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Slow Release Material from Epoxidized Natural Rubber and Rice Husk Composites for Agriculture Applications

Journal of Physics: Conference Series 2018年第1期1019卷

作者： Marwa N Al-Samarrai R Hamzah S T Sam N Z Noriman Omar S Dahham S Z Syed Idrus T Adam Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Faculty of Engineering Technology (FETech) Universiti Malaysia Perlis (UniMAP) Level 1 Block S2 UniCITI Alam Campus Sungai Chucuh Padang Besar 02100 Perlis Malaysia. School of Bioprocess Engineering Universiti Malaysia Perlis (UniMAP) Kompleks Pengajian Jejawi 3 02600 Arau Perlis Malaysia. School of Human Development and Techno-communication (iKOM) Green Advanced Computing and Technology (GREAT) Research Group – CEGeoGTech Universiti Malaysia Perlis (UniMAP Institute of Nano Electronic Engineering (INEE) Universiti Malaysia Perlis (UniMAP) 01000 Kangar Perlis Malaysia

This synthesis and characterization of slow release material from epoxidized natural rubber (ENR) and rice husks (RH) composites was conducted. The alkali modification of rice husks was carried using different NaOH concentration. ENR-50 and RH composites were analyzed by FTIR. Furthermore, the release behavior of the composites was invistigated using UV-visible spectroscopy. From the purified ENR-50, it can be characterized the peak epoxide group identified at 875 cm-1 and for the isoprene at 835 cm-1. For RH/urea beads, the CH2 peak of cellulose was obtained at 1457 cm-1. However, the CH2 is overlapped with cellulose at 1741 cm-1 and 1726 cm-1 that resulted for the partial opening of epoxide ring from carbonyl peak for hemi-cellulose and lignin, there are C=C stretching from aromatic group can be obtained at 1512 cm-1 and 1595 cm-1. The release of the composite at 7% NaOH concentration was higher than the composite at 1% NaOH concentration.

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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The Influences NaOH Treatment on Polypropylene/Cyperus Odoratus (PP/CY) Composites: Tensile and Morphology

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Journal of Physics: Conference Series 2018年第1期1019卷

作者： Omar S Dahham N Z Noriman R Hamzah Marwa N Al-Samarrai S Z Syed Idrus Z Shayfull T Adam M Mohammed Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Faculty of Engineering Technology (FETech) Universiti Malaysia Perlis (UniMAP) Level 1 Block S2 UniCITI Alam Campus Sungai Chucuh Padang Besar 02100 Perlis Malaysia. School of Human Development and Techno-communication (iKOM) Green Advanced Computing and Technology (GREAT) Research Group – CEGeoGTech Universiti Malaysia Perlis (UniMAP School of Manufacturing Engineering Universiti Malaysia Perlis Arau 02600 Malaysia Green Design and Manufacture Research Group Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Universiti Malaysia Perlis Kangar 0100 Malaysia Institute of Nano Electronic Engineering (INEE) Universiti Malaysia Perlis (UniMAP) 01000 Kangar Perlis Malaysia Center of Excellence Geopolymer and Green Technology (CEGeoGTech) School of Materials Engineering Universiti Malaysia Perlis (UniMAP) Kompleks Pengajian Jejawi 2 02600 Arau Malaysia

The bio-plastic composite has attracted the attention of people who work in academic and industrial fields due to the several advantages of this composite as compared with conventional plastic composites. However, the modification of the natural filler used in the bio-composites usually needs a modification before use. In the current work, the chemical modification on Cyperus Odoratus (CY) using different sodium hydroxide (NaOH) concentration at 1, 3, 6 and 9 wt.% were used to investigate the effect of this modification on the tensile properties of Polypropylene/Cyperus Odoratus (PP/CY) composites. The extrusion and injection molding processes were used respectively to prepare the composites. Results suggest that the presence of NaOH, particularly 3 wt.% has improved the tensile strength and the elongation at break of the composite. By contrast, the high NaOH concentration, particularly 9 wt.% has effected negatively on this properties. The tensile modulus increased linearly as NaOH concentration increased in the composites. The SEM micrograph of treated specimens at 3 wt.% NaOH shows improvement on CY surface while 9 wt.% NaOH shows deterioration on the DS surface.

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The Effects of SMR L Content on the Properties of ENR-25 / RSC Compounds

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Journal of Physics: Conference Series 2018年第1期1019卷

作者： Omar S Dahham R Hamzah N Z Noriman A M Alakrach S Z Syed Idrus Z Shayfull T Adam M Mohammed Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Faculty of Engineering Technology (FETech) Universiti Malaysia Perlis (UniMAP) Level 1 Block S2 UniCITI Alam Campus Sungai Chucuh Padang Besar 02100 Perlis Malaysia. School of Human Development and Techno-communication (iKOM) Green Advanced Computing and Technology (GREAT) Research Group – CEGeoGTech Universiti Malaysia Perlis (UniMAP School of Manufacturing Engineering Universiti Malaysia Perlis Arau 02600 Malaysia Green Design and Manufacture Research Group Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Universiti Malaysia Perlis Kangar 0100 Malaysia Institute of Nano Electronic Engineering (INEE) Universiti Malaysia Perlis (UniMAP) 01000 Kangar Perlis Malaysia Center of Excellence Geopolymer and Green Technology (CEGeoGTech) School of Materials Engineering Universiti Malaysia Perlis (UniMAP) Kompleks Pengajian Jejawi 2 02600 Arau Malaysia

Normally, direct introduction of recycled materials into polymer matrix could cause unwanted aggregations, which could effect on the properties of the final product. However, adding third material as a compatibilizer could enhance the properties of the product. This work studied the effects of different SMR L content (2,4,6,8 and 10 Phr) as a compatibilizer on the physical, tensile properties, and cure characteristics of ENR-25/rSC compounds. The results of cure characteristics showed decreasing on scorch time (t2) and cure time (t90) while increasing on minimum torque (ML) and maximum torque (MH) as SMR L increased in ENR-25/rSC/SMR L compounds. The values of modulus (M100), tensile strength (Ts), hardness and crosslinking density increased gradually with increasing SMR L while elongation at break (Eb) decreased.

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Workability, compressive strength, and efflorescence characteristics of one-part mix alkali-activated circulating fluidised bed combustion fly ash-based mortars

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CEMENT 2024年 18卷

作者： Jason Shun Fui Pei Chung Siung Choo Deni Shidqi Khaerudini Sing Muk Ng Dominic Ek Leong Ong Melvina Tan Jaka Sunarso Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia Research Centre for Sustainable Technologies Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia Research Centre for Innovative Society Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia Research Center for Advanced Materials National Research and Innovation Agency Bld. 440 Kawasan Puspiptek Serpong Indonesia Research and Development Department Sarawak Energy Berhad No. 1 The Isthmus 93050 Kuching Sarawak Malaysia School of Engineering and Built Environment Griffith University Nathan Queensland 4111 Australia

In this study, one-part alkali-activated mortars are formulated using circulating fluidised bed combustion (CFBC) fly ash, derived from lignite (brown coal) combustion, as the precursor, and sodium hydroxide (NaOH) and sodium metasilicate (Na 2 SiO 3 ) as the solid activators. Experimental findings indicate that an increase in solid activator-to-precursor ratio correlates with improved workability and compressive strength of the mortars. The influence of Na 2 SiO 3 -to-NaOH ratio on the compressive strength of the mortars is apparent only in mixes with a high solid activator-to-precursor ratio of 0.4 and 0.5, indicating its relatively lesser significance compared to the solid activator-to-precursor ratio. The mechanism through which an increase in the solid activator-to-precursor ratio improves the compressive strength of the mortars is elucidated using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The results indicate that increasing the solid activator-to-precursor ratio enhances the degree of alkali-activation for fly ash, thereby improving the compressive strength with increasing solid activator-to-precursor ratio.

关键词： Alkali-activated Just-add water One-part Solid activator Dry powder

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Correction to: Histone isoform H2A1H promotes attainment of distinct physiological states by altering chromatin dynamics

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Epigenetics & chromatin 2018年第1期11卷 67页

作者： Saikat Bhattacharya Divya Reddy Vinod Jani Nikhil Gadewal Sanket Shah Raja Reddy Kakoli Bose Uddhavesh Sonavane Rajendra Joshi Duane Smoot Hassan Ashktorab Sanjay Gupta Epigenetics and Chromatin Biology Group Gupta Lab Cancer Research Institute Advanced Centre for Treatment Research and Education in Cancer (ACTREC) Tata Memorial Centre Kharghar Navi Mumbai MH 410210 India. Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai MH 400085 India. Stowers Institute for Medical Research Kansas City MO 64110 USA. Bioinformatics Group Centre for Development of Advanced Computing (C‑DAC) University of Pune Campus Pune MH 411007 India. BTIS Cancer Research Institute Advanced Centre for Treatment Research and Education in Cancer (ACTREC) Tata Memorial Centre Kharghar Navi Mumbai MH 410210 India. Integrated Biophysics and Structural Biology Lab Cancer Research Institute Advanced Centre for Treatment Research and Education in Cancer (ACTREC) Tata Memorial Centre Kharghar Navi Mumbai MH 410210 India. Meharry Medical College 1005 Dr DB Todd Jr Blvd Nashville TN 37208 USA. Department of Medicine Howard University Cancer Center 2041 Georgia Avenue NW Suite 220 NW Washington DC 20059 USA. Epigenetics and Chromatin Biology Group Gupta Lab Cancer Research Institute Advanced Centre for Treatment Research and Education in Cancer (ACTREC) Tata Memorial Centre Kharghar Navi Mumbai MH 410210 India. sgupta@actrec.gov.in. Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai MH 400085 India. sgupta@actrec.gov.in.

After publication of this article [1], it was noticed Duane Smoot and Hassan Ashktorab who made and provided the cell line HFE145 were not included in the author list.

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Abstracts from the Food Allergy and Anaphylaxis Meeting 2016 Abstracts

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CLINICAL AND TRANSLATIONAL ALLERGY 2017年第SUPPL 1期7卷 10-10页

作者： [Anonymous] Department of Pediatrics Children’s Hospital Roubaix France Division of Pulmonology and Allergology Department of Pediatrics Faculty of Medicine and Children’s Hospital Lille France Allergy Vigilance Network Vandoeuvre les Nancy France Université Lille 2 CHU Lille EA 2694 - Santé Publique: épidémiologie et qualité des soins Lille France Biostatistics Unit Maison Régionale de la Recherche Clinique CHRU Lille Lille France Department of Allergology Emile Durkheim Hospital Epinal France Centre d’Epidémiologie sur les Causes Médicales de Décès INSERM CHU de Bicêtre Le Kremlin-Bicêtre France The Allergy Vigilance Network University Hospital Nancy Nancy France Pediatric Pulmonology and Allergy Department Hôpital Jeanne de Flandre CHU Lille Lille France Charité - Universitätsmedizin Berlin Berlin Germany Institute for Social Medicine Epidemiology and Health Economics Charité - Universitätsmedizin Berlin Berlin Germany Institute of Social Medicine Epidemiology and Health Economics Charité Universitätsmedizin Berlin Berlin Germany Department of Dermatology and Allergology Comprehensive Allergy Center Charité Charité – Universitätsmedizin Berlin Berlin Germany Department of Dermatology and Allergy Allergie-Centrum-Charité Charité-Universiätsmedizin Berlin Berlin Germany Department of Dermatology and Allergology Comprehensive Allergy Center Charité Charité Universitätsmedizin Berlin Berlin Germany University Hospital Basel Basel Switzerland Allergy Unit Department of Dermatology University Hospital Basel Switzerland Universitätsklinikum Salzburg Salzburg Austria Department of Dermatology and Allergy Centre Odense Research Center for Anaphylaxis (ORCA) Odense University Hospital Odense Denmark Odense University Hospital Odense Denmark Serviço de ImunoalergologiaCentro Hospitalar de São João E.P.E. Porto Portugal GRIAC Research Institute University of Groningen University Medical Center Groningen Groningen The Netherlands Serviço de Imunoalergologia Centr

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