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Optimization of Carbon Dot Deposition on Polycarbonate Substrates Via Non-Thermal Plasma: Exploring its Superhydrophobic Properties and Ph Sensing Capabilities

SSRN

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SSRN 2024年

作者： Selvaraj, Yuvaraj Venkatraman, M.R. Govindaraj, Rajesh Carrasco, Marcos Flores Natarajan, Muthukumarasamy Velauthapillai, Dhayalan Devaraj, Kamalraj Yoganand, Cp. Jaffer, Syed S. Department of Physics Coimbatore Institute of Technology Coimbatore641014 India Department of Physics Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Centre for Computational Modelling Chennai Institute of Technology Chennai600069 India Centre for Research in Pure and Applied Sciences JAIN [Deemed-to-be-University Bengaluru560069 India Department of Physics Faculty of Physical and Mathematical Sciences University of Chile Santiago265 Chile Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Science Bergen5063 Norway Department of Chemistry Coimbatore Institute of Technology Coimbatore641014 India

Carbon dots (CDs) are a class of highly photoluminescent nanomaterials that are less toxic, exhibit good compatibility in biological studies, and they are of sizes typically below 10 nm. In the present work, CDs were implanted on the surface of polycarbonate (PC) using the DC glow discharge non-thermal plasma technique. Plasma processing is a versatile technique to produce nanomaterials that can be used for biomedical and sensor applications. Monitoring the input voltage, processing time, and gas flow-rate are the crucial parameters that play a major role in optimal processing to impregnate CDs on PC. Acetylene plasma processed PC was subjected to an acid isolation process for accumulation of CDs and to test its usage in pH sensing and optical applications. Structural and graphitic layer formation of CDs on PC was studied through Raman spectroscopy, amorphous nature of the samples were observed from the XRD results. The uniform distribution of carbons over the PC was witnessed from the FESEM images and the isolated CDs were identified through and TEM microscopic images. The pH-sensitive photoemission property of acid isolated CDs was analyzed using fluorescence spectrophotometer and the observed results from 3D-contour fluorescent spectra revealed that the KMnO4 treated CDs showed interesting behaviour that can be used in biological pH sensors. The superhydrophobic nature of the prepared samples were observed through the contact angles measurements for water and blood respectively. Contact angle results suggest that the plasma processed CDs can be used in applications involving blood contact devices. © 2024, The Authors. All rights reserved.

关键词： pH sensors

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Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm

arXiv

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arXiv 2024年

作者： Cheng, Yu Yang, Qin Wang, Liyang Xiang, Ao Zhang, Jingyu Columbia University The Fu Foundation School of Engineering and Applied Science Operations Research New YorkNY United States Microelectronics Science and Engineering Sichuan Chengdu China Washington University in St. Louis Olin Business School Finance St. LouisMO United States University of Electronic Science and Technology of China School of Computer Science & Engineering School of Cybersecurity Digital Media Technology Sichuan Chengdu China The University of Chicago The Division of the Physical Sciences Analytics ChicagoIL United States

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural network models

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

arXiv

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

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Preparation of ultrafast ammonia sensor based on cross-linked ZnO nanorods coated with poly(3-hexylthiophene)

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Synthetic Metals 2023年第1期299卷

作者： Han, Kai-Xu Wu, Chia-Ching Hsu, Wei-Fan Chien, Wei Yang, Cheng-Fu Education Department of Guangxi Zhuang Autonomous Region Qinzhou535011 China Department of Applied Science National Taitung University Taitung95092 Taiwan Department of Chemical and Materials Engineering National University of Kaohsiung Kaohsiung811726 Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City333 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung413310 Taiwan

A gas sensing device was fabricated using poly(3-hexylthiophene) (P3HT)-coated ZnO nanorods, and the ammonia (NH3) gas-sensing properties of the heterojunction device were analyzed. Hexagonal ZnO nanorods were prepared using the hydrothermal method on a ZnO seed layer. The ZnO seed layer was deposited using the sputtering method on a Ti–Pt bilayer interdigital electrode. X-ray diffraction and field emission scanning electron microscopy characterized the crystal structure and morphology of the ZnO–P3HT nanorods heterojunction devices. The gas-sensing properties of the P3HT–ZnO nanorods heterojunction sensor were examined and compared with different concentrations of NH3 gas. The response and recovery times were less than 1.5 s with a NH3 gas concentration of 1 ppm. Comparison with sensors of other materials indicated that P3HT was the reason for the short response and recovery times. Plausible mechanisms explaining the enhanced NH3 selectivity of the P3HT–ZnO nanorods heterojunction structure are discussed. © 2023

关键词： Zinc oxide

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On the relevance of lift force modelling in turbulent wall flows with small inertial particles

arXiv

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arXiv 2024年

作者： Gao, Wei Shi, Pengyu Parsani, Matteo Costa, Pedro Mechanical Engineering Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Applied Mathematics and Computational Science Computer Electrical and Mathematical Science and Engineering Division Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Université de Toulouse CNRS Toulouse France Helmholtz-Zentrum Dresden – Rossendorf Institute of Fluid Dynamics Dresden01328 Germany Process & Energy Department TU Delft Leeghwaterstraat 39 Delft2628 CB Netherlands

In particle-laden turbulent wall flows, lift forces can influence the near-wall turbulence. This has been recently observed in particle-resolved simulations, which, however, are too expensive to be used in upscaled models. Instead, point-particle simulations have been the method of choice to simulate the dynamics of these flows during the last decades. While this approach is simpler, cheaper, and physically sound for small inertial particles in turbulence, some issues remain. In the present work, we address challenges associated with lift force modelling in turbulent wall flows and the impact of lift forces in the near-wall flow. We performed direct numerical simulations (DNS) of small inertial point particles in turbulent channel flow for fixed Stokes number and mass loading while varying the particle size. Our results show that the particle dynamics in the buffer region, causing the apparent particle-to-fluid slip velocity to vanish, raises major challenges for accurately modelling lift forces. While our results confirm that lift forces have little influence on particle dynamics for sufficiently small particle sizes, for inner-scaled diameters of order one and beyond, lift forces become quite important near the wall. The different particle dynamics under lift forces results in the modulation of streamwise momentum transport in the near-wall region. We analyze this lift-induced turbulence modulation for different lift force models, and the results indicate that realistic models are critical for particle-modelled simulations to correctly predict turbulence modulation by particles in the near-wall region. © 2024, CC BY.

关键词： Wall flow

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Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics

arXiv

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arXiv 2024年

作者： Xiang, Ao Zhang, Jingyu Yang, Qin Wang, Liyang Cheng, Yu University of Electronic Science and Technology of China School of Computer Science & Engineering School of Cybersecurity Digital Media Technology Sichuan Chengdu China The University of Chicago The Division of the Physical Sciences Analytics ChicagoIL United States Microelectronics Science and Engineering Sichuan Chengdu China Washington University in St. Louis Olin Business School Finance St. LouisMO United States Columbia University The Fu Foundation School of Engineering and Applied Science Operations Research New YorkNY United States

With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues. This paper introduces a new splicing image detection algorithm based on the statistical characteristics of natural images, aimed at improving the accuracy and efficiency of splicing image detection. By analyzing the limitations of traditional methods, we have developed a detection framework that integrates advanced statistical analysis techniques and machine learning methods. The algorithm has been validated using multiple public datasets, showing high accuracy in detecting spliced edges and locating tampered areas, as well as good robustness. Additionally, we explore the potential applications and challenges faced by the algorithm in real-world scenarios. This research not only provides an effective technological means for the field of image tampering detection but also offers new ideas and methods for future related research. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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HyperColor: A Hypernetwork approach for synthesizing auto-colored 3D models for game scenes population

arXiv

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arXiv 2021年

作者： Kostiuk, Ivan Stachura, Przemyslaw Tadeja, Slawomir K. Trzcinski, Tomasz Spurek, Przemyslaw Jagiellonian University Faculty of Mathematics and Computer Science Kraków Poland Jagiellonian University Institute of Applied Computer Science Kraków Poland Warsaw University of Technology Faculty of Electronics and Information Technology Warsaw Poland

Designing a 3D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves synthesis, coloring, and placement of 3D models within the game scene. To lessen this workload, we can apply machine learning to automate some aspects of the game scene development. Earlier research has already tackled automated generation of the game scene background with machine learning. However, model auto-coloring remains an underexplored problem. The automatic coloring of a 3D model is a challenging task, especially when dealing with the digital representation of a colorful, multipart object. In such a case, we have to "understand" the object's composition and coloring scheme of each part. Existing single-stage methods have their own caveats such as the need for segmentation of the object or generating individual parts that have to be assembled together to yield the final model. We address these limitations by proposing a two-stage training approach to synthesize auto-colored 3D models. In the first stage, we obtain a 3D point cloud representing a 3D object, whilst in the second stage, we assign colors to points within such cloud. Next, by leveraging the so-called triangulation trick, we generate a 3D mesh in which the surfaces are colored based on interpolation of colored points representing vertices of a given mesh triangle. This approach allows us to generate a smooth coloring scheme. Experimental evaluation shows that our two-stage approach gives better results in terms of shape reconstruction and coloring when compared to traditional single-stage techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Mesh generation

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Multiangle QAOA Does Not Always Need All Its Angles

Multiangle QAOA Does Not Always Need All Its Angles

引用

IEEE/ACM Symposium on Edge Computing (SEC)

作者： Kaiyan Shi Rebekah Herrman Ruslan Shaydulin Shouvanik Chakrabarti Marco Pistoia Jeffrey Larson Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Maryland College Park MD USA Industrial and Systems Engineering University of Tennessee at Knoxville Knoxville TN USA Global Technology Applied Research JPMorgan Chase New York NY USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Introducing additional tunable parameters to quantum circuits is a powerful way of improving per-formance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) signifi-cantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamilto-nian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance.

关键词： Costs Redundancy Approximation algorithms Hardware Quantum circuit Optimization Edge computing

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Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment

arXiv

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arXiv 2024年

作者： Wu, Jiaqi Zhang, Shihao Chen, Simin Wang, Lixu Wang, Zehua Chen, Wei He, Fangyuan Tian, Zijian Yu, F. Richard Leung, Victor C.M. Beijing100083 China Department of Electrical and Computer Engineering University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada University of Texas at dallas 800 W Campbell Rd RichardsonTX75080 United States Northwestern University 633 Clark St EvanstonIL60208 United States School of Computer Science and Technology China University of Mining and Technology Jiangsu province Xuzhou221116 China Department of Electrical and Computer Engineering The University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China College of Computer Science and Software Engineering Shenzhen University Shenzhen528060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada College of Applied Science and Technology Beijing Union University Beijing100012 China

Recently, edge computing has emerged as a prevailing paradigm in applying deep learning-based object detection models, offering a promising solution for time-sensitive tasks. However, existing edge object detection faces several challenges: 1) These methods struggle to balance detection precision and model lightweightness. 2) Existing generalized edge-deployment designs offer limited adaptability for object detection. 3) Current works lack real-world evaluation and validation. To address these challenges, we propose the Edge Detection Toolbox (ED-TOOLBOX), which leverages generalizable plug-and-play components to enable edge-site adaptation of object detection models. Specifically, we propose a lightweight Reparameterized Dynamic Convolutional Network (Rep-DConvNet) that employs a weighted multi-shape convolutional branch structure to enhance detection performance. Furthermore, ED-TOOLBOX includes a Sparse Cross-Attention (SC-A) network that adopts a localized-mapping-assisted self-attention mechanism to facilitate a well-crafted Joint Module in adaptively transferring features for further performance improvement. In real-world implementation and evaluation, we incorporate an Efficient Head into the popular You-Only-Look-Once (YOLO) approach to achieve faster edge model optimization. Moreover, we identify that helmet detection—one of the most representative edge object detection tasks—overlooks band fastening, which introduces potential safety hazards. To address this, we build a Helmet Band Detection Dataset (HBDD) and apply an edge object detection model optimized by the ED-TOOLBOX to tackle this real-world task. Extensive experiments validate the effectiveness of components in ED-TOOLBOX. In visual surveillance simulations, ED-TOOLBOX-assisted edge detection models outperform six state-of-the-art methods, enabling real-time and accurate detection. These results demonstrate that our approach offers a superior solution for edge object detection. Copyright © 2024, T

关键词： Deep learning

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An efficient algorithm for fuzzy frequent itemset mining

An efficient algorithm for fuzzy frequent itemset mining

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作者： Wu, Tsu-Yang Lin, Jerry Chun-Wei Yun, Unil Chen, Chun-Hao Srivastava, Gautam Lv, Xianbiao College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan Department of Mathematics Computer Science Brandon University Brandon Canada Shenzhen China

Association-rule mining (ARM) has concerned as an important and critical research issue in the field of data analytics and mining that aims at finding the correlations among the items in binary databases. However, the conventional algorithms considered the frequency of the item(set) in binary databases for ARM, which is not sufficient in real-life situations. Mining of useful information is not an easy task especially if the item(set) consists of the added values. Moreover, the discovered knowledge is not easy to understand if you are not the domain experts. For the past decades, several intelligent systems involved the fuzzy-set theory for many domains and applications due to it is interpretable for human reasoning. Before, the Apriori-based method for discovering fuzzy frequent itemsets (FFIs) based on the type-2 fuzzy-set theory was proposed, which requires the amount of computations with enormous candidates. In this study, we then first present a fast list-based multiple fuzzy frequent itemset mining (named as LFFT2)algorithm under type-2 fuzzy-set theory. It is developed by the type-2 membership functions to retrieve the multiple fuzzy frequent itemsets for presenting more useful and meaningful knowledge for making the efficient strategies or decisions. From the results shown in the experiments, it is clear to see that the developed LFFT2 outperforms the conventional Apriori-based approach regarding the execution time and the number of examined nodes in the search space. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy set theory

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