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SSRN 2025年

作者： Burke, Caitlin Gunawan, Johanna King, Jennifer Kshirsagar, Mihir Acquisti, Alessandro Chetty, Marshini Choffnes, David Egelman, Serge Gray, Colin M. Hartzog, Woodrow Im, Jane Mayer, Jonathan Mhaidli, Abraham Narayanan, Arvind Roesner, Franziska Schaffner, Brennan Schaub, Florian Strahilevitz, Lior Tong, Nicole Wang, Charlie Wilson, Christo Zeng, Eric Department of Communication Stanford University United States Khoury College of Computer Sciences Northeastern University United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States Heinz College Carnegie Mellon University United States Department of Computer Science University of Chicago United States Khoury College of Computer Sciences Northeastern University United States United States Purdue University United States Boston University School of Law United States School of Information Division of Computer Science and Engineering University of Michigan United States Princeton University United States School of Information University of Michigan United States Princeton University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States Department of Computer Science University of Chicago United States School of Information University of Michigan United States University of Chicago United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States CyLab Carnegie Mellon University United States Stanford University School of Law United States Princeton University United States

As documented in several research studies, consumers may encounter dark patterns in many online contexts, such as when making choices to consent to the disclosure of personal information or to cookies, when interacting with services and applications like games or content feeds that seek to capture and extend consumer attention and time spent, and in e-commerce, including at multiple points along a purchasing journey. Dark patterns may start with the advertising of a product or service, and can be present across the whole customer path, including sign-up, purchase, and cancellation. As a result, the Commission’s guidance should cover the business’s entire interaction with the consumer to ensure that they are allowed to engage in free and informed transactions. © 2025, The Authors. All rights reserved.

关键词： Sales

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PA-ResSeg: A phase attention residual network for liver tumor segmentation from multi-phase CT images

arXiv

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arXiv 2021年

作者： Xu, Yingying Cai, Ming Lin, Lanfen Zhang, Yue Hu, Hongjie Peng, Zhiyi Zhang, Qiaowei Chen, Qingqing Mao, Xiongwei Iwamoto, Yutaro Han, Xian-Hua Chen, Yen-Wei Tong, Ruofeng Collage of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China Department of Radiology Sir Run Run Shaw Hospital College of Medicine Zhejiang University Hangzhou Zhejiang China Department of Radiology The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou Zhejiang China College of Information Science and Engineering Ritsumeikan University Kusatsu Shiga Japan Artificial Intelligence Research Center Yamaguchi University Yamaguchi City Yamaguchi Japan Research Center for Healthcare Data Science Zhejiang Lab. Hangzhou China

Purpose: Liver tumor segmentation is a crucial prerequisite for computer aided diagnosis of liver tumors. In the clinical liver tumor diagnosis, radiologists usually examine multi-phase images as these images provide abundant and complementary information of tumors. However, most known automatic segmentation methods extract tumor features from CT images merely of a single phase, in which valuable multi-phase information is ignored. Therefore, it is highly demanded to develop a method effectively incorporating multi-phase information for automatic and accurate liver tumor segmentation. Methods: In this paper, we propose a phase attention residual network (PA-ResSeg) to model multiphase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra-phase attention (Intra-PA) module and an inter-phase attention (Inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies, respectively. Thus it enables the network to learn more representative multi-phase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA-based multi-scale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multi-scale features from multi-phase images. Moreover, a 3D boundary-enhanced loss (BE-loss) is proposed for training to make the network more sensitive to boundaries. Results: To evaluate the performance of our proposed PA-ResSeg, we conducted experiments on a multiphase CT dataset of focal liver lesions (MPCT-FLLs). Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of

关键词： computerized tomography

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Corrigendum to “Physical workplaces and human well-being: A mixed-methods study to quantify the effects of materials, windows, and representation on biobehavioral outcomes” [Building and Environment 224 (2022) 109516]

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Building and Environment 2024年 261卷

作者： Isabella P. Douglas Elizabeth L. Murnane Lucy Zhang Bencharit Basma Altaf Jean Marcel dos Reis Costa Jackie Yang Meg Ackerson Charu Srivastava Michael Cooper Kyle Douglas Jennifer King Pablo E. Paredes Nicholas P. Camp Matthew Louis Mauriello Nicole M. Ardoin Hazel Rose Markus James A. Landay Sarah L. Billington Department of Civil & Environmental Engineering Stanford University USA School of Engineering Dartmouth College USA Department of Psychology and Child Development California Polytechnic State University USA Department of Computer Science Stanford University USA Institute for Human-Centered Artificial Intelligence Stanford University USA Psychiatry & Behavioral Sciences Department Stanford University USA Department of Psychology Stanford University USA SPARQ Stanford University USA Department of Computer and Information Sciences University of Delaware USA School of Education Stanford University USA Woods Institute for the Environment Stanford University USA

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SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition

arXiv

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arXiv 2025年

作者： Lei, Mengqi Wu, Yihong Li, Siqi Zheng, Xinhu Wang, Juan Gao, Yue Du, Shaoyi BNRist THUIBCS BLBCI School of Software Tsinghua University Beijing100084 China Department of Mechanical Engineering Taiyuan University of Technology Taiyuan030024 China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China Department of Ultrasound the Second Affiliated Hospital Xi’an Jiaotong University China State Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Visual recognition relies on understanding both the semantics of image tokens and the complex interactions among them. Mainstream self-attention methods, while effective at modeling global pair-wise relations, fail to capture high-order associations inherent in real-world scenes and often suffer from redundant computation. Hypergraphs extend conventional graphs by modeling high-order interactions and offer a promising framework for addressing these limitations. However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, leading to excessive and redundant hyperedges with hard binary vertex memberships that overlook the continuity of visual semantics. To overcome these issues, we present Soft Hypergraph Neural Networks (SoftHGNNs), which extend the methodology of hypergraph computation, to make it truly efficient and versatile in visual recognition tasks. Our framework introduces the concept of soft hyperedges, where each vertex is associated with hyperedges via continuous participation weights rather than hard binary assignments. This dynamic and differentiable association is achieved by using the learnable hyperedge prototype. Through similarity measurements between token features and the prototype, the model generates semantically rich soft hyperedges. By using these soft hyperedges as the medium for message aggregation and dissemination, SoftHGNN models various high-order semantic relationships within visual features. To further enhance efficiency when scaling up the number of soft hyperedges, we incorporate a sparse hyperedge selection mechanism that activates only the top-k important hyperedges, along with a load-balancing regularizer to ensure adequate and balanced hyperedge utilization. Experimental results across three tasks on five datasets demonstrate that SoftHGNN efficiently captures high-order associations in visual scenes, achieving significant performance improvements. The source code of our framework is availa

关键词： computer vision

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Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial intelligence

arXiv

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arXiv 2022年

作者： Qian, Chen Gao, Yuncheng Han, Mingyang Wang, Zi Ruan, Dan Shen, Yu Wu, Yaping Zhou, Yirong Wang, Chengyan Jiang, Boyu Tao, Ran Wu, Zhigang Wang, Jiazheng Zhu, Liuhong Guo, Yi Kang, Taishan Lin, Jianzhong Gong, Tao Yang, Chen Fei, Guoqiang Lin, Meijin Guo, Di Zhou, Jianjun Wang, Meiyun Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University China Department of Medical Imaging Henan Provincial People's Hospital The People's Hospital of Zhengzhou University China Human Phenome Institute Fudan University China United Imaging Healthcare China Philips Healthcare China China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China Departments of Radiology Shandong Provincial Hospital Affiliated Shandong First Medical University China China Department of Neurology Zhongshan Hospital Fudan University China Department of Applied Marine Physics and Engineering Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Biomedical Research Institute Henan Academy of Sciences China

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability;(b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center;(c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets

arXiv

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arXiv 2022年

作者： Marzi, Chiara Giannelli, Marco Barucci, Andrea Tessa, Carlo Mascalchi, Mario Diciotti, Stefano Department of Statistics Computer Science and Applications "Giuseppe Parenti" University of Florence Florence Italy 50019 Italy Unit of Medical Physics Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana" Pisa56126 Italy Radiology Unit Apuane e Lunigiana Azienda USL Toscana Nord Ovest Massa54100 Italy Department of Experimental and Clinical Biomedical Sciences "Mario Serio" University of Florence Florence50139 Italy Florence50139 Italy Department of Electrical Electronic and Information Engineering "Guglielmo Marconi"-DEI University of Bologna Cesena47522 Italy Alma Mater Research Institute for Human-Centered Artificial Intelligence University of Bologna Bologna40121 Italy

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization;2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage. © 2022, CC BY-NC-SA.

关键词： Pipelines

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Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study

arXiv

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arXiv 2021年

作者： Luo, Luyang Chen, Hao Xiao, Yongjie Zhou, Yanning Wang, Xi Vardhanabhuti, Varut Wu, Mingxiang Han, Chu Liu, Zaiyi Fang, Xin Hao Benjamin Tsougenis, Efstratios Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong AI Research Lab Imsight Technology Co. Ltd. Shenzhen China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Radiology Shenzhen People's Hospital Shenzhen Luohu China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Radiology Queen Marry Hospital Hong Kong Artificial Intelligence Lab Head Office Information Technology and Health Informatics Division Hospital Authority Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Hong Kong

Purpose To evaluate the ability of fine-grained annotations to overcome shortcut learning in deep learning (DL)-based diagnosis using chest radiographs. Materials and Methods Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. A total of 34,501 chest radiographs from January 2005-September 2019 were retrospectively collected and annotated with cardiomegaly, pleural effusion, mass, nodule, pneumonia, pneumothorax, tuberculosis, fracture, and aortic calcification. The models' internal classification performance and lesion localization performance were compared on an testing set (n=2,922), external classification performance was compared on NIH-Google (n=4,376) and PadChest (n=24,536) datasets, and external lesion localization performance was compared on NIH-ChestX-ray14 dataset (n=880). The models were also compared to radiologists on a subset of the internal testing set (n=496). Performance was evaluated using receiver operating characteristic curve (ROC) analysis. Results Given sufficient training data, both models performed comparably to radiologists. CheXDet achieved significant improvement for external classification, such as in classifying fracture on NIH-Google (CheXDet area under the ROC curve [AUC]: 0.67, CheXNet AUC: 0.51;p © 2021, CC BY.

关键词： Classification (of information)

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One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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Retraction notice to “Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial” [Fuel 332 (2023) 125827]

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Fuel 2025年 391卷

作者： Tao Hai Kosar Hikmat Hama Aziz Jincheng Zhou Hayder A. Dhahad Kamal Sharma Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Teeba Ismail Kh Sadok Mehrez Anas Abdelrahman School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Mathura Uttar Pradesh 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belv’ed‘ere Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia

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