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Corrigendum to “Szeged and Mostar root-indices of graphs”

applied Mathematics and Computation 2025年 499卷

作者： Simon Brezovnik Matthias Dehmer Niko Tratnik Petra Žigert Pleteršek University of Ljubljana Faculty of Mechanical Engineering Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia Swiss Distance University of Applied Sciences Brig Switzerland Department for Biomedical Computer Science and Mechatronics UMIT-Private University for Health Sciences Medical Informatics and Technology Eduard-Wallnfer-Zentrum 1 6060 Hall in Tyrol Tyrol Austria College of Artificial Intelligence Nankai University Tianjin China School of Science Xian Technological University Xian Shaanxi 710021 China University of Maribor Faculty of Natural Sciences and Mathematics Slovenia University of Maribor Faculty of Chemistry and Chemical Engineering Slovenia

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The teacher-student chatroom corpus

arXiv

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arXiv 2020年

作者： Caines, Andrew Yannakoudakis, Helen Edmondson, Helena Allen, Helen Pérez-Paredes, Pascual Byrne, Bill Buttery, Paula ALTA Institute & Computer Laboratory University of Cambridge United Kingdom Department of Informatics King’s College London United Kingdom Theoretical & Applied Linguistics University of Cambridge United Kingdom Cambridge Assessment University of Cambridge United Kingdom Faculty of Education University of Cambridge United Kingdom Department of Engineering University of Cambridge United Kingdom

The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English. The lessons took place in an online chatroom and therefore involve more interactive, immediate and informal language than might be found in asynchronous exchanges such as email correspondence. The fact that the lessons were one-to-one means that the teacher was able to focus exclusively on the linguistic abilities and errors of the student, and to offer personalised exercises, scaffolding and correction. The TSCC contains more than one hundred lessons between two teachers and eight students, amounting to 13.5K conversational turns and 133K words: it is freely available for research use. We describe the corpus design, data collection procedure and annotations added to the text. We perform some preliminary descriptive analyses of the data and consider possible uses of the TSCC. Copyright © 2020, The Authors. All rights reserved.

关键词： Students

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

引用

Nature Water 2025年 1-11页

作者： Aßmann, Eva Greiner, Timo Richard, Hugues Wade, Matthew Agrawal, Shelesh Amman, Fabian Böttcher, Sindy Lackner, Susanne Landthaler, Markus Mangul, Serghei Munteanu, Viorel Psomopoulos, Fotis Smith, Maureen Trofimova, Maria Ullrich, Alexander von Kleist, Max Wyler, Emanuel Hölzer, Martin Irrgang, Christopher Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London United Kingdom Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA United States Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE. © Springer Nature Limited 2025.

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A PSO based optimal EVs Charging utilizing BESSs and PVs in buildings

A PSO based optimal EVs Charging utilizing BESSs and PVs in ...

引用

Compatibility in Power Electronics (CPE)

作者： Aggelos S. Bouhouras Paschalis A. Gkaidatzis Ioannis Panapakidis Anastasios Tsiakalos Dimitris P. Labridis Georgios C. Christoforidis Department of Electrical Engineering Western Macedonia University of Applied Sciences Kozani Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical Engineering University of Applied Sciences of Thessaly Larissa Greece Dept. of Applied Informatics University of Macedonia Thessaloniki Greece

The penetration of Electric Vehicles (EVs) is rapidly increasing at the Low Voltage (LV) level, thus increasing the electrification rate of transport. Moreover, Photovoltaic (PV) systems at the LV level have been constantly increasing as well. Buildings with PVs on their roof and EVs as the main transport means may well become the norm the following years, considering additionally that all new buildings in the EU should be Nearly Zero Energy Buildings (NZEBs). Such NZEBs should cover the majority of their low energy demand by Renewable Energy Sources, and PV technology is the main candidate to achieve that. Both these facts pose considerable challenges on the LV networks. In this context, this paper examines the potential of efficiently utilizing the stored energy of Battery Energy Storage Systems (BESSs) in order to face the increased load demand by the penetration of EVs in LV Distribution Networks (DNs), with the presence of PVs. A Particle Swarm Optimization (PSO) algorithm is developed in order to perform optimal charging schedule for the EVs under the aim of optimizing the DN voltage profile. The analysis is performed on a real LV DN with real load data and the results indicate that under proper charging schedule both the voltage profile and the energy losses of the DN could be improved.

关键词： Electric vehicles Battery powered vehicles Electric vehicle charging Energy storage Particle swarm optimization Photovoltaic systems

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Building a frame-based cyber security learning game 1

引用

1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Wang, Yuh-Jye Tseng, Shian-Shyong Yang, Tsung-Yu Weng, Jui-Feng Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Applied Informatics and Multimedia Asia University Taichung Taiwan

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

With the rapid growth of the Internet-based smart mobile phone applications, a variety of sophisticated threats or attacks have been developed on the Internet recently. Therefore, how to enhance the cyber-security literacy education to prevent or mitigate Internet crimes becomes a very important and challenging issue. In this paper, the frame structure of the knowledge base for the card game called iMonsters has been designed to help students to easily understand the attack or defense strategies and concepts in cyber-security domain, and the ontology crystallization method will be applied to refine the existing ontology. We then propose a rule generation algorithm to generate new rules based upon learning from examples approach, and the generated new rules can be used as the scaffoldings to help the users to learn the knowledge obtained from the real cases. 53 children have participated in the iMonster card game playing in 2015. Based upon the experimental results, we can find that the card game playing can assist students to learn the cyber-security knowledge. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Ontology

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Model -Free Adaptive Control With Fuzzy Component for Tower Crane Systems

Model -Free Adaptive Control With Fuzzy Component for Tower ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Raul-Cristian Roman Radu-Emil Precup Emil M. Petriu Elena-Lorena Hedrea Claudia-Adina Bojan-Dragos Mircea-Bogdan Radac Department of Automation and Applied Informatics Politehnica University of Timisoara Timisoara Romania School of Electrical Engineering and Computer Science University of Ottawa Otawa Canada

The objective of the current paper is to improve the performance of data-driven Model-Free Adaptive Control (MFAC) by adding a Proportional-Derivative Takagi-Sugeno fuzzy controller (PDTSFC) component. This MFAC improvement is called MFAC-Proportional-Derivative Takagi-Sugeno Fuzzy Controller (MFAC-PDTSFC). MFAC-PDTSFC is applied to a Multi Input-Multi Output control structure of a mathematical model that describes the behavior of a nonlinear tower crane system (TCS) laboratory equipment. The MFAC-PDTSFC parameters are tuned in a model-based manner using metaheuristic Grey Wolf optimizer algorithm. The performances of the new MFAC-PDTSFC are compared with those of MFAC and Proportional-Derivative Takagi-Sugeno Fuzzy Controller (PDTSFC).

关键词： PD control Mathematical model Cranes Poles and towers Automation informatics Takagi-Sugeno model

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A Survey on Dragonfly Algorithm and its Applications in engineering

TechRxiv

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TechRxiv 2020年

作者： Rahman, Chnoor M. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mirjalili, Seyedali Applied Computer Department College of Health and Applied Sciences Charmo University Sulaimany Iraq Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Charles Sturt University Sydney Australia Singidunum University Belgrade Serbia Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq Centre for Artificial Intelligence Research and Optimisation Torrens University Adelaide Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of University of Kurdistan Hewler Iraq

The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance,

关键词： Multiobjective optimization

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Global, regional, and national burden of asthma and atopic dermatitis, 1990–2021, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

引用

The Lancet Respiratory Medicine 2025年第5期13卷 425-446页

作者： Oh, Jiyeon Kim, Soeun Kim, Min Seo Abate, Yohannes Habtegiorgis Abd ElHafeez, Samar Abdelkader, Atef Abdi, Parsa Abdulah, Deldar Morad Aboagye, Richard Gyan Abolhassani, Hassan Abtahi, Dariush Abualruz, Hasan Abu-Gharbieh, Eman Aburuz, Salahdein Adane, Mesafint Molla Addo, Isaac Yeboah Adeleke, Olumide Thomas Aden, Bashir Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Afzal, Muhammad Sohail Ahmad, Sajjad Ahmad, Tauseef Ahmadi, Ali Ahmed, Syed Anees Al Awaidy, Salah Al Bakour, Mohamad Anas Alam, Khurshid Albashtawy, Mohammed Al-Eyadhy, Ayman Al-Gheethi, Adel Ali Saeed Alhalaiqa, Fadwa Naji Ali, Syed Shujait Ali, Waad Alif, Sheikh Mohammad Al-Jabi, Samah W. Alqahtani, Jaber S. AlQudah, Mohammad Alrawashdeh, Ahmad Alshahrani, Najim Z. Altaany, Zaid Altaf, Awais Al-Tammemi, Alaa B. Altirkawi, Khalid A. Alvis-Guzman, Nelson Alwafi, Hassan Al-Wardat, Mohammad Al-Worafi, Yaser Mohammed Aly, Hany Alyahya, Mohammad Sharif Ibrahim Alzoubi, Karem H. Amusa, Ganiyu Adeniyi Ansar, Adnan Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Asghari-Jafarabadi, Mohammad Ashraf, Tahira Athari, Seyyed Shamsadin Aujayeb, Avinash Ayana, Lemessa Assefa A. Aziz, Shahkaar Azzam, Ahmed Y. Barqawi, Hiba Jawdat Barrow, Amadou Bastan, Mohammad-Mahdi Batra, Kavita Behera, Priyamadhaba Behzadi, Payam Bell, Michelle L. Beneke, Alice A. Berhie, Alemshet Yirga Beyene, Kebede A. Bhattacharjee, Priyadarshini Bhatti, Jasvinder Singh Bolarinwa, Obasanjo Afolabi Bouaoud, Souad Bustanji, Yasser Butt, Nadeem Shafique Camargos, Paulo Cámera, Luis Alberto Carugno, Andrea Cenderadewi, Muthia Cerrai, Sonia Chakraborty, Sandip Chan, Jeffrey Shi Kai Chandika, Rama Mohan Chattu, Vijay Kumar Chaudhary, Anis Ahmad Cheng, Esther T. W. Chichagi, Fatemeh Chimoriya, Ritesh Ching, Patrick R. Chirinos-Caceres, Jesus Lorenzo Chong, Yuen Yu Chopra, Hitesh Chu, Dinh-Toi Corlateanu, Alexandru Cruz-Martins, Natalia da Silva, Alanna Gomes Dababo, Nour Dadras, Omid Dai, Xiaochen Damiani Department of Medicine Kyung Hee University College of Medicine Seoul South Korea Center for Digital Health Kyung Hee University College of Medicine Seoul South Korea Broad Institute of MIT and Harvard Cambridge MA United States Massachusetts General Hospital Boston MA United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia Department of Epidemiology Alexandria University Alexandria Egypt Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St John's NL Canada Community and Maternity Nursing Unit University of Duhok Duhok Iraq Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden Department of Anesthesiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan College of Pharmacy University of Jordan Amman Jordan Department of Pharmacology and Therapeutics Al Ain United Arab Emirates College of Medicine and Health Sciences Bahir Dar University Bahir Dar Ethiopia School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Department of Family Medicine Bowen University Iwo Nigeria Bowen University Teaching Hospital Ogbomoso Nigeria United Arab Emirates University Al Ain United Arab Emirates Institute of Public Health Walden University Al Ain United Arab Emirates Department of Public Health Padjadjaran University (Universitas Padjadjaran) Bandung Indonesia Clinical Sciences Department Sharjah United Arab Emirates Department

Background: Asthma and atopic dermatitis are common allergic conditions that contribute to substantial health loss, economic burden, and pain across individuals of all ages worldwide. Therefore, as a component of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we present updated estimates of the prevalence, disability-adjusted life-years (DALYs), incidence, and deaths due to asthma and atopic dermatitis and the burden attributable to modifiable risk factors, with forecasted prevalence up to 2050. Methods: Asthma and atopic dermatitis prevalence, incidence, DALYs, and mortality, with corresponding 95% uncertainty intervals (UIs), were estimated for 204 countries and territories from 1990 to 2021. A systematic review identified data from 389 sources for asthma and 316 for atopic dermatitis, which were further pooled using the Bayesian meta-regression tool. We also described the age-standardised DALY rates of asthma attributable to four modifiable risk factors: high BMI, occupational asthmagens, smoking, and nitrogen dioxide pollution. Furthermore, as a secondary analysis, prevalence was forecasted to 2050 using the Socio-demographic Index (SDI), air pollution, and smoking as predictors for asthma and atopic dermatitis. To assess trends in the burden of asthma and atopic dermatitis before (2010–19) and during (2019–21) the COVID-19 pandemic, we compared their average annual percentage changes (AAPCs). Findings: In 2021, there were an estimated 260 million (95% UI 227–298) individuals with asthma and 129 million (124–134) individuals with atopic dermatitis worldwide. Asthma cases declined from 287 million (250–331) in 1990 to 238 million (209–272) in 2005 but increased to 260 million in 2021. Atopic dermatitis cases consistently rose from 107 million (103–112) in 1990 to 129 million (124–134) in 2021. However, age-standardised prevalence rates decreased—by 40·0% (from 5568·3 per 100 000 to 3340·1 per 100 000) for asthma and 8·3% (from 1885·4

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GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows in Medical Imaging

arXiv

引用

arXiv 2021年

作者： Pati, Sarthak Thakur, Siddhesh P. Hamamcı, İbrahim Ethem Baid, Ujjwal Baheti, Bhakti Bhalerao, Megh Güley, Orhun Mouchtaris, Sofia Lang, David Thermos, Spyridon Gotkowski, Karol González, Camila Grenko, Caleb Getka, Alexander Edwards, Brandon Sheller, Micah Wu, Junwen Karkada, Deepthi Panchumarthy, Ravi Ahluwalia, Vinayak Zou, Chunrui Bashyam, Vishnu Li, Yuemeng Haghighi, Babak Chitalia, Rhea Abousamra, Shahira Kurc, Tahsin M. Gastounioti, Aimilia Er, Sezgin Bergman, Mark Saltz, Joel H. Fan, Yong Shah, Prashant Mukhopadhyay, Anirban Tsaftaris, Sotirios A. Menze, Bjoern Davatzikos, Christos Kontos, Despina Karargyris, Alexandros Umeton, Renato Mattson, Peter Bakas, Spyridon MLCommons Medical Working Group San FranciscoCA United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany International School of Medicine Istanbul Medipol University Marmara Istanbul Turkey Department of Bioengineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Mathematics School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Institute for Digital Communications School of Engineering The University of Edinburgh Scotland United Kingdom Department of Computer Science Technical University of Darmstadt Hesse Darmstadt Germany Intel Corporation Santa ClaraCA United States Department of Computer Science Stony Brook University Stony Brook New YorkNY United States Department of Biomedical Informatics Stony Brook University Stony Brook New YorkNY United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Institute of Image-Guided Surgery of Strasbourg Strasbourg France Department of Informatics & Analytics Dana-Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine Weill Cornell Medicine New YorkNY United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Biological Engineering Department of Mechanical Engineering Massachusetts Institute of Technology BostonMA United States Google Menlo ParkCA United States

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows. © 2021, CC BY.

关键词： Medical imaging

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IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial

引用

Electronic Notes in Theoretical computer Science 2019年 343卷 117-130页

作者： Chrysanthi Tziortzioti Dimitrios Amaxilatis Irene Mavrommati Ioannis Chatzigiannakis Hellenic Open University School of Applied Arts Patras Greece Computer Technology Institute and Press “Diophantus” & Computer Engineering and Informatics Department University of Patras Patras Greece Hellenic Open University School of Applied Arts & Computer Technology Institute and Press “Diophantus” Patras Greece Sapienza University of Rome Italy Computer Technology Institute and Press “Diophantus” Patras Greece

IoT sensors for measuring various sea water parameters, are explored here, aiming towards an educational context, in order to lead to a deeper understanding of the use of aquatic environments as natural resources, and towards the adoption of environmentally friendly behaviors. Sea-water sensing via IoT has not been extensively explored, due to practical difficulties in deployment, and the same applies to devising appropriate scenaria for understanding aquatic parameters in STEM education. A short hands-on IoT sensing trial, that has been conducted in various location of the Aegean sea, is reported in this paper. This research set out to gain insight into real data sets on which to base observations for devising realistic educational scenaria pertaining aquatic parameters. The results of this experiment are meant to guide research further, by shedding light into the IoT sensing issues that are involved in an educational scientific context. The goal is conducting broader research in the area of IoT water sensing towards its further utilization in STEM education.

关键词： IoT water sensors Science Technology engineering and Mathematics education (STEM) crowd sensing

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