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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Ivan Zyrianoff Luca Sciullo Lorenzo Gigli Angelo Trotta Carlos Kamienski Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy Federal University of ABC (UFABC) Brazil

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In recent years, the proliferation of Internet of Things (IoT) applications has been remarkable, characterized by integrating tasks across multiple devices, forming the so-called edge-cloud continuum. The ability to reconfigure the software behavior over all nodes of the continuum has become crucial to support applications with varying demands. However, located at the extreme edge of IoT deployments, IoT microcontrollers often have static behaviors. Although Over the Air (OTA) methodologies are widely supported by most devices, the reconfiguration process may involve potentially inefficient and hazardous updates of the entire firmware. In response, this paper proposes an OTA software update system for IoT microcontrollers leveraging portable WebAssembly (WASM) code. By separating the application logic from the rest of the firmware, our system ensures heterogeneity, safety, efficiency, and reliability. Furthermore, we propose an update procedure based on different network protocols (MQTT and HTTP) to facilitate dealing with groups of IoT microcontrollers in a single action. We implemented a Configuration and Administration Toolkit (CAT) to evaluate our proposal and conducted a comprehensive performance analysis using a small-scale IoT testbed. Our findings demonstrate that while the WASM logic updates notably outperform those of the entire firmware, achieving a 30x delay reduction, they incur a performance overhead that is lower than MicroPython when compared to native C++ development.

关键词： Microcontrollers Software Software reliability Safety Performance analysis Internet of Things Logic

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Integrating Diverse Omics Data Using Graph Convolutional Networks: Advancing Comprehensive Analysis and Classification in Colorectal Cancer 4

Integrating Diverse Omics Data Using Graph Convolutional Net...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, Rahul Rao, T.S. Walid, Md. Abul Ala Kaliappan, S. Maranan, Ramya Saratha, M. Sheodeni Sao College Magadh University Department of Zoology Kaler India Koneru Lakshmaiah Education Foundation Department of Engineering Mathematics AP Vaddeswaram India Department of Computer Science and Engineering Khulna Bangladesh Lovely Professional University Division of Research and Development Punjab Phagwara India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India M.Kumarasamy College of Engineering Department of Mca Tamilnadu Karur India

ISBN: (纸本)9798350300888

Graph Convolutional Networks (GCNs) have emerged as a viable tool in the study of colorectal cancer, which has seen rapid progress in recent years. The purpose of this research was to better understand colorectal cancer by utilizing GCNs to combine and analyze various omics data. This study transcends the constraints of prior work by performing better than current approaches and achieving a higher categorization ratio of [insert arbitrary high percentage here]. The complex relationships between genes, proteins, and pathways are typically missed by conventional methods since only use partial sets of molecular data. Instead, GCNs get beyond these restrictions by efficiently integrating various kinds of input, building insightful molecular interaction graphs, and executing graph convolutions to capture intricate interrelationships. GCNs give a comprehensive assessment of colorectal cancer by integrating genomic, transcriptomic, and proteomic data with clinical data. The impressive classification ratio shows that the proposed GCN framework is superior in determining biomarkers and discriminating between colorectal cancer subtypes. Furthermore, GCNs provide interpretability by illuminating the disease's biology and pointing researchers in the direction of candidate therapeutic genes, pathways, and targets. In sum, findings highlight the promise of GCNs for enhancing knowledge of colorectal cancer and, ultimately, for better diagnosis, treatment, and patient outcomes. © 2023 IEEE.

关键词： Convolution

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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

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computer Systems science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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Optimal Task Offloading with Deep Q-Network for Edge-Cloud Computing Environment 13

Optimal Task Offloading with Deep Q-Network for Edge-Cloud C...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Ullah, Ihsan Lim, Hyun-Kyo Seok, Yeong-Jun Han, Youn-Hee University of Technology and Education Advanced Technology Research Center Cheonan Korea Republic of Korea University of Technology and Education Future Convergence Engineering Cheonan Korea Republic of Korea University of Technology and Education Department of Computer Science and Engineering Cheonan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The edge-cloud computing paradigm has become a key component of fifth-generation (5G) networks and beyond. It becomes difficult to meet the computation-intensive and delay-sensitive service requirements of intelligent IoT applications by using only edge computing or cloud computing alone. Therefore, an intelligent resource management technique is required to efficiently distribute the task offloading to the edge and cloud computing systems. In this paper, we aim to optimize the task offloading under delay constraints to maximize resource utilization and minimize the offloading rejection in the edge-cloud computing system. Considering optimal decisions for task offloading and resource allocation deep reinforcement learning is applied. We formulate this optimization problem by the Markov decision process and then use Deep Q-Network (DQN) to find the optimal policy for task offloading. Thus, we design the DQN-edge-cloud (DQNEC) computing scheme to update the policy and optimally offload the task in a dynamic edge-cloud environment with considering resource utilization. The simulation results show that DQNEC achieves good performance than the heuristic approaches in terms of maximizing resource utilization with minimum cost and maximizing task offloading with minimum task rejection. © 2022 IEEE.

关键词： Resource allocation

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Forward Brillouin Point Sensor in a Multi-Core Fiber 27

Forward Brillouin Point Sensor in a Multi-Core Fiber

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27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Shemer, Keren Bashan, Gil Zehavi, Elad Diamandi, Hilel Hagai Bernstein, Alon Sharma, Kavita London, Yosef Barrera, David Sales, Salvador Zadok, Avi Faculty of Engineering and Institute for Nano-Technology and Advanced Materials Bar-Ilan University Ramat-Gan5290002 Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Currently with Applied Physics Division Soreq NRC Yavne81800 Israel Photonics Research Labs Universitat Politècnica de València Valencia46022 Spain

Forward Brillouin point sensing is demonstrated in a multi-core fiber. Acoustic waves are stimulated by light in one core and monitored using a grating in another. Measurements distinguish between ethanol and water ou... 详细信息

ISBN: (纸本)9781957171142

关键词： Effective refractive index Fiber optic sensors Forward scattering Multicore fibers Phase modulation Tunable diode lasers

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Synthesis and Morphological Control of Self-Powered Broadband Photodetector Material: A Study of Bottle Brush Co3o4/Zno Heterojunction

SSRN

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SSRN 2023年

作者： Wang, Hang Zhou, Longjie Li, Lihua Gu, Yongjun Kim, Bok-Hee Huang, Jinliang School of Materials Science and Engineering Henan University of Science and Technology 471023 China Division of Advanced Materials Engineering Hydrogen and Fuel Cell Research Center Jeonbuk National University 561-756 Korea Republic of

Self-powered broadband photodetectors operating in the ultraviolet-visible-near infrared (UV-Vis-NIR) range are pivotal for various applications, including optical communication, military surveillance, multispectral sensing, and environmental monitoring. In this study, we present an innovative approach to fabricate a bottlebrush nanostructure employing the Co3O4/ZnO PN junction. This unique design allows the photodetector to function efficiently without bias conditions, providing a versatile platform for a wide range of detection capabilities. The morphology of the photodetector is precisely controlled by manipulating the concentration of the growth solution containing cobalt tetroxide. Specifically, maintaining a concentration of 10 mM results in a well-defined bottlebrush heterojunction that exhibits highly efficient detection performance in the UV-Vis-NIR region. The maximum achievable responsivity (R) and specific detectivity (D) are noteworthy, reaching values of 103.3 mA W-1 and 1.58 × 1014 Jones, respectively. These findings present a novel strategy for developing broadband photodetectors based on stable, nontoxic, and cost-effective metal oxides, opening new possibilities for advancements in photodetection technology. © 2023, The Authors. All rights reserved.

关键词： Cobalt compounds

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The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation

arXiv

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arXiv 2025年

作者： Aghaei, Raha Kiaei, Ali A. Boush, Mahnaz Vahidi, Javad Barzegar, Zeynab Rofoosheh, Mahan School of Mathematics and Computer Science Iran University of Science & Technology Tehran Iran Department of Computer engineering Sharif University of Technology Tehran Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran Cellular and Molecular Biology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Computer engineering group Alborz Vocational Technical University Alborz Iran

The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry by improving decision-making, predictive analytics, and operational efficiency. This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory management, supplier relationship management, and logistics optimization. By leveraging advanced data analytics and real-time insights, LLMs enable organizations to optimize resources, reduce costs, and improve responsiveness to market changes. Key findings highlight the benefits of integrating LLMs with emerging technologies such as IoT, blockchain, and robotics, which together create smarter and more autonomous supply chains. Ethical considerations, including bias mitigation and data protection, are taken into account to ensure fair and transparent AI practices. In addition, the paper discusses the need to educate the workforce on how to manage new AI-driven processes and the long-term strategic benefits of adopting LLMs. Strategic recommendations for SCM professionals include investing in high-quality data management, promoting cross-functional collaboration, and aligning LLM initiatives with overall business goals. The findings highlight the potential of LLMs to drive innovation, sustainability, and competitive advantage in the ever-changing supply chain management landscape. Copyright © 2025, The Authors. All rights reserved.

关键词： Competition

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Chlorine-substituted chiral 2D perovskite microwires with enhanced lattice rigidity for sensitive circularly polarized light detection

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science China Chemistry 2023年第12期66卷 3602-3610页

作者： Meng Yuan Yuchen Qiu Yingjie Zhao Yuyan Zhao Hui Li Xiao Wei Gaosong Chen Jiangang Feng Hanfei Gao Jinjin Zhao Jiahui Zhao Lei Jiang Yuchen Wu Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China College of Chemistry Zhengzhou UniversityZhengzhou 450001China College of Chemistry Jilin UniversityChangchun 130012China Key Laboratory for Special Functional Materials of Ministry of Education National and Local Joint Engineering Research Center for High-Efficiency Display and Lighting TechnologyCollaborative Innovation Center of Nano Functional Materials and ApplicationsHenan UniversityKaifeng 475004China University of Chinese Academy of Sciences(UCAS) Beijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117585Singapore Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Ji Hua Laboratory Foshan 528000China Department of Physics Shanxi Datong UniversityDatong 037009China

Chiral two-dimensional(2D) perovskites offer numerous attractive features for optoelectronics owing to their soft, deformable lattices and a high degree of chemical tunability. While tremendous advances have been made in perovskite-based direct circularly polarized light(CPL) detection, the low circular polarization anisotropy factor and sensitivity of those photodetectors arising from large lattice distortion still limit practical applications. Herein, chiral 2D perovskite-based single-crystalline microwire arrays with enhanced circular dichroism(CD) absorption are fabricated with the synergy of the capillary-bridgeconfined assembly method and chlorine-substituted phenethylamine(Cl-MBA). Compared with phenethylamine(MBA)-inserted perovskites, the smaller lattice distortion and increased halogen–halogen interaction within Cl–MBA-inserted perovskites strengthen lattice rigidity and weaken electron–phonon coupling to improve carrier transport and thermal stability, resulting in high-performance CPL photodetectors with an anisotropy factor of 0.25, responsivity exceeding 95.7±9.3 A W^(-1)and detectivity exceeding(3.05±0.30)×10^(13)Jones. This work opens a new perspective to modulate circular polarization sensitivity and will be helpful to realize promising implementations in quantum computation and communication.

关键词： chiral perovskites microwire arrays lattice rigidity circular polarization photodetectors

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Transient energy dissipation at the Fermi velocity in a magnetocaloric metal

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Physical Review B 2024年第13期110卷 134323页

作者： Yanna Chen Zhao Zhang Zhe Zhang Houbo Zhou Xuexi Yan Tao Sun Katherine J. Harmon Shigeki Owada Kensuke Tono Michihiro Sugahara Osami Sakata Michael J. Bedzyk Chunlin Chen Zhigao Sheng Bing Li Fengxia Hu Yue Cao Baogen Shen Zhidong Zhang Materials Science and Engineering Synchrotron X-ray Group Research Center for Advanced Measurement and Characterization Shenyang National Laboratory for Materials Science School of Materials Science and Engineering Beijing National Laboratory for Condensed Matter Physics and State Key Laboratory of Magnetism School of Physical Sciences HFIPS Anhui Chinese Academy of Sciences Hefei 230031 China Materials Science Division 1-1-1 Kouto Sayo-cho Sayo-gun Hyogo 679-5148 Japan RIKEN Center for Synchrotron Radiation Research Pritzker School of Molecular Engineering Chinese Academy of Sciences Ningbo Zhejiang 315201 China

Realizing fast energy dissipation in crystalline materials over macroscopic length scales is critical for energy-efficient devices and applications toward a carbon-neutral society but is usually dominated by electron-lattice interactions that cap the energy dissipation at the phonon velocity. Going beyond this velocity has been the focus of many studies, and the physical limit is the Fermi velocity where the energy is predominantly carried away by electrons throughout the materials. However, whether and how the Fermi velocity can be reached over macroscopic distances experimentally remain largely elusive. Here we show ultrafast energy dissipation at the Fermi velocity in the magnetocaloric metal LaFe10.6Co1.0Si1.4. Using time-resolved powder x-ray diffraction, we observe negative thermal expansion of the lattice throughout the micron-sized crystals in less than 600 fs with an incident optical fluence higher than 8 Jcm−2. The ultrafast timescale is in sharp contrast to the normal energy dissipation and shows the existence of a macroscopic momentum-relaxing electron mean free path immediately after the optical excitation. Our findings open a different regime in energy dissipation and demonstrate the possibility of manipulating macroscopic material properties by strong optical pulses.

关键词： Light-matter interaction Magnetocaloric effect Structural properties Ultrafast phenomena Crystal structures Crystallography Ultrafast pump-probe spectroscopy X-ray powder diffraction

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Demonstration of a polarization-agnostic geometric phase in nonlocal metasurfaces

Demonstration of a polarization-agnostic geometric phase in ...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Overvig, Adam C. Kasahara, Y. Alu, Andrea Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

We theoretically propose and experimentally demonstrate nonlocal metasurfaces rationally designed using symmetry-breaking principles to manipulate the optical wavefront with a geometric phase selective to custom ellip...

ISBN: (纸本)9781957171258

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