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Effects of pyrolysis temperature and impregnation ratio on adsorption kinetics and isotherm of methylene blue on corn cobs activated carbons

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South African Journal of Chemical Engineering 2022年 42卷 91-97页

作者： Putranto, Aditya Ng, Zi Wei Hadibarata, Tony Aziz, Muhammad Yeo, Jason Yi Juang Ismadji, Suryadi Sunarso, Jaka Discipline of Chemical Engineering School of Engineering Monash University Malaysia Bandar Sunway Malaysia Department of Environmental Engineering Curtin University CDT 250 Miri 98009 Sarawak Malaysia Institute of Industrial Science The University of Tokyo 4-6-1 Komaba Meguro-ku 153-8550 Japan Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching 93350 Malaysia Department of Chemical Engineering Widya Mandala Surabaya Catholic University Kalijudan 37 Surabaya 60114 Indonesia

The rapid development of textile industries in developing countries has resulted in enormous dye waste, which remains problematic in some areas due to the lack of advanced technology. Adsorption appears as an efficient method for treating dye waste, which often relates to the use of conventional activated carbons produced from non-renewable sources such as coal. On the other hand, biomass is greatly available in developing countries and represents as a cheaper carbon source in the production of activated carbon for dye waste treatment. This research addresses the potential of corn cobs as the raw material of activated carbon, where the synthesis of corn cob-derived activated carbon consists of chemical impregnation by phosphoric acid into corn cobs followed by the pyrolysis process at varied impregnation ratios and temperatures. The experiment showed that the development of pores was enhanced by increasing the pyrolysis temperature but heating beyond 550 °C may cause the pores to collapse. Besides, the optimum impregnation ratio was found to be 4.5:1 as the adsorption kinetics ceased to improve at higher ratio. In addition, the adsorption kinetics were well represented by the pseudo-second-order kinetics model, whereas further investigation revealed that intra-particle step existed but did not control the adsorption process. Moreover, multilayer adsorption is implied by the good agreement of the adsorption data with the Freundlich isotherm. © 2022

关键词： Adsorption Corn cob Isotherm Kinetics

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Human Age Estimation Based on Dental Images

Human Age Estimation Based on Dental Images

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nur Ain Raidzan Itaza Afiani Mohtar Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Teknologi MARA Perak Branch Computer Science Studies College of Computing Informatics and Mathematics Universiti Perak Malaysia Faculty of Electrical Engineering & Technology Campus Pauh Putra University Malaysia Perlis Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

Predicting age is one of the most important characteristics in the forensic context for human identification because biological profiles can only be effectively rebuilt by estimating the most precise age. Humans usually count their age using calendars that are pre-programmed to keep track of days. It is not difficult for any living human being to measure their age, but it becomes a challenge when forensics needs to determine the exact age of a dead person involved in a criminal case. By comparing the stage of tooth development in X-rays to established dental growth norms, forensic scientists may estimate age. To deal with such problems, an age estimate system that predicts a person's real age group is required. In this paper, Convolutional Neural Network (CNN) was used to build Human Age Estimation System that can predict a person's actual age group. The system employs Inception-v3 architecture to create the model. The dataset consists of 1404 dental X-ray images of pre-teen in Malaysia. The model was trained using 60% of the dataset, and the remaining 40% was used for testing. Accuracy, precision, recall, and f-measure were used to assess the model. The model achieved 82%, 78%, 76%, and 77%, respectively. As a conclusion, a large amount of data is required for the training and testing process to acquaint the model with the pattern of tooth growth by age group. It is also crucial to rule out any illnesses that impact tooth growth, as well as radiographic images that are not of good quality.

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Beyond Reality: A Synthesis Analysis of Metaverse-Based Immersive Learning Experience

Beyond Reality: A Synthesis Analysis of Metaverse-Based Imme...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mohd Ekram Alhafis Bin hashim Wan Azani Mustafa Suraya Md Nasir Nur Safinas AlBakry Nor Hazlen Binti Kamaruddin Mohd Farizal Puadi Mohammed I. Habelalmateen Zainab abed Almoussawi Multimedia Creative Department Faculty of Art Sustainability and Creative Industry Sultan Idris Educational University Faculty of Electrical Engineering Technology Advanced Computing (AdvCOMP) UniCITI Alam Campus Centre of Excellence Universiti Malaysia Perlis Sungai Chuchuh Padang Besar Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Islamic Science Ahl Al Bayt University karbala Iraq

The incorporation of metaverse-based immersive learning experiences marks a paradigm leap beyond traditional pedagogical bounds in the ever-changing environment of education technology. This synthesis review digs into the world of metaverse and education, delving into the multiple elements and consequences of immersive learning in the metaverse. The review conducts a systematic analysis of a large body of literature on the design, implementation, and effects of metaverse-based educational initiatives. The synthesis illustrates the various applications of immersive technologies in promoting enhanced learning settings, transcending physical classroom boundaries. This review exposes the potential of metaverse-based immersive learning experiences to engage learners, promote creativity, and facilitate collaborative knowledge production by combining findings from numerous studies. Furthermore, the synthesis explores critically the obstacles and considerations involved in the adoption of these technologies, addressing themes such as accessibility, ethics, and pedagogical efficacy. This review presents a sophisticated perspective of the metaverse's role in determining the future of education as we venture beyond the confines of reality. It provides guidance for educators, academics, and policymakers interested in harnessing the transformative power of immersive learning experiences in the metaverse. The synthesis not only consolidates existing information, but it also prepares the path for future research projects aimed at maximizing the educational potential of this developing technological frontier.

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Enhancing drug-target interaction predictions in context of neurodegenerative diseases using bidirectional long short-term memory in male Swiss albino mice pharmaco-EEG analysis

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Heliyon 2024年第21期10卷 e39279页

作者： Qureshi, Shahnawaz Iqbal, Syed Muhammad Zeeshan Ameer, Asif Karrila, Seppo Ghadi, Yazeed Yasin Shah, Syed Aziz Intelligent Biomedical Application Lab Sino-Pak center for Artificial Intelligence School of Computing Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Haripur 22620 Pakistan Research and Development BrightWare LLC Riyadh 13323 Saudi Arabia Department of Computer Science National University of Computing and Emerging Sciences Faisalabad 38000 Pakistan Faculty of Science and Industrial Technology Prince of Songkla University Surat Thani Campus Muang Surat Thani 84000 Thailand Department of Computer Science Al Ain University Abu Dhab Al Ain United Arab Emirates Healthcare Sensing Technology Faculty Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom

Background and Objective: Emerging diseases like Parkinson or Alzheimer's, which are not curable, endanger human mental health and are challenging to research. Drug target interactions (DTI) are pivotal in the screening of candidate drugs and focus on a small pool of drug targets. Electroencephalogram shows the responses to psychotropic medicines in the brain bioelectric activity. Synaptic activity can be analyzed by using Local Field Potential recordings obtained from micro-electrodes implanted in the brain. The aim is to evaluate the effects of drug on brain bioelectric activity and increase the drug classification accuracy. The ultimate goal is to advance our understanding of how drugs affect synaptic activity and open the door to more focused treatment for neurodegenerative diseases. Methods: In this study, Pharmaco-EEG recordings are processed using Advanced neural network models, particularly Convolutional Neural Networks, to assess the effects of medications. The five different medicines used in this study are Ephedrine, Fluoxetine, Kratom, Morphine, and Saline. The signals observed are local field potential signals. To overcome some limits of DTI prediction, we propose Bidirectional Long Short-Term Memory (LSTM) for the categorization of intracranial EEG (i-EEG) data, departing from standard approaches. Similar EEG patterns are presumably caused by drugs that work by homologous pharmacological pathways, producing similar psychotropic effects. To improve accuracy and reduce training loss, our study introduces a bidirectional LSTM model for classification along with Bayesian optimization Results: High recall, precision, and F1-Scores, particularly a 95% F1-Score for morphine, ephedrine, fluoxetine, and saline, suggest good performance in predicting these drug classes. Kratom produces a somewhat lower recall of 94%, but a high F1-Score of 97% and perfect precision of 1.00. The weighted average F1-Score, macro average, and overall accuracy are all consistently h

关键词： Convolutional neural network Drug target interaction Intracranial EEG Local field potential Long short-term memory

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Highly Stable 3d-Printed Monolithic Structured Al2o3-Supported Ni-Based Catalysts for Dry Reforming of Methane

SSRN

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SSRN 2023年

作者： Zhang, Guangcai Zhang, Jinjin Song, Jian Yu, Fangyong Wong, Ngie Hing Sunarso, Jaka Yang, Naitao Meng, Bo Tan, Xiaoyao Liu, Shaomin School of Chemistry and Chemical Engineering Shandong University of Technology Zibo255000 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia State Key Laboratory of Separation Membranes and Membrane Processes Department of Chemical Engineering Tiangong University Tianjin300387 China

Nickel (Ni)-based powder catalysts with poor stability used in dry reforming of methane (DRM) suffer from severe coking and high bed pressure drop. We successfully prepared a monolithic structured NiAl2O4/Al2O3 catalyst using 3D printed α-Al2O3 topological support followed by Ni impregnation and calcination at 1,400 oC to improve the stability and decrease the pressure drop. The presence of Ni with strong metal-carrier binding in Ni/NiAl2O4/Al2O3 catalyst reduced from NiAl2O4/Al2O3 could inhibit coke accumulation effectively and exhibit excellent catalytic performance and stability. During the 750-h stability test, Ni/NiAl2O4/Al2O3 catalyst exhibited 3% CH4 conversion degradation and 0.37 mgC h-1 gcat-1 coke accumulation rate. The formation mechanisms of two different types of cokes were proposed to explain the performance. This work serves as a guide for preparing the monolithic structured Ni-based catalyst with outstanding stability and low pressure drop for practical application in DRM. © 2023, The Authors. All rights reserved.

关键词： Coke

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CAE-ResVGG FusionNet: A Feature Extraction Framework Integrating Convolutional Autoencoders and Transfer Learning for Immature White Blood Cells in Acute Myeloid Leukemia

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Heliyon 2024年第19期10卷 e37745页

作者： Elhassan, Tusneem Osman, Ahmed Hamza Mohd Rahim, Mohd Shafry Mohd Hashim, Siti Zaiton Ali, Abdulalem Elhassan, Esmaeil Elkamali, Yusra Aljurf, Mahmoud Cancer Center of Excellence King Faisal Specialist Hospital and Research Center Riyadh 11564 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia School of Computing Universiti Teknologi Malaysia Johor Johor Bahru 81310 Malaysia Faculty of Computer & Information Technology Sohar University Sohar Oman Institute of Computer Science and Digital Innovation UCSI University Federal Territory of Kuala Lumpur Faculty of mathematical science university of Khartoum Sudan Dept of Hematology Stem Cell Transplantation and Cellular Therapy King Faisal Specialist Hospital & Research Centre Riyadh Saudi Arabia

Acute myeloid leukemia (AML) is a highly aggressive cancer form that affects myeloid cells, leading to the excessive growth of immature white blood cells (WBCs) in both bone marrow and peripheral blood. Timely AML detection is crucial for effective treatment and patient well-being. Currently, AML diagnosis relies on the manual recognition of immature WBCs through peripheral blood smear analysis, which is time-consuming, prone to errors, and subject to inter-observers’ variation. This study aimed to develop a computer-aided diagnostic framework for AML, called "CAE-ResVGG FusionNet", that precisely identifies and classifies immature WBCs into their respective subtypes. The proposed framework leverages an integrated approach, by combining a convolutional autoencoder (CAE) with finely tuned adaptations of the VGG19 and ResNet50 architectures to extract features from CAE-derived embeddings. The process begins with a binary classification model distinguishing between mature and immature WBCs followed by a multiclassifier further classifying immature cells into four subtypes: myeloblasts, monoblasts, erythroblasts, and promyelocytes. The CAE-ResVGG FusionNet workflow comprises four primary stages, including data preprocessing, feature extraction, classification, and validation. The preprocessing phase involves applying data augmentation methods using geometric transformations and synthetic image generation using the CAE to address imbalance in the WBC distribution. Feature extraction involves image embedding and transfer learning, where CAE-derived image representations are used by a custom integrated model of VGG19 and ResNet50 pretrained models. The classification phase employs a weighted ensemble approach that leverages VGG19 and ResNet50, where the optimal weighting parameters are selected using a grid search. The model performance was assessed during the validation phase using the overall accuracy, precision, and sensitivity, while the area under the receiver charact

关键词： Acute myeloid leukemia Autoencoder blood smear microscopic images Cancer Computer-aided diagnosis model image classification Immature white blood cells ResNet VGG19

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Cognitive-inspired Post-processing of optical character recognition for Swedish addresses

Cognitive-inspired Post-processing of optical character reco...

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IEEE International Conference on Cognitive Informatics

作者： Moa Andersson Summrina Kanwal Faiza Khan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Sweden School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Centre for Applied Intelligent Systems Research Halmstad University Sweden Faculty of Computing Riphah International University Islamabad Pakistan

Optical character recognition (OCR) has many ap-plications, such as digitizing historical documents, automating processes, and helping visually impaired people read. However, extracting text from images into a digital format is not an easy problem to solve, and the outputs from the OCR frameworks often include errors. The complexity comes from the many variations in (digital) fonts, handwriting, lighting, etc. To tackle this problem, this thesis investigates two different methods for correcting the errors in OCR output. The used dataset consists of Swedish addresses. The methods are therefore applied to postal automation to investigate the usage of these methods for further automating postal work by automatically reading addresses on parcels using OCR. The first method, the lexical implementation, uses a dataset of Swedish addresses so that any valid address should be in this dataset (hence there is a known and limited vocabulary), and misspelled addresses are corrected to the address in the lexicon with the smallest Levenshtein distance. The second approach uses the same dataset, but with artificial errors, or artificial noise, added. The addresses with this artificial noise are then used together with their correct spelling to train a machine learning model based on Neural machine translation (NMT) to automatically correct errors in OCR read addresses. The results from this study could contribute by defining in what direction future work connected to OCR and postal addresses should go. The results were that the lexical implementation outperformed the NMT model. However, more experiments including real data would be required to draw definitive conclusions as to how the methods would work in real-life applications.

关键词： Vocabulary Automation Computational modeling Optical character recognition Lighting Machine learning Complexity theory

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Development of a techno-economic framework for natural gas dehydration via absorption using tri-ethylene glycol: A comparative study between DRIZO and other dehydration processes

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South African Journal of Chemical Engineering 2020年 31卷 17-24页

作者： Kong, Zong Yang Melvin Wee, Xin Jie Mahmoud, Ahmed Yu, Aimin Liu, Shaomin Sunarso, Jaka Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Kuching 93350 Sarawak Malaysia Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn 3122 Victoria Australia Department of Chemical Engineering Curtin University Perth 6845 WA Australia

In this work, the previously developed techno-economic framework in Kong et al. (2018) is extended to evaluate three different configurations of DRIZO process for natural gas dehydration: Case (i) – recycling of the regenerator overhead, Case (ii) – recycling of the flash vapour, and Case (iii) – recycling of both the flash vapour and the regenerator overhead into the dehydration system. The best configuration of DRIZO process is Case (iii), which is used as a representative case for subsequent comparison against other dehydration processes evaluated in our previous work. Our techno-economic analysis reveals that Case (iii) can regenerate tri-ethylene glycol (TEG) with the highest purity and the lowest TEG loss rate and volatile organic compound (VOC) emission. This translates to the lower water content in the dry product gas and also the higher HHV relative to Case (i) and Case (ii). A higher HHV generally signifies a higher gross profit margin. Nevertheless, DRIZO process is not economically feasible in comparison to the other dehydration processes from our previous work, mainly because of the high capital expenditure and the associated operating cost. Relative to the other dehydration processes, although the gross profit margin for DRIZO process is about $100,000 higher, it is not sufficient to compensate the high capital and operating expenses due to the high TCOP of about $500,000 to $600,000. Therefore, our analysis recommends the use of stripping gas dehydration process that consumes a portion of dry product gas to achieve the desired water dew point specification for its highest gross profit margin. © 2019 The Author(s)

关键词： Aspen HYSYS Dehydration Glycol regeneration Natural gas Techno-economic analysis Tri-ethylene glycol

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Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping

Continuous Human Activity Recognition using Radar Imagery an...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruchita Mehta Vasile Palade Sara Sharifzadeh Bo Tan Yordanka Karayaneva Centre for Computational Science and Mathematical Modelling Coventry University Coventry UK Department of Computer Science Swansea University Swansea UK Faculty of Technology and Communication Sciences Tampere University Finland School of Computing Engineering & Digital Technologies Teesside University Middlesbrough UK

Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using mm-wave Doppler radar. Unlike most previous research, this work records the data in terms of continuous series of activities rather than individual activities. These series of activities are similar to real-life activity patterns. The Dynamic Time Warping (DTW) algorithm is used for the detection of human activities in the recorded time series of data and compared to other time-series classification methods. DTW requires less amount of labelled data. The input for DTW was provided using three strategies, and the obtained results were compared against each other. The first approach uses the pixel-level data of frames (named UnSup-PLevel). In the other two strategies, a Convolutional Variational Autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. Results demonstrates the superiority of the Sup-EnLevel features over UnSup-EnLevel and UnSup-PLevel strategies. However, the performance of the UnSup-PLevel strategy worked surprisingly well without using annotations.

关键词： Heuristic algorithms Time series analysis Pipelines Radar detection Streaming media Radar imaging Feature extraction

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Evaluation of Machine Learning Techniques for Prediction of Breast Cancer

Evaluation of Machine Learning Techniques for Prediction of ...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Jared Tan Yi Hoong Samuel-Soma M. Ajibade Muhammed Basheer Jasser Anwar P.P. Abdul Majeed David Olayemi Alebiosu Bayan Issa Anthonia Oluwatosin Adediran Charis Shwu Chen Kwan Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Department of Smart Computing and Cyber Resilience School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

The prediction of breast cancer prognosis has been a significant challenge in medical research due to the complex nature of cancer progression and the variability in patient-specific factors. Machine learning techniques have emerged as powerful tools for enhancing prediction accuracy in cancer diagnosis and prognosis. This study evaluates the performance of three widely used machine learning classifiers—Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting breast cancer outcomes using the Wisconsin Breast Cancer Database. A robust framework was designed to preprocess the dataset, scale features, and assess the classifiers using key evaluation metrics, including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The results reveal that all classifiers demonstrated high predictive accuracy, with SVM achieving the highest accuracy of 97.08%, followed by RF and ANN with 96.67%. While RF recorded superior precision (95.40%), SVM outperformed others in recall (97.70%) and F1-score (96.05%), showcasing its ability to balance prediction precision and sensitivity. These findings underscore the potential of machine learning to augment traditional cancer prognosis methods, providing healthcare professionals with data-driven insights for personalized treatment strategies. This research highlights the critical role of machine learning in transforming cancer care and lays the groundwork for future studies focused on hybrid models and real-world implementation.

关键词： Support vector machines Accuracy Machine learning Medical services Artificial neural networks Breast cancer Prognostics and health management Random forests Medical diagnostic imaging Classification tree analysis

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