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The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

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The Lancet Psychiatry 2022年第6期9卷 477-486页

作者： Gumley, Andrew I Bradstreet, Simon Ainsworth, John Allan, Stephanie Alvarez-Jimenez, Mario Aucott, Lorna Birchwood, Maximillian Briggs, Andrew Bucci, Sandra Cotton, Sue M Engel, Lidia French, Paul Lederman, Reeva Lewis, Shôn Machin, Matthew MacLennan, Graeme McLeod, Hamish McMeekin, Nicola Mihalopoulos, Cathy Morton, Emma Norrie, John Schwannauer, Matthias Singh, Swaran P Sundram, Suresh Thompson, Andrew Williams, Chris Yung, Alison R Farhall, John Gleeson, John Glasgow Institute of Health and Wellbeing University of Glasgow Glasgow United Kingdom Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Orygen Melbourne Melbourne VIC Australia Centre for Youth Mental Health University of Melbourne Melbourne VIC Australia School of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Melbourne VIC Australia Centre for Healthcare Randomised Trials (CHaRT) University of Aberdeen Aberdeen United Kingdom Centre for Mental Health and Wellbeing Research Warwick Medical School University of Warwick Warwick United Kingdom Department of Health Services Research and Policy London School of Hygiene & Tropical Medicine London United Kingdom Greater Manchester Mental Health NHS Foundation Trust Manchester United Kingdom School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Psychiatry School of Clinical Sciences Monash University Melbourne VIC Australia School of Medicine Deakin University Melbourne VIC Australia Department of Psychiatry Manchester Metropolitan University Manchester United Kingdom Department of Psychiatry University of British Columbia Vancouver BC Canada The Usher Institute University of Edinburgh Edinburgh United Kingdom School for Health in Social Sciences University of Edinburgh Edinburgh United Kingdom Mental Health Program Monash Health Melbourne VIC Australia Department of Psychology and Counselling La Trobe University Melbourne VIC Australia NorthWestern Mental Health The Royal Melbourne Hospital Epping VIC Australia Healthy Brain and Mi

Background: Early warning signs monitoring by service users with schizophrenia has shown promise in preventing relapse but the quality of evidence is low. We aimed to establish the feasibility of undertaking a definitive randomised controlled trial to determine the effectiveness of a blended digital intervention for relapse prevention in schizophrenia. Methods: This multicentre, feasibility, cluster randomised controlled trial aimed to compare Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) with treatment as usual in community mental health services (CMHS) in Glasgow and Melbourne. CMHS were the unit of randomisation, selected on the basis of those that probably had five or more care coordinators willing to participate. Participants were eligible if they were older than 16 years, had a schizophrenia or related diagnosis confirmed via case records, were able to provide informed consent, had contact with CMHS, and had had a relapse within the previous 2 years. Participants were randomised within stratified clusters to EMPOWER or to continue their usual approach to care. EMPOWER blended a smartphone for active monitoring of early warning signs with peer support to promote self-management and clinical triage to promote access to relapse prevention. Main outcomes were feasibility, acceptability, usability, and safety, which was assessed through face-to-face interviews. App usage was assessed via the smartphone and self-report. Primary end point was 12 months. Participants, research assistants and other team members involved in delivering the intervention were not masked to treatment conditions. Assessment of relapse was done by an independent adjudication panel masked to randomisation group. The study is registered at ISRCTN (99559262). Findings: We identified and randomised eight CMHS (six in Glasgow and two in Melbourne) comprising 47 care coordinators. We recruited 86 service users between Jan 19 and Au

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Fast consensus clustering in complex networks

arXiv

引用

arXiv 2019年

作者： Tandon, Aditya Albeshri, Aiiad Thayananthan, Vijey Alhalabi, Wadee Fortunato, Santo School of Informatics Computing and Engineering Indiana University Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Algorithms for community detection are usually stochastic, leading to different partitions for different choices of random seeds. Consensus clustering has proven to be an effective technique to derive more stable and accurate partitions than the ones obtained by the direct application of the algorithm. However, the procedure requires the calculation of the consensus matrix, which can be quite dense if (some of) the clusters of the input partitions are large. Consequently, the complexity can get dangerously close to quadratic, which makes the technique inapplicable on large graphs. Here we present a fast variant of consensus clustering, which calculates the consensus matrix only on the links of the original graph and on a comparable number of additional node pairs, suitably chosen. This brings the complexity down to linear, while the performance remains comparable as the full technique. Therefore, our fast consensus clustering procedure can be applied on networks with millions of nodes and links. Copyright © 2019, The Authors. All rights reserved.

关键词： Cluster analysis

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Capacity approaching coding for low noise interactive quantum communication part I: Large alphabets

arXiv

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arXiv 2020年

作者： Leung, Debbie Nayak, Ashwin Shayeghi, Ala Touchette, Dave Yao, Penghui Yu, Nengkun Department of Combinatorics and Optimization Institute for Quantum Computing University of Waterloo and Perimeter Institute Department of Combinatorics and Optimization Institute for Quantum Computing University of Waterloo Department of Computer Science Institut Quantique Université de Sherbrooke Institute for Quantum Computing University of Waterloo Perimeter Institute State Key Laboratory for Novel Software Technology Nanjing University Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney

We consider the problem of implementing two-party interactive quantum communication over noisy channels, a necessary endeavor if we wish to fully reap quantum advantages for communication. For an arbitrary protocol with n messages, designed for a noiseless qudit channel over a poly (n) size alphabet, our main result is a simulation method that fails with probability less than 2-θ(n∈) and uses a qudit channel over the same alphabet n(1 + θ(√∈)) times, of which an ∈ fraction can be corrupted adversarially. The simulation is thus capacity achieving to leading order, and we conjecture that it is optimal up to a constant factor in the √ ∈ term. Furthermore, the simulation is in a model that does not require pre-shared resources such as randomness or entanglement between the communicating parties. Our work improves over the best previously known quantum result where the overhead is a non-explicit large constant [Brassard et al., FOCS'14] for low ∈. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum communication

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Reconstruction of Cherenkov image bymultiple telescopes of LHAASO-WFCTA

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Radiation Detection Technology and Methods 2022年第4期6卷 544-557页

作者： F.Aharonian Q.An Axikegu L.X.Bai Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi J.T.Cai Zhe Cao Zhen Cao J.Chang J.F.Chang E.S.Chen Liang Chen Liang Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen H.L.Cheng N.Cheng Y.D.Cheng S.W.Cui X.H.Cui Y.D.Cui B.D’Ettorre Piazzoli B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu Ddella Volpe K.K.Duan J.H.Fan Y.Z.Fan Z.X.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng X.T.Feng Y.L.Feng B.Gao C.D.Gao L.Q.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.H.Gong Q.B.Gou M.H.Gu F.L.Guo J.G.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He S.L.He X.B.He Y.He M.Heller Y.K.Hor C.Hou X.Hou H.B.Hu Q.Hu S.Hu S.C.Hu X.J.Hu D.H.Huang W.H.Huang X.T.Huang X.Y.Huang Y.Huang Z.C.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang Z.J.Jiang M.Jin M.M.Kang T.Ke D.Kuleshov K.Levochkin B.B.Li Cheng Li Cong Li F.Li H.B.Li H.C.Li H.Y.Li J.Li Jian Li Jie Li K.Li WLLi XRLi Xin Li Xin Li YZLi Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.S.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu W.J.Long R.Lu Q.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao A.Masood Z.Min W.Mitthumsiri Y.C.Nan Z.W.Ou B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi Y.Q.Qi B.Q.Qiao J.J.Qin D.Ruffolo A.Sáiz C.Y.Shao L.Shao O.Shchegolev X.D.Sheng J.Y.Shi H.C.Song Yu.V.Stenkin V.Stepanov Y.Su Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Z.B.Tang W.W.Tian B.D.Wang C.Wang H.Wang H.G.Wang J.C.Wang J.S.Wang L.P.Wang L.Y.Wang R.Wang R.N.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Y.P.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang D.X.Xiao G.Xiao G.G.Xin Y.L.Xin Y.Xing Z.Xiong D.L.Xu R.X.Xu L.Xue D.H.Yan J.Z.Yan C.W.Yang F.F.Yang H.W.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng Z.K.Zeng M.Zha X.X.Zhai B.B.Zhang F.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang L.X.Zhang Li Zhang Lu Zhang P.F.Zhang P.P.Zhang R.Zhang S.B.Zhang S.R.Zhang S. Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 10398069029 HeidelbergGermany State Key Laboratory of Particle Detection and Electronics BeijingChina University of Science and Technology of China Hefei 230026AnhuiChina School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031SichuanChina College of Physics Sichuan UniversityChengdu 610065SichuanChina School of Astronomy and Space Science Nanjing UniversityNanjing 210023JiangsuChina Center for Astrophysics Guangzhou UniversityGuangzhou 510006GuangdongChina Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023JiangsuChina Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Hebei Normal University Shijiazhuang 050024HebeiChina School of Physics and Astronomy(Zhuhai)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000China School of Physics(Guangzhou) Sun Yat-sen UniversityGuangzhou 510275GuangdongChina Dipartimento di Fisica dell’Universitàdi Napoli“Federico II” Complesso Universitario di Monte Sant’Angelovia Cinthia80126 NapoliItaly School of Physics and Astronomy Yunnan UniversityKunming 650091YunnanChina Département de Physique Nucléaire et Corpusculaire Facultéde SciencesUniversitéde Genève

Introduction One of main scientiﬁc goals of the Large High Altitude Air Shower Observatory(LHAASO)is to accurately measure the energy spectra of different cosmic ray compositions around the‘knee’*** Wide Field-of-View(FoV)Cherenkov Telescope Array(WFCTA),which is one of the main detectors of LHAASO and has 18 telescopes,is built to achieve this *** telescopes are put together and point to connected directions for a larger *** Telescopes are deployed spatially as close as possible,but due to their own size,the distance between two adjacent telescopes is about 10 ***,the Cherenkov lateral distribution and the parallax between the two telescopes should be considered in the event building process for images crossing over the boundaries of FoVs of the *** event building method for Cherenkov images measured by multiple telescopes of WFCTA is *** performance of the shower measurements using the combined images is evaluated by comparing with showers that are fully contained by a virtual telescope in *** and conclusion It is proved that the developed event building process can help to increase the FoV of WFCTA by 30%while maintaining the same reconstruction quality,compared to the separate telescope reconstruction method.

关键词： method directions conclusion

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An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for Cryptocurrency Forecasting

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Procedia Computer science 2019年 163卷 427-433页

作者： Nor Azizah Hitam Amelia Ritahani Ismail Faisal Saeed College of Computer Science and Engineering Taibah University Medina Saudi Arabia Information Systems Department Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia

Forecasting accurate future price is very important in financial sector. An optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) is introduced in forecasting the cryptocurrency future price. It is part of Artificial Intelligence (AI) that uses previous experience to forecast future price. Analysts and investors generally combine fundamental and technical analysis prior to decide the best price to execute their trades. Some may use Machine Learning Algorithms to execute their trades. However, forecasting result using basic SVM algorithms does not really promising. On the other hands, Particle Swarm Optimization (PSO) is known as a better algorithm for a static and simple optimization problem. Therefore, PSO is introduced to optimize the algorithms of SVM in cryptocurrency forecasting. The experiment of selected cryptocurrencies is conducted for this classifier. The experimental result demonstrates that an optimized SVM-PSO algorithm can effectively forecast the future price of cryptocurrency thus outperforms the single SVM algorithms.

关键词： Support Vector Machines Cryptocurrency Artificial Intelligence Machine Learning Particle Swarm Optimization

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When to automate spectroscopic data processing

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Spectroscopy Europe 2020年第4期32卷 20-22页

作者： Sharman, Gary Simons, Marcel G. Davies, Antony N. Mestrelab Research S.L Feliciano Barrera9B-Bajo Santiago de Compostela15706 Spain Nouryon Zutphenseweg 10 Deventer7418 AJ Netherlands SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom

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A Survey on Edge Detection based recent Marine Horizon Line Detection Methods and their Applications

A Survey on Edge Detection based recent Marine Horizon Line ...

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International Conference on computing, Mathematics and engineering Technologies (iCoMET)

作者： Manzoor Ahmed Hashmani Muhammad Umair Syed Sajjad Hussain Rizvi Abdul Rehman Gilal High-Performance Cloud Computing Center (HPC3) Center for Research in Data Science (CERDAS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia High-Performance Cloud Computing Center (HPC3) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Sea and sky boundary identification (i.e. marine horizon line detection) from a marine image is a problem of great interest for reasons such as, unmanned surface or aerial vehicle navigation, surveillance by object detection and tracking, and determining the spatial orientation of the ship. Due to the complexity of the marine environment, the problem poses its own unique challenges. In recent years, different methods have been proposed by the researchers to solve the problem. Those methods can be grouped into two categories; (i) edge detection based horizon detection, and (ii) machine learning-based horizon detection. In this paper, we present a survey on edge detection based recent marine horizon line detection methods and their applications. We have selected studies from the previous three years and discussed each study's approach to marine horizon line detection issue, the datasets used for testing purposes and its results. The authors' observations for each study are presented with a recommendation for their suitability for a specific application in the marine environment. Findings of the survey and future research directions for the researchers are also identified and presented. We hope that this survey paper provides a comprehensive overview of edge detection based recent marine horizon line detection methods and help the researchers in exploring new solutions to this challenging problem.

关键词： Image edge detection Transforms Gray-scale Feature extraction Real-time systems Computational efficiency Image color analysis

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Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions

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Physical Review D 2022年第1期106卷 014501-014501页

作者： Ed Bennett Deog Ki Hong Ho Hsiao Jong-Wan Lee C.-J. David Lin Biagio Lucini Michele Mesiti Maurizio Piai Davide Vadacchino Swansea Academy of Advanced Computing Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Pusan National University Busan 46241 Korea Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Extreme Physics Institute Pusan National University Busan 46241 Korea Center for High Energy Physics Chung-Yuan Christian University Chung-Li 32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom School of Mathematics and Hamilton Mathematics Institute Trinity College Dublin 2 Ireland Centre for Mathematical Sciences University of Plymouth Plymouth PL4 8AA United Kingdom

We consider the Sp(4) gauge theory coupled to Nf=2 fundamental and nf=3 antisymmetric flavors of Dirac fermions in four dimensions. This theory serves as the microscopic origin for composite Higgs models with SU(4)/Sp(4) coset, supplemented by partial top compositeness. We study numerically its lattice realization, and couple the fundamental plaquette action to Wilson-Dirac fermions in mixed representations, by adopting a (rational) hybrid Monte Carlo method, to perform nontrivial tests of the properties of the resulting lattice theory. We find evidence of a surface (with boundaries) of first-order bulk phase transitions in the three-dimensional space of bare parameters (one coupling and two masses). Explicit evaluation of the Dirac eigenvalues confirms the expected patterns of global symmetry breaking. After investigating finite-volume effects in the weak-coupling phase of the theory, for the largest available lattice we study the mass spectra of the lightest spin-0 and spin-1 flavored mesons composed of fermions in each representation, and of the lightest half-integer spin composite particle made of fermions in different representations—the chimera baryon. This work sets the stage for future systematical studies of the nonperturbative dynamics in phenomenologically relevant regions of parameter space.

关键词： Composite models Lattice field theory Non-Abelian gauge theories Spontaneous symmetry breaking Lattice gauge theory

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DistB-Condo: Distributed blockchain-based IoT-SDN model for smart condominium

arXiv

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arXiv 2020年

作者： Rahman, Anichur Md Jahidul, Islam Rahman, Ziaur Md Mahfuz, Reza Anwar, Adnan Parvez Mahmud, M.A. Nasir, Mostofa Kamal Noor, Rafidah M.D. Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh SavarDhaka Bangladesh Green University of Bangladesh Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Deakin University MelbourneVIC3220 Australia School of Engineering Deakin University GeelongVIC3216 Australia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Center for Mobile Cloud Computing Research Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model. © 2020, CC BY.

关键词： Internet of things

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When to automate spectroscopic data processing

Coating International

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Coating International 2020年第8期53卷 20-22页

作者： Sharman, Gary Simons, Marcel G. Davies, Antony N. Mestrelab Research S.L Feliciano Barrera 9B-Bajo Santiago de Compostela15706 Spain Nouryon Zutphenseweg 10 Deventer7418 AJ Netherlands SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom

So, it looks like there is a good clear case for continuing to develop faster and less error prone automated spectroscopic data processing. Jonathan's group have made their new software available under the Open Source MIT license, so if you feel like trying it out while you sit at home worrying about a second COVID- 19 wave it can be downloaded from GitHub. Gary was one of the authors on a recent paper that pulled together many of the topics discussed here.

关键词： Open source software

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