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Occupational Diseases and Environmental Medicine 2024年第2期12卷 78-114页

作者： Alyazya Alhosani Business College Aberystwyth University Aberystwyth UK

Research Problem: In Abu Dhabi, limited implementation of OSH Regulations contributes to the general unawareness among employees and workers about occupational hazards and safety measures, resulting in slow responsiveness toward enforcement measures and a lack of self-regulatory approaches within companies. Purpose: The purpose of this study is to examine the implementation methods practised in Abu Dhabi with those in developed countries with established OSH regulatory bodies. Methodology: Qualitative and quantitative research methods were employed to gather primary research data. Workers from various industries in Abu Dhabi were sampled on purpose and asked to respond to questionnaires and interviews on OSH protocol awareness and implementation, and circumstances of workplace incidence. Results: The findings of this study showed that the enforcement of OSH requirements in UAE positively correlated to a reduction in the rate of work-related injury and improved business performance. The quantitative research data showed that the energy sector had the highest score (15) while the tourism sector had the lowest score (5.3) in occupational health systems and improvements in business efficiency and productivity. Implications: The outcomes of this study shed light on the importance of implementing OSH Guidelines for companies to empower their safety managers to fully enforce OSH requirements in their organisations. In conclusion, effective OSH enforcement requires cooperation between general workers and OSH managers and facilitation from business owners.

关键词： Occupational Health and Safety Abu Dhabi The United Arab Emirates Implementation

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

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Neural Regeneration Research 2024年第7期19卷 1553-1558页

作者： Sihong Huang Jungong Han Hairong Zheng Mengjun Li Chuxin Huang Xiaoyan Kui Jun Liu Department of Radiology The Second Xiangya HospitalCentral South UniversityChangshaHunan ProvinceChina Department of Computer Science Aberystwyth UniversityAberystwythCeredigionUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong ProvinceChina School of Computer Science and Engineering Central South UniversityChangshaHunan ProvinceChina Department of Radiology Quality Control Center of Hunan ProvinceChangshaHunan ProvinceChina Clinical Research Center for Medical Imaging of Hunan Province ChangshaHunan ProvinceChina

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal ch

关键词： cognitive function cross-section follow-up functional connectivity graph theory longitudinal study mild traumatic brain injury prediction small-worldness structural connectivity subnetworks whole brain network

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无人飞行器在不确定扰动下的动态目标跟踪

Engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied environments.The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed.The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking.The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer.Thanks to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is mitigated.An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance ability.The stability of the velocity observer and IBVS controller is analyzed using the Lyapunov method.Comparative simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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Un système de signaux maritimes:Saussure’s Example of a Visual Code

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Language and Semiotic Studies 2022年第1期8卷 133-144页

作者： Daniel Chandler aberystwyth university UK

Saussure regarded nautical flag signalling codes as a prime example of semiological systems.They have something in common with language(e.g.,the principle of arbitrariness)but also some distinctive features(for instance,visual signifiers are unlike auditory ones in being able to deploy different features at the same time,such as shape and colour).The best-known example is the International Code of Signals(ICS).The system as it was during Saussure’s lifetime is explained with reference to his scattered observations,such as that a signal flag is merely“un morceau d’étoffe”until it is brought into use according to the prescriptions of the code shared by its users.Some commentators at the time envisaged this form of visual communication as a“universal language”.However,it is based on a restricted code,lacking the semantic universality of natural language.Each flag represents a letter,and combinations of flags represent predefined wordings in a“code book”.Such visual codes therefore depend on the prior existence of natural languages into and out of which they can be translated.

关键词： Saussure visual semiotics visual communication flags codes signals language

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Profiling Astronomical Objects Using Unsupervised Learning Approach

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Computers, Materials & Continua 2023年第1期74卷 1641-1655页

作者： Theerapat Sangpetch Tossapon Boongoen Natthakan Iam-On Center of Excellence in AI and Emerging Technologies School of Information TechnologyMae Fah Luang UniversityChiang Rai57100Thailand Department of Computer Science Aberystwyth UniversityAberystwythCeredigionUK

Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields.Instead of a manual inspection,various automated systems are invented to satisfy the need,including the classification of light curve profiles.A specific Kaggle competition,namely Photometric LSST Astronomical Time-Series Classification Challenge(PLAsTiCC),is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope(LSST)project.Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined types.As this challenge focuses on developing a predictive model that is robust to classifying unseen data,those previous attempts similarly encounter the lack of discriminate features,since distribution of training and actual test datasets are largely different.As a result,well-known classification algorithms prove to be sub-optimal,while more complicated feature extraction techniques may help to slightly boost the predictive performance.Given such a burden,this research is set to explore an unsupervised alternative to the difficult quest,where common classifiers fail to reach the 50%accuracy mark.A clustering technique is exploited to transform the space of training data,from which a more accurate classifier can be built.In addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning,a multiple-clustering alternative is also introduced with improved performance.In fact,it is able to yield a higher accuracy rate of 58.32%from 51.36%that is obtained using a simple clustering.For this difficult problem,it is rather good considering for those achieved by well-known models like support vector machine(SVM)with 51.80%and Naive Bayes(NB)with only 2.92%.

关键词： Astronomy sky survey light curve data classification data clustering

Classification of Adversarial Attacks Using Ensemble Clustering Approach

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Computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of attacks.To safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known intrusions.There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive traffics.The current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network applications.In contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class labels.These ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their combinations.Based on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the literature.This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated intrusions.In addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

Densely nested top-down flows for salient object detection

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Science China(Information Sciences) 2022年第8期65卷 57-70页

作者： Chaowei FANG Haibin TIAN Dingwen ZHANG Qiang ZHANG Jungong HAN Junwei HAN School of Artificial Intelligence Xidian University School of Mechano-Electronic Engineering Xidian University Brain and Artificial Intelligence Laboratory School of AutomationNorthwestern Polytechnical University Department of Computer Science Aberystwyth University

With the goal of identifying pixel-wise salient object regions from each input image, salient object detection(SOD) has been receiving great attention in recent years. One kind of mainstream SOD method is formed by a bottom-up feature encoding procedure and a top-down information decoding procedure. While numerous approaches have explored the bottom-up feature extraction for this task, the design of top-down flows remains under-studied. To this end, this paper revisits the role of top-down modeling in salient object detection and designs a novel densely nested top-down flows(DNTDF)-based framework. In every stage of DNTDF, features from higher levels are read in via the progressive compression shortcut paths(PCSPs). The notable characteristics of our proposed method are as follows.(1) The propagation of high-level features which usually have relatively strong semantic information is enhanced in the decoding procedure.(2) With the help of PCSP, the gradient vanishing issues caused by non-linear operations in top-down information flows can be alleviated.(3) Thanks to the full exploration of high-level features, the decoding process of our method is relatively memory-efficient compared to those of existing methods. Integrating DNTDF with EfficientN et, we construct a highly light-weighted SOD model, with very low computational complexity. To demonstrate the effectiveness of the proposed model, comprehensive experiments are conducted on six widely-used benchmark datasets. The comparisons to the most state-of-the-art methods as well as the carefully-designed baseline models verify our insights on the top-down flow modeling for SOD.

关键词： salient object detection top-down flow densely nested framework convolutional neural networks

Onfocus detection: identifying individual-camera eye contact from unconstrained images

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Science China(Information Sciences) 2022年第6期65卷 5-16页

作者： Dingwen ZHANG Bo WANG Gerong WANG Qiang ZHANG Jiajia ZHANG Jungong HAN Zheng YOU School of Mechano-Electronic Engineering Xidian University State Key Laboratory of Precision Measurement Technology and Instruments Tsinghua University Beijing Jingzhen Medical Technology Ltd. Computer Science Department Aberystwyth University

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status(e.g., lying or suffering mental disease) and special purposes(e.g., seeking help or attracting fans). Thus,developing effective onfocus detection algorithms is of significance for assisting the criminal investigation,disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector owing to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the onfocus detection in the wild(OFDIW). It consists of 20623 images in unconstrained capture conditions(thus called “in the wild”) and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network(ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN.

关键词： Computer Vision onfocus detection Deep Learning deep neural network capsule routing computer vision deep learning Deep neural network

Operational continental-scale land cover mapping of Australia using the Open Data Cube

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International Journal of Digital Earth 2022年第1期15卷 1715-1737页

作者： Christopher J.Owers Richard M.Lucas Daniel Clewley Belle Tissott Sean M.T.Chua Gabrielle Hunt Norman Mueller Carole Planque Suvarna M.Punalekar Pete Bunting Peter Tan Graciela Metternicht School of Environmental and Life Sciences University of NewcastleCallaghanAustralia Department of Geography and Earth Sciences Aberystwyth UniversityAberystwythUK Commonwealth Scientific and Industrial Research Organisation Land and WaterCanberraAustralia Centre for Geospatial Applications Plymouth Marine LaboratoryPlymouthUK Geoscience Australia SymonstonAustralia Earth and Sustainability Science Research Centre University of New South WalesSydneyAustralia School of Science Western Sydney UniversityRichmondAustralia

To comprehensively support national and international initiatives for sustainable development,land cover products need to be reliably and routinely generated within operational frameworks.Coupled with consistent semantics and taxonomies,ensuring confidence in mapping land cover for multiple time periods,facilitates informed decision-making at scales appropriate to multiple policy domains.The United Nations Food and Agriculture Organisation(FAO)Land Cover Classification System(LCCS)provides a taxonomy that comparable at different scales,level of detail and geographic location.The Open Data Cube(ODC)initiative offers a framework for operational continental-scale land cover mapping using analysis-ready Earth Observation data.This study utilised the FAO LCCS framework and the Landsat sensor data through Digital Earth Australia(DEA;Australia’s ODC instance)to generate consistent and continent-wide land cover mapping(DEA Land Cover)of the Australian continent.DEA Land Cover provides annual maps from 1988 to 2020 at 25 m resolution.Output maps were validated with∼12,000 independent validation points,giving an overall map accuracy of 80%.DEA Land Cover provides Australia with a nationally consistent picture of land cover,with an open-source software package using readily available global coverage data and demonstrates a pathway of adoption for national implementations across the world.

关键词： DEA land cover digital earth Australia FAO LCCS landsat sustainable development goals

Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

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General Psychiatry 2023年第6期36卷 472-480页

作者： Wenhan Yang Jungong Han Jing Luo Fei Tang Li Fan Yanyao Du Longtao Yang Jun Zhang Huiting Zhang Jun Liu The Second Xiangya Hospital of Central South university ChangshaHunanChina Department of Computer Science Aberystwyth UniversityAberystwythUK Department of Radiology The Second Xiangya Hospital of Central South UniversityChangshaHunanChina Hunan Judicial Police Academy ChangshaHunanChina siemens Healthineers Ltd WuhanHubeiChina Clinical Research Center for Medical Imaging in Hunan Province ChangshaHunanChina Department of Radiology Quality Control Center in Hunan Province ChangshaHunanChina

Background Individual differences have been detected in individuals with opioid use disorders(OUD)in rehabilitation following protracted abstinence.Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders(SUD).Aims This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling(CPM).Methods One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging(fMRI)scans at baseline.The Heroin Craving Questionnaire(HCQ)was used to assess craving levels at baseline and at the 8-month follow-up of abstinence.CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ(HCQtolow V-up-HCQpa baseline).Then,the follow-up aseline predictive ability of identified networks was tested in a separate,heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.Results CPM could predict craving changes induced by long-term abstinence,as shown by a significant correlation between predicted and actual HCQ fllow-up(r=0.417,p<0.001)and changes in HCQ(negative:r=0.334,p=0.002;positive:r=0.233,p=0.038).Identified craving-related prediction networks included the somato-motor network(SMN),salience network(SALN),default mode network(DMN),medial frontal network,visual network and auditory network.In addition,decreased connectivity of frontal-parietal network(FPN)-SMN,FPN-DMN and FPN-SALN and increased connectivity of subcortical network(SCN)-DMN,SCN-SALNandSCN-SMN were positively correlated with craving levels.Conclusions These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence,as well as the generalisation ability;the identified brain networks might be the focus of innovative therapies in the future.

关键词： opioid disorders network