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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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User communities: from nice-to-have to must-have

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Journal of Innovation and Entrepreneurship 2023年第1期12卷 25页

作者： Costa, Joana Amorim, Inês Reis, João Melão, Nuno DEGEIT Department of Economics Management Industrial Engineering and Tourism University of AveiroCampus Universitário de Santiago Aveiro 3810-193 Portugal GOVCOPP Research Unit on Governance Competitiveness and Public Policies Campus Universitário de Santiago Aveiro 3810-193 Portugal INESCTEC Institute for Systems and Computer Engineering Technology and Science R. Roberto Frias Porto 4200-465 Portugal FEP Faculty of Economics University of Porto R. Roberto Frias Porto 4200-464 Portugal Department of Industrial Engineering and Management Faculty of Engineering Lusofona University and EIGeS Campo Grande Lisbon 1749-024 Portugal CISeD Research Center in Digital Services Polytechnic Institute of Viseu Campus Politécnico Viseu 3504-510 Portugal

With Industry 5.0 already on the horizon, firms need to adapt their strategies to better cater to an increasingly demanding and sustainability-conscious customer base. At the same time, the role of customers has shifted from being mere passive buyers to active users, who not only demand personalized products and services to suit their needs and preferences, but also actively engage other users and stakeholders, thereby grounding the Quadruple Helix model. The objective of this paper is to assess the role of user communities in fostering firms’ innovation in all types of innovation. Econometric estimations identified the user community as a driver of innovation propensity. Additionally, the relevance of these communities across all types of innovation (mainly for product and process innovation) was proven. Robustness analysis confirmed the results obtained in different dimensions and established a connection to human capital. This finding revealed that absorptive capacity serves not only as a facilitator of innovation, but also as a moderator. The empirical contributions point towards the urgency of policy actions that consistently involve these agents as vehicles of responsible innovation, which can fine-tune the innovation paths towards an eco-friendlier innovation ecosystem. Further testing the connection between human capital and the user community is required, as the establishment of efficient communication channels promoting the knowledge flows inside the firm will leverage innovation outputs in the different innovation types. © 2023, The Author(s).

关键词： CIS Industry 5.0 Logit models Open innovation Quadruple Helix model User communities

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Real-time detection of Ochratoxin A in wine through insight of aptamer conformation in conjunction with graphene field-effect transistor

arXiv

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arXiv 2021年

作者： Nekrasov, Nikita Jaric, Stefan Kireev, Dmitry Emelianov, Aleksei V. Orlov, Alexey V. Gadjanski, Ivana Nikitin, Petr I. Akinwande, Deji Bobrinetskiy, Ivan National Research University of Electronic Technology Zelenograd Moscow124498 Russia BioSense Institute - Research and Development Institute for Information Technologies in Biosystems University of Novi Sad Novi Sad21000 Serbia Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX United States Prokhorov General Physics Institute The Russian Academy of Sciences Moscow119991 Russia

Mycotoxins comprise a frequent type of toxins present in food and feed. The problem of mycotoxin contamination has been recently aggravated due to the increased complexity of the farm-to-fork chains, resulting in negative effects on human and animal health and, consequently, economics. The easy-to-use, on-site, on-demand, and rapid monitoring of mycotoxins in food/feed is highly desired. In this work, we report on an advanced mycotoxin biosensor based on an array of graphene field-effect transistors integrated on a single silicon chip. A specifically designed aptamer against Ochratoxin A (OTA) was used as a recognition element, where it was covalently attached to graphene surface via pyrenebutanoic acid, succinimidyl ester (PBASE) chemistry. Namely, an electric field stimulation was used to promote more efficient π-π stacking of PBASE to graphene. The specific G-rich aptamer strand suggest its π-π stacking on graphene in free-standing regime and reconfiguration in G-quadruplex during binding an OTA molecule. This realistic behavior of the aptamer is sensitive to the ionic strength of the analyte solution, demonstrating a 10-fold increase in sensitivity at low ionic strengths. The graphene-aptamer sensors reported here demonstrate fast assay with the lowest detection limit of 1.4 pM for OTA within a response time as low as 10 s, which is more than 30 times faster compared to any other reported aptamer-based methods for mycotoxin detection. The sensors hold comparable performance when operated in real-time within a complex matrix of wine without additional time-consuming pre-treatment. © 2021, CC BY.

关键词： Graphene

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Financial Impact of Energy Efficient Retrofits on Design of Grid Tied Solar systems in Pakistan 1

Financial Impact of Energy Efficient Retrofits on Design of ...

引用

1st International Conference on Electrical, Communication and computer engineering, ICECCE 2019

作者： Ahmed, Tallal Yaqoob, Muhammad Ammar Sultan, Wajahat Latif, Muammad Hamza Khlaid, Waqas Shah, Imran Ali Center for Energy Research Development University of Engineering Technology Lahore Pakistan Al-Khawarzim Institute of Computer Science University of Engineering Technology Lahore Pakistan

ISBN: (纸本)9781728138251

Pakistan is an energy starving country. To eliminate the electricity's demand and supply gap, both energy conservation and energy generation is required. Demand side load reduction by energy efficient retrofits has gained attention in last few years. Consumers are encouraged to install LED lights, inverter based split air conditioners and energy efficient fans to reduce the electricity consumption. Besides electricity conservation, solar photovoltaic power systems are also widely installed to reduce the utility bills and grid dependency. Often both retrofitting and solar system are considered to implement simultaneously but consumers perceive it expensive and financially nonviable. This study is based on financial analysis of solar power system for a public institute. It includes comparison of two solar systems;one for existing load of institute and the other for retrofitted load. The study concludes that solar system for retrofitted load is economic and it also increases the distribution capacity of the existing electrical network. Installation of solar system on retrofitted load reduces the energy drawn from grid, likewise energy bills and stress on distribution and transmission lines. © 2019 IEEE.

关键词： Solar energy

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Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

arXiv

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arXiv 2024年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Wang, Ershuai Zhou, Qin Huang, Ziyan Lyu, Pengju He, Jian Wang, Bo The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Center UHN AI Hub University Health Network Vector Institute Toronto Canada Lenovo Research Beijing China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Ocean University of China Qingdao China Department of Research and Development ShenZhen Yorktal DMIT Co. LTD. Shenzhen China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Jiao Tong University Shanghai AI Laboratory Shanghai China City University of Macau China Hanglok-Tech Co. Ltd. Hengqin China The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing China The Peter Munk Cardiac Center University Health Network Department of Laboratory Medicine and Pathobiology Department of Computer Science University of Toronto Vector Institute UHN AI Hub University Health Network Toronto Canada

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient (DSC) scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://***/competitions/12239. © 2024, CC BY-NC-ND.

关键词： Lung cancer

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An Intelligent Age of Information Based Self-Energized Uav-Assisted Wireless Communication System

SSRN

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SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Muthuchidambaranathan, P. Ranchagodage Don, Sanjaya Arunapriya National Institute of Technology Tiruchirappalli India Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli620015 India Senior Member Technical Staff Baseband Algorithm Development Team Saankhya Labs Private Limited Bengaluru560001 India Centro de Investigaçä em Tecnologias - Autónoma TechLab Universidade Autónoma de Lisboa Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka10500 Sri Lanka

Internet-of-things (IoT) is an enabling technology in the fourth generation industrial revolution. An unified performance metric named age of information (AoI) is introduced to quantify the freshness of data and its applications. Thus, this paper study the combination of AoI and full-duplex enabled unmanned aerial vehicle (FD-UAV) as a promising solution to improve latency and spectrum efficiency of a wireless powered sensor network. A sensor node (SN), located ina transport infrastructure, harvests energy from radio frequency signals transmitted by the FD-UAV. This uses to transmit real time sensor observations to the data sink via FD-UAV. The SN generates an update after replenished the battery and transmits by using the harvested energy. A closed-form expression for average AoI (AAoI) is derived as a function of time allocated for energy harvesting (EH). The optimal time allocation for EH that maximizes the freshness of data update is identified. Deep learning algorithms such as Long-Short Term Memory (LSTM) is used to predict the AAoI. Simulation results demonstrate the usefulness performance bounds in-terms of freshness of data updates. © 2022, The Authors. All rights reserved.

关键词： Sensor nodes

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

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Hardware implementation of memristor-based artificial neural networks

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Nature Communications 2024年第1期15卷 1974页

作者： Aguirre, Fernando Sebastian, Abu Le Gallo, Manuel Song, Wenhao Wang, Tong Yang, J. Joshua Lu, Wei Chang, Meng-Fan Ielmini, Daniele Yang, Yuchao Mehonic, Adnan Kenyon, Anthony Villena, Marco A. Roldán, Juan B. Wu, Yuting Hsu, Hung-Hsi Raghavan, Nagarajan Suñé, Jordi Miranda, Enrique Eltawil, Ahmed Setti, Gianluca Smagulova, Kamilya Salama, Khaled N. Krestinskaya, Olga Yan, Xiaobing Ang, Kah-Wee Jain, Samarth Li, Sifan Alharbi, Osamah Pazos, Sebastian Lanza, Mario Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Departament d’Enginyeria Electrònica Universitat Autònoma de Barcelona (UAB) Barcelona 08193 Spain IBM Research – Zurich Rüschlikon Switzerland Department of Electrical and Computer Engineering University of Southern California (USC) Los Angeles 90089 CA United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor 48109 MI United States Department of Electrical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano and IUNET Piazza L. da Vinci 32 Milano 20133 Italy School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Electrical Engineering University College London (UCL) Torrington Place WC1E 7JE London United Kingdom Departamento de Electrónica y Tecnología de Computadores Facultad de Ciencias Universidad de Granada Avenida Fuentenueva s/n Granada 18071 Spain Engineering Product Development (EPD) Pillar Singapore University of Technology & amp Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province Hebei University Baoding 071002 China Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore (NUS) Singapore Singapore

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. © The Author(s) 2024.

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Experimental Analysis for Position Estimation using Trilateration and RSSI in Industry 4.0

Experimental Analysis for Position Estimation using Trilater...

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International Conference on System Modeling & Advancement in research Trends (SMART)

作者： Robin Singh Chouhan Anand Singh Rajawat S.B. Goyal Pradeep Bedi Neagu Bogdan Constantin Maria Simona Raboaca Chaman Verma Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Galgotais University Greater Noida Department of Power Engineering “Gheorghe Asachi” Technical University of Iasi Iasi ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eötvös Loránd Budapest Hungary

ISBN: (纸本)9781665487351

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map.

关键词： Visualization Time-frequency analysis Coordinate measuring machines Service robots Extraterrestrial measurements Chatbots Mathematical models

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