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Corrigendum to “Efficiency and accuracy of GPU-parallelized Fourier spectral methods for solving phase-field models” [Comput. Mater. sci. 228 (2023) 112313]

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Computational Materials science 2024年 231卷

作者： A.D. Boccardo M. tong s.B. Leen D. tourret J. segurado Mechanical Engineering School of Engineering College of Science and Engineering University of Galway University Road Galway H91 HX31 Ireland I-Form Advanced Manufacturing Research Centre University of Galway University Road Galway H91 HX31 Ireland IMDEA Materials Institute C/ Eric Kandel 2 28906 Getafe Madrid Spain Universidad Politécnica de Madrid Department of Materials Science E.T.S.I. Caminos C/ Profesor Aranguren 3 28040 Madrid Spain

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Generating 3D brain tumor regions in MRi using vector-quantization Generative Adversarial Networks

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computers in Biology and Medicine 2025年 185卷 109502-109502页

作者： Zhou, Meng Wagner, Matthias W. tabori, Uri Hawkins, Cynthia Ertl-Wagner, Birgit B. Khalvati, Farzad Department of Computer Science University of Toronto 40 St George St. TorontoONM5S 2E4 Canada Neurosciences & Mental Health Research Program The Hospital for Sick Children 686 Bay St. TorontoONM5G 0A4 Canada Department of Diagnostic and Interventional Radiology The Hospital for Sick Children 170 Elizabeth St. TorontoONM5G 1H3 Canada Division of Neuroradiology Neurooncology The Hospital for Sick Children 170 Elizabeth St. TorontoONM5G 1H3 Canada Paediatric Laboratory Medicine Division of Pathology The Hospital for Sick Children 170 Elizabeth St. TorontoONM5G 1H3 Canada Institute of Medical Science University of Toronto 1 King's College Circle TorontoONM5S 1A8 Canada Department of Medical Imaging University of Toronto 263 McCaul St. TorontoONM5T 1W7 Canada Department of Mechanical and Industrial Engineering University of Toronto 5 King's College Road TorontoONM5S 3G8 Canada Institute of Diagnostic and Interventional Neuroradiology University Hospital Augsburg Stenglinstraße 2 Augsburg86156 Germany

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. the common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROi). Research on deep learning-based brain tumor classification using MRi has shown that it is easier to classify the tumor ROis compared to the entire image volumes. in this work, we present a novel framework that uses vector-quantization GAN and a transformer incorporating masked token modeling to generate high-resolution and diverse 3D brain tumor ROis that can be used as additional data for tumor ROi classification. We apply our method to two imbalanced datasets where we augment the minority class: (1) low-grade glioma (LGG) ROis from the Multimodal Brain tumor segmentation Challenge (Brats) 2019 dataset;(2) BRAF V600E Mutation genetic marker tumor ROis from the internal pediatric LGG (pLGG) dataset. We show that the proposed method outperforms various baseline models qualitatively and quantitatively. the generated data was used to balance the data to classify brain tumor types. Our approach demonstrates superior performance, surpassing baseline models by 6.4% in the area under the ROC curve (AUC) on the Brats 2019 dataset and 4.3% in the AUC on the internal pLGG dataset. the results indicate the generated tumor ROis can effectively address the imbalanced data problem. Our proposed method has the potential to facilitate an accurate diagnosis of rare brain tumors using MRi scans. © 2024

关键词： Generative adversarial networks

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incorporation of gallinaceous egg shell waste as an alternative raw material for porous ceramic support

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Materials today: Proceedings 2023年

作者： R.M. supritha s. Kaliappan J. Anita Lett Durgeshwar Pratap singh B. Uma Maheswari G. Jerome Nithin Gladson V. Ranjith Kumar Department of Civil Engineering Dr. Ambedkar Institute of Technology Bangalore Karnataka 560056 India Division of Research and Development Lovely Professional University Jalandhar - Delhi G.T. Road Phagwara Punjab 144411 India Department of Physics Sathyabama Institute of Science and Technology Chennai Tamil Nadu 600119 India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai Tamil Nadu 600119 India Department of Mechanical Engineering Saveetha Engineering College Chennai Tamil Nadu 602105 India Department of Mechanical Engineering Sri Sairam Engineering College Chennai Tamil Nadu 600044 India

Recycled aggregate from roller compacted concrete (RCD) has become a more popular building material with widespread use in many countries around the world, as the amount of construction and demolition waste produced daily has become an environmental concern. therefore, the present work aims to analyze the characteristics of the behavior of concrete using recycled aggregate. For the development of the study, a narrative bibliographical review was used, through the deductive method, obtaining a qualitative approach on the behavior of concrete using recycled aggregate. A material produced in large quantities in hatcheries is eggshell, which is difficult to remove and dispose of in large quantities. Concrete was tested for compressive strength at 14 and 28 days. in relation to concrete with treated eggshell waste, the reference concrete mix was expanded; Relative replacement percentages of fine aggregate with eggshell waste (0% to 30%). the results showed that the compressive strength decreased by 30%, with the increase in the percentage of eggshell residue. it is indicating that the mixture was suitable for blocks and slabs.

关键词： Concrete Eggshell waste Compression resistance Modulus of elasticity

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Multi-site, Multi-domain Airway tree Modeling (AtM’22): A Public Benchmark for Pulmonary Airway segmentation

arXiv

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arXiv 2023年

作者： Zhang, Minghui Wu, Yangqian Zhang, Hanxiao Qin, Yulei Zheng, Hao tang, Wen Arnold, Corey Pei, Chenhao Yu, Pengxin Nan, Yang Yang, Guang Walsh, simon Marshall, Dominic C. Komorowski, Matthieu Wang, Puyang Guo, Dazhou Jin, Dakai Wu, Ya'nan Zhao, shuiqing Chang, Runsheng Zhang, Boyu Lv, Xing Qayyum, Abdul Mazher, Moona su, Qi Wu, Yonghuang Liu, Ying'ao Zhu, Yufei Yang, Jiancheng Pakzad, Ashkan Rangelov, Bojidar Estepar, Raul san Jose Espinosa, Carlos Cano sun, Jiayuan Yang, Guang-Zhong Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Department of Respiratory and Critical Care Medicine Department of Respiratory Endoscopy Shanghai Chest Hospital Shanghai China InferVision Medical Technology Co. Ltd. Beijing China Imperial College London London United Kingdom Alibaba DAMO Academy 969 West Wen Yi Road Zhejiang Hangzhou China Alibaba DAMO Academy USA 860 Washington Street 8F New York United States College of Medicine and Biological Information Engineering Northeastern University Shenyang China A.I R&D Center Sanmed Biotech Inc. No. 266 Tongchang Road Xiangzhou District Guangdong Zhuhai China A.I R&D Center Sanmed Biotech Inc. T220 Trade st. San DiegoCA United States ENIB UMR CNRS 6285 LabSTICC Brest29238 France Shanghai Jiao Tong University Shanghai China School of Information Science and Technology Fudan University Shanghai China University of Science and Technology of China Anhui Hefei China Dianei Technology Shanghai China EPFL Lausanne Switzerland Department of Surgery and Cancer Imperial College London London United Kingdom University of California Los AngelesCA United States Department of Computer Engineering and Mathematics University Rovira I Virgili Tarragona Spain Brigham and Women’s Hospital Harvard Medical School SomervilleMA02145 United States Medical Physics and Biomedical Engineering Department University College London London United Kingdom Center for Medical Image Computing University College London London United Kingdom

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. in recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. since EXACt’09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. to provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway tree Modeling (AtM’22), which was held as an official challenge event during the MiCCAi 2022 conference. AtM’22 provides large-scale Ct scans with detailed pulmonary airway annotation, including 500 Ct scans (300 for training, 50 for validation, and 150 for testing). the dataset was collected from different sites and it further included a portion of noisy COViD-19 Cts with ground-glass opacity and consolidation. twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. AtM’22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://***/). Copyright © 2023, the Authors. All rights reserved.

关键词： computerized tomography

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Prediction of energy resolution in the JUNO experiment

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Chinese Physics C 2025年第1期49卷 35-59页

作者： Angel Abusleme thomas Adam Kai Adamowicz shakeel Ahmad Rizwan Ahmed sebastiano Aiello Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli tatiana Antoshkina Joao Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Marco Beretta Antonio Bergnoli Daniel Bick Lukas Bieger svetlana Biktemerova thilo Birkenfeld iwan Blake David Blum simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai stéphane Callier steven Calvez Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Vanessa Cerrone Jinfan Chang Yun Chang Auttakit Chatrabhuti Chao Chen Guoming Chen Pingping Chen shaomin Chen Xin Chen Yiming Chen Yixue Chen Yu Chen Zelin Chen Zhangming Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng YuChin Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Yen-ting Chin Po-Lin Chou Ziliang Chu Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Marta Colomer Molla selma Conforti Di Lorenzo Alberto Coppi Daniele Corti simon Csakli Chenyang Cui Flavio Dal Corso Olivia Dalager Jaydeep Datta Christophe De La taille Zhi Deng Ziyan Deng Xiaoyu Ding Xuefeng Ding Yayun Ding Bayu Dirgantara Carsten Dittrich sergey Dmitrievsky tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Wei Dou Marcos Dracos Frédéric Druillole Ran Du shuxian Du Katherine Dugas stefano Dusini Hongyue Duyang Jessica Eck timo Enqvist Andrea Fabbri Ulrike Fahrendholz Lei Fan Jian Fang Wenxing Fang Dmitry Fedoseev Li-Cheng Feng Qichun Feng Federico Ferraro Amélie Fournier Fritsch Fritsch Haonan Gan Feng Gao Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Marco Grassi Maxim Gromov Vasily Gromov Minghao G Yerevan Physics Institute YerevanArmenia Université Libre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Millennium Institute for SubAtomic Physics at the High-energy Frontier(SAPHIR) ANIDChile Pontificia Universidad Católica de Chile SantiagoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi'an Jiaotong University Xi'anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhouChina Institute of Physics National

this paper presents an energy resolution study of the JUNO experiment,incorporating the latest knowledge acquired during the detector construction *** determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 *** achieve this ambitious goal,significant efforts have been undertaken in the design and production of the key components of the JUNO *** factors affecting the detection of inverse beta decay signals have an impact on the energy resolution,extending beyond the statistical fluctuations of the detected number of photons,such as the properties of the liquid scintillator,performance of photomultiplier tubes,and the energy reconstruction *** account for these effects,a full JUNO simulation and reconstruction approach is *** enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy *** results of this study reveal an energy resolution of 2.95% at 1 ***,this study assesses the contribution of major effects to the overall energy resolution *** analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data ***,it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

关键词： energy resolution liquid scintillator detector JUNO simulation reconstruction

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self-Propelled Detachment upon Coalescence of surface Bubbles

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Physical Review Letters 2021年第23期127卷 235501-235501页

作者： Pengyu Lv Pablo Peñas Hai Le the Jan Eijkel Albert van den Berg Xuehua Zhang Detlef Lohse State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science Beijing Innovation Center for Engineering Science and Advanced Technology College of Engineering Peking University Beijing 100871 People’s Republic of China Physics of Fluids group Faculty of Science and Technology Max Planck—University of Twente Center for Complex Fluid Dynamics MESA+Institute and J. M. Burgers Centre for Fluid Dynamics University of Twente P.O. Box 217 7500 AE Enschede Netherlands BIOS Lab-on-a-Chip group Faculty of Electrical Engineering Max Planck—University of Twente Center for Complex Fluid Dynamics Mathematics and Computer Science MESA+Institute University of Twente P.O. Box 217 7500 AE Enschede Netherlands Department of Chemical & Materials Engineering University of Alberta Edmonton Alberta T6G1H9 Canada Max Planck Institute for Dynamics and Self-Organization 37077 Göttingen Germany

the removal of microbubbles from substrates is crucial for the efficiency of many catalytic and electrochemical gas evolution reactions in liquids. the current work investigates the coalescence and detachment of bubbles generated from catalytic decomposition of hydrogen peroxide. self-propelled detachment, induced by the coalescence of two bubbles, is observed at sizes much smaller than those determined by buoyancy. Upon coalescence, the released surface energy is partly dissipated by the bubble oscillations, working against viscous drag. the remaining energy is converted to the kinetic energy of the out-of-plane jumping motion of the merged bubble. the critical ratio of the parent bubble sizes for the jumping to occur is theoretically derived from an energy balance argument and found to be in agreement with the experimental results. the present results provide both physical insight for the bubble interactions and practical strategies for applications in chemical engineering and renewable energy technologies like electrolysis.

关键词： Drops & bubbles

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Fetal Brain tissue Annotation and segmentation Challenge Results

arXiv

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arXiv 2022年

作者： Payette, Kelly Li, Hongwei De Dumast, Priscille Licandro, Roxane Ji, Hui siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Liu, Hao Pei, Yuchen Wang, Lisheng Peng, Ying Xie, Juanying Zhang, Huiquan Dong, Guiming Fu, Hao Wang, Guotai Rieu, ZunHyan Kim, Donghyeon Kim, Hyun Gi Karimi, Davood Gholipour, Ali torres, Helena R. Oliveira, Bruno Vilaça, João L. Lin, Yang Avisdris, Netanell Ben-Zvi, Ori Bashat, Dafna Ben Fidon, Lucas Aertsen, Michael Vercauteren, tom sobotka, Daniel Langs, Georg Alenyà, Mireia Villanueva, Maria inmaculada Camara, Oscar Fadida, Bella specktor Joskowicz, Leo Weibin, Liao Yi, Lv Xuesong, Li Mazher, Moona Qayyum, Abdul Puig, Domenec Kebiri, Hamza Zhang, Zelin Xu, Xinyi Wu, Dan Liao, KuanLun Wu, YiXuan Chen, Jintai Xu, Yunzhi Zhao, Li Vasung, Lana Menze, Bjoern Cuadra, Meritxell Bach Jakab, Andras Center for MR Research University Children's Hospital Zurich University of Zurich Zurich Switzerland Neuroscience Center Zurich University of Zurich Zurich Switzerland Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University of Munich Munich Germany Medical Image Analysis Laboratory Department of Diagnostic and Interventional Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Lausanne Switzerland Laboratory for Computational Neuroimaging Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States Medical University of Vienna Vienna Austria Arizona State University United States NVIDIA United States Shanghai Jiaotong University China School of Computer Science Shaanxi Normal University Xi'An710119 China Research Institute NEUROPHET Inc. Seoul06247 Korea Republic of Department of Radiology The Catholic University of Korea Eunpyeong St. Mary's Hospital Seoul06247 Korea Republic of Boston Children's Hospital Harvard Medical School BostonMA United States 2Ai - School of Technology IPCA Barcelos Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal School of Medicine University of Minho Braga Portugal ICVS 3B's - PT Government Associate Laboratory Guimarães Braga Portugal School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Sagol Brain Institute Tel Aviv Sourasky Medical Center Israel Sagol School of Neuroscience Tel Aviv University Israel Sackler Faculty of Medicine Tel Aviv University Israel School of Biomedical Engineering & Imaging Sciences King's College London LondonSE1 7EU United Kingdom Department of Radiology University Hospitals Leuven Leuven3000 Belgium Computational Imaging Research Lab Department of Biomedical Imaging and Image-guided Therapy Medical

in-utero fetal MRi is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. therefore, we organized the Fetal tissue Annotation (FetA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. the challenge utilized FetA Dataset, an open dataset of fetal brain MRi reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. in this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. the majority of teams used existing medical imaging deep learning frameworks. the main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. the challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. this paper provides a first of its kind benchmark for future automatic multi.t.ssue segmentation algorithms for the developing human brain in utero. © 2022, CC BY.

关键词： Magnetic resonance imaging

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the 2024 Active Metamaterials Roadmap

arXiv

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arXiv 2024年

作者： Pope, simon A. Roth, Diane J. Bansal, Aakash Mousa, Mostafa Rezanejad, Ashkan Forte, Antonio E. Nash, Geoff.R. singleton, Lawrence Langfeldt, Felix Cheer, Jordan Henthorn, stephen Hooper, ian R. Hendry, Euan Powell, Alex W. souslov, Anton Plum, Eric sun, Kai de Groot, C.H. Muskens, Otto L. shields, Joe De Galarreta, Carlota Ruiz Wright, C. David Kocabas, Coskun Ergoktas, M. said Xiao, Jianling schulz, sebastian A. Di Falco, Andrea Krasavin, Alexey V. Zayats, Anatoly V. Galiffi, Emanuele Department of Automatic Control and Systems Engineering The University of Sheffield Amy Johnson Building Portobello Street SheffieldS1 3JD United Kingdom Department of Physics London Centre for Nanotechnology King’s College London Strand WC2R 2LS United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom Department of Engineering King’s College London LondonWC2R 2LS United Kingdom Natural Sciences University of Exeter Harrison Building North Park Road Exeter United Kingdom Institute of Sound & Vibration Research University of Southampton University Road Southampton United Kingdom Department of Electronic and Electrical Engineering University of Sheffield Sheffield United Kingdom Centre for Metamaterial Research and Innovation University of Exeter Stoker Road ExeterEX4 4QL United Kingdom T.C.M. Group Cavendish Laboratory University of Cambridge CambridgeCB3 0HE United Kingdom Optoelectronics Research Centre Centre for Photonic Metamaterials University of Southampton Highfield SouthamptonSO17 1BJ United Kingdom School of Physics and Astronomy University of Southampton Southampton United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom Institute of Materials Science of Barcelona Universitat Autònoma de Barcelona Bellaterra Barcelona08193 Spain Department of Materials National Graphene Institute University of Manchester United Kingdom Schoolof Physics and Astronomy University of St Andrews North Haugh Fife St. AndrewsKY16 9SS United Kingdom Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Photonics Initiative Advanced Science Research Centre City University of New York 85 St. Nicholas Terrace New YorkNY10027 United States

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). they possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. they have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab -based experiments. this roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. the objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field. Copyright © 2024, the Authors. All rights reserved.

关键词： Microwave acoustics

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Application of optimization technique for best blend selection: A mini review

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AiP Conference Proceedings 2024年第1期2835卷

作者： Prashant Kumar Ch. siva Ramakrishna Y. Anupam Rao Renuka shyam Narain ila Dixit Rahul Kumar Anil singh Yadav tabish Alam Abhishek sharma School of Mechanical Engineering Vellore Institute of Technology Vellore 632014 Tamil Nadu India Departtment of Mechanical Engineering Vignan’s Institute of Information Technology Visakhapatnam 530049 Andhra Pradesh India Department of Mechanical Engineering Oriental Institute of Science and Technology Jabalpur 482003 Madhya Pradesh India Interdisciplinary Institute of Human Security and Governance Alwar 301019 Rajasthan India Department of Management Maharaja Agrasen International College Raipur 492001 India School of Mechanical Engineering Lovely Professional University Punjab 144001 India Mechanical Engineering Department IES College of Technology Bhopal 462044 Madhya Pradesh India CSIR-Central Building Research Institute Roorkee Uttarakhand 247667 India Department of Mechanical Engineering B.I.T. Sindri Dhanbad 828123 Jharkhand India

Fossil fuels are depleting at a significantly higher rate, global warming reaching its new high and entire ecosystem suffering from air pollution. Biodiesel has emerged as potential substitute of diesel fuel that can be operated in a conventional diesel engine without any sort of major modification and provides comparable performance and lower emissions. One of the cumbersome tasks is to find the optimum blend of diesel-biodiesel and engine operating parameters. in this regard, many optimization and simulation methods have been used to optimize the biodiesel percentage in blend and engine operating parameters, which gives maximum power output and minimum emissions. there are several optimization techniques such as Genetic algorithm, Ant colony optimization, Grey relational analysis, Response surface methodology, taguchi, artificial neural network, ViKOR and tOPsis, which have been employed to optimize the working of a diesel engine run on biodiesel blends. in this research paper, the various optimization methods that have been used in a diesel engine for predicting & optimizing the several output parameters of the input model and ranking them accordingly are discussed.

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Phylogenomics,divergence times and notes of orders in Basidiomycota

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Fungal Diversity 2024年第3期126卷 127-406页

作者： Mao-Qiang He Bin Cao Fei Liu teun Boekhout teodor t.Denchev Nathan schoutteten Cvetomir M.Denchev Martin Kemler sergio P.Gorjón Dominik Begerow Ricardo Valenzuela Naveed Davoodian tuula Niskanen Alfredo Vizzini scott A.Redhead Virginia Ramírez-Cruz Viktor Papp Vasiliy A.Dudka Arun Kumar Dutta Ricardo García-sandoval Xin-Zhan Liu teeratas Kijpornyongpan Anton savchenko Leho tedersoo Bart theelen Larissa trierveiler-Pereira Fang Wu Juan Carlos Zamora Xiang-Yu Zeng Li-Wei Zhou shi-Liang Liu Masoomeh Ghobad-Nejhad Admir J.Giachini Guo-Jie Li Makoto Kakishima ibai Olariaga Danny Haelewaters Bobby sulistyo Junta sugiyama sten svantesson Andrey Yurkov Pablo Alvarado Vladimír Antonín AndréFelipe da silva irina Druzhinina tatiana B.Gibertoni Laura Guzmán-Dávalos Alfredo Justo samantha C.Karunarathna Mahesh C.A.Galappaththi Merje toome-Heller tsuyoshi Hosoya Kare Liimatainen Rodrigo Márquez Armin Mešić Jean-Marc Moncalvo LászlóG.Nagy torda Varga takamichi Orihara tania Raymundo isabel salcedo Alexandre G.s.silva-Filho Zdenko tkalčec Felipe Wartchow Chang-Lin Zhao tolgor Bau Milay Cabarroi-Hernández Alonso Cortés-Pérez Cony Decock Ruben De Lange Michael Weiss Nelson Menolli Jr. R.Henrik Nilsson Yu-Guang Fan Annemieke Verbeken Yusufjon Gafforov Angelina Meiras-Ottoni Renato L.Mendes-Alvarenga Nian-Kai Zeng Qi Wu Kevin D.Hyde Paul M.Kirk Rui-Lin Zhao State Key Laboratory of Mycology Institute of MicrobiologyChinese Academy of SciencesBeijing 100101People’s Republic of China The Yeasts Foundation Amsterdamthe Netherlands Institute of Biodiversity and Ecosystem Research Bulgarian Academy of Sciences2 Gagarin St.1113 SofiaBulgaria IUCN SSC Rusts and Smuts Specialist Group SofiaBulgaria Research Group Mycology Department of BiologyGhent UniversityK.L.Ledeganckstraat 359000 GhentBelgium Institute of Plant Science and Microbiology Organismic Botany and MycologyUniversität HamburgOhnhorststr.1822609 HamburgGermany Department of Botany and Plant Physiology Plant DNA-BiobankUniversity of SalamancaC/Licenciado Méndez Nieto s/n37007 SalamancaSpain Departamento de Botánica Instituto Politécnico NacionalEscuela Nacional de Ciencias BiológicasPlan de Ayala y Carpio s/nSanto TomásDel.Miguel HidalgoCP 11340 MexicoMexico Royal Botanic Gardens Victoria MelbourneVIC 3004Australia Fungal Diversity and Systematics Team Royal Botanic GardensKewSurrey TW93ABUK Botany Unit Finnish Museum of Natural HistoryUniversity of HelsinkiP.O.Box 700014 HelsinkiFinland Department of Life Sciences and Systems Biology University of TorinoViale P.A.Mattioli 2510125 TurinItaly Institute for Sustainable Plant Protection(IPSP-SS Turin) C.N.R.Viale P.A.Mattioli2510125 TurinItaly National Mycological Herbarium/DAOM Ottawa Research and Development CentreScience and Technology BranchAgriculture and Agri-Food Canada960 Carling Ave.William Saunders Building(Bldg.49)OttawaON K1A0C6Canada University of Guadalajara Apdo.postal 1-13945147 ZapopanJaliscoMexico Department of Botany Hungarian University of Agriculture and Life Sciences1518 BudapestHungary Komarov Botanical Institute of the Russian Academy of Sciences Prof.Popov Str.2St PetersburgRussia 197376 Yugra State University Chekhova str.16Khanty-MansiyskKhanty-Mansi Autonomous Okrug-YugraRussia 628012 Molecular&Applied Mycology Laboratory Department of BotanyGauhati UniversityGopinath Bordoloi

Basidiomycota is one of the major phyla in the fungal tree of *** outline of Basidiomycota provides essential taxonomic information for researchers and workers in *** this study,we present a time-framed phylogenomic tree with 487 species of Basidiomycota from 127 families,47 orders,14 classes and four subphyla;we update the outline of Basidiomycota based on the phylogenomic relationships and the taxonomic studies since 2019;and we provide notes for each order and discuss the history,defining characteristics,evolution,justification of orders,problems,significance,and *** phylogenomic analysis suggests that the subphyla diverged in a time range of 443-490 Myr(million years),classes in a time range of 312-412 Myr,and orders in a time range of 102-361 *** diverged in a time range of 50-289 Myr,76-224 Myr,and 62-156 Myr in Agaricomycotina,Pucciniomycotina,and Ustilaginomycotina,*** on the phylogenomic relationships and divergence times,we propose a new suborder Mycenineae in Agaricales to accommodate *** the current outline of Basidiomycota,there are four subphyla,20 classes,77 orders,297 families,and 2134 genera *** building a robust taxonomy of Basidiomycota in the genomic era,the generation of molecular phylogenetic data has become relatively *** phenotypical characters,especially those that can be applied for identification and classification,however,has become increasingly challenging.

关键词： Classification Fungi Molecular clock systematics taxonomy

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