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A secure mist-fog-assisted cooperative offloading framework for sustainable smart city development

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Digital Communications and Networks 2024年

作者： Subhranshu Sekhar Tripathy Sujit Bebortta Mazin Abed Mohammed Muhammet Deveci Haydar Abdulameer Marhoon Radek Martinek School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 India Department of Computer Science Ravenshaw University Cuttack 753003 India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Telecommunications VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Industrial Engineering Turkish Naval Academy National Defence University 34942 Tuzla Istanbul Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon The Bartlett School of Sustainable Construction University College London 1-19 Torrington Place London WC1E 7HB UK Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Iraq College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq

Practical applications of smart cities and the Internet of Things (IoT) have multiplied, posing many difficulties in network performance, dependability, and security. Concerns of accessibility, reliability, sustainability, and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems. Fog computing offers a foundation for various applications, including cognitive support, health and social services, intelligent transportation systems, and pervasive computing and communications. Fog computing can help enhance these apps' productivity and lower the end-to-end delay experienced by such time-sensitive applications. In this research, we propose a reliable and secure service delivery strategy at the network edge for smart cities. To improve the availability and dependability, along with the security of smart city applications, the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system. Simulation findings suggest a reduction of 40.3% in the delay incurred by each service request for highly dense areas and 60.6% for moderately dense environments. Furthermore, the system has low false-negative rates and high detection and accuracy rates, decreasing service requests 2%.

关键词： Security Reliability Trustworthy computing Resource provisioning Threat detection Fog computing Internet of things

IoT-based Irrigation Management for Smallholder Farmers in Rural Sub-Saharan Africa

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Procedia computer science 2020年 177卷 86-93页

作者： Ethiopia Nigussie Thomas Olwal George Musumba Tesfa Tegegne Atli Lemma Fisseha Mekuria Department of Future Technologies Turku Finland Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa School of Computer Science and Information Technology Dedan Kimathi University of Technology Nyeri Kenya Center for ICT for Development Bahir Dar University Bahir Dar Ethiopia School of Electircal Engineering Haramaya University Dire Dawa Ethiopia Council for Scientific and Industrial Research Pretoria South Africa

Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management, irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. It is expected that an intelligent data analysis will help to discover new knowledge that support further development of agricultural expertise. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands.

关键词： Internet of things smart irrigation smallholder farmers intelligent data analysis low-cost sensors

Convex optimization for non-equilibrium steady states on a hybrid quantum processor

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arXiv 2022年

作者： Lau, Jonathan Wei Zhong Lim, Kian Hwee Bharti, Kishor Kwek, Leong-Chuan Vinjanampathy, Sai Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore Joint Center for Quantum Information and Computer Science Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore National Institute of Education Nanyang Technological University 1 Nanyang Walk 637616 Singapore School of Electrical and Electronic Engineering Block S2.1 50 Nanyang Avenue Singapore639798 Singapore Department of Physics Indian Institute of Technology-Bombay Powai Mumbai400076 India Centre of Excellence in Quantum Information Computation Science and Technology Indian Institute of Technology Bombay Powai Mumbai400076 India

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semidefinite program, we bypass several well known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

Diffusive topological transport in spatiotemporal thermal lattices

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arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

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arXiv 2024年

作者： Garrucho, Lidia Kushibar, Kaisar Reidel, Claire-Anne Joshi, Smriti Osuala, Richard Tsirikoglou, Apostolia Bobowicz, Maciej del Riego, Javier Catanese, Alessandro Gwoździewicz, Katarzyna Cosaka, Maria-Laura Abo-Elhoda, Pasant M. Tantawy, Sara W. Sakrana, Shorouq S. Shawky-Abdelfatah, Norhan O. Abdo-Salem, Amr Muhammad Kozana, Androniki Divjak, Eugen Ivanac, Gordana Nikiforaki, Katerina Klontzas, Michail E. García-Dosdá, Rosa Gulsun-Akpinar, Meltem Lafcı, Oğuz Mann, Ritse Martín-Isla, Carlos Prior, Fred Marias, Kostas Starmans, Martijn P.A. Strand, Fredrik Díaz, Oliver Igual, Laura Lekadir, Karim Barcelona Spain Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany 2nd Dept. of Radiology Medical University of Gdansk Gdansk Poland Parc Taulí Hospital Universitari Sabadell Spain Hospital Germans Trias i Pujol Badalona Spain Centro Mamario Instituto Alexander Fleming Buenos Aires Argentina Department of Diagnostic & Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo Egypt Department of Radiology University Hospital of Heraklion Stavrakia Greece Department of Diagnostic and Interventional Radiology University Hospital Dubrava Zagreb Croatia University of Zagreb School of Medicine Zagreb Croatia Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research and Technology—Hellas Heraklion Greece Department of Radiology School of Medicine University of Crete Heraklion Greece Medical Imaging and Radiology Universitary and Politechnic Hospital La Fe Valencia Spain Department of Radiology Hacettepe University Faculty of Medicine Sihhiye Ankara Turkey Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria Department of Radiology and Nuclear Medicine Radboud University Medical Center Netherlands University of Arkansas for Medical Sciences Little RockAR United States Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Heraklion Greece Department of Radiology and Nuclear Medicine Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Department of Pathology Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Breast Radiology Karolinska University Hospital Stockholm Sweden Passeig Lluís Companys 23 Barcelona Sp

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pretreatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

A novel stochastic epidemic model with application to COVID-19

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arXiv 2021年

作者： Arruda, Edilson F. Alexandre, Rodrigo e A. Fragoso, Marcelo D. do Val, João B.R. Thomas, Sinnu S. Department of Decision Analytics and Risk Southampton Business School University of Southampton 12 University Rd SouthamptonSO17 1BJ United Kingdom Alberto Luiz Coimbra Institute-Graduate School and Research in Engineering Federal University of Rio de Janeiro. CP 68507 Rio de Janeiro21941-972 Brazil National Laboratory for Scientific Computation Av. Gettúlio Vargas 333 Quitandinha Petrópolis RJ25651-075 Brazil School of Electrical Engineering University of Campinas Av. Albert Einstein 400 Cidade Universitária Campinas SP13083-852 Brazil School of Computer Science and Engineering Kerala University of Digital Sciences Innovation and Technology Technocity Mangalapuram Thonnakkal PO ThiruvananthapuramKerala695317 India

MSC Codes 9010, 93E20In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system’s occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated. © 2021, CC BY-NC-ND.

关键词： Queueing theory

Integrative multi-target analysis of Urtica dioica for gout arthritis treatment: a network pharmacology and clustering approach

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In silico pharmacology 2024年第2期12卷 88页

作者： Maryam Qasmi Muhammad Mazhar Fareed Haider Ali Zarmina Khan Sergey Shityakov Faculty of Life Sciences Department of Bioinformatics and Biotechnology Government College University Faisalabad Pakistan. Department of Computer Science School of Science and Engineering Università degli Studi di Verona Verona Italy. Department of Biotechnology Applied Bioinformatics Group Università degli Studi di Verona Verona Italy. Department of Computer Science Systems and Communications Università degli Studi di Milano-Bicocca Milan Italy. Department of Meat Science and Technology University of Veterinary and Animal Sciences Lahore Pakistan. Laboratory of Chemoinformatics Infochemistry Scientific Center ITMO University Saint-Petersburg Russian Federation.

Urtica dioica (stinging nettle) has been traditionally used in Chinese medicine for the treatment of joint pain and rheumatoid arthritis. This study aims to elucidate the active compounds and mechanisms by which it acts against gout arthritis (GA). Gout-related genes were identified from the DisGeNet, GeneCards, and OMIM databases. These genes may play a role in inhibiting corresponding proteins targeted by the active compounds identified from the literature, which have an oral bioavailability of ≥ 30% and a drug-likeness score of ≥ 0.18. A human protein-protein interaction network was constructed, resulting in sixteen clusters containing plant-targeted genes, including ABCG2, SLC22A12, MAP2K7, ADCY10, RELA, and TP53. The key bioactive compounds, apigenin-7-O-glucoside and kaempferol, demonstrated significant binding to SLC22A12 and ABCG2, suggesting their potential to reduce uric acid levels and inflammation. Pathway enrichment analysis further identified key metabolic pathways involved, highlighting a dual mechanism of anti-inflammatory and urate-lowering effects. These findings underscore the potential of U. dioica in targeting multiple pathways involved in GA, combining traditional medicine with modern pharmacology. This integrated approach provides a foundation for future research and the development of multi-target therapeutic strategies for managing gout arthritis.

关键词： Gout arthritis Leiden algorithm Network pharmacology OMIM Urtica dioica

Abstracting Fairness: Oracles, Metrics, and Interpretability

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arXiv 2020年

作者： Dwork, Cynthia Ilvento, Christina Rothblum, Guy N. Sur, Pragya Harvard John A. Paulson School of Engineering and Applied Sciences Radcliffe Institute for Advanced Study Microsoft Research CambridgeMA United States Harvard John A. Paulson School of Engineering and Applied Sciences CambridgeMA United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Center for Research on Computation and Society Harvard John A. Paulson School of Engineering and Applied Sciences CambridgeMA United States

It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of "true" fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth;moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every "truly fair" classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness. Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracle’s conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair. Our results have implications for interpretablity – a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be "unfair" or illegitimately derived. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

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