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RSC Applied Polymers 2024年第4期2卷 634-641页

作者： Deswal, Swati Arab, Shima He, Nanfei Gao, Wei Lee, Bongmook Misra, Veena Department of Electrical and Computer Engineering North Carolina State University Raleigh NC United States Department of Textile Engineering Chemistry and Science North Carolina State University Raleigh 27606 NC United States College of Engineering SUNY Polytechnic Institute Utica NY United States

Ceramic-based ferroelectric materials have long been used as functional ferroelectric materials for applications in triboelectric-energy harvesting. However, their drawbacks (brittleness, energy-intensive fabrication methods, and minimal mechanical flexibility) have intensified the search for alternatives where flexible form factors are needed. Molecular ferroelectrics are an emerging class of multifunctional materials. To date, the intriguing piezo/ferroelectric properties of these materials have led to the construction of piezoelectric nanogenerators. However, their numerous advantages warrant their expansion towards triboelectric nanogenerators (TENGs). We aimed to demonstrate the potential of a molecular ferroelectric, diisopropylammonium bromide (DIPAB), in the realm of triboelectric-energy harvesting. Combining ferroelectric properties with enhanced surface modification, we designed an electrospun-based TENG comprising DIPAB/P(VDF-TrFE) as an active negative layer. The synergistic effects emanating from highly aligned polymeric chains and ferroelectric particles in conjunction with a high surface area of the as-designed TENG generated an output voltage of 203.8 V and resulted in a maximum power density of 416.2 mW m⁻² when operated in contact separation mode. Its practical application as a sustainable power supply for low-power electronics was demonstrated through powering of commercial electrolytic capacitors and LEDs. This study presents a reliable, cost-effective, and readily scalable method for enhancing TENG performance. This strategy holds potential for application of TENGs in wearable biomechanical-energy harvesting, and paves the way for further advances involving a sustainable energy solution for wearable electronics. © 2024 RSC.

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WAVEFIELD CHARACTERIZATION OF LAMB WAVE PROPAGATION IN COMPOSITE PLATES BASED ON GUIDED WAVE OMNIDIRECTIONAL WAVENUMBER SIMULATION 18

WAVEFIELD CHARACTERIZATION OF LAMB WAVE PROPAGATION IN COMPO...

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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Wang, Lei Liu, Hongye Sun, Maoxun Chen, Xiaorong Shen, Liang Qiu, Chongpeng Tian, Zhenhua School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Mechanical Engineering Virginia Polytechnic Institute State University 1145 Perry Street BlacksburgVA24061 United States

ISBN: (纸本)9798331523732

With the growing application of composite materials in sectors such as aerospace, automotive, and wind energy, accurately assessing their mechanical properties is critical for effective non-destructive testing (NDT) and structural health monitoring (SHM). In this paper, a three-dimensional finite element model was developed to generate the fundamental A0 Lamb wave mode within an 8-layer orthotropic laminate, addressing the complexities associated with the anisotropic behavior of composites. Utilizing the Legendre Orthogonal Polynomial Expansion (LOPE) method, we characterized the wave number profile, revealing significant directional dependence in wave propagation across multilayer laminates. The results demonstrated strong agreement between the analytical predictions from the LOPE method and the finite element simulations, confirming the robustness of our modeling approach. Additionally, we introduced an innovative omnidirectional wavefield representation method, effectively mitigating the influence of phase and group velocity steering angles. These findings enhance our understanding of guided wave dynamics in composite materials, offering valuable insights for the development of more effective detection strategies and assessments of structural integrity in complex composite systems. © 2024 IEEE.

关键词： Gravity waves

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ELASTIC CONSTANTS INVERSION OF ZIRCONIUM TUBE BASED ON THE LONGITUDINAL MODE OF GUIDED WAVE 18

ELASTIC CONSTANTS INVERSION OF ZIRCONIUM TUBE BASED ON THE L...

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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Ji, Haoshuai Liu, Hongye Sun, Maoxun Chen, Xiaorong Shen, Liang Qiu, Chongpeng Tian, Zhenhua School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Mechanical Engineering Virginia Polytechnic Institute State University 1145 Perry Street BlacksburgVA24061 United States

ISBN: (纸本)9798331523732

Estimating elastic constants in materials is markedly important in engineering and technology. Ultrasonic guided wave non-destructive testing provides an effective means for accurately assessing material properties. This paper presents a method for inverting the elastic constants of hollow pipes using the longitudinal mode of ultrasonic guided waves combined with a genetic algorithm (GA). The Legendre orthogonal polynomial expansion (LOPE) method serves as the forward model for analyzing the dispersion curves of axisymmetric guided wave modes, while the GA performs the inversion. A carefully designed finite element (FE) model generates pseudo-experimental data, which is transformed into the frequency-wave number domain for input into the GA. The comparison between simulation results and theoretical analysis confirms the validity of the FE model and the accuracy of the detected modes. The results demonstrate that the proposed method effectively inverts the elastic constants of isotropic hollow cylindrical structures, with an inversion error of less than 2% based on pseudo-experimental data. © 2024 IEEE.

关键词： Nondestructive examination

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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An effective fault localization approach for Verilog based on enhanced contexts

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Frontiers of computer science 2024年第5期18卷 223-225页

作者： Zhuo ZHANG Ya LI Lei XIA Jianxin XUE Jiang WU Xiaoguang MAO School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingbo 315000China No.83 Army Joint and Truma Disease Treatment Centre of PLA Xinxiang 453000China Department of Software Engineering Shanghai Second Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

New computer architecture innovationswith diverse functionalities and comprehensive features continue to emerge incessantly,resulting in a rising trend of incorporating a larger number of circuit devices into these products[1].In the case of a sophisticated and expansive integrated circuit chip,the presence of defective or malfunctioning components can significantly impact the overall performance of the *** situation may even result in costly repercussions.

关键词： Verilog computer circuit.

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

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computer Modeling in engineering & sciences 2024年第2期138卷 1051-1075页

作者： Shazia Shamas Surya Narayan Panda Ishu Sharma Kalpna Guleria Aman Singh Ahmad Ali AlZubi Mallak Ahmad AlZubi Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Higher Polytechnic School Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain Department of Engineering Universidad Internacional IberoamericanaAreciboPR00613USA Uttaranchal Institute of Technology Uttaranchal UniversityDehradunUttarakhand248007India Computer Science Department King Saud UniversityRiyadhSaudi Arabia Faculty of Medicine Jordan University of Science and TechnologyIrbidJordan

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and *** detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by resea

关键词： Lesion lung cancer segmentation medical imaging meta-heuristic Artificial Bee Colony(ABC) Cuckoo Search Algorithm(CSA) Particle Swarm Optimization(PSO) Firefly Algorithm(FFA) segmentation

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Surface-dominated conductance scaling in Weyl semimetal NbAs

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npj Computational Materials 2024年第1期10卷 2370-2378页

作者： Sushant Kumar Yi-Hsin Tu Sheng Luo Nicholas A.Lanzillo Tay-Rong Chang Gengchiau Liang Ravishankar Sundararaman Hsin Lin Ching-Tzu Chen Department of Materials Science&Engineering Rensselaer Polytechnic Institute1108th StTroyNY 12180USA IBM Thomas J.Watson Research Center 1101 Kitchawan RoadYorktown HeightsNY 10598USA Department of Physics Cheng Kung UniversityTainan CityTaipei Department of Electrical and Computer Engineering College of Design and EngineeringNational University of SingaporeSingaporeSingapore IBM Research 257 Fuller RoadAlbanyNY 12203USA Institute of Physics Academia SinicaTaipeiTaipei

Protected surface states arising fromnon-trivial bandstructure topology in semimetals can potentially enable advanced device functionalities in compute,memory,interconnect,sensing,and *** necessitates a fundamental understanding of surface-state transport in nanoscale topological ***,we investigate quantum transport in a prototypical topological semimetal NbAs to evaluate the potential of this class of materials for beyond-Cu interconnects in highly-scaled integrated *** density functional theory(DFT)coupled with non-equilibrium Green’s function(NEGF)calculations,we show that the resistance-areaRAproduct in NbAs films decreases with decreasing thickness at the nanometer scale,in contrast to a nearly constant RA product in ideal Cu *** anomalous scaling originates from the disproportionately large number of surface conduction states which dominate the ballistic conductance by up to 70%in NbAs thin *** also show that this favorable RA scaling persists even in the presence of surface defects,in contrast to RA sharply increasing with reducing thickness for films of conventional metals,such as Cu,in the presence of surface *** results underscore the potential of topological semimetals as future back-end-of-line(BEOL)interconnect metals.

关键词： scaling topological conductance

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Password Strength Analysis and its Classification by Applying Machine Learning Based Techniques 2

Password Strength Analysis and its Classification by Applyin...

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2nd International Conference on computer science, engineering and Applications, ICCSEA 2022

作者： Sarkar, Sakya Nandan, Mauparna Asansol Institute of Engineering & Management-Polytechnic Department of Computer Science & Technology Asansol India Haldia Institute of Technology Department of Computer Applications Haldia India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Passwords serve as a vital authentication mechanism for any system's protection. Although numerous more secure methods of authentication exists such as biometrics and smart cards but still the largely popular technique is password authentication for ensuring protection of any system on account of its ease of implementation. But, passwords are exposed to various types of vulnerabilities due to the predictable patterns which human employs for setting passwords likely dictionary words, commonly used people and places names, keyboards patterns, date of birth, known phrases etc. and thereby, pave a route for online or offline muggers to intrude into the system by assuming the passwords. For guessing the passwords, various password cracking tools are available either online or offline and most of the tools can readily crack such accounts possessing passwords with weak strengths or passwords with common patterns. Therefore, the organizations should enforce unassailable strategies governing the use of strong passwords such that those vulnerabilities can be addressed. Hence, the most efficient way to protect the system against these online and offline attacks is by enforcing the users to implement a strong password. In this proposed work, prediction of the strength of a password has been modeled as a classification task with the aid of multiple supervised machine learning algorithms which were used for the learning purpose. The novelty of this work lies in the fact, that two new algorithms namely, XGBoost and Multilayer Perceptron have been implemented to determine the strength and the corresponding category of a password which has not been evaluated in similar other works. However, during testing the models with the test dataset, it has been observed that XGBoost outperformed the other machine learning classifiers with an accuracy of 99%. The final results reveal that machine learning approaches possesses ample capability to classify the passwords into different categories,

关键词： Smart cards

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