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Effect of channel thickness on the particle diffusion and permeability of carbon nanotubes a membrane in reverse electrodialysis process using molecular dynamics simulation

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International Communications in Heat and Mass Transfer 2025年 166卷

作者： Sun, Shuai Basem, Ali Singh, Narinderjit Singh Sawaran Al-zahy, Younis Mohamed Atiah Saeidlou, Salman Muzammil, Khursheed Salahshour, Soheil Sajadi, S. Mohammad Sahramaneshi, Hani School of Energy and Constructional Engineering Shandong Huayu University of Technology Shandong Dezhou253034 China Faculty of Engineering DongShin University Jeollanam-do Naju-si58245 Korea Republic of Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai71800 Malaysia Department of Physics College of Education Misan University Maysan Iraq School of Engineering Technology and Design Canterbury Christ Church University Kent United Kingdom Department of Public Health College of Applied Medical Sciences Khamis Mushait Campus King Khalid University Abha62561 Saudi Arabia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Chemistry Payam e Noor University Saqqez Branch Kurdistan Saqqez Iran Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Adopting innovative technology and solutions is critical for ensuring clean water. Several methods may be used to remove salts from water. They may be divided into two categories: membranes and heat. Reverse electrodialysis, which uses a membrane, is an efficient way of separating substances. Prior research investigated system-level factors, but the nanoscale mechanisms that drive ion and water penetration across membranes were poorly understood. This study closed a research gap by investigating the influence of carbon nanotube membrane thickness on particle mobility and fluid dynamics in reverse electrodialysis systems. The research is contributed to the enhancement of energy conversion efficiency and membrane performance in reverse electrodialysis systems by offering a comprehensive understanding of the influence of channel thickness on particle transport and selectivity through the carbon nanotube membrane. Molecular dynamics simulations using the LAMMPS software package are conducted to examine the effect of carbon nanotube thickness variation (1-layer vs 2-layer) on fluid flow, ionic current, hydrogen bonding, and fluid density. To the findings, increasing the thickness of a carbon nanotube from one layer to two layers decreases the fluid flow rate to 203.79 atoms/ns and the current from 5.31 e/ns to 5.15 e/ns. Additionally, the number of broken hydrogen bonds decreases from 116 to 105, indicating decreased permeability and increased stability of the hydrogen-bonding network. In addition to offering useful information for the construction of more effective and selective membranes in renewable energy applications, these results provided a molecular understanding of how carbon nanotube thickness affected reverse electrodialysis effectiveness. © 2025 Elsevier Ltd

关键词： Flow separation

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A hybrid steganography framework using DCT and GAN for secure data communication in the big data era

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Scientific Reports 2025年第1期15卷 1-23页

作者： Malik, Kaleem Razzaq Sajid, Muhammad Almogren, Ahmad Malik, Tauqeer Safdar Khan, Ali Haider Altameem, Ayman Rehman, Ateeq Ur Hussen, Seada Department of Computer Science Air University Islamabad 44230 Pakistan Chair of Cyber Security Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia Department of Information and Communication Technology Bahauddin Zakariya University Punjab Multan 60800 Pakistan Department of Software Engineering Faculty of Computer Science Lahore Garrison University Punjab Lahore 54000 Pakistan College of Computer Science Beijing University of Technology Beijing 100124 China School of Computing Gachon University Seongnam-si 13120 South Korea Department of Electrical Power Adama Science and Technology University Adama 1888 Ethiopia

The growth of the internet and big data has spurred the demand for more extensive information hoarding to store and distribute information. In today’s digital era, ensuring the security of data transmission is paramount. Advancements in digital technology have facilitated the proliferation of high-resolution graphics over the Internet, raising security concerns and enabling unauthorized access to sensitive data. Researchers have increasingly explored steganography as a reliable method for secure communication because it plays a crucial role in concealing and safeguarding sensitive information. This study introduces a novel and comprehensive steganography framework using the discrete cosine transform (DCT) and the deep learning algorithm, generative adversarial network. By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. While conventional steganography methods are typically classified into spatial and transform domains, extensive research and analysis demonstrate that the hybrid approach surpasses individual techniques in performance. The experimental results validate the effectiveness of the proposed steganography approach, showcasing superior visual image quality with a mean square error (MSE) of 93.30%, peak signal-to-noise ratio (PSNR) of 58.27%, root mean squared error (RMSE) of 96.10%, and structural similarity index measure (SSIM) of 94.20%, in comparison to existing leading methodologies. The proposed model achieved reconstruction accuracies of 96.2% using Xu Net and 95.7% with SR Net. By combining DCT with deep learning algorithms, the proposed approach overcomes the limitations of spatial domain methods, offering a more flexible and effective steganography solution. Furthermore, simulation results confirm that the proposed technique outperforms state-of-the-art methods across key performance metrics, inc

关键词： DCT Deep learning GAN Spatial domain Steganography

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Quantum Differentially Private Sparse Regression Learning

arXiv

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arXiv 2020年

作者： Du, Yuxuan Hsieh, Min-Hsiu Liu, Tongliang You, Shan Tao, Dacheng School of Computer Science Faculty of Engineering The University of Sydney JD Explore Academy Beijing101111 China Hon Hai Quantum Computing Research Center Taipei114 Taiwan Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Sydney2006 Australia SenseTime Beijing100080 China

The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differentially private (QDP) Lasso estimator to solve sparse regression tasks. Concretely, given N d-dimensional data points with N d, we first prove that the optimal classical and quantum non-private Lasso requires Ω(N+d) and Ω(√N+ √d) runtime, respectively. We next prove that the runtime cost of QDP Lasso is dimension independent, i.e., O(N5/2), which implies that the QDP Lasso can be faster than both the optimal classical and quantum non-private Lasso. Last, we exhibit that the QDP Lasso attains a near-optimal utility bound Õ(N−2/3) with privacy guarantees and discuss the chance to realize it on near-term quantum chips with advantages. © 2020, CC BY-NC-ND.

关键词： Machine learning

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COO-DuDo: computation overhead optimization methods for dual-domain sparse-view CT reconstruction

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Expert Systems with Applications 2025年 286卷

作者： Deng, Zihan Wang, Zhisheng Shan, Yuanlin He, Guohang Du, Tiantian Wang, Shunli Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin150080 China Ministry of Industry and Information Technology Harbin150080 China Artificial Intelligence Media Computing Laboratory School of Computer Science and Technology Harbin Engineering University Harbin150080 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning530000 China Faculty of Education University of Hong Kong Hong Kong

Recently, deep learning methods have shown exciting effects in Sparse-view CT reconstruction. The Dual-Domain (DuDo) deep learning method is one of the representative methods, and it can process the information in both the sinogram and image domains. However, the existing DuDo methods do not pay enough attention to the allocation of training costs and strategies for the two domains, which will result in wasted computing overhead or insufficient training in one of the two domains. In this paper, we propose a Computation-Overhead Optimization (COO) DuDo training strategy for sparse-view CT reconstruction, i.e., COO-DuDo. The training ratio of different domains is controlled by calculating their computation overhead, loss, and gradient variation of the loss. To make our COO-DuDo strategy enable sparse-view CT reconstruction better, we adopt a DuDo-Network (COO-DDNet) structure based on two coding-decoding-type subnetworks. The evaluation, animal and clinical experiments have verified the effectiveness of our training strategies and methods. Our research provides a broader perspective for dual-domain image restoration tasks from the perspective of computational overhead. © 2025 Elsevier Ltd

关键词： Deep learning

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Analytical simulations for rate type nanomaterial stretching flow with dual convection

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Ahmad, Manzoor Khan, Sami Ullah Quratulain, Syeda Adnan Waqas, M. Garalleh, Hakim AL Zamri, Nurnadiah Abduvalieva, Dilsora Gupta, Manish Department of Mathematics University of Azad Jammu & Kashmir Muzaffarabad 13100 Pakistan Department of Mathematics Namal University Mianwali 42250 Pakistan Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif 12080 AJ&K Pakistan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan Division of Research and Development Lovely Professional University Phagwara India

Owing to high thermal performances and stable properties, multiple applications of nanomaterials have been studied in different industrial and engineering processes. Ongoing continuous research in nanofluid suggested different applications of nanomaterials in catalysis, aerospace engineering, oil industry, various optical devices, medical imaging etc. The objective for exploring current investigation is to disclose the thermal impact of Oldroyd-B nanofluid comprising the buoyancy driven flow. The heat and mass transfer analysis is predicted with insight of Brownian motion and thermophoresis phenomenon. The flow is subject to bidirectional surface maintaining the uniform velocity. The zero-mass constraints are utilized for analyzing the flow phenomenon. The analytical treatment is suggested regarding the computations of developed system. Fundamental of thermal transport phenomenon are suggested with physical aspects. It is observed that velocity profile enhances with applications of buoyancy forces. The temperature profile reduces due to velocity ratio parameter. Current results present applications in cooling processes, thermal management devices, heating control, extrusion processes, chemical systems etc. © 2025

关键词： Analytical solution Buoyancy forces Mixed convection Oldroyd-B nanofluid Zero-mass constraints

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Heart failure prediction using machine learning technique

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AIP Conference Proceedings 2023年第1期2608卷

作者： Mohammad Naser Hasan Radaideh Hiam Alquran Wan Azani Mustafa Mohd Aminudin Jamlos Syed Zulkarnain Syed Idrus Department of Biomedical Engineering Jordan University of Science and Technology Al-Ramtha Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University 21163 Irbid Jordan Faculty of Electrical Engineering Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh 02100 Padang Besar Perlis Malaysia Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Faculty of Electronic Engineering Technology University of Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh 02100 Padang Besar Perlis Malaysia Faculty of Applied and Human Sciences Universiti Malaysia Perlis 01000 Kangar Perlis Malaysia

Heart failure is one of the familiar diseases that needs a special care and its prediction in early stage helps to take caution of its symptoms. By the invention of machine learning, some tasks become easier to handle like expectations, classification and clusstering. The main goal of this article is to predict heart failure using the patient record. This paper focus on building more than one model to predict the prescne or absence of heart failure. These models aim to reduce the risk of death and to make the life is more adaptive with this non-cureable disease. The three models are built with different number of features. The comparisin between these models is evaluated. The first model is build with 12 features and it achived the highest accurcy 84.6% in Bagged Tree classifier, whereas the second model is exploited with 6 features, its accuracy reduced to 75.6% Cosine KNN classifier. The last model is performed around 75.9 % with just two features. This study recommend s to use the whole record fetures to obtain the highest accuracy for predicting heart failar cases.

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Effects of Green Solvents and Surfactants on the Characteristics of Few-Layer Graphene Produced by Dual-Frequency Ultrasonic Liquid Phase Exfoliation Technique

SSRN

引用

SSRN 2022年

作者： Tyurnina, Anastasia V. Morton, Justin A. Kaur, Amanpreet Mi, Jiawei Grobert, Nicole Porfyrakis, Kyriakos Tzanakis, Iakovos Eskin, Dmitry G. Brunel Centre for Advanced Solidification Technology Brunel University London Kingston Lane UB8 3PH United Kingdom School of Engineering Computing and Mathematics Oxford Brookes University College Cl Wheatley OxfordOX33 1HX United Kingdom Department of Engineering University of Hull Cottingham Rd HullHU6 7RX United Kingdom Department of Materials University of Oxford Parks Rd OxfordOX1 3PH United Kingdom Faculty of Engineering and Science University of Greenwich Park Row LondonSE10 9LS United Kingdom Tomsk State University 36 Lenin. Ave. Tomsk634050 Russia

Nowadays, one of the promising methods for scalable graphene production is ultrasound-aided liquid phase exfoliation (ULPE) of graphite. Two current limiting factors of ULPE are the use of harmful solutions (such as NMP or DMF) and a relatively low graphene yield. In this study, we demonstrate a new dual frequency (20 kHz and 1174 kHz) ULPE approach in various eco-friendly media, which enabled us to produce various FLG solutions of high quality. By implementing sophisticated characterisation techniques consisting of Raman spectroscopy, UV-vis spectroscopy and high-resolution electron microscopy, the final graphene flakes structure was confirmed to correlate the properties of each individual solution. The thinner (3 Ls) and larger (~1.5 μm2) flakes were observed while using just water, with the highest yield (11%) of smaller FLG flakes to be achieved in the mixture of water and a surfactant. In order to understand the cavitation mechanism in different solutions, the ULPE process was investigated by acoustic measurements. This study demonstrates the crucial role of ethanol (as a solvent) and surfactants as it regulates the cavitation power and intensity of the ultrasonic field and, thereby, the cavitation effectiveness. It is suggested that the mixture of water, ethanol and a surfactant is the best medium for ULPE process where a high yield of low-defective FLG flakes can be obtained in a solution stable at least for 3 months (around 80%). © 2022, The Authors. All rights reserved.

关键词： Cavitation

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Covid-19 and the tourism industry: An early stage sentiment analysis of the impact of social media and stakeholder communication

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International Journal of Information Management Data Insights 2021年第2期1卷

作者： Obembe, Demola Kolade, Oluwaseun Obembe, Funmi Owoseni, Adebowale Mafimisebi, Oluwasoye Leicester Castle Business School De Montfort University The Gateway UK Leicester LE1 9BH United Kingdom School of Computer Science and Informatics Faculty of Computing Engineering and Media De Montfort University The Gateway UK Leicester LE1 9BH United Kingdom

This paper examines tourist public responses to crisis communications during the early stages of Covid-19. Using the social-mediated crisis communication model, the paper explores the key factors that influence public sentiments during nascent periods of the crisis. The choice of data collection dates was determined by key milestones events with significant implications in relation to UK tourism. Sentiment analysis of data sets of public tweets and news articles were done in order to interrogate how the trends and performance of the airlines and the tourism sector have been shaped by the sentiments of the tourism publics, the crisis communication interventions from key institutional actors, and the news sentiments about tourism organizations, particularly airlines. Sentiment analysis, also known as opinion mining, falls under natural language processing (NLP) and is used to identify different sentiments and polarities in texts. Our findings indicate that institutional actors have a significant impact on the sentiments of tourism publics. Our study contributes to existing research on crisis communication by illuminating how public narrative about, and stakeholder responses to, crisis are shaped not just by organizational communication strategies but also institutional actors, on the one hand, and the interested publics too. © 2021 The Author(s)

关键词： Airline Covid-19 Crisis communication Sentiment analysis Social media Tourism

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Investigation of the Ultra-High-Energy gamma-ray emission from the Northern Fermi Bubble with LHAASO-KM2A

Investigation of the Ultra-High-Energy gamma-ray emission fr...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Yi He, Jiayin Zhang, Rui Zhao, Shiping Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 9 Jiangsu Nanjing210023 China University of Science and Technology of China Anhui Hefei230026 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Cor

We analyze gamma-ray emission from the Northern Fermi bubble region at the ultra-high-energy range, using the data collected by LHAASO-KM2A from December 2019 to September 2022. Employing an improved gamma/hadron separation method, the median energy of the gamma rays is above 25 TeV. We perform the "direct integral method" in the background estimation;however, no significant excess is observed. Consequently, we present the expected upper limits for gamma-ray emissions within the Fermi bubble region at this energy range. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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You Drive Me Crazy! Interactive QoE Assessment for Telepresence Robot Control

You Drive Me Crazy! Interactive QoE Assessment for Teleprese...

引用

International Workshop on Quality of Multimedia Experience, QoMEx

作者： Hamed Z. Jahromi Ivan Bartolec Edwin Gamboa Andrew Hines Raimund Schatz School of Computer Science University College Dublin Ireland Faculty of Electrical Engineering and Computing University of Zagreb Croatia Institute of Media Technology TU Ilmenau Germany Center for Technology Experience AIT Austrian Institute of Technology Austria

ISBN: (数字)9781728159652

ISBN: (纸本)9781728159669

Telepresence robots (TPRs) are versatile, remotely controlled vehicles that enable physical presence and human-to-human interaction over a distance. Thanks to improving hardware and dropping price points, TPRs enjoy the growing interest in various industries and application domains. Still, a satisfying experience remains key for their acceptance and successful adoption, not only in terms of enabling remote communication with others, but also in terms of managing robot mobility by means of remote navigation. This paper focuses on the latter aspect of remote operation which has been hitherto neglected. We present the results of an extensive subjective study designed to systematically assess remote navigation Quality of Experience (QoE) in the context of using a TPR live over the Internet. Participants were `beamed' into a remote office space and asked to perform characteristic TPR remote operation tasks (driving, turning, parking). Visual and control dimensions of their experience were systematically impaired by altering network characteristics (bandwidth, delay and packet loss rate) in a controlled fashion. Our results show that users can differentiate well between visual and navigation/control aspects of their experience. Furthermore, QoE impairment sensitivity varies with the actual task at hand.

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