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3D free-assembly modular microfluidics inspired by movable type printing

Microsystems & Nanoengineering 2023年第5期9卷 191-201页

作者： Shaoqi Huang Jiandong Wu Lulu Zheng Yan Long Junyi Chen Jianlang Li Bo Dai Francis Lin Songlin Zhuang Dawei Zhang Engineering Research Center of Optical Instrument and System the Ministry of EducationShanghai Key Laboratory of Modern Optical SystemUniversity of Shanghai for Science and TechnologyShanghai 200093China Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Physics and Astronomy University of ManitobaWinnipegMB R3T 2N2Canada

Reconfigurable modular microfluidics presents an opportunity for flexibly constructing prototypes of advanced microfluidic ***,the strategy of directly integrating modules cannot easily fulfill the requirements of common applications,e.g.,the incorporation of materials with biochemical compatibility and optical transparency and the execution of small batch production of disposable chips for laboratory trials and initial ***,we propose a manufacturing scheme inspired by the movable type printing technique to realize 3D free-assembly modular ***-layer 3D microfluidic structures can be produced by replicating the assembled molds.A library of modularized molds is presented for flow control,droplet generation and manipulation and cell trapping and *** addition,a variety of modularized attachments,including valves,light sources and microscopic cameras,have been developed with the capability to be mounted onto chips on *** systems,including those for concentration gradient generation,droplet-based microfluidics,cell trapping and drug screening,are *** scheme enables rapid prototyping of microfluidic systems and construction of on-chip research platforms,with the intent of achieving high efficiency of proof-of-concept tests and small batch manufacturing.

关键词： batch proof mounted

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Comparative Analysis of mmWave Radar-based Object Detection in Autonomous Vehicles

Comparative Analysis of mmWave Radar-based Object Detection ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Sharifisoraki, Ziaaddin Amini, Marzieh Rajan, Sreeraman Carleton University School of Information Technology Ottawa Canada Carleton University System and Computer Engineering Department Ottawa Canada

ISBN: (纸本)9798350324136

Millimeter-wave radar technology is gaining popularity as a perception sensor in autonomous vehicles. This is due to its ability to detect nearby objects in adverse weather conditions, such as rain, snow, or fog, as well as its cost-effectiveness. In this paper, we explore the impact of different backbones and object detector heads on the performance of radar-based object detection algorithms. More specifically, we employ the RADDet dataset and its object detection algorithm which provides the entire Range-Azimuth-Doppler spectrum and incorporates an automatic annotation approach. We examine different backbones and object detector heads to identify optimal model combinations for autonomous driving applications. Our results show that using a YOLOv4 head integrated with a modified ResNet backbone leads to the highest mean average precision, reaching 66.3% with an intersection over union (IoU) of 0.1, and 43.6% with an IoU of 0.3. This observation will help to advance radar-based object detection, thereby enhancing safety and reliability in diverse environmental conditions. © 2024 IEEE.

关键词： mmWave Radar Object Detection ResNet VGG YOLO

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Wu, Guotong Nan, Yang Shang, Ke Ishibuchi, Hisao Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC. © 2024 IEEE.

关键词： Neural network models

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Performance Analysis of N-Beats and Regression Learners for Wind Speed Forecasting and Predictions 9th

Performance Analysis of N-Beats and Regression Learners for ...

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9th National Conference on Wind engineering, NCWE 2023

作者： Prakash, Jatin Kankar, P.K. Miglani, Ankur System Dynamics Lab Department of Mechanical Engineering Indian Institute of Technology Indore Indore453552 India Microfluidics and Droplet Dynamics Lab Department of Mechanical Engineering Indian Institute of Technology Indore Indore453552 India

ISBN: (纸本)9789819941827

Wind energy has enormous potential to fulfil industrial and other power requirement demands specifically in remote areas. The amount of power generated from the wind turbine depends on several factors, namely wind speed, wind direction, rotor area, the height of the tower, etc. As the wind speed is highly dynamic, it highly affects the power generation capacity of the windmills. Thus, it is highly desired that wind shall be monitored as well as forecasted earlier to prevent any sudden ups or downs in the power generation. This manuscript presents a regression-based methodology to predict the wind speed using XGBoost and AdaBoost regression learners. Their learning capabilities have been compared using mean absolute error. XGBoost is found to have lesser value of MAE at 0.392. Parallelly, N-Beats, the time series forecasting model is trained to forecast the wind speed. This way, the present study showcases the utility of time series forecasting method to accurately predict and forecast the wind speed. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wind power

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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Investigating the Effectiveness of Artificial Intelligence in Watermarking and Steganography for Digital Media Security 21

Investigating the Effectiveness of Artificial Intelligence i...

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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Abdalwahid, Shadan M.J.A Hashim, Wassan Adnan Saeed, Mohammed Ganim Altaie, Sarmad A. Kareem, Shahab Wahhab Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq AIQAlam University College Medical Instruments Techniques Dept Kirkuk Iraq Dohuk Polytechnic University Department of Information Technology Dohuk Iraq University of Technology Computer Engineering Department Baghdad Iraq Erbil Technical Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq

ISBN: (纸本)9798350374131

Watermarking and Steganography are methods of embedding digital information within images or other media, such as text or audio, for the purpose of Copyright Protection or covert communication. This field of study is not recent and has been ongoing for several years, culminating in its current advanced stage. The utilization of Artificial Intelligence algorithms has played a pivotal role in revolutionizing various aspects, including security concerns and the precision of outcomes, as compared to traditional methods. This paper focuses on doing an in-depth analysis of cutting-edge research, techniques, and methodologies employed in the domain of Watermarking and Steganography, specifically in conjunction with Artificial Intelligence. By thoroughly examining and evaluating a collection of recent studies in this domain, we have scrutinized the outcomes of each study with respect to its research goal, the acquired results, the employed algorithm, and the research's robustness in terms of susceptibility to various forms of attacks and the technique of data embedding. Our findings indicate that the use of Artificial Intelligence algorithms has a substantial influence on enhancing result precision, system resilience, and establishing data ownership. Deep Neural Networks (DNN) are essential in Watermarking and Steganography due to their robustness, effectiveness, and accuracy. Novel methodologies and systems improve security, incorporation rates, precision of detection, and speed of convergence. Deep learning is being investigated in techniques such as data concealment, information hiding, and steganography to improve security. © 2024 IEEE.

关键词： Embeddings

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Antibiofilm and antithrombotic hydrogel coating based on superhydrophilic zwitterionic carboxymethyl chitosan for blood-contacting devices

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Bioactive Materials 2024年第4期34卷 112-124页

作者： Dong Uk Lee Mukhammad Kayumov Junghun Park Se Kye Park Yeongkwon Kang Yejin Ahn Woojin Kim Seung Hwa Yoo Jun-Kyu Park Bong-Gi Kim Yong Suk Oh In-Seok Jeong Dong Yun Choi Biomedical Manufacturing Technology Center Korea Institute of Industrial TechnologyYeongcheon38822Republic of Korea Department of Thoracic and Cardiovascular Surgery Chonnam National University Hospital and Medical SchoolGwangju61469Republic of Korea Research Institute of Advanced Materials Seoul National UniversitySeoul08826Republic of Korea Department of Organic and Nano System Engineering Konkuk UniversitySeoul05029Republic of Korea Department of Quantum System Engineering Jeonbuk National UniversityJeonju-si54896Republic of Korea IMT Inc. Gwangju61086Republic of Korea Department of Mechanical Engineering Changwon National UniversityChangwon51140Republic of Korea

Blood-contacting devices must be designed to minimize the risk of bloodstream-associated infections,thrombosis,and intimal lesions caused by surface ***,achieving effective prevention of both bloodstream-associated infections and thrombosis poses a challenge due to the conflicting nature of antibacterial and antithrombotic activities,specifically regarding electrostatic *** study introduced a novel biocompatible hydrogel of sodium alginate and zwitterionic carboxymethyl chitosan(ZW@CMC)with antibacterial and antithrombotic activities for use in *** ZW@CMC hydrogel demonstrates a superhydrophilic surface and good hygroscopic properties,which facilitate the formation of a stable hydration layer with low *** zwitterionic-functionalized CMC incorporates an additional negative sulfone group and increased negative charge density in the carboxyl *** augmentation enhances electrostatic repulsion and facilitates the formation of hydration *** leads to exceptional prevention of blood clotting factor adhesion and inhibition of biofilm ***,the ZW@CMC hydrogel exhibited biocompatibility with tests of in vitro cytotoxicity,hemolysis,and catheter ***,in vivo tests of antithrombotic and systemic inflammation models with catheterization indicated that ZW@CMC has significant advantages for practical applications in cardiovascular-related and sepsis *** study opens a new avenue for the development of chitosan-based multifunctional hydrogel for applications in blood-contacting devices.

关键词： Zwitterionic Hydrogel Blood-clotting Antimicrobial Sepsis

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Anode surface engineering of zinc-ion batteries using tellurium nanobelt as a protective layer for enhancing energy storage performance

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Journal of Energy Chemistry 2024年第5期92卷 113-123页

作者： Soobeom Lee Yeonjin Je Boeun Seok Hyun Tae Kim Yong-Ryun Jo Soong Ju Oh Byoungyong Im Dae Guen Kim Sang-Soo Chee Geon-Hyoung An Department of Energy System Engineering Gyeongsang National UniversityJinju 52725Republic of Korea Nano Convergence Materials Center Korea Institute of Ceramic Engineering and Technology(KICET)Jinju 52851Republic of Korea Department of Materials Science and Engineering Korea UniversitySeoul 02841Republic of Korea School of Materials Science and Engineering Gyeongsang National UniversityJinju 52828Republic of Korea GIST Advanced Insitute of Instrumental Analysis(GAIA) Electron Microscopy LaboratoryGwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Center for Advanced Materials and Processing Institute for Advanced EngineeringYongin 17180Republic of Korea Department of Energy Engineering Gyeongsang National UniversityJinju 52725Republic of Korea

Over the years,zinc-ion batteries(ZIBs)have attracted attention as a promising next-generation energy storage technology because of their excellent safety,long cycling performance,eco-friendliness,and high-power ***,issues,such as the corrosion and dissolution of the Zn anode,limited wet-tability,and lack of sufficient nucleation sites for Zn plating,have limited their practical *** introduction of a protective layer comprising of tellurium(Te)nanobelts onto the surface of Zn anode has emerged as a promising approach to overcome these limitations and improve the electrochemical behav-ior by enhancing the safety and wettability of ZIBs,as well as providing numerous nucleation sites for Zn *** the presence of a Te-based protective layer,the energy power density of the surface-engineered Zn anode improved significantly(ranging from 310 to 144 W h kg^(-1),over a power density range of 270 to 1,800 W kg^(-1)),and the lifespan capability was *** results demonstrate that the proposed strategy of employing Te nanobelts as a protective layer holds great promise for enhancing the energy storage performance of zIBs,making them even more attractive as a viable energy storage solution forthefuture.

关键词： Zn ion battery Anode Protective layer Tellurium Nanobelt

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A systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

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IEEE Access 2024年 12卷 53408-53423页

作者： Por, Lip Yee Ng, Ian Ouii Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes. © 2013 IEEE.

关键词： Authentication

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