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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc networks

Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Plasma–enhanced chemical vapor deposition of Co3O4 thin films as a new approach for improving oxygen evolution activity

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Catalysis Today 2025年 458卷

作者： Knozowski, Dominik Kędzierska-Sar, Aleksandra Ranecki, Robert Kuświk, Piotr Fronczak, Maciej Gierlotka, Stanislaw Arabasz, Sebastian Aligayev, Amil Jabbarli, Ulkar Dominguez–Gutierrez, Francisco Javier Gmurek, Marta Lodz University of Technology Faculty of Process and Environmental Engineering Wólczańska 213 St Lodz93–005 Poland Lodz University of Technology Faculty of Chemistry Institute of General and Ecological Chemistry Żeromskiego 116 St. Lodz90–924 Poland Institute of Molecular Physics Polish Academy of Sciences Mariana Smoluchowskiego 17 St. Poznań60–179 Poland Polish Academy of Sciences Laboratory of Nanostructures Sokolowska 29/37 St. Warsaw01–142 Poland Lukasiewicz Research Network – PORT Polish Center for Technology Development Stablowicka 147 St. Wroclaw54–066 Poland CAS Key Laboratory of High Magnetic Field and Iron Beam Physical Biology Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Science Hefei230031 China Science Island Branch of Graduate School University of Science and Technology of China Hefei230026 China NOMATEN Centre of Excellence National Centrefor Nuclear Research A. Soltana 7 St. Otwock05–400 Poland Institute of Theoretical Physics Faculty of Physics University of Warsaw Pasteura 5 St Warsaw02–093 Poland

The development of efficient and durable electrocatalysts for the oxygen evolution reaction (OER) remains a key challenge in advancing sustainable energy technologies. Among cobalt – based materials, spinel Co3O4 is a widely studied catalyst;however, its bulk form often suffers from poor electrical conductivity and limited accessibility of active sites. In this study, we demonstrate that plasma–enhanced chemical vapor deposition (PECVD), followed by thermal annealing in air, provides an effective route for vacancy engineering in CoxOy thin films inducing both oxygen and cobalt vacancies without relying on foreign dopants or ion–exchange processes. The Co3O4 films demonstrated exceptional OER performance, with the best sample achieving a low overpotential of 343 mV at 10 mA cm–2, and a high capability for Co4+ formation, a key intermediate in the OER process. Additionally, the films exhibited catalytic activity not only at the surface, but also throughout their entire volume, highlighting the significance of deposition time and film thickness in optimizing OER performance. Ab–initio calculations, including Bader charge analysis and density of states evaluations, further elucidate the role of defect chemistry: oxygen vacancies enhance *OH adsorption and drive structural reconstruction, while cobalt vacancies reduce the energy barrier for deprotonation and facilitate the Co3+ → Co4+ oxidation process. Overall, this work establishes PECVD combined with thermal treatment as a powerful and scalable strategy for tailoring defect concentrations in cobalt oxide catalysts. The approach offers valuable insights into the rational design of high–performance OER catalysts, with broad implications for electrochemical energy conversion technologies. © 2025 The Authors

关键词： Thermal Engineering

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A microstrip-based sensor for glucose monitoring: towards non-invasive blood glucose detection

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Measurement 2025年

作者： Guodao Zhang Qiwen Zhang Chaochao Wang Xiaojun Ji Zhengqiu Weng Abdulilah Mohammad Mayet Xinjun Miao Institute of Intelligent Media Computing Hangzhou Dianzi University Hangzhou 310018 China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing 314000 China Department of Cardiology Wenzhou Central Hospital Wenzhou 325000 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou 325000 China Electrical Engineering Department College of Engineering King Khalid University Abha Kingdom of Saudi Arabia Department of Emergency Wenzhou Central Hospital Wenzhou 325000 China

In this study, a microstrip sensor with U-shaped resonators was designed and developed for non-invasive blood glucose measurement. The sensor comprises five U-shaped resonators forming a microstrip circuit, transmitting in the frequency ranges of 2–2.3 GHz and 3.9–4.2 GHz. Glucose solutions with concentrations from 50 mg/dL to 500 mg/dL were prepared and placed in a 3D-printed PLA container for testing. Samples were positioned non-contact on the sensor, and frequency response changes up to 6 GHz were recorded. The sensor exhibited high sensitivity, measuring 1.50 MHz per mg/dL. Due to environmental susceptibilities and the relatively low repeatability of microstrip sensors, each sample was tested five times, yielding a total of 50 measurements. These recorded responses were processed using a multilayer perceptron (MLP) neural network, which predicted glucose concentrations with a mean relative error (MRE%) of less than 2.48 %. This research highlights the development of a highly sensitive, non-invasive glucose sensor integrated with a neural network, offering precise and rapid blood glucose measurements. This system holds promise as a valuable tool for managing diabetes and other blood glucose-related conditions.

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An application of insurance mechanism in risk management for energy internet

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High technology Letters 2018年第2期24卷 196-202页

作者： Yang Yunxue Zhang Qin Zhang Libo CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 P. R. China College of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Business College Yantai Nanshan University Yantai 265713 P. R. China

As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the *** paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.

关键词： energy internet insurance risk management optimal control fuzzy theory

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Knowledge-guided pairwise reconstruction network for weakly supervised referring expression grounding

arXiv

引用

arXiv 2019年

作者： Liu, Xuejing Li, Liang Wang, Shuhui Zha, Zheng-Jun Su, Li Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image region (proposal) and the query is unknown in the training stage. In referring expressions, people usually describe a target entity in terms of its relationship with other contextual entities as well as visual attributes. However, previous weakly supervised REG methods rarely pay attention to the relationship between the entities. In this paper, we propose a knowledge-guided pairwise reconstruction network (KPRN), which models the relationship between the target entity (subject) and contextual entity (object) as well as grounds these two entities. Specifically, we first design a knowledge extraction module to guide the proposal selection of subject and object. The prior knowledge is obtained in a specific form of semantic similarities between each proposal and the subject/object. Second, guided by such knowledge, we design the subject and object attention module to construct the subject-object proposal pairs. The subject attention excludes the unrelated proposals from the candidate proposals. The object attention selects the most suitable proposal as the contextual proposal. Third, we introduce a pairwise attention and an adaptive weighting scheme to learn the correspondence between these proposal pairs and the query. Finally, a pairwise reconstruction module is used to measure the grounding for weakly supervised learning. Extensive experiments on four large-scale datasets show our method outperforms existing state-of-the-art methods by a large margin1. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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A Deep Look into Neural Ranking Models for Information Retrieval

arXiv

引用

arXiv 2019年

作者： Guo, Jiafeng Fan, Yixing Pang, Liang Yang, Liu Ai, Qingyao Zamani, Hamed Wu, Chen Croft, W. Bruce Cheng, Xueqi University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Center for Intelligent Information Retrieval University of Massachusetts Amherst AmherstMA United States

Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Information retrieval

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

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15th EAI International Conference on Collaborative computing: networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Temporal Knowledge Propagation for Image-to-Video Person Re-Identification

Temporal Knowledge Propagation for Image-to-Video Person Re-...

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International Conference on Computer Vision (ICCV)

作者： Xinqian Gu Bingpeng Ma Hong Chang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.

关键词： Videos Image representation Feature extraction Knowledge engineering Visualization Robustness Training

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RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation

arXiv

引用

arXiv 2019年

作者： Niu, Xuesong Shan, Shiguang Han, Hu Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Institute of Computing Technology CAS Beijing100190 China Peng Cheng Laboratory Shenzhen China Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos;however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HR1), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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