检索结果-内蒙古大学图书馆

Work in Progress: Management of technological development projects in the engineering undergraduate-A knowledge management approach and interdisciplinarity 3

学校读者我要写书评

暂无评论

Work in Progress: Management of technological development pr...

3rd IEEE World engineering Education Conference, EDUNINE 2019

作者： Rojas, Julio Ernesto Quispe Tuesta, Julio Enrique Quispe Computing and Electronics Department Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru Systems Engineering Program Universidad Nacional Tecnológica de Lima sur Lima Peru

ISBN: (纸本)9781728116662

The professional training of the electronic engineer must include competencies such as research, technological development and innovation. According to Peruvian Technical Standard NTP 732.001, technological development is the application of knowledge for the creation or improvement of products or processes or management up to the level of demonstrative pilots, and the technological development project activity (TDP), is linked to the design and implementation of a prototype that solves a requirement of reality. The research question is, which approach should address the management of activities RDI so that technological development projects allow contributing to develop new competencies in the engineer and preserve the knowledge generated? The UNPRG's Electronic engineering program aims to be a successful program thanks by its competitive professionals, its links with companies for activities RDI, valued by the knowledge generated by its students and professors, and the appropriate approach should be based on knowledge management and interdisciplinarity. © 2019 IEEE.

关键词： Knowledge management

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Mao, Lingchao Wang, Hairong Hu, Leland S. Tran, Nhan L. Canoll, Peter D. Swanson, Kristin R. Li, Jing H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Mathematical NeuroOncology Lab Precision Neurotherapeutics Innovation Program Mayo Clinic Arizona 5777 East Mayo Blvd Support Services Building Suite 2-700 PhoenixAZ85054 United States Department of Radiology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Neurosurgery Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Cancer Biology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Pathology and Cell Biology Columbia University Medical Center 630 West 168th Street New YorkNY10032 United States

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine *** Codes 92B99 Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge representation

Assessing Engraftment Following Fecal Microbiota Transplant

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Herman, Chloe Barker, Bridget M. Bartelli, Thais F. Chandra, Vidhi Krajmalnik-Brown, Rosa Jewell, Mary Li, Le Liao, Chen McAllister, Florencia Nirmalkar, Khemlal Xavier, Joao B. Caporaso, J. Gregory Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Clinical Cancer Prevention University of Texas MD Anderson Cancer Center HoustonTX United States Biodesign Center for Health Through Microbiomes Arizona State University TempeAZ United States School of Sustainable Engineering and the Built Environment Arizona State University TempeAZ United States Independent author Salt Lake CityUT United States Program for Computational and Systems Biology Memorial Sloan-Kettering Cancer Center New YorkNY United States Department of Gastrointestinal Medical Oncology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Immunology The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biological Sciences Northern Arizona University FlagstaffAZ United States

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn’t respond because the engrafted microbiome didn’t produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn’t engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient’s microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment;investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria,

关键词： Diseases

A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

学校读者我要写书评

暂无评论

IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

What we should learn from pandemic publishing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Research to complete the professional career of engineering – Motivation and facilitation for the initiation of research

学校读者我要写书评

暂无评论

Research to complete the professional career of engineering ...

International Symposium on engineering Accreditation (ICACIT)

作者： Julio Ernesto Quispe Rojas Julio Enrique Quispe Tuesta Computing and Electronics Department Universidad Nacional Pedro Ruiz Gallo Lambayeque Perú Systems Engineering Program Universidad Nacional Tecnológica de Lima Sur Lima Perú

ISBN: (数字)9781728135670

ISBN: (纸本)9781728135687

The purpose of this research aims to show a thesis motivation strategy for systems engineering and electronic engineering undergraduates that allow them to obtain their bachelor's degree and professional title according to the three-stage methodology,, first: know the technology developments of companies and universities from developed countries, second: to select research topics taking into account the global development, companies issues and the community of university environment, third: disseminate the selected topics and conduct research on these issues with students in their courses. As a methodology application, two universities were visited, one in Germany and another in the United States; each professor developed a research topics portfolio related to their research lines, professional interest and specialization. Students are able to start their final thesis research when they are at the fifth or sixth level.

关键词： Motivation to pursue research facilitation to initiate investigations portfolio of research subjects for thesis

Improving the Vocabulary Learning by Personalized Proficiency

学校读者我要写书评

暂无评论

Improving the Vocabulary Learning by Personalized Proficienc...

IEEE International Conference on Ubi-Media computing

作者： Yi-Zhen Lin Kai-Hsiang Chen Jen-Wei Huang Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (数字)9781728128207

ISBN: (纸本)9781728128214

关键词：

Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Diaz, Keith Anshilo Dailisan, Damian Sharaf, Umang Santos, Carissa Gan, Qijian Uy, Francis Aldrine Lim, May T. Bayen, Alexandre M. The School of Civil Environmental and Geological Engineering Mapua University Manila Philippines The National Institute of Physics University of the Philippines Diliman Philippines The Analytics Computing and Complex Systems Laboratory Asian Institute of Management Philippines The Electrical Engineering and Computer Sciences department at University of California Berkeley United States The PATH program in the Institute of Transportation Studies University of California Berkeley United States The Electrical Engineering and Computer Sciences Department The Department of Civil and Environmental Engineering The Institute of Transportation Studies University of California Berkeley United States

Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low-volume links for several cycles to optimize the network-level traffic flow. Here we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods. This framework improves arterial coordination while maintaining phase order and timing predictability. Using a validated and calibrated traffic model, we trained the policy of a deep RL agent that aims to reduce travel delays in the network. To evaluate the resulting policy, we compared its performance against the phase offsets deployed along a segment of Huntington Drive in the city of Arcadia. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed the baseline on average, effectively reducing delay time by 13.21% in the AM Scenario, 2.42% in the Noon scenario, and 6.2% in the PM scenario when offsets are adjusted in 15-minute intervals. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges in off-peak hours and localized traffic incidents. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning