-Ai Pham, Duy Phong Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics Faculty of Applied Sciences HCMC University of Technology and Education Ho Chi Minh City700000 Viet Nam College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In this study, we aim to enhance the efficiency of p-type tunnel oxide passivated contact (p-TOPCon) solar cells by improving charge carrier extraction while achieving higher fill factor (FF) and open circuit voltage (Voc). We explore various potential methods for the photovoltaic industrial procedure, including optimizing metallization patterns and techniques, such as the utilization of a high efficiency grid pattern (HEGP) in place of the conventional straight finger-line pattern (SFLP). Co-firing conditions, peak firing temperature, and belt speed are investigated for their impact on FF enhancement, along with evaluating the effects of single and double print processes. Furthermore, we examine the efficacy of Nafion treatment as a surface modification technique to enhance FF. Experimental results indicate that optimizing co-firing conditions and employing Nafion treatment significantly increased FF to approximately 83.26% in p-TOPCon solar cells. Adjusting peak firing temperature and belt speed, as well as implementing the double print process, demonstrate superior FF enhancement compared to the single print process. Additionally, Nafion treatment contributes to FF improvement by passivating surface defects and enhancing charge carrier collection. Overall, this study provides valuable insights into optimizing co-firing conditions and surface treatment techniques to enhance p-TOPCon solar cell efficiency (Eff) up to 23.50%, thus advancing high-efficiency photovoltaic technology. © 2024 The Korean Society of Industrial and engineering Chemistry

关键词： Metallizing

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Risk assessment of data protection in the maritime industry using system-theoretic process analysis

Results in Engineering

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Results in engineering 2025年 26卷

作者： Yungratog, Sutthipong Kim, Hyungju Punurai, Wonsiri Thammaboosadee, Sotarat Technology of Information System Management Division Faculty of Engineering Mahidol University Nakorn Pathom Thailand Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology Trondheim Norway Department of Civil & Environmental Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand

The maritime industry is one of the most hazardous industries in the world. Risk assessment is applied in various contexts within this industry to prevent hazardous situations affecting systems and humans. Risk assessment in the maritime industry relates to accident situations and cybersecurity. In addition, many industries have studied data protection to mitigate risks associated with their customers’ personal data. The maritime industry has collected a significant amount of personal data within its systems, including passenger information, cargo details, and ship location data. Data protection assessment is a significant issue to security and privacy enhancement. In terms of data protection, The General Data Protection Regulation (GDPR) outlines risk assessment provisions in Article 35 (Data Protection Impact Assessment, DPIA), setting the standard for all industries. The risk assessment method is not specified as it depends on the organization's context and other related factors. system-Theoretic Process Analysis (STPA) is chosen and applied with DPIA as the assessment method in this study through a comparison with other methods commonly used in the maritime industry. STPA identifies risk causes in systems and human interactions, guiding mitigation efforts. This research aims to assess risk in data protection by applying STPA with DPIA for regulation alignment. The outcome shows the STPA approach in data protection assessment with four steps of analysis. This generic approach will enhance the processing of data protection in the maritime industry, both in operations and assessment, which could be applied in real business in the future. © 2025

关键词： Risk assessment

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Development and modeling of advanced systems Na2SnBr6-based perovskite solar cells: a comprehensive study on electron transport layers

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Optics Express 2025年第10期33卷 21152-21172页

作者： Dipta Roy Md Selim Reza Avijit Ghosh Anup Kumar Roy Bivash Ranjan Chowdhury Md Aktarujjaman H A Alrafai Abeer A Hassan Department of Electrical and Computer Engineering California State University at Northridge Northridge CA 91330 USA Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur-5400 Bangladesh Phillip M. Drayer Department of Electrical and Computer Engineering Lamar University Beaumont TX 77710 USA Department of Management Information System International American University Los Angeles CA 90010 USA Department of Information Technology Management Webster University St. Louis MO 63119 USA Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study simulates a n-i-p planar heterojunction for Na 2 SnBr 6 -based perovskite solar cells (PSCs) , with Na 2 SnBr 6 as the absorber layer, fluorine-doped tin oxide (FTO) as the substrate, and In 2 S 3 , IGZO, and SnS 2 as electron transport layers (ETLs). Gold (Au) is used as the back contact. Na 2 SnBr 6 was chosen for its low cost, non-toxicity, tunable band gap, excellent electrical properties, and stability. The SCAPS-1D simulator was employed to model the solar cell performance under AM 1.5 G light irradiation, focusing on factors like doping concentration, layer thickness, defect density, and interface defects across three device configurations: Device I (Au/Na 2 SnBr 6 / In 2 S 3 /FTO/Al), Device II (Au/ Na 2 SnBr 6 /IGZO/FTO/Al), and Device III (Au/ Na 2 SnBr 6 /SnS 2 /FTO/Al). Device III achieved the highest PCE of 31.35%, FF of 85.95%, V OC of 0.7926 V, and J SC of 46.01 mA/cm 2 2 , making it a promising candidate for high-efficiency, lead-free perovskite solar cells. Devices I and II showed lower efficiencies of 27.47% and 30.98%, respectively. The study also analyzed quantum efficiency (QE), carrier dynamics, and recombination rates, highlighting the potential of Device III in advancing Na 2 SnBr 6 -based hybrid perovskite solar technologies for future solar energy applications.

关键词： Material properties Solar cell efficiency Solar cells Solar energy Thin film solar cells Transparent conducting oxides

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Innovative Solutions in Breath Alcohol Measurement for Improving Road Safety

Innovative Solutions in Breath Alcohol Measurement for Impro...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Patiyuth Pramkeaw Pimyada Suksomboon Warit Leangruttananon Thittaporn Ganokratanaa Davids Makararpong Narumol Chumuang Worawut Yimyam Chayawee Angsusingha Adil Farooq Mahasak Ketcham Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Faculty of Science King Mongkut's University of Technology Thonburi Bangkok Thailand Thailand Business school The University of the Thai Chamber of Commerce Bangkok Thailand Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Business Information Management Phetchaburi Rajabhat University Phetchaburi Thailand The national broadcasting and Telecommunications Commission Bangkok Thailand Department of Electronic Engineering Maynooth University Ireland Management of Information Systems King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research presents the development and evaluation of a breath alcohol measurement device utilizing the MQ-3 alcohol sensor and an Arduino Nano-based control system. The primary objective was to design an accurate and reliable system for detecting breath alcohol concentration, with real-time feedback provided through an OLED display and an active buzzer for alerts. The device operates by measuring the alcohol concentration in the breath and converting the sensor data into parts per million (ppm). When the alcohol concentration exceeds the predefined threshold of 5% ABV, the system activates an audible alarm via the buzzer and displays the corresponding ppm value on the OLED screen. A series of tests were conducted with eight volunteers who consumed varying amounts of alcohol. The results demonstrated the system's high accuracy and responsiveness, with the ability to detect alcohol concentrations ranging from safe levels to dangerous thresholds. The device was tested multiple times to ensure consistency and minimize errors, with the final results confirming its effectiveness in providing reliable alcohol measurements. This system holds significant potential for applications in monitoring alcohol levels in drivers and individuals involved in safety-critical activities. The research underscores the importance of real-time alcohol detection systems in enhancing public safety and ensuring compliance with safety regulations.

关键词： Accuracy Measurement uncertainty Organic light emitting diodes Control systems Real-time systems Public security Nanoscale devices Monitoring Vehicles Testing

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An approach to parameter optimizer for music mood classification in machine learning

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AIP Conference Proceedings 2025年第1期3223卷

作者： Nunik Destria Arianti T. Thoyyibah Achmad Udin Zailani Neny Rosmawarni Rinna Rachmatika Achmad Lutfi Fuadi Information System University of Nusaputra Sukabumi Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten Indonesia Faculty of Information Technology Veterans National Development University Indonesia

Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an object that can be studied both through lyrics, audio, biographies and others. This research focuses on the mood of Indonesian music in the 70s and 80s. The data set used consists of text data and audio data. The mood tested consisted of sad, happy and neutral. This area of interest was investigated using the Crisp-DM method derived from business understanding, data understanding, data preparation, modelling, evaluation, deployment. In building the Confusion Matrix, this study uses several parameters, namely epoch, dimensions, set size, learning rate and others. The value generated by the confusion matrix contains a good 85 % value.

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Survival Analysis of Kasturi Tobacco Plants in Jember District using the Stratified Cox and Extended Cox Models

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MethodsX 2025年

作者： Angga Iryanto Pratama Dwi Rantini Ratih Ardiati Ningrum Arip Ramadan Fazidah Othman Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

关键词： Productive Agriculture Cox Proportional Hazard Stratified Cox Extended Cox Kasturi Tobacco

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