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Petri Net Model of a Smart Factory in the Frame of Industry 4.0

IFAC-PapersOnLine 2018年第2期51卷 266-271页

作者： Latorre-Biel, Juan-Ignacio Faulín, Javier Juan, Angel A. Jiménez-Macías, Emilio Institute of Smart Cities Public University of Navarre Spain Internet Interdisciplinary Institute Dept. of Computer Science Open University of Catalonia Spain Dept. of Electrical Engineering University of La Rioja Spain

Industry 4.0 advocates an intensive use of information and communication technologies in manufacturing facilities for achieving sustainability and success in a market with exigent and informed consumers willing personalized products and services. Three main concepts of the Industry 4.0 paradigm are addressed: cyber-physical systems, the Internet of Things, and virtualization. A Petri net model of a smart factory regarding the Industry 4.0 paradigm is proposed as virtualization for decision making support. Enhanced computing and communication capabilities of machines and products allow implementing the approach of mass customization. Any product is associated to a sequence of services, being able to request them to the appropriate machines. The formalism of the object Petri nets has been chosen, where a token net is associated to the product and a system net to the facility. The resulting model can be considered for analysis, performance evaluation, and decision making support. © 2018

关键词： Industry 4.0 Artificial intelligence Decision making Decision support systems Embedded systems Information use Models Petri nets Virtual reality Virtualization Communication capabilities Decision making support Information and Communication Technologies Manufacturing facility Performance evaluations Personalized products simulation Smart manufacturing

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BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text

arXiv

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arXiv 2025年

作者： Wu, Jiageng Gu, Bowen Zhou, Ren Xie, Kevin Snyder, Doug Jiang, Yixing Carducci, Valentina Wyss, Richard Desai, Rishi J. Alsentzer, Emily Celi, Leo Anthony Rodman, Adam Schneeweiss, Sebastian Chen, Jonathan H. Romero-Brufau, Santiago Lin, Kueiyu Joshua Yang, Jie Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Siebel School of Computing and Data Science The Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Otorhinolaryngology – Head & Neck Surgery Mayo Clinic RochesterMN United States Department of Biostatistics Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Division of General Internal Medicine Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Stanford Clinical Excellence Research Center Stanford University StanfordCA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, current evaluations of LLMs in clinical contexts remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world electronic health record (EHR) data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. We systematically evaluated 52 state-of-the-art LLMs (including DeepSeek-R1, GPT-4o, Gemini, and Llama 4) under various inference strategies. With a total of 13,572 experiments, our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. © 2025, CC BY-NC-SA.

关键词： Electronic health record

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A novel approach to multi-class atopic dermatitis disease severity scoring using multi-class SVM

A novel approach to multi-class atopic dermatitis disease se...

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2019 IEEE International Conference on Cybernetics and Computational Intelligence, CYBERNETICSCOM 2019

作者： Suhendra, Rivansyah Arnia, Fitri Idroes, Rinaldi Earlia, Nanda Suhartono, Eko Faculty of Engineering Universitas Syiah Kuala Department of Electrical and Computer Banda Aceh Indonesia Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Department of Pharmacy Banda Aceh Indonesia Dermatology Division Zainoel Abidin Regional Public Hospital Banda Aceh Indonesia Faculty of Medicine Lambung Mangkurat University Banjarmasin Indonesia

ISBN: (纸本)9781728108674

The severity scoring of atopic dermatitis skin disease is a crucial stage for a dermatologist. Accurate and reliable decision-making by a dermatologist are needed for better therapy and risk stratification to the patient. Generally, the dermatologist used visual assessment to make decisions of the severity score. The procedure is very subjective and carries a lack of reliability. The paper presents a novel approach to classify the atopic dermatitis severity scoring based on machine learning algorithms using color, texture, and redness features of the skin. The redness features showed significant indication compared to other features. The overall accuracy achieved by the Multi-class SVM classifier was around 86%, and the multiclass SVM successfully classified the severity score into four classes. © 2019 IEEE.

关键词： Machine learning

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Correction: Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3

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BMC medical informatics and decision making 2024年第1期24卷 264页

作者： Md Sohanur Rahman Khandaker Reajul Islam Johayra Prithula Jaya Kumar Mufti Mahmud Mohammed Fasihul Alam Mamun Bin Ibne Reaz Abdulrahman Alqahtani Muhammad E H Chowdhury Department of Electrical and Electronics Engineering University of Dhaka Dhaka 1000 Bangladesh. Department of Physiology Faculty of Medicine University Kebangsaan Malaysia 56000 Kuala Lumpur Malaysia. Department of Physiology Faculty of Medicine University Kebangsaan Malaysia 56000 Kuala Lumpur Malaysia. jayakumar@ukm.edu.my. Department of Computer Science Nottingham Trent University Clifton Lane Nottingham NG11 8NS UK. Department of Public Health College of Health Sciences QU Health Qatar University Doha 2713 Qatar. Department of Electrical Engineering Independent University Bangladesh Dhaka Bangladesh. Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia. Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City 11952 Saudi Arabia. Department of Electrical Engineering Qatar University Doha 2713 Qatar. mchowdhury@qu.edu.qa.

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Initial ASEAN digital security and interoperability standard proposal

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AIP Conference Proceedings 2023年第1期2739卷

作者： Sangsuree Vasupongayya Gunawan Hutagalung Obrina Candra Chalee Vorakulpipat Doni Purnomo Parinda Wattanasri Eko Supriyanto Shukor Abd Razak Othman Ibrahim Anazida Zainal Sri Devi Ravana Hiep Cong Pham Tharith Sriv Daw Khin Myo Su Mora Hertanto Agus Winarno Prabaswari Raden Muhammad Taufik Yuniantoro Badrus Zaman Fatmawati Retna Apsari Yutika Amelia Effendi Norjihan Abdul Ghani Zarina Shukur Hamidah Ibrahim Nur Izura Udzir Marnel Peradilla Jude Michael Teves Nestor Michael Tiglao Cenon Alfonso Pg Emeroylariffion Abas Mueen Uddin Agustin Kusumayati Brian Sri Prahastuti Department of Computer Engineering Faculty of Engineering Prince of Songkla University Hatyai Songkhla 90110 THAILAND Directorate General of Post and Information Technology Ministry of Communication and Information Technology Jakarta INDONESIA Politeknik Siber dan Sandi Negara National Cyber & Crypto Agency (BSSN) Depok West Java INDONESIA Information Security Research Team at National Electronics and Computer Technology Center Bangkok THAILAND National Standardization Agency of Indonesia Jakarta INDONESIA Department of Disease Control Ministry of Public Health Mueang Nonthaburi Nonthaburi THAILAND School of Biomedical Engineering and Health Sciences Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA IJN-UTM Cardiovascular Engineering Center Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur MALAYSIA RMIT University Vietnam Ho Chi Minh City VIETNAM Royal University of Phnom Penh Phnom Penh CAMBODIA Yangon University of Economics Yangon MYANMAR Agency for the Assessment and Application of Technology (BPPT) Jakarta INDONESIA Faculty of Science and Technology Universitas Airlangga Surabaya East Java INDONESIA Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya East Java INDONESIA Centre for Cyber Security Universiti Kebangsaan Malaysia Bangi Selangor MALAYSIA Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor MALAYSIA Computer Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Software Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City P

In this work, the result of the initial attempt to propose an Association of Southeast Asian Nations (ASEAN) digital security and interoperability standard to support the mobility among ASEAN countries is presented. Literature reviews on existing coronavirus disease 2019 (COVID-19) related digital technologies of six ASEAN member countries are conducted. Several concerns related to creating/implementing the ASEAN digital security and interoperability standard are discussed including the technology accessing concern, the data security and privacy concern, and the data interoperability and availability concern. The proposed solution toward creating an ASEAN digital security and interoperability standard is then presented. To evaluate the proposed solution, a survey has been conducted on representatives of nine ASEAN countries on four main issues including the security standards and requirements, the interoperability standard and requirements, the certifications, and the implementation challenges. According to the survey and the discussion, seven recommendations were proposed toward creating an ASEAN digital security and interoperability standard. First, each country collects and maintains the data of its own citizens and also provides a method to authenticate and access the data by authorized requests. Second, QR code standard or ASEAN QR code standard must be agreed upon so that it can be authenticated and verified by ASEAN countries. Third, health risk assessment must be agreed upon to be used. Forth, the security standard, the data, the data format and the exchange protocol must be agreed upon at the ASEAN policy level while adoption of international standards as an implementation guideline will ease of the process and neutralize different requirements from various countries. Fifth, any accountability of incidents must be agreed upon at the ASEAN policy level. Sixth, certification might be extended to certification of risk assessment for organizations, products or

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基于大语言模型的糖尿病管理:潜力与展望

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Science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Data Privacy Quantification and De-identification Model Based on Information Theory

Data Privacy Quantification and De-identification Model Base...

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International Conference on Networking and Network Applications (NaNA)

作者： Zeyu Zhang Zhiyang Lu Youliang Tian State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China College of Electrical Engineering Guizhou University Guiyang China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

De-identification enables to protect privacy of data from different attacks. At present, the specific de-identification standards and privacy quantification methods are given in many models, such as K-anonymity model and differential privacy model. But, the K-anonymity model does not provide an effective method to prove its degree of privacy protection, and when the model parameters change, the degree of privacy protection cannot be quantified. And due to rigorous calculation method of the differential privacy model, which quantifies the degree of privacy protection based on a mathematical basis, it is difficult to be used widely in organizations and institutions. So, this paper proposes a de-identification model, which includes quantification solution of identifying the sensitivity of the personal information, de-identification approach for adaptively matching different standard de-identification methods to personal information with different sensitivities and de-identification effect detection function. The objective is to provide an automatic, efficient and widely used de-identification model, which is built by quantitatively analyzing degree of privacy protection based on conditional entropy. Finally, performance analysis results show that the model makes the trade-off between privacy and secure of data and the widespread use of data.

关键词： Data privacy Privacy Entropy Encryption Standards Sensitivity Analytical models

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Moth-mobility-induced outages in thz: A beyond 5g (b5g) application

arXiv

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arXiv 2019年

作者： Singh, Rohit Sicker, Douglas Huq, Kazi Mohammed Saidul Engineering and Public Policy Carnegie Mellon University Pittsburgh United States School of Computer Science Carnegie Mellon University Pittsburgh United States Instituto de Telecomunicaes Aveiro Portugal

5G will enable the growing demand for Internet of Things (IoT), high-resolution video streaming, and low latency wireless services. Demand for such services is expected to growth rapid, which will require a search for Beyond 5G technological advancements in wireless communications. Part of these advancements is the need for additional spectrum, namely moving toward the terahertz (THz) range. To compensate for the high path loss in THz, narrow beamwidths are used to improve antenna gains. However, with narrow beamwidths, even minor fluctuations in device location (such as through body movement) can cause frequent link failures due to beam misalignment. In this paper, we provide a solution to these small-scale indoor movement that result in mobility-induced outages. Like a moth randomly flutters about, Mobilityinduced Outages in THz (MOTH) can be ephemeral in nature and hard to avoid. To deal with MOTH we propose two methods to predict these outage scenarios: (i) Align-After-Failure (AAF), which predicts based on fixed time margins, and (ii) Align-Before-Failure (ABF), which learns the time margins through user mobility patterns. In this paper, two different online classifiers were used to train the ABF model to predicate if a mobility-induced outage is going to occur;thereby, significantly reducing the time spent in outage scenarios. Simulation results demonstrate a relationship between optimal beamwidth and human mobility patterns. Additionally, to cater to a future with dense deployment of Wireless Personal Area Network (WPAN), it is necessary that we have efficient deployment of resources (e.g., THz-APs). One solution is to maximize the user coverage for a single AP, which might be dependent on multiple parameters. We identify these parameters and observe their tradeoffs for improving user coverage through a single THz-AP. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

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Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study ( vol 14, 17380, 2024 )

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kember, Allan J. Zia, Hafsa Elangainesan, Praniya Hsieh, Min-En Adijeh, Ramak Li, Ivan Ritchie, Leah Akbarian, Sina Taati, Babak Hobson, Sebastian R. Dolatabadi, Elham Department of Obstetrics and Gynaecology University of Toronto 123 Edward Street Suite 1200 Toronto ON M5G 0A8 Canada Institute of Health Policy Management and Evaluation University of Toronto 155 College Street Suite 425 Toronto ON M5T 3M6 Canada Maternal-Fetal Medicine Division Department of Obstetrics and Gynaecology Mount Sinai Hospital 600 University Avenue Toronto ON M5G 1X5 Canada School of Health Policy and Management York University Stong College Room 314 4700 Keele Street Toronto ON M3J 1P3 Canada Temerty Faculty of Medicine University of Toronto Medical Sciences Building 1 King’s College Circle Toronto ON M5S 1A8 Canada Electrical Engineering and Computer Science National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Regulatory Affairs Program Northeastern University First Canadian Place 100 King Street West Suite 4620 Toronto ON M5X 1E2 Canada Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada KITE Research Institute Toronto Rehabilitation Institute University Health Network 550 University Avenue #12-165 Toronto ON M5G 2A2 Canada Department of Biological Sciences University of Toronto Scarborough 1265 Military Trail Toronto ON M1C 1A4 Canada Vector Institute 661 University Avenue Suite 710 Toronto ON M5G 1M1 Canada

Sleeping on the back after 28 weeks of pregnancy has recently been associated with giving birth to a small-for-gestational-age infant and late stillbirth, but whether a causal relationship exists is currently unknown and difficult to study prospectively. This study was conducted to build a computer vision model that can automatically detect sleeping position in pregnancy under real-world conditions. Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present.

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