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Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15464-15479页

作者： Chandrasekhar, Abudukeremu Kadier ab Junying Wang ab K. Wang, Junying Chandrasekhar, K. Abdeshahian, Peyman Islam, M. Amirul Ghanbari, Farshid Bajpai, Mukul Katoch, Surjit Singh Bhagawati, Prashant Basavaraj Li, Hui Kalil, Mohd Sahaid Hamid, Aidil Abdul Abu Hasan, Hassimi Ma, Peng-Cheng Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China Center of Material and Opto-electronic Research University of Chinese Academy of Sciences Beijing 100049 China School of Civil and Environmental Engineering Yonsei University Seoul 03722 Republic of Korea Department of Biotechnology Engineering School of Lorena University of São Paulo São Paulo 12602-810 Brazil Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology Department of Electrical and Computer Engineering Faculty of Engineering Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Research Center for Environmental Contaminants (RCEC) Abadan University of Medical Sciences Abadan Iran Environmental Engineering Lab Civil Engineering Department National Institute of Technology Hamirpur Hamirpur Himachal Pradesh 177005 India Faculty of Engineering Department of Civil Engineering Annasaheb Dange College of Engineering and Technology Ashta Maharashtra 416301 India Department of Chemical and Process Engineering Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia School of Biosciences and Biotechnology Faculty of Science and Technology National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia Research Centre for Sustainable Process Technology Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H-2). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m(3) H-2/m(3) d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ?, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m(3) H-2/m(3) d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m(3) H-2/m(3) d was produced. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Microbial electrolysis cell (MEC) Hydrogen production Palm oil mill effluent (POME) Wastewater Optimization Response surface methodology (RSM)

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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Design and Control of a Two Switch Forward Converter with Supertwisting Sliding Mode

Design and Control of a Two Switch Forward Converter with Su...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Korhan Kayisli Ruhi Zafer Caglayan Resul Coteli Mariacristina Roscia Abdelkader Harrouz Abdelhakim Belkaid Electrical-Electronic Engineering Engineering Faculty Gazi University Ankara Turkiye Electrical- Electronic Engineering Engineering Faculty Computer Engineering Engineering Faculty Gazi University TED University Ankara Türkiye Dept. of Energy Systems Eng. Technology Faculty Firat University Elazig Turkiye University of Bergamo Bergamo Italy Dept. of Hydrocarbon and Renewable energy LDDI laboratory Ahmed Draia University Adrar Algeria Dept. of Electrical Engineering University of Bejaia Bejaia Algeria

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper presents the design and control of a two-switch forward converter utilizing a supertwisting sliding mode control (STSMC) strategy. The two-switch forward converter, known for its efficiency in medium-power applications, is equipped with a robust control method to ensure improved stability and dynamic performance under varying operating conditions. The supertwisting sliding mode approach is chosen for its ability to mitigate chattering effects, a common drawback in traditional sliding mode control, while ensuring precise tracking and disturbance rejection. Through comprehensive simulation, the proposed system demonstrates enhanced performance in terms of voltage regulation and improved transient response. The results indicate that the integration of STSMC significantly improves the converter's efficiency and robustness, making it suitable for various power conversion applications.

关键词： Robust control Transient response Renewable energy sources Simulation Switches Robustness Stability analysis Power conversion Voltage control Sliding mode control

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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IEEE Symposium on computers & Informatics (ISCI)

作者： Mas Omar Fitri Yakub Mohamad Sofian Abu Talip Mohd Nazmin Maslan Vijay Kumar Sinha Muljono Muljono WEE Laboratory Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Electronic System Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Universiti Teknikal Malaysia Melaka Malaysia Department of Computer Science & Engineering School of Engineering & Technology Chitkara University Himachal Pradesh India Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement System. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications.

关键词： Accuracy Neurons Transportation Predictive models Traffic control Transformers Road traffic

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A systematic review of ulcer detection methods in wireless capsule endoscopy

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Informatics in Medicine Unlocked 2024年 51卷

作者： Musha, Ahmmad Hasnat, Rehnuma Mamun, Abdullah Al Hossain, Md Sohag Hossen, Md Jakir Ghosh, Tonmoy Department of Electrical and Electronic Engineering Pabna University of Science and Technology Bangladesh Department of Electrical and Computer Engineering Oakland University United States School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Engineering and Technology Multimedia University Malaysia Department of Electrical and Computer Engineering The University of Alabama Tuscaloosa AL United States Department of Electrical and Electronic Engineering Feni University Feni Bangladesh

Background: Ulcers are one of the most prevalent disorders in the gastrointestinal (GI) tract, affecting many people worldwide. Wireless capsule endoscopy (WCE) emerges as the most non-invasive way to diagnose ulcers in the GI tract. However, manually reviewing images captured by WCE is a tedious and time-consuming process. Implementing a computer-aided ulcer detection system can facilitate the automatic evaluation of these images. Methods: Many researchers have proposed various models to develop automatic ulcer detection methods. This research aims to conduct a systematic review by scouring four repositories (Scopus, PubMed, IEEE Xplore, and ScienceDirect) for all original publications on computer-aided ulcer detection published between 2011 and 2024. The review follows the the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Results: The full texts of 89 scientific articles were reviewed. The contributions of this paper are two-fold: I) it reports and summarizes the current state-of-the-art ulcer detection algorithms;and II) it finds the most appropriate and preferable method in terms of color space, region of interest selection, feature extraction, and classifier. Conclusion: The paper concludes with a discussion of the challenges and futuredirections for ulcer detection. © 2024 The Authors

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Watermarking and Encryption for electronic Medical Records (EMR) Security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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Real-Time Power Monitoring and Fault Detection in Power Distribution System Using FPGA and cRIO 9039

Real-Time Power Monitoring and Fault Detection in Power Dist...

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International Conference on Innovations in Science, engineering and technology (ICISET)

作者： Md Salman Khan Mithil Md Abu Talha Shameem Ahmad Khandoker Mainul Islam Department of Electrical Engineering and Computer Science Electrical Engineering (MS) South Dakota School of Mines & Technology Rapid City SD United States Department of Electrical and Electronic Engineering Faculty of Engineering American International University–Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This paper details developing and implementing a comprehensive power monitoring system, designed for real-time data acquisition and fault detection. Utilizing a cRIO 9039 platform, the system measures voltage and current, calculates RMS values, frequency, and complex power, and identifies fault conditions in voltage levels, frequency, or power factor. A unique aspect of our approach integrates FPGA-based data processing, ensuring high-speed data handling and error detection without interrupting primary data flow. The system also features a user-friendly interface for real-time data display and fault management. If any errors have been detected, the user will be notified using visual and audio warning and the reading and timestamp corresponding to the fault will be recorded. The system accurately monitors power metrics and detects deviations, enhancing industrial power management systems. The architecture, workflow, and user interface design align with industrial standards, offering practical insights into power system monitoring.

关键词： Visualization Voltage measurement Power measurement Fault detection Power system management User interfaces Real-time systems Frequency measurement Monitoring Field programmable gate arrays

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Optimal Design to Dual-Scale Channel Estimation for Sensing-Assisted Communication Systems

arXiv

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arXiv 2024年

作者： Bai, Zhiyue Hou, Fen Cai, Lin X. Shan, Hangguan State Key Laboratory of IOTSC Department of ECE Faculty of Science and Technology University of Macau China Department of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States

Sensing-assisted communication is critical to enhance the system efficiency in integrated sensing and communication (ISAC) systems. However, most existing literature focuses on large-scale channel sensing, without considering the impacts of small-scale channel aging. In this paper, we investigate a dual-scale channel estimation framework for sensing-assisted communication, where both large-scale channel sensing and small-scale channel aging are considered. By modeling the channel aging effect with block fading and incorporating CRB (Cramér-Rao bound)-based sensing errors, we optimize both the time duration of large-scale detection and the frequency of small-scale update within each subframe to maximize the achievable rate while satisfying sensing requirements. Since the formulated optimization problem is non-convex, we propose a two-dimensional search-based optimization algorithm to obtain the optimal solution. Simulation results demonstrate the superiority of our proposed optimal design over three counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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An Iterative Clipping and Filtering Algorithm for PAPR Reduction in OFDM System

An Iterative Clipping and Filtering Algorithm for PAPR Reduc...

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Microelectronics (IICM), Iranian International Conference on

作者： Abbas Nasri Mostafa Katebi Sirous Toofan Mohammad Mostafavi Department of Electronic Faculty of Engineering University of Zanjan Zanjan Iran Department of Electronic Faculty of Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tariz Tabriz Iran

Peak-to-average power ratio (PAPR) one of the most important issues in orthogonal frequency division multiplexing (OFDM). Iterative clipping and filtering (CF) is a high efficiency technique to reduce PAPR in OFDM systems, but it needs numerous iterations to minimize the peak regrowth problem. This paper presents a new CF method which requires one iteration. The previous method needs 2K+1 IFFT/FFT blocks where K is iterations number. By using this algorithm, the number of FFT and IFFT operations have been decreased to three. The simulated results show that the QPSK modulated OFDM signal with 528 sub-carriers achieves a P APR reduction of 6.1 dB by passing the OFDM signal from white Gaussian noise channel with a good bit error rate performance at the receiver.

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Stacking Classifier with Random Forest functioning as a Meta Classifier for Diabetes Diseases Classification 26

Stacking Classifier with Random Forest functioning as a Meta...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Ali, Maria Haider, Muhammad Nasim Lashari, Saima Anwar Sharif, Wareesa Khan, Abdullah Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Faculty of Computing The Islamia University of Bahawalpur Pakistan School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Pulau Pinang Nibong Tebal14300 Malaysia

Diabetes has been an offensive condition in recent years, and it can lead to major health problems. If diabetes is not addressed, it can lead to a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Diabetes condition must be addressed promptly to avoid a significant health risk. Machine learning algorithms can assist the doctor in identifying and diagnosing diabetes and other diseases. Different types of classifiers have been used to diagnose diabetes. To improve the performance of integrated flexible individual classifiers and lower the possibility of misclassifying a single instance, an ensemble approach named "Stacking Classifier" was developed. Several classifiers, such as Naïve Bayes, KNN, Linear regression, and decision tree (DT) were used but all these models have low accuracy. However, additional study is needed to detect diabetic condition due to a lack of major work and low accuracy. Therefore this study proposed an ensemble technique termed "Stacking Classifier" was designed to increase the performance of integrated flexible individual classifiers and reduce the probability of misclassifying a single instance. This study uses a variety of classifiers, including Naïve Bayes, KNN, Linear Discriminant Analysis, and Decision Tree, with Random Forest functioning as a Meta classifier. In terms of F-measure, Recall, Accuracy, and Precision, the proposed stacking classifier achieves a higher accuracy of 97.35 % when compared to current models such as Nave Naïve Bayes, KNN, Decision Tree, and Linear Discriminant Analysis, which are 74.60 %, 78.57 %, and 77.35 %, respectively. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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