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Combination of cellulose nanocrystal and graphene oxide as modifying agent for improving the performance of PVDF membranes

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Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Sofyana Muslim, Abrar Supardan, Muhammad Dani Ambarita, Aulia Chintia Arahman, Nasrul Doctoral Program School of Engineering Science Syiah Kuala University Department of Chemical Engineering Syiah Kuala University Graduate School of Environmental Management Universitas Syiah Kuala Research Center for Environmental and Natural Resources Universitas Syiah Kuala Oil Palm and Coconut Research Center Universitas Syiah Kuala

The effectiveness of polyvinylidene fluoride (PVDF) membranes in various applications is often hampered by fouling, necessitating the exploration of modification strategies to enhance their antifouling properties. This study investigates the effect of combining cellulose nanocrystal (CNC) and graphene oxide (GO) on PVDF membranes to address this issue. Six flat sheet membranes with different compositions were fabricated using a non-solvent-induced phase separation method. The resulting membrane (M5: CNC 1 % w/w and GO 0.25 % w/w) exhibited increased surface pores, a finger-like cross-sectional structure, and higher water content. The pure water flux (PWF) in the M5 membrane increased to 37 L/m²∙h, with a humic acid (HA) rejection rate of 60 % and excellent antifouling properties (98.32 %). In conclusion, this study successfully modified PVDF membranes by combining CNC and GO, resulting in membranes with improved properties and performance. The present study provides valuable insights into composite membrane design and offers potential solutions to fouling challenges. © 2024 The Authors

关键词： Antifouling Cellulose nanocrystals Composite membrane Graphene oxide Polyvinylidene fluoride

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A Systematic Literature Review of Knowledge Sharing Practices in Academic Institutions 3

A Systematic Literature Review of Knowledge Sharing Practice...

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Purba, Mariana Ermatita, Ermatita Abdiansah, Abdiansah Ayumi, Vina Noprisson, Handrie Ratnasari, Anita Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science South Sumatera Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665427333

Many knowledge-sharing activities take place in educational institutions, both formally and informally. If this activity is organized with good knowledge management, then this will support a faster knowledge sharing process to get quality and innovative ideas. This paper aims to obtain a summary of existing research, issues, and opportunities for further analysis on knowledge-sharing (KS) practices in academic institutions. The phases of the systematic literature review (SLR) method are described based on Kitchenham (2009) guideline. We found 21 articles, seven articles from IEEE, nine articles from Science Direct, and five articles from Springer. The collection of research data on knowledge sharing has been carried out in the United States, Australia, China, Cyprus, United Kingdom, Ghana, Hungary, Indonesia, Iran, Israel, Italy, Jordanian, Malaysia, and Saudi Arabia, with the most research locations being Malaysia (14%) and Saudi Arabia (19%). We also attempted to identify the research topic trend of knowledge sharing research in academic institutions. We found two types of research trends of knowledge sharing research in university, including factor analysis and architecture development. Of the two categories of research trends, 76% discussed factor analysis, and 24% discussed architectural development. We also identified six methods in KS research including Partial Least Square Structure Equation Modeling (PLS-SEM), questionnaire, data triangulation, focus groups discussion, observation and interview. Moreover, the most used method in related research is questionnaire, because it is a relatively inexpensive, fast and efficient way of collecting large amounts of information. © 2021 IEEE.

关键词： Factor analysis

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks

A Novel Blockchain-Based Strategy for Energy Conservation in...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Khemraj Sharma Rohit Goyal Sukesh Kumar Bhagat Sheenu Agarwal Girish Singh Bisht Mohemmed Hussien Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Department of Computer Science & Engineering Uttaranchal University Dehradun India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun India Department of Computer Science and Engineering Tula's Institute Dehradun India Dehradun India The Islamic university Najaf Iraq

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency.

关键词： Wireless communication Wireless sensor networks Adaptation models Machine learning algorithms Heuristic algorithms Scalability Sensors Blockchains Internet of Things Reliability

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Ravindra Pandey Aruna Sri Rongali S. Gopinath Nilesh Anute Forda Nama Sachin Pund Department - Management Hult International Business School Doctoral Information Technology/Information Technology Emphasis/Information Technology University of The Cumberlands US Department of Electronics And Communication Engineering Karpagam Institute of Technology Coimbatore Management ASM's Institute of Business Management and Research Pune Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets.

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TREATMENT OF DIRECT BLUE 15 (DB15) BY FERED-FENTON

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2022年第3期57卷 363-372页

作者： Marlina, Elin Purwanto, Purwanto Sudarno, Sudarno Sari, Ajeng Arum Doctoral Program of Environmental Science School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Environmental Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Research Unit for Clean Technology Indonesian Institute of Sciences Bandung Indonesia

The Fered-Fenton process is one of the proposed options across the set of electrochemical oxidation processes. The primacy of this process is in-situ production of oxidation factors such as the hydroxyl radical (OH•). In this study, the Fered-Fenton process was adopted for the degradation of Direct Blue 15 (DB15) in wastewater by variating operational factors such as initial pH, applied current, and H2O2 and Fe2+ concentrations. The efficiency of color removal, chemical oxygen demand (COD) removal, functional group removal, and energy consumption of the Fered-Fenton process was evaluated. According to the results of this research, color and COD was degraded up to 90% and 86%, respectively, using graphite and Ti/Pt as electrodes. The presence of C-O absorption at wave number 1129.17 cm-1 also strengthens the presence of OH groups attached to the carbon chain. The energy consumption of this treatment was 20.4 kWh/L after 120 min. Therefore, the Fered-Fenton method is environmentally friendly for dye wastewater treatment. © 2022 Science Press. All rights reserved.

关键词： Wastewater treatment

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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods 4

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Kesuma, Lucky Indra Ermatita, Ermatita Erwin, Erwin Sari, Purwita Purabaya, Rudhy Ho Universitas Sriwijaya Doctoral Program in Engineering Faculty of Engineering Palembang Indonesia Universitas Sjakhyakirti India Universitas Sriwijaya Information System Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer System Palembang Indonesia Universitas Sriwijaya Informatic Management Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well. © 2022 IEEE.

关键词： Image quality

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Specific chemical bioexergy and microwave-assisted torrefaction optimization via statistical and artificial intelligence approaches

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Fuel 2023年 333卷

作者： Chen, Wei-Hsin Aniza, Ria Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan International Doctoral Degree Program on Energy Engineering National Cheng Kung University Tainan701 Taiwan

The artificial neural network (ANN) in artificial intelligence (AI) is a computational model that portrays how nerve cells (neurons) work in the human brain. Meanwhile, specific chemical bioexergy (SCB) is a vital indicator to provide essential information on how high the actual energy may be contained within biomass fuel (biofuel). In this study, the Taguchi method, analysis of variance (ANOVA), and artificial neural network (AI-ANN) are utilized to predict the SCB of biofuels from the spent mushroom substrate (SMS) torrefaction via microwave-assisted heating (MAH). In Taguchi's orthogonal array, washing, catalyst, and power operating parameters are considered. Acid and water washings lower the SMS's ash content by 58.45 % and 36.29 %, respectively. The optimum conditions for combining with acid washing, a catalyst with higher Fe2O3 (35 %), and microwave power 540 W render the highest total SCB in biofuels (biochar + bio-oil) of 47.90 MJ·kg−1, which is close to the SCB of crude oil derivatives (41–49 MJ·kg−1). The enhancement of biomass's SCB value from optimal torrefaction approximately 3 folds (256.30 %) from 12.84 (raw) to 47.90 MJ·kg−1. The ANN model with an architecture of 1 hidden layer (sigmoid activation function) with 3 neurons and the output layer (piecewise linear activation function) with a quick propagation algorithm for the training process of all layers poses excellent prediction with high accuracy R2 = 1. This result demonstrates that ANN with the designed scheme is suitable for predicting the SCB of SMS-derived biofuels. © 2022 Elsevier Ltd

关键词： Taguchi methods

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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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Informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： Computer vision Fetal echocardiography Object detection YOLOv7

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Optimizing Task Execution in Robotic Swarms: Coordination Strategies for Enhanced Efficiency

Optimizing Task Execution in Robotic Swarms: Coordination St...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vandna Bansla Inderjeet Kaur Rudramani Bhutia Girish Singh Bisht Khemraj Sharma Shivalik Engineering College Dehradun U.K India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P. India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Business Administration Doctoral Program in Management Sciences NANHUA UNIVERSITY Dalin Chiayi R.O.C

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The “swarm robotics” is a relatively recent field of study that investigates the ways in which several robot systems interact with one another throughout the course of time. The organization is able to accomplish its goals by delegating work to a large number of robots that are capable of doing activities that are beyond the capabilities of a single robot. This is accomplished via the use of a broad variety of different methods. It is possible to make use of optimization algorithms in robotic swarms in order to discover answers to difficult trans disciplinary challenges that are encountered in real-world environments. The fact that algorithms for swarm intelligence are able to adjust to changing circumstances makes the potential of this happening a reality. In order to achieve the highest possible level of performance, this study analyses a broad range of different swarm optimization algorithms that may be used to robotic swarm scenarios. This category contains a variety of methodologies, including PSO, GPSO, CSO, ACO, and GSO, among others. Within this section, a comprehensive analysis is provided for each of the strategies that came before it, and an inquiry into the potential future course of action for swarm optimization is furthermore carried out. The ramifications of these tactics are also discussed in this section, which is included in the overall debate.

关键词： Robot kinematics Swarm robotics Organizations Planning Particle swarm optimization Informatics Optimization

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Sensing Architecture in Smart City: A Review Paper

Sensing Architecture in Smart City: A Review Paper

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International Conference on ICT For Smart Society (ICISS)

作者： Mindit Eriyadi Suhono Harso Supangkat Fadhil Hidayat Doctoral Program of Instiut Teknologi Bandung Bandung Indonesia School of Applied Science Universitas Telkom Bandung Indonesia School of Electrical Engineering and Informatics Instiut Teknologi Bandung Bandung Indonesia Smart City and Community Innovation Center Institut Teknologi Bandung Bandung Indonesia

Relevant smart city research involves many highly futuristic applications, such as smart transportation, in which smart roads warn drivers of impending traffic jams, smart parking, which communicates the location of available parking spaces to drivers, and smart environments, which allow homes and workplaces to fully automatically adjust their temperature to save energy. Complexity in the city sensing section is caused by the complexity of urban systems. The enormous number of sensor types and technologies utilized for municipal data collecting provide a problem. Some of the issues in city sensing are node deployment, sensing management, sensor network types, and their derivative variables, necessitating the development of a city sensing architecture capable of contributing to advances in efficiency, accuracy, and real-timeness.

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