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Improving the ephemerides of Neptune’s satellites with Gaia, Voyager 2, and ground-based data

Celestial Mechanics and Dynamical Astronomy 2025年第4期137卷 1-18页

作者： Dolgakov, Ivan Pavlov, Dmitry Institute of Applied Astronomy RAS St. Petersburg Russia Faculty of Computer Science and Technology St. Petersburg Electrotechnical University St. Petersburg Russia

Precise ephemerides of planetary satellites are essential for spacecraft mission planning and studying of the central planet’s properties. We present numerical theories for Neptune’s natural satellites—Triton, Nereid, and Proteus—based on ground-based CCD observations and space-based data from the Voyager 2 flyby mission and Gaia space observatory, whose Data Release 3 (DR3) includes positional observations of Triton and Nereid from 2014 to 2019. Parameters of gravity and rotation of Neptune are determined alongside satellites’ orbital parameters.

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MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors 23

MotionNeXt: A Deep Learning Neural Network for Recognizing H...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350353952

Recognition of human activities using wearable sensors is essential for applications related to health and well-being. However, the complex data captured by inertial sensors poses challenges in accurately identifying actions. This research introduces MotionNeXt, a residual deep neural network incorporating aggregated transformation. MotionNeXt utilizes residual and multi-kernel blocks to extract spatial and temporal characteristics from raw IMU data. Subsequently, it employs global average pooling, a fully connected layer, and softmax for classification purposes. An attention layer enables the model to focus on specific segments of the input sequence while categorizing each time step. MotionNeXt is evaluated using a publicly available dataset of activities collected from wearable inertial measurement devices. It achieves state-of-the-art accuracy, boasting an F1-score of 99.37%, surpassing previous deep learning methods by 2- 5%. By integrating accelerometer, gyroscope, and magnetometer modalities, MotionNeXt mitigates the limitations of individual inputs, significantly improving accuracy across various models. © 2024 IEEE.

关键词： Deep neural networks

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing 29

Physics-Informed Neural Networks: A Step Towards Data-Driven...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Depaoli, Fabio Felicioni, Stefano Ponzio, Francesco Aliberti, Alessandro MacIi, Enrico Bondioli, Federica Padovano, Elisa Di Cataldo, Santa Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (纸本)9798350361230

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach. © 2024 IEEE.

关键词： Smart manufacturing

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on Whale Optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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An Efficient AES 32-Bit Architecture Resistant to Fault Attacks

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computers, Materials & Continua 2022年第2期70卷 3667-3683页

作者： Hassen Mestiri Imen Barraj Abdullah Alsir Mohamed Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia METS Research Group Electrical Engineering DepartmentNational Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

The Advanced Encryption Standard cryptographic algorithm,named AES,is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information *** of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret *** now,a several AES fault detection schemes against fault injection attacks have been *** this paper,so as to ensure a high level of security against fault injection attacks,a new efficient fault detection scheme based on the AES architecture modification has been *** this reason,the AES 32-bit round is divided into two half rounds and input and pipeline registers are implemented between *** proposed scheme is independent of the procedure the AES is ***,it can be implemented to secure the pipeline and iterative *** evaluate the robustness of the proposed fault detection scheme against fault injection attacks,we conduct a transient and permanent fault attacks and then we determine the fault detection capability;it is about 99.88585%and 99.9069%for transient and permanent faults *** have modeled the AES fault detection scheme using VHDL hardware language and through hardware FPGA *** FPGA results demonstrate that our scheme can efficiently protect the AES hardware implementation against fault *** can be simply implemented with low *** addition,the FPGA implementation performances prove the low area overhead and the high efficiency and working frequency for the proposed AES detection scheme.

关键词： Security cryptographic circuits AES hardware implementation fault detection fault attacks

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aterials structure-property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

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npj Computational Materials 2022年第1期 574-580页

作者： Dan Guevarra Lan Zhou Matthias H.Richter Aniketa Shinde Di Chen Carla P.Gomes John M.Gregoire Division of Engineering and Applied Science and Liquid Sunlight Alliance California Institute of TechnologyPasadenaCAUSA Department of Computer Science Cornell UniversityIthacaNYUSA

Properties can be tailored by tuning composition in high-order composition *** spaces with complex phase behavior,modeling the properties as a function of composition and phase distribution remains a formidable *** present materials structure–property factorization(MSPF)as an approach to automate modeling of such data and identify synergistic phase *** is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks(DRNets)to matrix factorization-based modeling of the representative properties of each phase in a *** is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation,which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity *** the measured photoactivity to a learned model for non-interacting phases,synergistic phase interactions are identified to guide further photoactivity optimization and *** identifies synergistic interactions of a BiVO_(4)-like phase with both Cu_(2)V_(2)O_(7)-like and CuV_(2)O_(6)-like phases,creating avenues for understanding complex photoelectrocatalysts.

关键词： synergistic phase property

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Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings

Mental Health Stigma: Strategies for Destigmatization in Hea...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Verma, Aishwarya Panda, Krishna Tanaya Sharma, Vandana Tripathy, Hrudaya Kumar Al-Khasawneh, Mahmoud Ahmad Alkhayyat, Ahmed Kalinga Institute of Industrial Technology India CHRIST University Computer Science Department India Skyline University College University City Sharjah School of Computing Sharjah1797 United Arab Emirates Applied Science Private University Applied Science Research Center Amman Jordan Jadara University Research Center Jadara University Jordan The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350378092

Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. © 2024 IEEE.

关键词： Electronic health record

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Review of Fundamental, Security, and Privacy in Metaverse

Review of Fundamental, Security, and Privacy in Metaverse

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Al-Nowfleh, Nadia Mohammed Al-Dmour, Nidal A. Hamadi, Hussam Al Taher, Fatma Ghazal, Taher M. Mutah University Department of Computer Engineering Al-Karak Jordan College of Engineering and It University of Dubai Dubai United Arab Emirates College of Technological Innovation Zayed University Dubai United Arab Emirates Information Science and Technology Selangor Bangi Malaysia Applied Science Private University Applied Science Research Center Amman Jordan

ISBN: (纸本)9798350304602

The Metaverse, a living environment, and cyberspace are used to virtualize and digitize the real world. It utilizes a variety of existing technologies to map the physical world and potentially realms beyond it. Future forecasts imply that Metaverse will be used in a variety of applications. The creation of numerous related technologies serves as the foundation for the Metaverse's upkeep. It follows that the security threats associated with the growth of the Metaverse may be increasingly obvious and complicated. Metaverse is the name of the most recent Internet-based platform. In order to establish a completely engross extremely spatiotemporal, self-sustaining common virtual world that allows individuals to play, work, and socialize, the Metaverse-a concept for the future version of the Internet-was developed. In order to provide security for the metaverse, it may be difficult given its inherent characteristics, such as sensory reality, hyper-spatial duration, long-term viability and variety. We present in this paper some of the technologies used in the Metaverse as well as some potential security and privacy issues. © 2023 IEEE.

关键词： Virtual reality

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Utilizing Artificial Intelligence for Plant Phenotyping in Soilless Farming: An Innovative Deep Learning Approach on a Unique Dataset 1

Utilizing Artificial Intelligence for Plant Phenotyping in S...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Reddy, D. Jaya Narayana Krishna Priya, R. Kumar, J. Arun Prasad, P. Dharani Nawaz, Mustafa Venu, Nookala G Pullaiah College of Engineering and Technology India University of Technology and Applied Sciences Research and Consultancy Department Governorate of Musandam Oman Vel Tech Rangarajan Sagunthala R&d Institute of Science and Technology Department of Ece India Dept of Pharmacology Tirupati India Sri Sairam Institute of Technology Department of Computer Science and Engineering India Centre for IoT Madhya Pradesh Gwalior474005 India

ISBN: (纸本)9798350377002

In the dynamic landscape of agriculture, the convergence of technological advancements and sustainable practices has become imperative. The emergence of soilless farming, driven by hydroponics and aeroponics, presents a promising solution to address food security challenges while mitigating environmental concerns. Central to the success of soilless farming is efficient plant phenotyping, which traditionally relies on labour-intensive methodologies. However, this paradigm is shifting with the integration of Artificial Intelligence (AI) and Deep Learning techniques. This research endeavours to pioneer an innovative approach to plant phenotyping in soilless farming by harnessing the power of AI. Leveraging a unique dataset meticulously curated from controlled hydroponic and aeroponic environments, our study aims to redefine the boundaries of agricultural research and practice. By employing state-of-the-art Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we dissect complex phenotypic traits encompassing leaf morphology, biomass accumulation, and physiological responses. Through iterative model refinement and validation, we strive to develop a robust framework capable of real-time phenotypic assessment across diverse plant species and growth stages. By synthesizing diverse environmental conditions and perturbations, we augment the original dataset, enhancing model generalization and adaptability. Moreover, through transfer learning techniques, Furthermore, the scalability and accessibility of AI technologies pave the way for democratizing agricultural innovation, fostering inclusive growth and resilience in the face of global challenges. © 2024 IEEE.

关键词： Hydroponics

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