359 research outputs found

    An empirical evaluation of m-health service users’ behaviours: A case of Bangladesh

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Mobile health (m-health) services are revolutionising healthcare in the developing world by improving accessibility, affordability, and availability. Although these services are revolutionising healthcare in various ways, there are growing concerns regarding users' service quality perceptions and overall influence on satisfaction and usage behaviours. In developing countries, access to healthcare and low healthcare costs are insufficient if users lack confidence in healthcare service quality. Bangladesh's Directorate General of Health Services (DGHS) provides the only government-sponsored m-health service available to the entire population. DGHS's m-health service, available since 2009, is yet to be evaluated in terms of users' perceptions of the quality of service and its impact on satisfaction and usage. Hence, this study developed a conceptual model for evaluating the associations between overall DGHS m-health service quality, satisfaction, and usage behaviours. This study operationalised overall m-health service quality as a higher-order construct with three dimensions- platform quality, information quality, and outcome quality, and nine corresponding subdimensions-privacy, systems availability, systems reliability, systems efficiency, responsiveness, empathy, assurance, emotional benefit, and functional benefit. Moreover, researchers in various service domains, including- healthcare, marketing, environmental protection, and information systems, evaluated and confirmed the influence of social and personal norms on satisfaction and behavioural outcomes like- intention to use. Despite this, no research has been conducted to determine whether these normative components affect m-health users' service satisfaction and usage behaviours. As a result, this study included social and personal norms along with overall service quality into the conceptual model to assess the influence of these variables on users' satisfaction and m-health service usage behaviours. Data was collected from two districts in Bangladesh- Dhaka and Rajshahi, utilising the online survey approach. A total of 417 usable questionnaires were analysed using partial least squares structural equation modelling to investigate the relationships between the constructs in Warp PLS. The study confirms that all three dimensions of service quality and their corresponding subdimensions influence users' overall perceptions of DGHS m-health service quality. Moreover, overall DGHS m-health service quality has a significant direct association with satisfaction and an indirect association with usage behaviours through satisfaction. While social norms do not influence satisfaction and usage behaviours within the DGHS m-health context, personal norms directly influence users' satisfaction and indirectly influence usage behaviours through satisfaction. Theoretically, the study contributes by framing the influence of users' overall m-health service quality perceptions, social and personal norms on their actual usage behaviours rather than the intention to use. It also extends the existing knowledge by assessing and comparing m-health users' continuous and discontinuous behaviours. Methodologically this study confirms the usefulness of partial least squares structural equational modelling to analyse a complex model including a higher order construct (i.e., overall perceived service quality). Practically, the study demonstrates the importance of users' satisfaction in addition to service quality, as service quality only affects usage behaviours through satisfaction in the current study context. Additionally, knowing that personal norms significantly influence service satisfaction motivates providers of m-health services to strive to enhance users' personal norms toward m-health service to enhance service satisfaction and usage. Overall, the study will help enhance patient outcomes and m-health service usage

    Learning Dynamic Priority Scheduling Policies with Graph Attention Networks

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    The aim of this thesis is to develop novel graph attention network-based models to automatically learn scheduling policies for effectively solving resource optimization problems, covering both deterministic and stochastic environments. The policy learning methods utilize both imitation learning, when expert demonstrations are accessible at low cost, and reinforcement learning, when otherwise reward engineering is feasible. By parameterizing the learner with graph attention networks, the framework is computationally efficient and results in scalable resource optimization schedulers that adapt to various problem structures. This thesis addresses the problem of multi-robot task allocation (MRTA) under temporospatial constraints. Initially, robots with deterministic and homogeneous task performance are considered with the development of the RoboGNN scheduler. Then, I develop ScheduleNet, a novel heterogeneous graph attention network model, to efficiently reason about coordinating teams of heterogeneous robots. Next, I address problems under the more challenging stochastic setting in two parts. Part 1) Scheduling with stochastic and dynamic task completion times. The MRTA problem is extended by introducing human coworkers with dynamic learning curves and stochastic task execution. HybridNet, a hybrid network structure, has been developed that utilizes a heterogeneous graph-based encoder and a recurrent schedule propagator, to carry out fast schedule generation in multi-round settings. Part 2) Scheduling with stochastic and dynamic task arrival and completion times. With an application in failure-predictive plane maintenance, I develop a heterogeneous graph-based policy optimization (HetGPO) approach to enable learning robust scheduling policies in highly stochastic environments. Through extensive experiments, the proposed framework has been shown to outperform prior state-of-the-art algorithms in different applications. My research contributes several key innovations regarding designing graph-based learning algorithms in operations research.Ph.D

    Prognostic and health management of critical aircraft systems and components: an overview

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    This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.European Union funding: 95568

    Assuring safe and efficient operation of UAV using explainable machine learning

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    The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements

    Geometric Inhomogeneous Random Graphs for Algorithm Engineering

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    The design and analysis of graph algorithms is heavily based on the worst case. In practice, however, many algorithms perform much better than the worst case would suggest. Furthermore, various problems can be tackled more efficiently if one assumes the input to be, in a sense, realistic. The field of network science, which studies the structure and emergence of real-world networks, identifies locality and heterogeneity as two frequently occurring properties. A popular model that captures these properties are geometric inhomogeneous random graphs (GIRGs), which is a generalization of hyperbolic random graphs (HRGs). Aside from their importance to network science, GIRGs can be an immensely valuable tool in algorithm engineering. Since they convincingly mimic real-world networks, guarantees about quality and performance of an algorithm on instances of the model can be transferred to real-world applications. They have model parameters to control the amount of heterogeneity and locality, which allows to evaluate those properties in isolation while keeping the rest fixed. Moreover, they can be efficiently generated which allows for experimental analysis. While realistic instances are often rare, generated instances are readily available. Furthermore, the underlying geometry of GIRGs helps to visualize the network, e.g.,~for debugging or to improve understanding of its structure. The aim of this work is to demonstrate the capabilities of geometric inhomogeneous random graphs in algorithm engineering and establish them as routine tools to replace previous models like the Erd\H{o}s-R{\\u27e}nyi model, where each edge exists with equal probability. We utilize geometric inhomogeneous random graphs to design, evaluate, and optimize efficient algorithms for realistic inputs. In detail, we provide the currently fastest sequential generator for GIRGs and HRGs and describe algorithms for maximum flow, directed spanning arborescence, cluster editing, and hitting set. For all four problems, our implementations beat the state-of-the-art on realistic inputs. On top of providing crucial benchmark instances, GIRGs allow us to obtain valuable insights. Most notably, our efficient generator allows us to experimentally show sublinear running time of our flow algorithm, investigate the solution structure of cluster editing, complement our benchmark set of arborescence instances with a density for which there are no real-world networks available, and generate networks with adjustable locality and heterogeneity to reveal the effects of these properties on our algorithms

    Automatization of Attack Trees

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    Oggigiorno la cybersecurity è più critica che mai. L'uso estensivo di dispositivi elettronici espone i nostri dati sensibili a sempre più minacce e vulnerabilità, e tutto ciò può portare a dei cyber-attacchi. Il problema della protezione dei dati e dei sistemi dalle minacce informatiche non ha una risoluzione banale a causa dell'eterogeneità dei sistemi e dei dispositivi esistenti, che possono richiedere mezzi protettivi molto diversi fra loro. Pertanto, giocano un ruolo centrale la prevenzione ed il rilevamento di potenziali minacce nei vari tipi di sistema esistenti. Lo scopo di questa tesi è quello di sviluppare un tool di analisi automatica dei cosiddetti event log dei sistemi che sono stati attaccati o hackerati. L'obiettivo è quello di ottenere un process tree che rappresenti le azioni dell'attaccante partendo dai log, e di usare successivamente alcune regole di traduzione per ottenere un attack tree del sistema in questione. Quest'ultimo può essere visto come una rappresentazione grafica di tutti i potenziali attacchi. Il lavoro proposto può essere utile come mezzo per identificare quali possano essere le possibili debolezze e vulnerabilità che un attaccante potrebbe sfruttare all'interno di un sistema

    Comparative process mining:analyzing variability in process data

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