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<p>Efficient strategies for diabetes management in primary care provide avenues through which the disease may be monitored and controlled, but systems and processes must be more than adequate. The use of Electronic Medical Record systems (EMRs) assist healthcare providers in delivering quality care to patients to help better manage chronic conditions, and integrate services throughout the healthcare system so that relevant chronic disease programs may be made available to individuals and communities. Usability issues have often been blamed for poor EMR adoption rates, underutilization of systems, endangerment of patient health and inadequacies in providing positive health outcomes for patients while improving the quality of chronic disease management.</p> <p>This thesis investigates the use of EMRs in managing diabetes within primary care, and evaluates their usability and its effects in managing diabetes in patients, with special reference to patient safety, healthcare provider workflow and adherence to clinical practice guidelines (CPGs).</p> <p>Existing evidence emphasizing the management of diabetes and the role of the EMR in primary care is presented, while three levels of usability and several usability guidelines are identified and investigated. Data gathered from the local environment, show the relationships between EMR usability, patient safety, clinician workflow and adherence to CPGs in managing diabetes, and three models of EMR usability are suggested.</p> <p>The primary proposition for this study is that EMRs provide promise in helping to control diabetes in patients. However EMR usability may present significant hindrances in maximizing outcomes for individuals and in providing support programs and services to communities.</p> / Master of Science (MSc)

The number of IoT and cyber-physical systems will be growing in the comingfuture. According to estimates, more than 21 billion IoT devices are expectedto exist by 2025. The adoption of Digital Twins and AI-enhanced IoTapplications is projected to fuel the expected increase in IoT spending.It is essential to accelerate the development., deployment, and administrationof these IoT applications, which can be accomplished by orchestrating IoTcomponents, devices, services, and systems. IoT Intelligence orchestrationposes several obstacles that must be overcome for a wide range of domainspecific use cases and applications to follow and support business logic.This thesis aims to create a secure and full proof way of orchestratingintelligence within IoT devices from multiple ecosystems.It also aims tobreak down the current approach of monolithic development and introduce amixture of visual programming and distributed systems considering all thenecessary cyber-security aspects,.A comparison of the developed framework and the existing tool with thenecessary characteristics like security, response time and the accuracy too isincluded.of the thesis.

<p dir="ltr">The success of machine learning techniques in widespread applications has taught us that with respect to accuracy, the more data, the better the model. However, for fairness, data quality is perhaps more important than quantity. Existing studies have considered the impact of data preprocessing on the accuracy of ML model tasks. However, the impact of preprocessing on the fairness of the downstream model has neither been studied nor well understood. Throughout this thesis, we conduct a systematic study of how data quality issues and data preprocessing steps impact model fairness. Our study evaluates several preprocessing techniques for several machine learning models trained over datasets with different characteristics and evaluated using several fairness metrics. It examines different data preparation techniques, such as changing categories into numbers, filling in missing information, and smoothing out unusual data points. The study measures fairness using standards that check if the model treats all groups equally, predicts outcomes fairly, and gives similar chances to everyone. By testing these methods on various types of data, the thesis identifies which combinations of techniques can make the models both accurate and fair.The empirical analysis demonstrated that preprocessing steps like one-hot encoding, imputation of missing values, and outlier treatment significantly influence fairness metrics. Specifically, models preprocessed with median imputation and robust scaling exhibited the most balanced performance across fairness and accuracy metrics, suggesting a potential best practice guideline for equitable ML model preparation. Thus, this work sheds light on the importance of data preparation in ML and emphasizes the need for careful handling of data to support fair and ethical use of ML in society.</p>

This study explores operationalizing UX practices in agile software development teams to integrate accessibility in B2B software products. We conducted a qualitative case study, collecting data through a user-centered approach with interviews, a workshop, prototyping, and user testing. Our findings highlight that the sporadic involvement of UX professionals, the B2B context’s restriction of communication with end-users, and the perception of accessibility as a non-critical concern, all limit the operationalization of UX practices. Additionally, the importance of implementing structured approaches to integrate UX practices. This study contributes theoretically by broadening the literature on integrating accessibility through UX practices within agile processes, providing insights into the challenges and strategies of B2B environments, and practically by introducing a prototype ecosystem to help product teams embed UX practices into their workflow for enhancing accessibility.

Robotic assistance is applied in orthopedic interventions for pedicle screw placement (PSP). While current robots do not act autonomously, they are expected to have higher autonomy under surgeon supervision in the mid-term. Augmented reality (AR) is promising to support this supervision and to enable human–robot interaction (HRI). To outline a futuristic scenario for robotic PSP, the current workflow was analyzed through literature review and expert discussion. Based on this, a hypothetical workflow of the intervention was developed, which additionally contains the analysis of the necessary information exchange between human and robot. A video see-through AR prototype was designed and implemented. A robotic arm with an orthopedic drill mock-up simulated the robotic assistance. The AR prototype included a user interface to enable HRI. The interface provides data to facilitate understanding of the robot’s ”intentions”, e.g., patient-specific CT images, the current workflow phase, or the next planned robot motion. Two-dimensional and three-dimensional visualization illustrated patient-specific medical data and the drilling process. The findings of this work contribute a valuable approach in terms of addressing future clinical needs and highlighting the importance of AR support for HRI.

Yes / In the last 10 years the University of Bradford Library Acquisitions Department has shrunk from 13 members of staff to 5. This has led to us embracing new technology to help streamline workflows within the department. As well as utilising EDI functionality, changing processing workflows and using shelf-ready books, we have devised innovative ways of dealing with everyday tasks. Topics included cover: PDA deduplication; spine labelling on a large scale; the weeding of discarded books; using saved global updates on incoming MARC records and using load profiles innovatively.

Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.

Businesses do evolve. Their evolution necessitates the re-engineering of their existing "business processes”, with the objectives of reducing costs, delivering services on time, and enhancing their profitability in a competitive market. This is generally true and particularly in domains such as manufacturing, pharmaceuticals and education). The central objective of workflow technologies is to separate business policies (which normally are encoded in business logics) from the underlying business applications. Such a separation is desirable as it improves the evolution of business processes and, more often than not, facilitates the re-engineering at the organisation level without the need to detail knowledge or analyses of the application themselves. Workflow systems are currently used by many organisations with a wide range of interests and specialisations in many domains. These include, but not limited to, office automation, finance and banking sector, health-care, art, telecommunications, manufacturing and education. We take the view that a workflow is a set of "activities”, each performs a piece of functionality within a given "context” and may be constrained by some security requirements. These activities are coordinated to collectively achieve a required business objective. The specification of such coordination is presented as a set of "execution constraints” which include parallelisation (concurrency/distribution), serialisation, restriction, alternation, compensation and so on. Activities within workflows could be carried out by humans, various software based application programs, or processing entities according to the organisational rules, such as meeting deadlines or performance improvement. Workflow execution can involve a large number of different participants, services and devices which may cross the boundaries of various organisations and accessing variety of data. This raises the importance of _ context variations and context-awareness and _ security (e.g. access control and privacy). The specification of precise rules, which prevent unauthorised participants from executing sensitive tasks and also to prevent tasks from accessing unauthorised services or (commercially) sensitive information, are crucially important. For example, medical scenarios will require that: _ only authorised doctors are permitted to perform certain tasks, _ a patient medical records are not allowed to be accessed by anyone without the patient consent and _ that only specific machines are used to perform given tasks at a given time. If a workflow execution cannot guarantee these requirements, then the flow will be rejected. Furthermore, features/characteristics of security requirement are both temporal- and/or event-related. However, most of the existing models are of a static nature – for example, it is hard, if not impossible, to express security requirements which are: _ time-dependent (e.g. A customer is allowed to be overdrawn by 100 pounds only up-to the first week of every month. _ event-dependent (e.g. A bank account can only be manipulated by its owner unless there is a change in the law or after six months of his/her death). Currently, there is no commonly accepted model for secure and context-aware workflows or even a common agreement on which features a workflow security model should support. We have developed a novel approach to design, analyse and validate workflows. The approach has the following components: = A modelling/design language (known as CS-Flow). The language has the following features: – support concurrency; – context and context awareness are first-class citizens; – supports mobility as activities can move from one context to another; – has the ability to express timing constrains: delay, deadlines, priority and schedulability; – allows the expressibility of security policies (e.g. access control and privacy) without the need for extra linguistic complexities; and – enjoy sound formal semantics that allows us to animate designs and compare various designs. = An approach known as communication-closed layer is developed, that allows us to serialise a highly distributed workflow to produce a semantically equivalent quasi-sequential flow which is easier to understand and analyse. Such re-structuring, gives us a mechanism to design fault-tolerant workflows as layers are atomic activities and various existing forward and backward error recovery techniques can be deployed. = Provide a reduction semantics to CS-Flow that allows us to build a tool support to animate a specifications and designs. This has been evaluated on a Health care scenario, namely the Context Aware Ward (CAW) system. Health care provides huge amounts of business workflows, which will benefit from workflow adaptation and support through pervasive computing systems. The evaluation takes two complementary strands: – provide CS-Flow’s models and specifications and – formal verification of time-critical component of a workflow.