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arXiv 2021年

作者： Fonseca, Bruna Fonseca, Caio Barros, Michael White, Mark Abhyankar, Vinay Borkholder, David A. Balasubramaniam, Sasitharan Telecommunication Software & Systems Group Waterford Institute of Technology Waterford Ireland Research Innovation & Graduate Studies Waterford Institute of Technology Waterford Ireland School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Kate Gleason College of Engineering Rochester Institute of Technology Rochester United States

The recent COVID-19 pandemic has resulted in high fatality rates, especially for patients who suffer from underlying health issues. One of the more serious symptoms exhibited from patients suffering from an acute COVID-19 infection is breathing difficulties and shortness of breath, which is largely due to the excessive fluid (cellular leakage and cytokine storm) and mucoid debris that have filled lung alveoli, and reduced the surfactant tension resulting in heavy and stiff lungs. In this paper we propose the use of micro-bubbles filled with exosomes that can be released upon exposure to ultrasound signals as a possible rescue therapy in deteriorating COVID-19 patients. Recent studies have shown that exosomes can be used to repair and treat lung damage for patients who have suffered from the viral infection. We have conducted simulations to show the efficacy of the ultrasound signals that will penetrate through layers of tissues reaching the alveoli that contains the micro-bubbles. Our results have shown that ultrasound signals with low frequencies are required to oscillate and rupture the polymer-based micro-bubbles. Our proposed system can be used for patients who require immediate rescue treatments for lung damage, as well as for recovered patients who may suffer from viral relapse infection, where the micro-bubbles will remain dormant for a temporary therapeutic window until they are exposed to the ultrasound signals. © 2021, CC BY-NC-ND.

关键词： Biological organs

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Evidence-Based Quality-Aware Agile Software Development Process: Design and Evaluation

SSRN

引用

SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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Reconfigurable Quantum Local Area Network Over Deployed Fiber

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PRX Quantum 2021年第4期2卷 040304-040304页

作者： Muneer Alshowkan Brian P. Williams Philip G. Evans Nageswara S.V. Rao Emma M. Simmerman Hsuan-Hao Lu Navin B. Lingaraju Andrew M. Weiner Claire E. Marvinney Yun-Yi Pai Benjamin J. Lawrie Nicholas A. Peters Joseph M. Lukens Quantum Information Science Group Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Applied Physics Stanford University Stanford California 94305 USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time synchronized via the Global Positioning System. We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we realize the first deployed fiber network demonstration of remote state preparation (RSP), a fundamental quantum communications protocol with utility for performing remote private “blind” quantum computing. We further demonstrate RSP not only at one location but over three nodes in three locations. In general, our results highlight an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands.

关键词： Nonlinear optics Quantum communication Quantum entanglement Quantum networks Quantum optics Timing & synchronization Bayesian methods

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Deterministic Identification For MC ISI-Poisson Channel

arXiv

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arXiv 2022年

作者： Salariseddigh, Mohammad Javad Jamali, Vahid Pereg, Uzi Boche, Holger Deppe, Christian Schober, Robert Institute For Communication Engineering Technical University of Munich Munich80333 Germany Group of Resilient Communication Systems Technical University of Darmstadt Germany Department of Electrical and Computer Eng. Technion – Israel Institute of Technology Haifa80333 Israel Theoretical Information Technology Technical University of Munich Munich80333 Germany Institute for Digital Communications Friedrich-Alexander-University Erlangen-Nürnberg Erlangen Germany

Several applications of molecular communications (MC) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC) with inter-symbol interference (ISI) where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule counting receivers. We derive lower and upper bounds on the DI capacity of the DTPC with ISI when the number of ISI channel taps K may grow with the codeword length n (e.g., due to increasing symbol rate). As a key finding, we establish that for deterministic encoding, the codebook size scales as 2(n log n)R assuming that the number of ISI channel taps scales as K = 2κlogn, where R is the coding rate and κ is the ISI rate. Moreover, we show that optimizing κ leads to an effective identification rate [bits/s] that scales linearly with n, which is in contrast to the typical transmission rate [bits/s] that is independent of n. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel capacity

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Integrated carbon and nitrogen management for cost-effective environmental policies in China

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Science (New York, N.Y.) 2025年第6751期388卷 1098-1103页

作者： Xin Xu Xiuming Zhang Yiyang Zou Tianrun Chen Jingfang Zhan Luxi Cheng Wilfried Winiwarter Shaohui Zhang Peter M Vitousek Wim de Vries Baojing Gu State Key Laboratory of Soil Pollution Control and Safety Zhejiang University Hangzhou China. College of Environmental and Resource Sciences Zhejiang University Hangzhou China. International Institute for Applied Systems Analysis Laxenburg Austria. Ministry of Education Key Laboratory of Environment Remediation and Ecological Health Zhejiang University Hangzhou China. College of Computer Science and Technology Zhejiang University Hangzhou China. Policy Simulation Laboratory Zhejiang University Hangzhou China. Institute of Environmental Engineering University of Zielona Góra Zielona Góra Poland. School of Economics and Management Beihang University Beijing China. Department of Earth System Science Stanford University Stanford CA USA. Wageningen University and Research Earth Systems and Global Change Group Wageningen Netherlands. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment Zhejiang University Hangzhou China.

Carbon and nitrogen are central elements in global biogeochemical cycles. To effectively manage carbon and nitrogen in China, we developed a comprehensive model for quantifying their fluxes, investigating their interplay across 16 human and natural subsystems. Between 1980 and 2020, nitrogen losses in China increased 2.3-fold and carbon emissions surged 6.5-fold. Integrated carbon and nitrogen management holds the potential for a 74% reduction in nitrogen losses to air and water and a 91% decrease in carbon emissions to the atmosphere by 2060. Compared with separate control of carbon or nitrogen, integrated management delivers an additional reduction of 1.8 million tons of nitrogen and 26.5 million tons of carbon by 2060, bringing out a 37% decrease in unit abatement cost and a net societal benefit of 1384 billion USD.

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Towards a Taxonomy of Industrial Challenges and Enabling Technologies in Industry 4.0

arXiv

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arXiv 2022年

作者： Figliè, Roberto Amadio, Riccardo Tyrovolas, Marios Stylios, Chrysostomos Pasko, Lukasz Stadnicka, Dorota Carreras-Coch, Anna Zaballos, Agustín Navarro, Joan Mazzei, Daniele The Computer Science Department University of Pisa Pisa56127 Italy The Department of Informatics and Telecommunications University of Ioannina Arta47150 Greece Industrial Systems Institute Athena Research Center Patras26504 Greece The Faculty of Mechanical Engineering and Aeronautics Rzeszów University of Technology Rzeszów35959 Poland Research Group in Internet Technologies & Storage Universitat Ramon Llull Barcelona08022 Spain

Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies and innovations from scientific research. At the same time, academics struggle to find and focus on real-world applications for their developing technological solutions. Moreover, the increasing complexity of industrial challenges and technologies is widening this hiatus. To reduce this knowledge and communication gap, this article proposes a mixed approach of humanistic and engineering techniques applied to the technological and enterprise fields. The study’s results are represented by a taxonomy in which industrial challenges and I4.0-focused technologies are categorized and connected through academic and grey literature analysis. This taxonomy also formed the basis for creating a public web platform where industrial practitioners can identify candidate solutions for an industrial challenge. At the same time, from the educational perspective, the learning procedure can be supported since, through this tool, academics can identify real-world scenarios to integrate digital technologies’ teaching process. © 2022, CC BY-SA.

关键词： Industry 4.0

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Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

arXiv

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arXiv 2022年

作者： Xu, Han Zuo, Zheming Li, Jie Chang, Victor Institute of Microelectronics Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100029 China Department of Computer Science Durham University DurhamDH1 3LE United Kingdom Cybersecurity Information Systems and AI Research Group School of Computing Engineering & Digital Technologies Teesside University MiddlesbroughTS3 6DR United Kingdom

Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage. © 2022, CC BY-NC-SA.

关键词： Internet of things

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Model Predictive Self-healing Control Scheme for Dual Active Bridge Converter 2

Model Predictive Self-healing Control Scheme for Dual Active...

引用

2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019

作者： Hosseinzadehtaher, Mohsen Khan, Ahmad Baker, Matthew W. Shadmand, Mohammad B. Power Electronics Autonomous Systems Research Group Kansas State University Dept. of Electrical and Computer Engineering ManhattanKS United States

ISBN: (纸本)9781728129600

This paper presents a model predictive self-healing control (MPSC) of dual active bridge (DAB) converter for battery energy storage system (BESS) application. The main feature of the proposed MPSC scheme is an autonomous corrective action at the prediction stage in comparison to conventional model predictive control (MPC) approach. This feature is required in particular for predictive controlled DAB converter to ensure and validate the predicted AC link inductor's current n-step ahead in horizon of time. The proposed MPSC for DAB converter doesn't require substantial tuning effort in comparison to conventional control schemes for DAB converters. The MPSC cost function is developed based on the desired phase shift between the primary and secondary sides' voltages of the DAB transformer. The self-healing loop utilizes the feasible range of power transfer in conjunction with the AC link inductor's voltage status i.e. profile, to validate the predicted current and phase shift for the cost function. The AC link inductor voltage profile in DAB converter could cause high error in predicted current, thus the proposed self-healing loop autonomously corrects potential error in current prediction. The theoretical concepts are validated via several case studies for the considered BESS application. The performance and design effort of the controller is compared with conventional PI-based control schemes for DAB. © 2019 IEEE.

关键词： Model predictive control

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On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control

引用

IFAC-PapersOnLine 2020年第2期53卷 1450-1455页

作者： Maurice Poot Jim Portegies Tom Oomen Control Systems Technology Group Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands

Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this paper is to develop a model-free approach for fast and safe learning for mechatronic systems. The developed actor-critic iterative learning control (ACILC) framework uses a feedforward parameterization with basis functions. These basis functions encode implicit model knowledge and the actor-critic algorithm learns the feedforward parameters without explicitly using a model. Experimental results on a printer setup demonstrate that the developed ACILC framework is capable of achieving the same feedforward signal as preexisting model-based methods without using explicit model knowledge.

关键词： Feedforward control iterative learning control learning algorithms reinforcement learning Markov decision problems function-approximation model-free control model-based control

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Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

arXiv

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arXiv 2022年

作者： Chandra, Rohitash Bansal, Chaarvi Kang, Mingyue Blau, Tom Agarwal, Vinti Singh, Pranjal Wilson, Laurence O.W. Vasan, Seshadri Transitional Artificial Intelligence Research Group School of Mathematics and Statistics Unsw Sydney Sydney Australia Australian E-Health Research Centre Commonwealth Scientific and Industrial Research Organisation North Ryde Australia Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan India Data 61 Csiro Sydney Australia Department of Computer Science and Engineering Indian Institute of Technology Guwathi Assam India Department of Health Sciences University of York York United Kingdom

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron, emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future. © 2022, CC BY.

关键词： COVID-19

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