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SSRN 2023年

作者： Li, Aoxuan D’Angelo, Gabriele Tang, Su-Kit Fang, Frank Gong, Baron Faculty of Applied Sciences Macao Polytechnic University Rua de Luís Gonzaga Gomes China Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 BolognaI-40127 Italy Mystiko.Network

Blockchain exposes all users’ transaction data to the public, including account balances, asset holdings, trading history, etc. Such data exposure leads to potential security and personal privacy risks that restrict blockchain from broader adoption. Although some existing projects focus on single-chain confidential payment, no existing cross-chain system supports private transactions yet, which is incompatible with privacy regulations such as GDPR. Also, current confidential payment systems require users to pay high extra fees. However, a private and anonymous protocol encrypting all transaction data raises concerns about malicious and illegal activities since the protocol is difficult to audit. We need to balance privacy and auditability in *** propose an auditable and affordable protocol for cross-chain and single-chain transactions. This protocol leverages zero-knowledge proofs to encrypt transactions and perform validation without disclosing sensitive users' data. To meet regulations, each auditor from an auditing committee will have an encrypted secret share of the transaction data. Auditors may view the private transaction data only if a majority of the committee agrees to decrypt the data. We employ a ZK-rollup scheme by processing multiple transactions in batches, which reduces private transaction costs of 90\% compared with solutions that do not employ the ZK-rollup. The proposed scheme has been implemented using Zokrates and Solidity and evaluated running on the Ethereum test network. The results show that the total one-to-one private transactions latency is about 5 seconds. Moreover, the security of the protocol is analyzed by mean of the standard real/ideal world paradigm. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

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Hyperspectral Brain Tissue Classification using a Fast and Compact 3D CNN Approach

Hyperspectral Brain Tissue Classification using a Fast and C...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Hamail Ayaz David Tormey Ian McLoughlin Muhammad Ahmad Saritha Unnikrishnan Faculty of Engineering and Design and Mathematical Modelling and Intelligent Systems for Health and Environment Atlantic Technological University Sligo Ireland Faculty of Engineering and Design Centre for Precision Engineering Materials and Manufacturing Research (PEM) Atlantic Technological University Sligo Ireland Department of Computer Science and Applied Physics Atlantic Technological University Galway Ireland Department of Computer Science National University of Computer and Emerging Sciences Chiniot Pakistan

ISBN: (纸本)9781665462204

Glioblastoma (GB) is a malignant brain tumor and requires surgical resection. Although complete resection of GB improves prognosis, supratotal resection may cause neurological abnormalities. Therefore, intraoperative tissue classification techniques are needed to delineate infected tumor regions to remove reoccurrences. To delineate the affected regions, surgeons mostly rely on traditional magnetic resonance imaging (MRI) which often lacks accuracy and precision due to the brain-shift phenomenon. Hyperspectral Imaging (HSI) is a noninvasive advanced optical technique and has the potential to classify tissue cells accurately. However, HSI tumor classification is challenging due to overlapping regions, high interclass similarity, and homogeneous information. Additionally, HSI models using 2D Convolutional Neural Network (CNN) models works with spectral information eliminating spatial features and 3D followed by 2D hybrid model lacks abstract level spatial information. Therefore, in this study, we have used a minimal layer 3D CNN model to classify the GB tumor region from normal tissues using an intraoperative VivoHSI dataset. The HSI data have normal tissue (NT), tumor tissue (TT), hypervascularized tissue or blood vessels (BV), and background (BG) tissue cells. The proposed 3D CNN model consists of only two 3D layers using limited training samples (20%), which are further divided into 50% for training and 50% for validation and blind tested (80%) on the rest of the data. This study outperformed then state-of-the-art hybrid architecture by achieving an overall accuracy of 99.99%.

关键词： Training Solid modeling Three-dimensional displays Magnetic resonance imaging Surgery Brain modeling Data models

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Exploring the Roles of Agents and Multi-Agent in Improving Mobile Ad Hoc Networks 4

Exploring the Roles of Agents and Multi-Agent in Improving M...

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4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Jubair, Mohammed Ahmed Alabdullah, Abdullah I. M. Mostafa, Salama A. Mustapha, Aida Hassan, Mustafa Hamid Gunasekaran, Saraswathy Shamini College of Information Technology Imam ja'Afar Al-Sadiq University Department of Computer Technical Engineering Al-Muthanna Iraq University of Mosul College of Science Department of Physics Mosul Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia College of Computer Science and Informatics Universiti Tenaga Nasional Kajang43000 Malaysia

ISBN: (纸本)9781728166544

We are experiencing a rapid evolution and advancement of technologies that are capable of supporting the formulation and implementation of robust and adaptive network architectures designed for mobile and dynamic environments. An example of such technologies is the Mobile Ad-Hoc Network (MANET), which supports self-organizing multi-hop heterogeneous network routing services and organization. The importance of such technologies for the future of DoD networking is evident, particularly in the front edge of the battlespace where there is a need for robust and self-organizing network. Another area of such technological advancement is the Multi-Agent Systems (MAS), which are capable of supporting both team-based and independent problem solving in many different environments. These two technologies have been extensively explored, but their combination is largely unexplored. Consequently, this paper aims to investigate the joint operation of agents and multi-agent in the MANET environment. The paper also discusses the research efforts that have been made to optimally improve the technologies. © 2021 IEEE.

关键词： Multi agent systems

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Generating datasets for software requirements prioritization research

Generating datasets for software requirements prioritization...

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2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

作者： Nazim, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Faculty of Engineering and Technology Department of Applied Sciences and Humanities New Delhi110025 India Indian Institute of Science Department of Computer Science and Automation Bangalore560012 India

ISBN: (纸本)9781728150703

Software requirements are the need of the stakeholders which are identified during requirements elicitation process. There are different types of the software requirements like functional requirements, non-functional requirements, testing requirements, etc. After requirements elicitation process, a system may have hundreds or thousands of requirements;and during the development process it is not possible to implement all the software requirements because of the several limitations of an organization as well as the stakeholders. To develop the successful software product, the elicited software requirements must be prioritized before the development of software so that high ranked software requirements can be identified. Computation of the ranking values of the software requirements is a difficult task because it involves lot of computational steps like evaluation of software requirements, computation of group preferences from the individual preferences, computation of the ranking values after normalizing the preference matrices, etc. Among these steps, in this paper, we mainly focus on the evaluation of the software requirements using linguistic variables because in real life applications people use linguistic variables to specify their preferences on software requirements. The objective of this paper is to generate a dataset of 32 functional requirements after evaluating it on the basis of 3 non-functional requirements so that the generated dataset can be used as an input into the software requirements prioritization methods. The institute examination system is used as case study to explain the steps for the generation of the dataset. © 2020 IEEE.

关键词： Requirements engineering

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Simulation of Conducted Emissions From Power Converters Using Leading Switching Technologies

Simulation of Conducted Emissions From Power Converters Usin...

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IEEE Global Electromagnetic Compatibility Conference (GEMCCON)

作者： Amr Madi Francinei Vieira Piotr Lezynski Sebastian Koj Robert Smolenski Electrical Engineering Mathematics & Computer Science (EEMCS) University of Twente Enschede Netherlands Institute of Electrical Engineering and Measurement Technology Leibniz University Hannover Hannover Germany Institute of Automatic Control Electronics and Electrical Engineering University of Zielona Gora Zielona Gora Poland Department of Engineering Sciences Jade University of Applied Sciences Wilhelmshaven Germany

This paper presents a comparative study between the commonly used switches (IGBT, MOSFET, SiC and GaN) for power inverters of electric vehicles. A 400 V, three-phase inverter system simulation test is implemented using LTspice and the conducted emissions are compared to variant CISPR standards and switching frequencies in order to show the possible interferences with the other car equipment.

关键词： MOSFET Silicon carbide Switching frequency Interference Predictive models SPICE Inverters

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Supervised Multi-Regional Segmentation Machine Learning Architecture for Digital Twin Applications in Coastal Regions

arXiv

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arXiv 2023年

作者： Ahmadi, Mohsen Lonbar, Ahmad Gholizadeh Nouri, Mohammadsadegh Javidi, Amir Sharifzadeh Beris, Ali Tarlani Sharifi, Abbas Salimi-Tarazouj, Ali Department of Electrical Engineering and Computer Science Florida Atlantic University FL United States Department of Civil Architectural and Environmental Engineering Missouri University of Science and Technology RollaMO United States School of Civil Engineering College of Engineering University of Tehran Iran Center for Applied Coastal Research Department of Civil and Environmental Engineering University of Delaware NewarkDE United States Department of Mechanical Engineering College of Engineering Boston University BostonMA United States School of Mechanical Engineering College of Engineering University of Tehran Iran Department of Civil and Environmental Engineering Florida International University MiamiFL United States College ParkMD United States Lynker LeesburgVA20176 United States University of Maryland College ParkMD20742 United States

This study explores the use of a digital twin model and deep learning method to build a global terrain and altitude map based on USGS information. The goal is to artistically represent various landforms while incorporating precise elevation modifications in the terrain map and encoding land height in the altitude map. A random selection of 5000 segments from the worldwide map guarantees the inclusion of significant characteristics in the subsets, with rescaling according to latitude accounting for distortions caused by map projection. The process of generating segmentation maps involves using unsupervised clustering and classification methods, segmenting the terrain into seven groups: Water, Grassland, Forest, Hills, Desert, Mountain, and Tundra. Each group is assigned a unique color, and median filtering is used to improve map characteristics. Random parameters are added to provide diversity and avoid duplication in overlapping image sets. The U-Net network is deployed for the segmentation task, with training conducted on the seven terrain classes. Cross-validation is carried out every 10 epochs to gauge the model's performance. The segmentation maps produced accurately categorize the terrain, as evidenced by the ROC curve and AUC values. The main goal of this research is to create a digital twin model of Florida's coastal area. This is achieved through the application of deep learning methods and satellite imagery from Google Earth, resulting in a detailed depiction of the coast of Florida. The digital twin acts as both a physical and a simulation model of the area, emphasizing its capability to capture and replicate real-world locations. The model effectively creates a global terrain and altitude map with precise segmentation and capture of important land features. The results confirm the effectiveness of the digital twin, especially in depicting Florida's coastline. Copyright © 2023, The Authors. All rights reserved.

关键词： Forestry

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Mixed convection in a Rayleigh-Bénard cell with an imposed mean wind 14

Mixed convection in a Rayleigh-Bénard cell with an imposed ...

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14th European Turbulence Conference, ETC 2013

作者： Scagliarini, Andrea Gylfason, Ármann Toschi, Federico School of Science and Engineering Reykjavik University Iceland Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Applied Physics Eindhoven University of Technology Netherlands

We present a numerical study of mixed convection in a Rayleigh-Bénard cell with a lateral wind. We discuss the scaling properties of the heat flux with the Rayleigh number Ra at changing the intensity of the lateral forcing as well as the statistics of small scale fluctuations of thermohydrodynamic fields. Copyright © ETC 2013 - 14th European Turbulence *** rights reserved.

关键词： Mixed convection

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Numerical approach toward ternary hybrid nanofluid flow with nonlinear heat source-sink and fourier heat flux model passing through a disk

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International Journal of Thermofluids 2023年 18卷

作者： Alqawasmi, Khaled Alharbi, Khalid Abdulkhaliq M. Farooq, Umar Noreen, Sobia Imran, Muhammad Akgül, Ali Kanan, Mohammad Asad, Jihad Cyber Security Department Zarqa University Jordan Mechanical Engineering Department College of Engineering Umm Al-Qura University Makkah24382 Saudi Arabia Department of Mathematics Government College University Faisalabad 38000 Pakistan School of Mathematics Minhaj University Lahore Pakistan Department of Chemistry Government College Women University Faisalabad 38000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Siirt University Art and Science Faculty Department of Mathematics Siirt 56100 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey Jeddah College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Dep. of Physics Faculty of Applied Science Palestine Technical University- Kadoorie TulkarmP 305 Palestine

The use of a ternary hybrid nanofluid, a new form of nanofluid, can improve heat movement. The current research employs a two-dimensional steady model to investigate nonlinear thermal radiation via ternary hybrid nanofluid flow across a revolving disc. The study of manganese zinc ferrite, copper, and silver nanocomposite base hybrid nanofluid across a spinning disc is gaining traction in invention and research due to its broad range of applications. To derive dimensionless forms of regulating paired nonlinear partial differential equations, a collection of pertinent similarity transformations is used. Based on the shooting method, the modified collection of ODEs is then analytically solved by bvp4c via the computational tool MATLAB. Furthermore, as the magnetic parameter values are reduced, the fluid velocity declines while the fluid temperature grows over a spinning disc. For larger levels, the temperature of the distribution and the radiant heat component rises. The ternary composite nanofluid has the largest impact on the surface. Furthermore, compared to the hybrid and based nanofluids, the heat generation rate of the ternary nanofluid combination is the higher transmission. The significance of Ag nanoparticles in the food industry is because of their anti-bacterial and anti-fungicidal properties, silver (Ag) is widely used in everyday products such as fabrics, pastes, polymers, food, and detergents. Manganese zinc ferrites (MnZnFe2O4) have recently piqued the interest of material science researchers due to their broad variety of applications. They are extensively used in energy storage devices, catalysts, adsorbents, sensing and imaging, therapeutic action, and other applications. © 2023 The Author(s)

关键词： Numerical methods

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A constitutive model incorporating grain refinement strengthening on metallic alloys

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Journal of Materials science & Technology 2021年第29期88卷 233-239页

作者： Shan Cecilia Cao Xiaochun Zhang Yuan Yuan Pengyau Wang Lei Zhang Na Liu Yi Liu Jian Lu Material Genome Institute Shanghai UniversityShanghai 200444China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Department of Mechanical Engineering City University of Hong KongHong KongChina Department of Computer Science University of CaliforniaBerkeleyCA 94720USA College of Material and Textile Engineering Jiaxing UniversityZhejiang 314001.China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China CityU-Shenzhen Futian Research Institute Shenzhen 518045China Centre for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstituteGreater Bay Joint DivisionShenyang National Laboratory for Materials ScienceShenzhen 518057China

Surface nano-crystallization techniques have been recently developed as one of the most effient ways to optimize materials'structure,and therefore develop the local and global mechanical behavior as to increase strength without compromising *** this work,we present a constitutive model incorporating grain refinement hardening to simulate the nano-crystallization technique,specifically,surface mechanical attrition *** computation is implemented using user-defined VUMAT *** an example of its application,a geometry model with full coverage of random impacts are *** results show that the model has rather precise predictability of grain size evolution during plastic *** readily embedded with a computational code of material dynamics enables this novel model to be a promising tool to study the dynamic evolution of microstructures under plastic deformation.

关键词： Surface grain refinement Constitutive model Full coverage random impact Surface mechanical attrition treatment(SMAT)

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