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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

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Computational and Mathematical Methods in Medicine 2021年第1期2021卷 9025470页

作者： Tufail, Ahsan Bin Ma, Yong-Kui Kaabar, Mohammed K. A. Martínez, Francisco Junejo, A.R. Ullah, Inam Khan, Rahim School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China Department of Electrical and Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Gofa Camp Near Gofa Industrial College and German Adebabay Nifas Silk-Lafto Addis Ababa26649 Ethiopia Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur50603 Malaysia Department of Applied Mathematics and Statistics Technological University of Cartagena Cartagena30203 Spain School of Control Science and Control Engineering Harbin Institute of Technology Harbin150001 China Changzhou Campus 213022 China

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods. © 2021 Ahsan Bin Tufail et al.

关键词： Forecasting

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Proactive user engagement via friendly survey and data-driven methodologies

Proactive user engagement via friendly survey and data-drive...

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International Conference on Data engineering Workshops

作者： Paolo Bethaz Riccardo Callà Tania Cerquitelli Arianna Montorsi Claudia De Giorgi Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Architecture and Design Politecnico di Torino Turin Italy

ISBN: (数字)9781728142661

ISBN: (纸本)9781728142678

In this paper, we present CAROL (unConventionAl suRvey methOdoLogy) to collect users' opinions, to analyze them effectively, and to graphically provide interesting summaries in near-real-time. CAROL has been designed to collect citizens' opinions, needs, and a perception that can be very useful to redesign cities and drive the innovation process. To this aim, particular importance is given to the architecture implemented to collect the data, which involves sending voice messages instead of text, allowing users greater freedom of expression. Moreover, to make the survey more friendly, an initial analysis is done in real-time thanks to an intuitive dashboard, which shows the first results in the form of graphs and charts, updated after each message. Finally, a more in-depth analysis can be carried out at the end of the survey, thanks to text-mining techniques adapted to the context. As use case, the proposed methodology has been tested in a public event organized by Politecnico di Torino and addressed to female students, with the aim to collect their ideas about new services/opportunities that can be offered to support women in STEM.

关键词： Real-time systems Servers computer architecture Technological innovation Data visualization Smart cities

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Correction to: Resolving molecular contributions of ion channel noise to interspike interval variability through stochastic shielding

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Biological cybernetics 2021年第3期115卷 303-304页

作者： Shusen Pu Peter J Thomas Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland OH USA. sxp600@case.edu. Department of Biomedical Engineering Vanderbilt University Nashville TN USA. sxp600@case.edu. Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland OH USA. Department of Biology Case Western Reserve University Cleveland OH USA. Department of Cognitive Science Case Western Reserve University Cleveland OH USA. Department of Data and Computer Science Case Western Reserve University Cleveland OH USA. Department of Electrical Control and Systems Engineering Case Western Reserve University Cleveland OH USA.

A Correction to this paper has been published: https://***/10.1007/s00422-021-00877-7 .

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Recent advancement in Mxene-based nanomaterials for flame retardant polymers and composites

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Advanced Industrial and engineering Polymer Research 2025年

作者： Yakubu Adekunle Alli Abayomi Bamisaye Onome Ejeromedoghene Olusegun Oluwaseun Jimoh Samuel Oluwadadepo Oni Gerald Chekwube Ezeamii Chukwurimazu Ozoemezim Adeniyi Sunday Ogunlaja Suraya Abdul Rashid Baljinder K. Kandola Department of Chemistry Nelson Mandela University Port Elizabeth South Africa Department of Chemistry Faculty of Natural and Applied Sciences Lead City University Ibadan Oyo State Nigeria College of Chemistry Chemical Engineering and Materials Science Soochow University 199 Renai Road 215123 Suzhou Jiangsu Province PR China Department of Pharmacology Southern Illinois University Carbondale 62702 USA Department of Physics Georgia Southern University Statesboro 30458 USA Department of Applied Instrumentation and control school of Computing Engineering and Built Environment Glasgow Caledonian University UK Department of Electrical and Computer Engineering University of Florida Institute of Nanoscience & Nanotechnology Universiti Putra Malaysia Institute for Materials Research and Innovation University of Bolton Bolton BL3 5AB UK

This review explores the advancements in MXene-based nanomaterials as flame-retardant additives for polymers and composites, driven by increasing fire safety demands across industries. It highlights the critical role of flame-retardant materials in mitigating fire hazards in structures, electronics, transportation, and textiles, emphasizing the need for innovative solutions due to stricter safety regulations. MXenes, a class of two-dimensional nanomaterials with unique structural properties such as high surface area, tunable composition, and superior thermal stability, are presented as promising candidates. The review discusses various synthesis and incorporation techniques for MXenes in polymer matrices, showcasing improvements in flame retardancy, mechanical properties, and thermal stability. Additionally, it emphasizes the multifunctionality of MXenes, which offer conductivity, electromagnetic shielding, and mechanical reinforcement alongside flame suppression. In conclusion, the review underscores MXenes' potential to address challenges in flame-retardant materials, advocating for further research to optimize their applications and explore synergies with other agents to enhance safety and sustainability in engineering materials.

关键词： MXene flame-retardant nanomaterials polymer

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The impact of generative artificial intelligence on socioeconomic inequalities and policy making

arXiv

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arXiv 2023年

作者： Capraro, Valerio Lentsch, Austin Acemoglu, Daron Akgun, Selin Akhmedova, Aisel Bilancini, Ennio Bonnefon, Jean-François Brañas-Garza, Pablo Butera, Luigi Douglas, Karen M. Everett, Jim A.C. Gigerenzer, Gerd Greenhow, Christine Hashimoto, Daniel A. Holt-Lunstad, Julianne Jetten, Jolanda Johnson, Simon Kunz, Werner H. Longoni, Chiara Lunn, Pete Natale, Simone Paluch, Stefanie Rahwan, Iyad Selwyn, Neil Singh, Vivek Suri, Siddharth Sutcliffe, Jennifer Tomlinson, Joe van der Linden, Sander Van Lange, Paul A.M. Wall, Friederike Van Bavel, Jay J. Viale, Riccardo Department of Psychology University of Milan-Bicocca Italy Department of Economics MIT United States Institute Professor and Department of Economics MIT United States College of Education Michigan State University United States IMT School of Advanced Studies Lucca Italy Toulouse School of Economics France Department of Economics Loyola Andalucia University Spain Department of Economics Copenhagen Business School Denmark School of Psychology University of Kent United Kingdom Max Planck Institute for Human Development Berlin Germany Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery Perelman School of Medicine University of Pennsylvania United States Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania United States Department of Psychology and Neuroscience Brigham Young University United States School of Psychology University of Queensland Australia MIT Sloan School of Management United States Department of Marketing University of Massachusetts Boston United States Department of Marketing Bocconi University Italy Economic & Social Research Institute Dublin Ireland Department of Humanities University of Turin Italy Department of Service and Technology Marketing Aarhus University Denmark Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Faculty of Education Monash University Australia Microsoft Research United States York Law School United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Experimental and Applied Psychology Vrije Universiteit Amsterdam Netherlands Department of Management Control and Strategic Management University of Klagenfurt Austria Department of Psychology Center for Neural Science New York University United States Norwegian School of Economics Bergen Norway Department of Economics University of Milan-Bicocca Italy

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. © 2023, CC BY.

关键词： Artificial intelligence

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Nonlinear Model Predictive control for an Exothermic Reaction in an Adiabatic CSTR

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IFAC-PapersOnLine 2020年第1期53卷 500-505页

作者： Morten Ryberg Wahlgreen Eskild Schroll-Fleischer Dimitri Boiroux Tobias K.S. Ritschel Hao Wu Jakob Kjøbsted Huusom John Bagterp Jørgensen Department of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kgs. Lyngby Denmark 2-control ApS DK-2800 Kgs. Lyngby Denmark Department of Chemical and Biochemical Engineering Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

We model and simulate an exothermic reaction conducted in an adiabatic continuous stirred tank reactor (CSTR). The system has multiple steady states in part of its operating window. We demonstrate that the three-state model representing the mass and energy balances of the system can be well approximated with a two-state as well as a one-state model. The reduced-order models are relevant for efficient implementation of nonlinear model predictive control (NMPC). We extend the drift term in the system of deterministic ordinary differential equations (ODEs) with a diffusion term such that the process is modeled by a system of stochastic differential equations (SDEs). The SDE representation is well-suited for development of the NMPC that is based on a continuous-discrete extended Kalman filter (CD-EKF) and an optimal control problem. The optimal control problem (OCP) is solved using a multiple-shooting algorithm as the NMPC must be able to stabilize the system at stable as well as unstable steady states. Simulations demonstrate that the NMPC based on a one-state reduced SDE can track any reactor temperature setpoint at stable as well as unstable steady states.

关键词： Nonlinear model predictive control Multiple steady states Adiabatic continuous stirred tank reactor Exothermic reaction Stochastic differential equations

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Climate warming restructures seasonal dynamics of grassland soil microbial communities

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mLife 2022年第3期1卷 245-256页

作者： Xue Guo Mengting Yuan Jiesi Lei Zhou Shi Xishu Zhou Jiabao Li Ye Deng Yunfeng Yang Liyou Wu Yiqi Luo James MTiedje Jizhong Zhou State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina Institute for Environmental Genomics and Department of Microbiology and Plant Biology University of OklahomaNormanOklahomaUSA Department of Environmental Science Policyand ManagementUniversity of CaliforniaBerkeleyCaliforniaUSA Gladstone Institutes and Chan‐Zuckerberg Biohub San FranciscoCaliforniaUSA School of Minerals Processing and Bioengineering Central South UniversityChangshaChina Key Laboratory of Environmental and Applied Microbiology Chinese Academy of Sciences and Environmental Microbiology&Key Laboratory of Sichuan ProvinceChengdu Institute of BiologyChinese Academy of SciencesChengduChina Key Laboratory of Environmental Biotechnology Research Center for Eco‐Environmental SciencesChinese Academy of SciencesBeijingChina Department of Biological Sciences Center for Ecosystem Science and SocietyNorthern Arizona UniversityFlagstaffArizonaUSA Center for Microbial Ecology Michigan State UniversityEast LansingMichiganUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA Earth and Environmental Sciences Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA School of Computer Sciences University of OklahomaNormanOklahomaUSA.

Soil microbial community's responses to climate warming alter the global carbon *** temperate ecosystems,soil microbial communities function along seasonal ***,little is known about how the responses of soil microbial communities to warming vary when the season *** this study,we investigated the seasonal dynamics of soil bacterial community under experimental warming in a temperate tall‐grass prairie *** results showed that warming significantly(p=0.001)shifted community structure,such that the differences of microbial communities between warming and control plots increased nonlinearly(R^(2)=0.578,p=0.021)from spring to ***,warming significantly(p<0.050)increased microbial network complexity and robustness,especially during the colder seasons,despite large variations in network size and complexity in different *** addition,the relative importance of stochastic processes in shaping the microbial community decreased by warming in fall and winter but not in spring and *** study indicates that climate warming restructures the seasonal dynamics of soil microbial community in a temperate *** seasonality of microbial responses to warming may enlarge over time and could have significant impacts on the terrestrial carbon cycle.

关键词： climate warming microbial community microbial network seasonal dynamics

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

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Science 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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Optimal control of Joint Multi-Virus Infection and Information Spreading

arXiv

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arXiv 2020年

作者： Taynitskiy, Vladislav Gubar, Elena Fedyanin, Denis Petrov, Ilya Zhu, Quanyan St. Petersburg State University Faculty of Applied Mathematics and Control Processes Saint-Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia Department of Electrical and Computer Engineering New York University United States

Nowadays, epidemic models provide an appropriate tool for describing the propagation of biological viruses in human or animal populations, or rumours and other kinds of information in social networks and malware in both computer and ad hoc networks. Commonly, there are exist multiple types of malware infecting a network of computing devices, or different messages can spread over the social network. Information spreading and virus propagation are interdependent processes. To capture such independencies, we integrate two epidemic models into one holistic framework, known as the modified Susceptible-Warned-Infected-Recovered-Susceptible (SWIRS) model. The first epidemic model describes the information spreading regarding the risk of malware attacks and possible preventive procedures. The second one describes the propagation of multiple viruses over the network of devices. To minimize the impact of the virus spreading and improve the protection of the networks, we consider an optimal control problem with two types of control strategies: information spreading among healthy nodes and the treatment of infected nodes. We obtain the structure of optimal control strategies and study the condition of epidemic outbreaks. The main results are extended to the case of the network of two connected clusters. Numerical examples are used to corroborate the theoretical findings. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

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State of the Art of Audio- and Video-Based Solutions for AAL

arXiv

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arXiv 2022年

作者： Aleksic, Slavisa Atanasov, Michael Agius, Jean Calleja Camilleri, Kenneth Čartolovni, Anto Climent-Pérez, Pau Colantonio, Sara Cristina, Stefania Despotovic, Vladimir Ekenel, Hazım Kemal Erakin, Ekrem Florez-Revuelta, Francisco Germanese, Danila Grech, Nicole Sigurðardóttir, Steinunn Gróa Emirzeoğlu, Murat Iliev, Ivo Jovanovic, Mladjan Kampel, Martin Kearns, William Klimczuk, Andrzej Lambrinos, Lambros Lumetzberger, Jennifer Mucha, Wiktor Noiret, Sophie Pajalic, Zada Pérez, Rodrigo Rodriguez Petrova, Galidiya Petrovica, Sintija Pocta, Peter Poli, Angelica Pudane, Mara Spinsante, Susanna Salah, Albert Ali Santofimia, Maria Jose Islind, Anna Sigríður Stoicu-Tivadar, Lacramioara Tellioğlu, Hilda Zgank, Andrej Leipzig University of Applied Sciences Germany TU Wien Austria Department of Anatomy University of Malta Malta Centre for Biomedical Cybernetics Faculty of Engineering Department of Systems and Control Engineering Malta Catholic University of Croatia Croatia Universidad de Alicante Spain Institute of Information Science and Technologies The National Research Council of Italy Italy Department of Systems and Control Engineering University of Malta Malta University of Luxembourg Luxembourg Dept. of Computer Engineering Istanbul Technical University Turkey Reykjavik University School of Computer Science Iceland Sağlık Bilimleri Fakültesi Fizyoterapi ve Rehabilitasyon Karadeniz Technical University Research Information System Turkey TU Sofia Bulgaria Singidunum University Belgrade Serbia University of South Florida United States SGH Warsaw School of Economics Poland Department of Communication and Internet Studies Cyprus University of Technologies Cyprus Technical University of Vienna Austria VID Specialized University Oslo Norway Getafe University Hospital Spain Riga Technical University Latvia University of Zilina Slovakia Università Politecnica Marche Italy Utrecht University Netherlands Universidad de Castilla-La Mancha Spain Reykjavik University Iceland University Politehnica Timisoara Romania University of Maribor Slovenia

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may

关键词： computer vision

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