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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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Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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Computers in Biology and Medicine 2025年 194卷 110406页

作者： Saleh, Hager El-Sappagh, Shaker McCann, Michael Alsamhi, Saeed Hamood Breslin, John G. Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person's blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model's generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system's potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Electrotherapeutics

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Optimal control of microalgae growth using linear quadratic regulator method with firefly algorithm optimization

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Journal of Physics: Conference Series 2020年第1期1538卷

作者： Suci Yongki Setyowati Mardlijah Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember

Microalgae is an important potential resource for various industrial applications. In the industry, there are a variety of processed products from algae biomass. Lipids from microalgae used for biofuell, humans and animals in food supplements while in the pharmaceutical, lipids used as a vitamin or protein. The other application of microalgae captures CO2 and wastewater treatment. This research discusses the optimal control of microalgae growth with variable control are carbon dioxide and nutrients. In this case, the methodology is the Linear Quadratic Regulator method. In the Linear Quadratic Regulator method, there are weights in which the value is determined by trial and error in the matrices Q and R. The method of optimizing the weight value is required to optimize the weight. In this paper, the firefly algorithm is used to optimize the matrix values for Q and R. Application of firefly algorithm is able to optimally increase microalgae growth, so that the biomass produced is also abundant. The using of Firefly Algorithms optimization in the LQR method can increase algal concentration by 63.3% of the initial concentration.

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Wind disturbance elimination on dual axis solar tracker using fuzzy logic control 3

Wind disturbance elimination on dual axis solar tracker usin...

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3rd International Conference on Natural and Environmental sciences 2019, ICONES 2019

作者： Ikhwan, M. Rizal, S. Ramli, M. Muchlisin, Z.A. Mardlijah, M. Graduate School of Mathematics and Applied Science Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Marine Sciences Faculty of Marine and Fisheries Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Science Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Aquacultures Faculty of Marine and Fisheries Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

The use of solar energy using solar panels began to be encouraged. Solar panels utilize solar radiation and convert it into clean and renewable energy. The problems of the accuracy of the panel facing right towards radiation are a challenge in the development of a driving system. Solar panels have limited motion, which is a maximum of phi, so that wind disturbances are very dangerous for motor drives and solar panels. This paper aims to eliminate wind disturbances in solar panel drive systems. The fuzzy logic control is used to determine the amount of power given to the driving motor to direct the panel towards solar radiation. Inputs and outputs of fuzzy controllers are arranged by dividing into each of the five members. Simulation results show that the position error of solar panels without wind disturbances reaches 0.3325° in zonal and 0.5210° in meridional. By using wind disturbances, this system managed to maintain errors smaller than 0.4285° in zonal and 0.6116° in meridional. After modifying the rule base and membership function, systems show decreasing of error half of recent trial. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Solar panels

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Cell membrane vaccine delivery system: A review of the recent advances against breast cancer

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International Journal of Pharmaceutics 2025年 681卷 125849页

作者： Ayah Rebhi Hilles Syed Mahmood Riyanto Teguh Widodo Ahmad Khusairi Azemi Abu Hassan Nordin Abdin Shakirin Mohamad Norpi Muhammad Fauzi Abd Jalil Zarif Mohamed Sofian Mazlan Mohamed Kamarul Ariffin Hambali Nani Izreen Mohd Sani Roslina Rashid Muhammad Luqman Nordin INHART International Islamic University Malaysia Jalan Gombak 53100 Kuala Lumpur Malaysia Department of Pharmaceutical Technology Faculty of Pharmacy Universiti Malaya 50603 Kuala Lumpur Malaysia Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Advanced Materials (CAM) Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia Faculty of Pharmaceutical Sciences Chulalongkorn University Pathum Wan Bangkok 10330 Thailand Institute of Climate Change Adaptation and Marine Biotechnology Universiti Malaysia Terengganu 21030 Kuala Terengganu Terengganu Malaysia Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Campus Arau 02600 Perlis Malaysia Faculty of Pharmacy and Health Sciences Universiti Kuala Lumpur Royal College of Medicine Perak Perak 30450 Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kota Bharu 16100 Kelantan Malaysia Faculty of Earth Science Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Animal and Wildlife Research Group Faculty of Earth Science Jeli Campus Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Faculty of Veterinary Medicine Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kelantan Malaysia

Natural cells exhibit unique properties, such as homotypic targeting and prolonged circulation in the bloodstream, making them attractive candidates for breast cancer therapy vaccine delivery systems (VDSs). To improve delivery efficiency, these cells can be integrated with nanocarriers, paving the way for next-generation VDSs in the form of biomimetic nanomedicines. This review highlights recent progress in cell membrane-based vaccine delivery systems (CVDSs) and other types of vaccine delivery systems as a potential for breast cancer vaccine development. Cell membrane sources, including red blood cells (RBCs), cancer cells, and platelets are explored. Additionally, the methods for extracting these membranes and the mechanisms driving the functionality of biomimetic CVDSs are detailed. This review also highlights recent advancements and cutting-edge strategies of cell membrane vaccine delivery systems, with a particular focus on the use of cell-derived biomimetic nanotechnology for breast cancer. It also highlights breast cancer vaccines under development encompass protein-based (e.g., peptide vaccines), nucleic acid-based (DNA and RNA vaccines), immune cell-based (including dendritic cell and autologous tumor cell vaccines), and bacterial/viral vector-based vaccines. Each platform aims to elicit targeted anti-tumor immune responses.

关键词： Biomimetic Breast cancer Cell membrane Nanotechnology Vaccine

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Federated learning for 6G communications: Challenges, methods, and future directions

arXiv

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arXiv 2020年

作者： Liu, Yi Yuan, Xingliang Xiong, Zehui Kang, Jiawen Wang, Xiaofei Niyato, Dusit School of Data Science of Technology Heilongjiang University Harbin China Faculty of Information Technology Monash University ClaytonVIC3800 Australia School of Computer Science and Engineering Nanyang Technological University Singapore Singapore College of Intelligence and Computing Tianjin University Tianjin China Energy Research Institute Nanyang Technological University Singapore

—As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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Pattern-based Wordiness Reduction System for Thai Texts

Pattern-based Wordiness Reduction System for Thai Texts

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International Computer science and Engineering Conference (ICSEC)

作者： Nattapong Tongtep Thatsanee Charoenporn College of Computing Prince of Songkla University Phuket Thailand Asia AI Institute Faculty of Data Science Musashino University Tokyo Japan

In this paper, we propose a method and web application to reduce wordiness in Thai by adapting pattern-based analysis with wordiness initial corpus. The small amount of Thai text is gathered to create the initial corpus for grammatically analyzing the patterns of Thai wordiness. 18 wordiness have been decided to intensely analyze the co-occurrence, patterns contexts and construction rules. And 99 wordiness patterns are discovered for the experiments. The experiment has been done with 100 documents from Thai National Corpus by 15 students. For the user satisfaction which is analyzed from the questionnaire, users are highly satisfied with the use of the proposed system. And the wordiness reduction result of the experiment shows the effectiveness of the wordiness identification and reduction which the accuracy is 86.51%. The 13.49% of errors come from word segmentation and unknown words. Wordiness automatically corpus will be developed for further experiment in the future.

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Forest Mapping from Hyperspectral Image Using Deep Belief Network

Forest Mapping from Hyperspectral Image Using Deep Belief Ne...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xianxian Luo Songya Xu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou China Faculty of Educational Science Quanzhou Normal University Quanzhou China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Forest type is very important in the management of the forest ecosystem. Previous literatures demonstrate that deep belief network (DBN) gain the excellent classification performance. This research attempts to classify the forest type from hyperspectral image using DBN. To begin with, the Restricted Boltzmann Machines and deep belief networks are briefly reviewed. Then, the effect of network depth and the number of hidden units on the accuracy and Kappa efficient is discussed. Furthermore, forest type is classified using DBN by Python language, comparing with typical classification methods, such as support vector machine (SVM). The experimental results show that the number of layers is 3 and the number of nodes is 256 are the optimal network structure for forest type identification. The overall accuracy is 85.8% and the coefficient is 0.785, which is better than the classification performance of support vector machine. In general, deep belief network has the potential to be the dominant method of forest mapping by hyperspectral image.

关键词： Forestry Support vector machines Hyperspectral imaging Feature extraction Machine learning Training

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Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference

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Optik 2022年 270卷

作者： Romany F. Mansour Eatedal Alabdulkreem Heba F. Eid Sathishkumar K Mohd Abdul Rahim Khan Anil Kumar Department of Mathematics Faculty of Science New Valley University El-Kharja 72511 Egypt Department of Computer Science College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O.Box 84428 Riyadh 11671 Saudi Arabia Faculty of Science AL-Azhar University Cairo Egypt Department of Computer Science Gobi Arts & Science College(Autonomous) Gobichettipalayam Tamilnadu India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India

In power systems engineering, early faults detection, as well asa location in networks is a major challenge that results in damage and revenue to facilities and equipment, energy loss .The reason for the delay in detection is that most network operators rely on data/complaints supplied by customers without having a system in place to check and verify whether the information is misleading or *** paper proposed the novel fault detection in power system operators for sub-station equipment by a convolutional neural network (CNN)for detecting and relegating blemish of the network by training voltage and power values by analyzing the circuit breakers of the network. Then, at that point, discrete wavelet change (DWT) has been coordinated with the preparation part to get qualities of voltage and current signs in numerous recurrence groups of the organization at the sub-stations and this will bean master arrangement of issue location. The fault locations have been done using a fuzzy interference-based fault location system (FI-FLS) of the network. By this fault detection and locator, the aberrant and mundane conditions of the networks has been relegated and located. The simulation results show feasibility, effectiveness, average precision. The fault located by this proposed method is line to ground, line to line and, phase fault predicted on the fault parameter *** proposed approach attains 96 % average accuracy for the detection that shows the efficiency of the performance.

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