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Unconventional spin polarization at Argon ion milled SrTiO3 Interfaces.

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arXiv 2023年

作者： Kumar, Amrendra Shashank, Utkarsh Maharana, Suman Kumar Mohan, John Rex Gupta, Surbhi Asada, Hironori Fukuma, Yasuhiro Medwal, Rohit Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka820-8502 Japan Department of Physics Motilal Nehru National Institute of Technology Barrister Mullah Colony Uttar PradeshTeliarganj Prayagraj211004 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

Interfacial two-dimensional electron gas (2DEG) formed at the perovskite-type oxide, such as SrTiO3, has attracted significant attention due to its properties of ferromagnetism, superconductivity, and its potential application in oxide-based low-power consumption electronics. Recent studies have investigated spin-to-charge conversion at the STO interface with different materials, which could affect the efficiency of this 2DEG interface. In this report, we presented a Ar+ ion milling method to create a 2DEG at STO directly by inducing oxygen vacancies. To quantify the spin-to-charge conversion of this interface, we measured the angular-dependent spin torque ferromagnetic resonance (ST-FMR) spectra, revealing an unconventional spin polarization at the interface of of Argon ion milled STO and NiFe . Furthermore, a micromagnetic simulation for angular dependent spin torque ferromagnetic resonance (ST-FMR) has been performed, confirming the large unconventional spin type spin polarization at the interface. Copyright © 2023, The Authors. All rights reserved.

关键词： Ferromagnetism

An Effective Model for Drug-Drug Interactions Prediction in Cold-start Scenario via Counterfactual Data Augmentation

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An Effective Model for Drug-Drug Interactions Prediction in ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xueling Yuan Weizhong Zhao Xiaowei Xu Xinhui Tu Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China School of Computer Central China Normal University Wuhan China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Wuhan China Department of Information Science University of Arkansas at Little Rock Little Rock AR USA

Drug-drug interaction (DDI) pertains to the occurrence where the concomitant use of two or more drugs may lead to interactions in terms of their pharmacokinetic or pharmacodynamic behavior, resulting in unexpected effects. Accurately predicting DDIs holds significant importance in ensuring drug safety. Despite the numerous approaches proposed for DDI prediction, a majority of these methods often overlook the challenge presented by cold-start scenario, consequently limiting their applicability. This paper presents a novel data augmentation approach for the prediction of DDIs in cold-start scenarios. This method leverages counterfactual inference to generate meaningful pseudo samples for drugs with limited prior information. To achieve this, a HIN relevant to DDIs is initially established by amalgamating various associations between drugs and proteins. Subsequently, the identification of drug communities within this HIN is regarded as a form of counterfactual inference treatment, facilitating the generation of counterfactual links for cold-start drugs and thereby augmenting the training dataset. Lastly, we enhance our understanding of drug characteristics through a meta-path-based fusion mechanism, ultimately improving the accuracy of DDIs prediction in cold-start scenarios. We substantiate the effectiveness of our proposed method through an extensive series of experiments.

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Timeliness of Status Update System: The Effect of Parallel Transmission Using Heterogeneous Updating Devices

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arXiv 2024年

作者： Chen, Zhengchuan Lang, Kang Pappas, Nikolaos Yang, Howard H. Wang, Min Tian, Zhong Quek, Tony Q.S. School of Microelectronics and Communication Engineering Chongqing University Chongqing China Department of Computer and Information Science Linköping University Linköping581 83 Sweden Zhejiang University University of Illinois at Urbana-Champaign Institute Zhejiang University Haining314400 China School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Postdoctoral Research Center of Chongqing Key Laboratory of Optoelectronic Information Sensing and Transmission Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information. However, parallel status updating leads to out-of-order arrivals at the monitor, significantly challenging timeliness analysis. This work studies the Age of information (AoI) of a multi-queue status update system where multiple devices monitor the same physical process. Specifically, two systems are considered: the Basic System, which only has type-1 devices that are ad hoc devices located close to the source, and the Hybrid System, which contains additional type-2 devices that are infrastructure-based devices located in fixed points compared to the Basic System. Using the Stochastic Hybrid Systems (SHS) framework, a mathematical model that combines discrete and continuous dynamics, we derive the expressions of the average AoI of the considered two systems in closed form. Numerical results verify the accuracy of the analysis. It is shown that when the number and parameters of the type-1 devices/type-2 devices are fixed, the logarithm of average AoI will linearly decrease with the logarithm of the total arrival rate of type-2 devices or that of the number of type-1 devices under specific condition. It has also been demonstrated that the proposed systems can significantly outperform the FCFS M/M/N status update system. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

Secret key agreement and secure omniscience of tree-PIN source with linear wiretapper

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arXiv 2021年

作者： Vippathalla, Praneeth Kumar Chan, Chung Kashyap, Navin Zhou, Qiaoqiao Department of Computer Science City University of Hong Kong Hong Kong Institute of Network Coding Department of Information Engineering Chinese University of Hong Kong Hong Kong Department of Electrical Communication Engineering Indian Institute of Science Bangalore560012 India

While the wiretap secret key capacity remains unknown for general source models even in the two-user case, we obtained a single-letter characterization for a large class of multi-user source models with a linear wiretapper who can observe any linear combinations of the source. We introduced the idea of irreducible sources to show existence of an optimal communication scheme that achieves perfect omniscience with minimum leakage of information to the wiretapper. This implies a duality between the problems of wiretap secret key agreement and secure omniscience, and such duality potentially holds for more general sources. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Analysis of Cognitive Emotional and Behavioral Aspects of Alzheimer's Disease Using Hybrid CNN Model

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Analysis of Cognitive Emotional and Behavioral Aspects of Al...

computer, Power and Communications (ICCPC), International Conference on

作者： R. Prabha G. A Senthil P Suganthi Divya Boopathi M Razmah A. Lazha Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Information Technology Agni College of Technology Chennai India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India Department of Physiology Sri Sairam Siddha Medical College and Research Center Chennai India

Alzheimer's disease is a brain related disorder which occurs by the growth upon unnecessary growth of protein in and around the brain cells. This disease causes memory loss in the human that makes them do activities slowly and repeatedly. The patients who suffer the disease couldn't handle the money properly, they repeat the questions often and they suffer challenges in planning. To some extent it makes the interactions with the environment complicated. Being a cruel disease, this should be analyzed and treated in the initial state. Thus, predicting disease is essential. This paper explains how machine learning algorithms helps patients to get predicted and classified on Alzheimer's disease. The algorithms used in the papers includes VGG-16, DENSENET-121, CNN. As it is a hybrid model, the efficiencies of the algorithms are compared and found an efficient result at the end of the research.

关键词： Proteins Analytical models Machine learning algorithms Optimized production technology Predictive models Prediction algorithms Classification algorithms

Numerical treatment based on artificial neural network to Soret and Dufour effects on MHD squeezing flow of Jeffrey fluid in horizontal channel with thermal radiation

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International Journal of Thermofluids 2024年 23卷

作者： Ullah, Hakeem Alqahtani, Aisha M. Raja, Muhammad Asif Zahoor Fiza, Mehreen Ullah, Kashif Omer, Abdoalrahman S.A. Khan, Ilyas Shoaib, Muhmmad Department of Mathematics Abdul Wali Khan University KP Mardan23200 Pakistan Department of Mathematical Science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section.3Yunlin Douliou64002 Taiwan Department of Mathematics College of Education Al Fashir University Sudan Department of Information System College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Yuan Ze University AI centre Taoyun320 Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Mathematics Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Al-Ayen research center Al-Ayen University Iraq

The current investigation formulated an efficient computing technique by incorporating the algorithm of Artificial Neural network Levenberg Marquardt Method (ANNLMM) to approximate Soret and Dufour effects on Magnetohydrodynamic squeezing flow of Jeffrey fluid (MHDSFJF) in MATLAB environment. Due to its efficiency and flexibility, the applied neural network model can accurately predict the output results. In the ANNLMM, 80% of the data is used for training and 10% is set for both the testing and validation processes. The flow under consideration is mathematically formulated with the help of Navier Stokes Equations. The partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) by similarity variables. The effects of the physical parameters are discussed briefly. When the Hartmann number and Jeffrey fluid parameter increase, the concentration, temperature, and velocity profiles decreases. The temperature profile and heat transfer rate are improved by the effects of joule dissipation, Dufour numbers, and heat generation/absorption. On the other hand, as thermal radiation rises, the temperature profile decreases and the heat transfer rate increases. With an augment in Soret number, the mass transfer rate increases and the concentration profile slows down. Additionally, increasing chemical reactions, the mass transfer rate increases, but in convective chemical reactions, the contradiction is observed. © 2024

关键词： Magnetohydrodynamics

A GPT-4 Reticular Chemist for Guiding MOF Discovery

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arXiv 2023年

作者： Zheng, Zhiling Rong, Zichao Rampal, Nakul Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry Kavli Energy Nanoscience Institute Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States KACST—UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia Department of Electrical Engineering and Computer Sciences Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences Department of Statistics Department of Mathematics School of Information Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthesis (chemical)

A Hybrid network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

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Research Square

Research Square 2021年

作者： Khilar, Rashmita Mariyappan, K. Christo, Mary Subaja Amutharaj, J. Anitha, T. Thavasimuthu, Rajendran Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of Computer Science School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Information Science & Engineering RajaRajeswari College of Engineering Bangalore India Makeit Technologies Center for Industrial Research Coimbatore India

The security of the network is a significant issue in any distributed system. For that intrusion detection system (IDS), have been proposed for securing the network from malicious activities. This research is proposed to design and develop an anomaly detection model for detecting attacks and unusual activities in IoT networks. The primary objective of this research is to design efficient IDS for IoT network. The intrusion detection plays an essential role in detecting different attacks on IoT and enhances the performance of the IoT. In this research, anomaly detection in IoT networks using glowworm swarm optimization (GSO) algorithm with principal component analysis (PCA) is proposed. However, the proposed model is metaheuristic algorithm-based anomaly detection model to identify attacks by using the NSL-KDD dataset. The GSO algorithm based on PCA is implemented to perform the anomaly detection. For feature extraction, the PCA is used, and for classification, the GSO algorithm is used. For performance analysis, various parameters like accuracy, precision, recall, detection rate and FAR are evaluated. For normal class the proposed model achieved 94.14% accuracy, for DoS 95.52%, for R2L 93.15%, for probe 93.50% and for U2R 88.62% accuracy. Overall the detection rate was 94.08% and FAR was 3.41%. © 2021, CC BY.

关键词： Principal component analysis

Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel