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SSRN 2023年

作者： Zhao, Huan Zheng, Chuantao Pi, Mingquan Liang, Lei Song, Fang Zhang, Yu Wang, Yiding Tittel, Frank K. State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun130012 China State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States

Polymeric SU8 waveguide is suitable for large-scale fabrication due to its simple fabrication process and low cost, but it has never been employed for on-chip gas measurement based on infrared absorption. A near-infrared on-chip acetylene (C2H2) sensor using SU8 polymer spiral waveguide was proposed, and the sensing performances based on wavelength modulation spectroscopy (WMS) was verified experimentally for the first time to our knowledge. The proposed Euler-S bend and Archimedean spiral SU8 waveguide reduces the sensor size by more than a half. By virtue of WMS technique, the C2H2 sensing performances of a 7.4cm- and a 13cm-long SU8 waveguide were derived at 1532.83 nm, and the obtained limit of detection (LoD) are 2197.1 and 425.5 parts-per-million (ppm) with an averaging time of 0.2 s, respectively. The experimentally obtained optical power confinement factor (PCF) is 0.0172, which is close to the simulated value of 0.016, and the measured waveguide loss is 3 dB/cm. The rise time and fall time are ~ 2.05 s and ~ 3.27 s, respectively. This work proves that SU8 waveguide has the potential of high-performance on-chip gas sensing in the near-infrared waveband. © 2023, The Authors. All rights reserved.

关键词： Infrared devices

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NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products

arXiv

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arXiv 2025年

作者： Ding, Yuheng Wang, Yusong Qiang, Bo Yu, Jie Li, Qi Zhou, Yiran Liu, Zhenmin State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing100191 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA98105 United States

Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products research primarily rely on supervised learning approaches designed for specific downstream tasks. However, such one-model-for-a-task paradigm often lacks generalizability and leaves significant room for performance improvement. Additionally, existing molecular characterization methods are not well-suited for the unique tasks associated with natural products. To address these limitations, we have pre-trained a foundation model for natural products based on their unique properties. Our approach employs a novel pretraining strategy that is especially tailored to natural products. By incorporating contrastive learning and masked graph learning objectives, we emphasize evolutional information from molecular scaffolds while capturing side-chain information. Our framework achieves state-of-the-art (SOTA) results in various downstream tasks related to natural product mining and drug discovery. We first compare taxonomy classification with synthesized molecule-focused baselines to demonstrate that current models are inadequate for understanding natural synthesis. Furthermore, by diving into a fine-grained analysis at both the gene and microbial levels, NaFM demonstrates the ability to capture evolutionary information. Eventually, our method is experimented with virtual screening, illustrating informative natural product representations that can lead to more effective identification of potential drug candidates. © 2025, CC BY.

关键词： Metabolites

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Automated Intrusion Detection and Classification using Binary Metaheuristics with Deep Learning on Smart Cities

Automated Intrusion Detection and Classification using Binar...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Haritha V R. Sindhuja Kumutha R. Aviral Srivastava Shobana. G Suma Christal Mary Sundararajan Department of CSE RMK Engineering College Kavarapettai Department of Medical Electronics Saveetha Engineering college Department of Humanities and science (Physics) S. A. Engineering College Chennai India college of information sciences and technology Pennsylvania state university State college Pennsylvania USA Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Information Technology Panimalar Engineering College Poonamalle Chennai

With the dramatic increase of interconnected devices in smart cities, ensuring the integrity and security of the basic infrastructure has become a major concern. Intrusion detection plays a major role in protecting smart city systems against cyber-attacks and threats. Deep learning (DL) techniques, namely recurrent neural networks (RNN) or convolutional neural networks (CNNs), were employed for analyzing the network traffic data. The model was trained by labelled dataset, where network traffic instance was categorized as either intrusive or normal. This study proposes a novel automated Intrusion Detection and Classification design using Binary Metaheuristics with Deep Learning (AIDC-BMDL) techniques on Smart Cities. The AIDC-BMDL technique makes use of metaheuristic feature selection and DL based classification process. For the election of optimal features in the intrusion data, the AIDC-BMDL technique uses binary gray wolf optimizer (BGWO) algorithm. Besides, the AIDC-BMDL technique exploits Stacked Autoencoder (SAE) model for the effectual recognition and classification of the intrusions in the smart city environment. The simulation results illustrated the ability of the AIDC-BMDL technique to accurately identify intrusions in smart city environments. The AIDC-BMDL technique gained maximum performance, pointing out the significance of improving the security of smart cities.

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Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Efficient and Resilient Placement Strategy for Electric Vehi...

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Technological engineering and its applications in Sustainable Development (ICTEASD), International Conference for

作者： Rakesh Kumar S. Shajathi Begam Dev Singh Tuychiev Komiljon Lazizovich S. Muthubalaji Mohammed Al-Farouni Srinivas Varanasi Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science and Business System E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Mathematics Department Allenhouse Institute of Technology Kanpur Department of Marketing Tashkent State University of Economics Uzbekistan Department of Electrical and Electronics Engineering CMR College of Engineering & Technology Hyderabad Telangana India The Islamic University Najaf Iraq GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness.

关键词： Photovoltaic systems Renewable energy sources Power distribution Production Electric vehicle charging Wind turbines Reliability

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A Comparison of Point Cloud Registration Techniques for on-site Disaster Data from the Surfside Structural Collapse

A Comparison of Point Cloud Registration Techniques for on-s...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Ananya Ball Robert Ladig Pranav Goyal John Galeotti Howie Choset David Merrick Robin Murphy The Robotics Institute Carnegie Mellon University Pittsburgh PA USA The Department of Science and Engineering Ritsumeikan University Kusatsu Japan The Birla Institute of Technology & Science - Pilani Goa India The College of Social Science and Public Policy Florida State University USA Raytheon Professor of Computer Science and Engineering Texas A&M University USA

ISBN: (纸本)9781665456814

3D representations of geographical surfaces in the form of dense point clouds can be a valuable tool for documenting and reconstructing a structural collapse, such as the 2021 Champlain Towers Condominium collapse in Surfside, florida. Point cloud data reconstructed from aerial footage taken by uncrewed aerial systems at frequent intervals from a dynamic search and rescue scene poses significant challenges. Properly aligning large point clouds in this context, or registering them, poses noteworthy issues as they capture multiple regions whose geometries change over time. These regions denote dynamic features such as excavation machinery, cones marking boundaries and the structural collapse rubble itself. In this paper, the performances of commonly used point cloud registration methods for dynamic scenes present in the raw data are studied. The use of Iterative Closest Point (ICP), Rigid - Coherent Point Drift (CPD) and PointNetLK for registering dense point clouds, reconstructed sequentially over a time-frame of five days, is studied and evaluated. All methods are compared by error in performance, execution time, and robustness with a concluding analysis and a judgement of the preeminent method for the specific data at hand is provided.

关键词： Point cloud compression Surface reconstruction Three-dimensional displays Poles and towers Robustness Safety Registers

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Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks

Cloud-Powered Smart Rehabilitation and Assistive Robotics fo...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： M Maria Sampoornam Mothiram Rajasekaran Visumathi J Balachandra Pattanaik S. Kalaichelvi S. Sujatha Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Pine Candle Way Saint Augustine Florida USA Department of Information Technology Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia PG and Research Department of Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Elayampalayam Tiruchengode Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients.

关键词： Cloud computing Technological innovation Recurrent neural networks Transforms Stroke (medical condition) Robot sensing systems Real-time systems Wearable sensors Streams Research and development

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Persistent hyperdigraph homology and persistent hyperdigraph Laplacians

arXiv

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arXiv 2023年

作者： Chen, Dong Liu, Jian Wu, Jie Wei, Guo-Wei Department of Mathematics Michigan State University MI48824 United States Mathematical Science Research Center Chongqing University of Technology Chongqing400054 China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing101408 China Department of Electrical and Computer Engineering Michigan State University MI48824 United States Department of Biochemistry and Molecular Biology Michigan State University MI48824 United States

Hypergraphs are useful mathematical models for describing complex relationships among members of a structured graph, while hyperdigraphs serve as a generalization that can encode asymmetric relationships in the data. However, obtaining topological information directly from hyperdigraphs remains a challenge. To address this issue, we introduce hyperdigraph homology in this work. We also propose topological hyperdigraph Laplacians, which can extract both harmonic spectra and non-harmonic spectra from directed and internally organized data. Moreover, we introduce persistent hyperdigraph homology and persistent hyperdigraph Laplacians through filtration, enabling the capture of topological persistence and homotopic shape evolution of directed and structured data across multiple scales. The proposed methods offer new multiscale algebraic topology tools for topological data analysis. © 2023, CC BY.

关键词： Algebra

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On Convergence of Tabu-Enhanced Quantum Annealing Algorithm 24th

On Convergence of Tabu-Enhanced Quantum Annealing Algorithm

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24th International Conference on Distributed computer and Communication Networks, DCCN 2021

作者： Rumyantsev, A.S. Pastorello, D. Blanzieri, E. Cavecchia, V. Institute of Applied Mathematical Research Karelian Research Centre of RAS Pushkinskaya Street 11 Republic of Karelia Petrozavodsk185000 Russia Petrozavodsk State University 33 Lenina Pr. Petrozavodsk Russia Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Trento Institute of Fundamental Physics and Applications via Sommarive 14 Povo Trento38123 Italy via alla Cascata 56/c Povo Trento38123 Italy

ISBN: (纸本)9783030971090

The convergence of a recently proposed tabu-enhanced quantum annealing algorithm depends critically on the finiteness of memory of the related stochastic process. We discuss the background of quantum annealing and the convergence issues of the tabu-enhanced algorithm. Given the details of the tabu data structure, the so-called tabu matrix, we consider the sequences of solutions that result in a tabu matrix collisions. As such, convergence of the algorithm is related to the problem of studying the so-called matrix kernel, which we investigate and give an example of such a collision as well. © 2022, Springer Nature Switzerland AG.

关键词： Stochastic systems

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A Multi-Conditional Proxy Broadcast Re-Encryption Scheme for Sensor Networks

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computers, Materials & Continua 2020年第12期65卷 2079-2090页

作者： Pang Li Lifeng Zhu Brij B.Gupta Sunil Kumar Jha Nanjing Vocational College of Information Technology Nanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China Department of Computer Engineering National Institute of TechnologyKurukshetra136119India Department of Computer Science and Information Engineering Asia UniversityTaichung41449Taiwan IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

In sensor networks,it is a challenge to ensure the security of data exchange between packet switching nodes holding different private *** order to solve this problem,the present study proposes a scheme called multi-conditional proxy broadcast re-encryption(MC-PBRE).The scheme consists of the following roles:the source node,proxy server,and the target *** the condition is met,the proxy can convert the encrypted data of the source node into data that the target node can directly *** allows the proxy server to convert the ciphertext of the source node to a new ciphertext of the target node in a different group,while the proxy server does not need to store the key or reveal the *** the same time,the proxy server cannot obtain any valuable information in the *** paper formalizes the concept of MC-PBRE and its security model,and proposes a MC-PBRE scheme of ciphertext ***,the scheme security has been proved in the random oracle.

关键词： Proxy re-encryption sensor network security broadcast re-encryption

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A Survey on the Cybersecurity of IoT From The Perspective of SoC

A Survey on the Cybersecurity of IoT From The Perspective of...

引用

International Conference on Internet of Things: Systems, Management and Security

作者： Xianping Wang Hao Qiu Jiayue Shen Weiru Chen Department of Computer Science Florida Polytechnic University Lakeland FL USA Department of Engineering Technology Fort Valley State University Fort Valley GA USA Department of Engineering Technology SUNY Polytechnic Institute Utica NY USA Department of Information Systems and Technology Management Slippery Rock University of Pennsylvania Slippery Rock PA USA

Internet of Things is permeating every aspects of our society. Concerning critical infrastructures, people’s privacy and the availability of IoT services, assessed with the standard Confidentiality-Integrity-Availability triad model, we investigate the cybersecurity of IoT from the perspective of System on Chip - the core physical components of IoT. The cybersecurity of IoT has been improved drastically in the recent decade, especially in the core of IoT - the System on Chip, most of them integrated hardware security modules for cybersecurity. However, it is still a challenge to design, run and maintain a secure IoT system.

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