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Recent developments and trends in silver nanoparticles for biomedical applications

AiP Conference Proceedings 2024年第1期3178卷

作者： subhendu Chakroborty Nibedita Nath Amit Jain tarun Yadav Anil singh Yadav Fanindra Pati Pandey Abhishek sharma Yogesh Agrawal Department of Basic Sciences IITM IES University Bhopal Madhya Pradesh 462044 India Department of Chemistry D.S Degree College Laida Sambalpur Odisha 768214 India Department of Chemical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Chhattisgarh 495009 India Department of Mechanical Engineering Bakhtiyarpur College of Engineering (Science Technology and Technical Education Department Govt. of Bihar) Bakhtiyarpur Patna Bihar 803212 India Scitechesy Research and Technology Private Limited Varanasi Uttar Pradesh 221005 India Department of Mechanical Engineering B.I.T. Sindri Dhanbad Jharkhand 828123 India Department of Mechanical Engineering Sandip Institute of Technology and Research Centre Nashik 422213 Maharashtra India

today, nanotechnology generates a significant amount of interest among researchers because of the vast array of applications it provides for addressing various aspects of the eco-system. Additionally, it enables researchers to traverse the boundaries of a more specialized area of study than an already well-established one. it is an interdisciplinary field of study that focuses on the production and manipulation of nanoscale materials via scaling down the particle size scale from micrometers to nanometers (1–100 nm). they provide excellent options in the fields of health, industry, and medicine as they present a new vision for the treatment of medical conditions thanks to the fact that the particle size may be reduced, which gives them a distinct prospective characteristic. Researchers continue to be interested in finding new uses for metal nanoparticles (NPs) as the field of nanotechnology advances since these particles display distinctive properties. the public is interested in several different types of metal nanoparticles, including silver NPs. silver nanoparticles (AgNPs) are versatile materials that have many potential applications. some examples include spectrally selective coatings for solar energy absorption, intercalation material for electrical batteries, optical receptors, catalysts in chemical processes, and so on. Lots of studies have focused on silver nanoparticles because of their potential biological applications, such as their antibacterial and antifungal properties. Recent developments and trends in the field of AgNPs for use in biomedical applications are the main focus of this review.

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A compact polarization-independent Dual-Band Frequency selective surface for smart phone safety Application

A compact polarization-independent Dual-Band Frequency Selec...

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international Conference on smart structures and systems (iCsss)

作者： Rahul Krishnan Vanaja selvaraj D. sasirekha K. Lalitha K. Jayamani Rajasekhar Atla Rajalakshmi Institute of Technology Chennai India Rajalakshmi Engineering College Chennai India SRM Institute of Science and Technology Chennai India N.B.K.R.I.S.T Vidyanagar India

ISBN: (数字)9781728172231

ISBN: (纸本)9781728172286

this paper consists of a compact polarization-independent dual-band pass Frequency selective surface (Fss) for smart phone safety application. the Fss structure is entrenched in FR-4 substrate, which acts as a band pass filter for a frequency of 2.4 GHz and 5 GHz. these two pass band frequencies are independently controlled by tuning the unit cell geometrical parameters. the unit cell shows a symmetrical shape to enable constant performance in both tE and tM mode. By considering different angles (0°- 60°) for the incidence of waves and the angular stability is verified. the proposed unit cell structure is developed with the help of Cst microwave studio. the performance of the proposed Fss were validated with the help of simulated and measured results. in reflection mode analysis, the specific frequency is passed and the remaining frequencies received at the receiving end are attenuated near to 30 dB. the accurate results are obtained at 2.25 GHz and 4.77 GHz.

关键词： Frequency selective surfaces Resonant frequency Dual band Band-pass filters shape Reflection Copper

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A decentralized power injection-based approach for voltage imbalance mitigation in three-phase distribution networks

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scientific Reports 2025年第1期15卷 1-21页

作者： tsobze, saatong Kenfack Mbasso, Wulfran Fendzi Harrison, Ambe Khishe, Mohammad Dagal, idriss Jangir, Pradeep smerat, Aseel Al-Gahtani, saad F. Elbarbary, Z.M.s. Unité de Recherche d’Automatique et d’Informatique Appliquée I.U.T. Fotso Victor University of Dschang P.O. Box 134 Bandjoun Cameroon Technology and Applied Sciences Laboratory U.I.T. of Douala University of Douala P.O. Box 8689 Douala Cameroon Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Department of Electrical and Electronics Engineering College of Technology (COT) University of Buea P.O. Box Buea 63 Buea Cameroon Department of Electrical Engineering Imam Khomeini Naval Science University of Nowshahr Nowshahr Iran Beykent University Istanbul Turkey University Centre for Research and Development Chandigarh University Gharuan Mohali 140413 India Innovation Center for Artificial Intelligence Applications Yuan Ze University Taoyuan 320315 Taiwan Department of Electrical and Electronics Engineering J.J. College of Engineering and Technology Tamilnadu Tiruchirappalli India Applied Science Research Center Applied Science Private University Amman 11937 Jordan Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Centre for Research & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 1404010 India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Electrical Engineering Department Faculty of Engineering King Khalid University Abha Saudi Arabia Department of Electrical Engineering College of Engineering King Khalid University P.O. Box 394 KSA Abha 61421 Saudi Arabia Center for Engineering and Technology Innovations King Khalid University Abha 61421 Saudi Arabia

this voltage imbalance in four-wire, three-phase distribution networks gives rise to negative-sequence and zero-sequence voltage components which increases the total apparent power received from the network. this also increases the energy losses from the network. traditional methods employed for load compensation provide partial fixes at the local area without any form of system-wide solution. this work presents a new decentralized control strategy for the inverter of a photovoltaic-based three-phase power source (DPs) aimed at instantaneously correcting phase voltage imbalances. the method does not require load current measurement because it depends entirely on real-time voltage measurements at the point of common coupling (PCC). the capability to mitigate the unbalance depends on the available power of the DPs. to test how effective the proposed method is, simulations have been conducted using MAtLAB/siMULiNK on a distribution network with a four-leg inverter connected to a line with cascading single and three-phase loads, where a four-leg inverter enables independent phase control and mitigation of neutral current disturbances. the results show that this control enables the comparison of balancing for three-phase powers with a 96.4% improvement. the phase-to-phase voltage deviation was also reduced by around 8 V (3.6% of nominal voltage). Furthermore, the total harmonic distortion (tHD) of the output current from the inverter did not rise about 3.75%, hence improving the power quality. its real-time applicability in decentralized renewable energy integration is possible due to the method’s effectiveness in reducing voltage imbalances even when network conditions are extremely distorted. © the Author(s) 2025.

关键词： Decentralized power injection Photovoltaic system Power quality three-Phase inverter Voltage imbalance

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sELFiEs and the future of molecular string representations

arXiv

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arXiv 2022年

作者： Krenn, Mario Ai, Qianxiang Barthel, senja Carson, Nessa Frei, Angelo Frey, Nathan C. Friederich, Pascal Gaudin, théophile Gayle, Alberto Alexander Jablonka, Kevin Maik Lameiro, Rafael F. Lemm, Dominik Lo, Alston Moosavi, seyed Mohamad Nápoles-Duarte, José Manuel Nigam, AkshatKumar Pollice, Robert Rajan, Kohulan schatzschneider, Ulrich schwaller, Philippe skreta, Marta smit, Berend strieth-Kalthoff, Felix sun, Chong tom, Gary von Rudorff, Guido Falk Wang, Andrew White, Andrew Young, Adamo Yu, Rose Aspuru-Guzik, Alán Erlangen Germany Department of Chemistry Fordham University The Bronx New York United States Department of Mathematics Vrije Universiteit Amsterdam Amsterdam Netherlands Syngenta Jealott’s Hill International Research Centre Bracknell Berkshire United Kingdom Department of Chemistry Imperial College London Molecular Sciences Research Hub White City Campus Wood Lane London United Kingdom Massachusetts Institute of Technology CambridgeMA United States Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Nanotechnology Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Department of Computer Science University of Toronto Canada IBM Research Europe Zürich Switzerland Unaffiliated Sapporo Japan Sion Valais Switzerland Medicinal and Biological Chemistry Group São Carlos Institute of Chemistry University of São Paulo São Paulo Brazil Faculty of Physics University of Vienna Vienna Austria Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Facultad de Ciencias Químicas Universidad Autónoma de Chihuahua Chihuahua Mexico Department of Computer Science Stanford University CA United States Chemical Physics Theory Group Department of Chemistry University of Toronto Canada Institute for Inorganic and Analytical Chemistry Friedrich-Schiller Universität Jena Jena Germany Institut für Anorganische Chemie Julius-Maximilians-Universität Würzburg Würzburg Germany Lausanne Switzerland Lausanne Switzerland Vector Institute for Artificial Intelligence Toronto Canada Solar Fuels Group Department of Chemistry University of Toronto Canada Department of Chemical Engineering University of Rochester United States Department of Computer Science and Engineering University of California San Diego United States Department of Chemical Engineering and Applied Chemistry University of Toronto Canada Department of Materials Science University of Toronto Canada Toronto

Artificial intelligence (Ai) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. the machine needs to read and write fluently in a chemical language for each of these tasks. strings are a common tool to represent molecular graphs, and the most popular molecular string representation, sMiLEs, has powered cheminformatics since the late 1980s. However, in the context of Ai and ML in chemistry, sMiLEs has several shortcomings – most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. to overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: sELFiEs (sELF-referencing Embedded strings). sELFiEs has since simplified and enabled numerous new applications in chemistry. in this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. these involve the extension toward new chemical domains, exciting questions at the interface of Ai and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of Ai in chemistry and materials science. © 2022, CC BY.

关键词： Molecules

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Why is the winner the best?

arXiv

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arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. tizabi, M.D. isensee, F. Adler, t.J. Ali, s. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, s. Balu, N. Bano, s. Bernal, J. Bodenstedt, s. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, s. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, s. Payette, K. Rädsch, t. Rafael-Patiño, J. Bawa, V. singh speidel, s. sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, i. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, s. Jahanifar, M. Jang, i. Kadkhodamohammadi, A. Kang, i. Kofler, F. Kondo, s. Kuijf, H. Li, M. Luu, M. Martinčič, t. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, s. Park, J. Park, s. Plotka, s. Puybareau, E. Rajpoot, N. Ryu, K. saeed, N. shephard, A. shi, P. Štepec, D. subedi, R. tochon, G. torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, s. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany 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 Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? to address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of iEEE isBi 2021 and MiCCAi 2021. statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. these typically include the use of multi.t.sk learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). the "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. the insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

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Carbon nanotubes–Elium nanocomposite sensor for structural health monitoring of unidirectional glass fibre reinforced epoxy composite

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Composites Communications 2025年 58卷

作者： Omid sam-Daliri Conor Kelly Michael Walls tomas Flanagan William Finnegan Noel M. Harrison Pouyan Ghabezi College of Science and Engineering University of Galway Galway Ireland Ryan Institute for Environmental Marine and Energy Research University of Galway Galway Ireland Construct Innovate and SFI MaREI Research Centre School of Engineering University of Galway Galway Ireland Éire Composites Teo Údarás Industrial Estate An Choill Rua Inverin Co. Galway Ireland CTL Tástáil Teo Údarás Industrial Estate An Choill Rua Inverin Co. Galway Ireland I-Form The SFI Research Centre for Advanced Manufacturing Ireland

the emergence of new technologies in composite manufacturing and inspection has contributed to significant advancements in the wind energy industry. Unidirectional (UD) glass fibre reinforced epoxy composites are widely used in the manufacturing of wind turbine blades due to their good fatigue resistance. One of the important subjects in wind turbine blades is periodic repair and maintenance. Delamination and crack propagation are common structural issues that make regular inspection necessary in the composites. this study presents a structural health monitoring approach using a novel thermoplastic sensor for damage assessment in UD glass fibre epoxy composite. the thermoplastic sensor, composed of thermoplastic Elium and carbon nanotube materials, was prepared using the material extrusion filament technique. the electrical resistance of the filament was assessed under cyclic loading. the prepared material in the filament shape was embedded in the intermediate layer of UD glass fibre-epoxy composite. to evaluate damage propagation, flexural and electrical tests were carried out on the prepared smart composite laminate simultaneously. three point flexural bending test was conducted for mechanical test and relative resistance change was recorded using a Wheatstone bridge circuit. sharpe increase in the electrical output indicated that the smart composite is sensitive to damage extension under flexural load. this electro-mechanical evaluation on the composite laminate highlights the potential of the proposed technology for health monitoring of large composite structures, such as wind turbine blades.

关键词： Carbon nanotube Material extrusion filament Piezoresistive sensor Electrical resistance

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Roadmap on Quantum thermodynamics

arXiv

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arXiv 2025年

作者： Campbell, steve D’Amico, irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.s. Busch, thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, salambô Deffner, sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya s. Hernández-Gómez, santiago Hu, Chang-Kang iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel t. Lasek, Aleksander Lemziakov, sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark t. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario sánchez, Rafael santos, Alan C. sarthour, Roberto s. sela, Eran solfanelli, Andrea souza, Alexandre M. splettstoesser, Janine tan, Dian tesser, Ludovico Van Vu, tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

the last two decades has seen quantum thermodynamics become a well established field of research in its own right. in that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. this Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, the Authors. All rights reserved.

关键词： temperature

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Classes and phyla of the kingdom Fungi

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Fungal Diversity 2024年第5期124卷 1-165页

作者： Nalin N.Wijayawardene Kevin D.Hyde Kirill V.Mikhailov Gabor Peter Andre Aptroot Carmen L.A.Pires‑Zottarelli Bruno t.Goto Yuri s.tokarev Danny Haelewaters samantha C.Karunarathna Paul M.Kirk Andre L.C.M.de A.santiago Ramesh K.saxena Nathan schoutteten Madhara K.Wimalasena Vladimir V.Aleoshin Abdullah M.s.Al‑Hatmi Kahandawa G.s.U.Ariyawansa Amanda R.Assuncao thushara C.Bamunuarachchige Hans‑Otto Baral D.Jayarama Bhat Janusz Błaszkowski teun Boekhout Nattawut Boonyuen Michael Brysch‑Herzberg Bin Cao Jonathan Cazabonne Xue‑Mei Chen Claudia Coleine Dong‑Qin Dai Heide‑Marie Daniel suzana B.G.da silva Francisco Adriano de souza somayeh Dolatabadi Manish K.Dubey Arun K.Dutta Aseni Ediriweera Eleonora Egidi Mostafa s.Elshahed Xinlei Fan Juliana R.B.Felix Mahesh C.A.Galappaththi Marizeth Groenewald Li‑su Han Bo Huang Vedprakash G.Hurdeal Anastasia N.ignatieva Gustavo H.Jeronimo Ana L.de Jesus serhii Kondratyuk Jaturong Kumla Martin Kukwa Qirui Li Juliana L.R.Lima Xiao‑Yong Liu Wenhua Lu H.thorsten Lumbsch Hugo Madrid Franco Magurno Guy Marson Eric H.C.McKenzie Audrius Menkis Armin Mešić Elaine C.R.Nascimento Elena s.Nassonova Yong Nie Naasson V.L.Oliveira Emilia A.Ossowska Julia Pawłowska Ursula Peintner igor R.Pozdnyakov Bhagya M.Premarathne A.K.Hasith Priyashantha C.Alisha Quandt Mariana B.Queiroz Kunhiraman C.Rajeshkumar Mubashar Raza Niranjan Roy Milan C.samarakoon Alessandra A.santos Lidiane A.santos Felix schumm Laura selbmann Faruk selcuk D.Rabern simmons Anastasia V.simakova Maudy th.smith Onden Paraparath sruthi Nakarin suwannarach Kazuaki tanaka saowaluck tibpromma Elias O.tomas Merve Ulukapı Nicolas Van Vooren Dhanushka N.Wanasinghe Evi Weber Qianzhen Wu Er Fu Yang Ryuichi Yoshioka Noha H.Youssef Annemarie Zandijk Gui‑Qing Zhang Jin‑Yong Zhang Heng Zhao RuiLin Zhao Oleg A.Zverkov Marco thines sergey A.Karpov Center for Yunnan Plateau Biological Resources Protection and Utilization College of Biological Resource and Food EngineeringQujing Normal UniversityQujing 655011YunnanPeople’s Republic of China Tropical Microbiology Research Foundation 96/N/10Meemanagoda RoadPannipitiya 10230Sri Lanka Department of Bioprocess Technology Faculty of TechnologyRajarata UniversityMihintaleSri Lanka Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiangRai 57100Thailand Innovative Institutefor Plant Health Zhongkai University of Agriculture and EngineeringGuangzhou 510225People’s Republic of China CAS Key Laboratory for Plant Biodiversity and Biogeography of East Asia(KLPB) Kunming Institute of BotanyChinese Academy of ScienceKunmingYunnanChina Belozersky Institute of Physicochemical Biology Lomonosov Moscow State UniversityLeninskye Gory1Build.40Moscow 119991Russian Federation Kharkevich Institute for Information Transmission Problems Russian Academy of SciencesMoscow 127051Russian Federation National Collection of Agricultural and Industrial Microorganisms Institute of Food Science and TechnologyHungarian University of Agriculture and Life SciencesSomlóiút 14‑161118 BudapestHungary Instituto de Biociências Universidade Federal de Mato Grosso do SulAvenida Costa e Silvas/n Bairro UniversitárioCampo GrandeMS CEP 79070‑900Brazil Instituto de Pesquisas Ambientais Secretaria do Meio AmbienteInfraestrutura e LogísticaAv.Miguel Stéfano 3687São PauloSP CEP 04301‑902Brazil Departamento de Botânica e Zoologia Universidade Federal do Rio Grande do NorteCampus UniversitárioNatalRN 59078-900Brazil All-Russian Institute of Plant Protection Sh.Podbelskogo 3PushkinSt.Petersburg 196608Russia Research Group Mycology Department of BiologyGhent University9000 GhentBelgium Faculty of Science University of South Bohemia37005ČeskéBudějoviceCzech Republic Department of Ecology and Evolutionary Biology University of Colora

Fungi are one of the most diverse groups of organisms with an estimated number of species in the range of 2–3 *** higher-level ranking of fungi has been discussed in the framework of molecular phylogenetics since Hibbett et al.,and the definition and the higher ranks(e.g.,phyla)of the‘true fungi’have been revised in several subsequent *** accumulation of novel genomic data and the advancements in phylogenetics now facilitate a robust and precise foundation for the higher-level classification within the *** study provides an updated classification of the kingdom Fungi,drawing upon a comprehensive phylogenomic analysis of Holomycota,with which we outline well-supported nodes of the fungal tree and explore more contentious *** accept 19 phyla of Fungi,***,Ascomycota,Basidiobolomycota,Basidiomycota,Blastocladiomycota,Calcarisporiellomycota,Chytridiomycota,Entomophthoromycota,Entorrhizomycota,Glomeromycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota,Mucoromycota,Neocallimastigomycota,Olpidiomycota,Rozellomycota,sanchytriomycota,and *** the phylogenies,Caulochytriomycota resides in Chytridiomycota;thus,the former is regarded as a synonym of the latter,while Caulochytriomycetes is viewed as a class in *** provide a description of each phylum followed by its classes.A new subphylum,sanchytriomycotina Karpov is introduced as the only subphylum in *** subclass Pneumocystomycetidae Kirk et *** Pneumocystomycetes,Ascomycota is invalid and thus *** of fossil fungi in phyla and classes are also discussed,providing examples.

关键词： Caulochytriomycota Phylogenomic analyses Pneumocystomycetidae sanchytriomycotina

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integrability of Goldilocks quantum cellular automata

arXiv

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arXiv 2024年

作者： Hillberry, Logan E. Piroli, Lorenzo Vernier, Eric Halpern, Nicole Yunger Prosen, tomaž Carr, Lincoln D. Department of Physics The University of Texas at Austin AustinTX78712 United States Dipartimento di Fisica e Astronomia Università di Bologna INFN Sezione di Bologna via Irnerio 46 BolognaI-40126 Italy Laboratoire de Probabilités Statistique et Modélisation CNRS Université Paris Cité Sorbonne Université Paris France Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Faculty of Mathematics and Physics University of Ljubljana Jadranska 19 LjubljanaSI-1000 Slovenia Institute of Mathematics Physics and Mechanics Jadranska 19 LjubljanaSI-1000 Slovenia Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Goldilocks quantum cellular automata (QCA) have been simulated on quantum hardware and produce emergent small-world correlation networks. in Goldilocks QCA, a single-qubit unitary is applied to each qubit in a one-dimensional chain subject to a balance constraint: a qubit is updated if its neighbors are in opposite basis states. Here, we prove that a subclass of Goldilocks QCA—including the one implemented experimentally—map onto free fermions and therefore can be classically simulated efficiently. We support this claim with two independent proofs, one involving a Jordan–Wigner transformation and one mapping the integrable six-vertex model to QCA. We compute local conserved quantities of these QCA and predict experimentally measurable expectation values. these calculations can be applied to test large digital quantum computers against known solutions. in contrast, typical Goldilocks QCA have equilibration properties and quasienergy-level statistics that suggest nonintegrability. still, the latter QCA conserve one quantity useful for error mitigation. Our work provides a parametric quantum circuit with tunable integrability properties with which to test quantum hardware. © 2024, CC BY.

关键词： Qubits

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Constrained Optimization of Charged Particle tracking with Multi-Agent Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Kortus, tobias Keidel, Ralf Gauger, Nicolas R. Kieseler, Jan Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Gauger, Nicolas R. Genov, Georgi Grøttvik, Ola Helstrup, Håvard igolkin, sergey Keidel, Ralf Kobdaj, Chinorat Kortus, tobias Leonhardt, Viktor Mehendale, shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, thomas Pettersen, Helge Egil seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter santana, Joshua schilling, Alexander seco, Joao songmoolnak, Arnon sudár, Ákos sølie, Jarle Rambo tambave, Ganesh tymchuk, ihor Ullaland, Kjetil Varga-Kofarago, Monika Wagner, Boris Xiao, RenZheng Yang, shiming Yokoyama, Hiroki Kaiserslautern Germany Karlsruhe Germany Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway University of Applied Sciences Worms Worms Germany Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus – Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India College of Mechanical & Power Engineering China Three Gorges University Yichang China

Reinforcement learning demonstrated immense success in modelling complex physics-driven systems, providing end-to-end trainable solutions by interacting with a simulated or real environment, maximizing a scalar reward signal. in this work, we propose, building upon previous work, a multi-agent reinforcement learning approach with assignment constraints for reconstructing particle tracks in pixelated particle detectors. Our approach optimizes collaboratively a parametrized policy, functioning as a heuristic to a multidimensional assignment problem, by jointly minimizing the total amount of particle scattering over the reconstructed tracks in a readout frame. to satisfy constraints, guaranteeing a unique assignment of particle hits, we propose a safety layer solving a linear assignment problem for every joint action. Further, to enforce cost margins, increasing the distance of the local policies predictions to the decision boundaries of the optimizer mappings, we recommend the use of an additional component in the blackbox gradient estimation, forcing the policy to solutions with lower total assignment costs. We empirically show on simulated data, generated for a particle detector developed for proton imaging, the effectiveness of our approach, compared to multiple single- and multi-agent baselines. We further demonstrate the effectiveness of constraints with cost margins for both optimization and generalization, introduced by wider regions with high reconstruction performance as well as reduced predictive instabilities. Our results form the basis for further developments in RL-based tracking, offering both enhanced performance with constrained policies and greater flexibility in optimizing tracking algorithms through the option for individual and team rewards. Copyright © 2025, the Authors. All rights reserved.

关键词： Constrained optimization

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