1,386 research outputs found
Posthuman Creative Styling can a creative writer’s style of writing be described as procedural?
This thesis is about creative styling — the styling a creative writer might use to make their writing
unique. It addresses the question as to whether such styling can be described as procedural. Creative
styling is part of the technique a creative writer uses when writing. It is how they make the text more
‘lively’ by use of tips and tricks they have either learned or discovered. In essence these are rules, ones
the writer accrues over time by their practice. The thesis argues that the use and invention of these
rules can be set as procedures. and so describe creative styling as procedural.
The thesis follows from questioning why it is that machines or algorithms have, so far, been
incapable of producing creative writing which has value. Machine-written novels do not abound on
the bookshelves and writing styled by computers is, on the whole, dull in comparison to human-crafted
literature. It came about by thinking how it would be possible to reach a point where writing by people
and procedural writing are considered to have equal value. For this reason the thesis is set in a
posthuman context, where the differences between machines and people are erased.
The thesis uses practice to inform an original conceptual space model, based on quality dimensions
and dynamic-inter operation of spaces. This model gives an example of the procedures which a
posthuman creative writer uses when engaged in creative styling. It suggests an original formulation
for the conceptual blending of conceptual spaces, based on the casting of qualities from one space to
another. In support of and informing its arguments are ninety-nine examples of creative writing
practice which show the procedures by which style has been applied, created and assessed. It provides
a route forward for further joint research into both computational and human-coded creative writing
Impacts of coffee fragmented landscapes on biodiversity and microclimate with emerging monitoring technologies
Habitat fragmentation and loss are causing biodiversity declines across the globe. As biodiversity is unevenly distributed, with many hotspots located in the tropics, conserving and protecting these areas is important to preserve as many species as possible. Chapter 2 presents an overview of the Ecology of the Atlantic Forest, a highly fragmented biodiversity hotspot. A major driver of habitat fragmentation is agriculture, and in the tropics coffee is major cash crop. Developing methods to monitor biodiversity effectively without labour intensive surveys can help us understand how communities are using fragmented landscapes and better inform management practices that promote biodiversity. Acoustic monitoring offers a promising set of tools to remotely monitor biodiversity. Developments in machine learning offer automatic species detection and classification in certain taxa. Chapters 3 and 4 use acoustic monitoring surveys conducted on fragmented landscapes in the Atlantic Forest to quantify bird and bat communities in forest and coffee matrix, respectively. Chapter 3 shows that acoustic composition can reflect local avian communities. Chapter 4 applies a convolutional neural network (CNN) optimised on UK bat calls to a Brazilian bat dataset to estimate bat diversity and show how bats preferentially use coffee habitats. In addition to monitoring biodiversity, monitoring microclimate forms a key part of climate smart agriculture for climate change mitigation. Coffee agriculture is limited to the tropics, overlapping with biodiverse regions, but is threatened by climate change. This presents a challenge to countries strongly reliant on coffee exports such as Brazil and Nicaragua. Chapter 5 uses data from microclimate weather stations in Nicaragua to demonstrate that sun-coffee management is vulnerable to supraoptimal temperature exposure regardless of local forest cover or elevation.Open Acces
Experiences of living and dying with Lewy body dementia:A longitudinal narrative study
Background Lewy body dementia is a life-limiting condition with multiple, complex symptoms. As the condition progresses much of the caring and nursing duties fall to families. However, little is known about how people with Lewy body dementia and their families are affected by the condition. The overall aim of this thesis was to explore the experiences of people living with Lewy body dementia, and their family carers over time. An integrative systematic review of the literature was conducted. A convergent integrated design was applied to facilitate the synthesis of published research exploring the experiences of people living with Lewy body dementia and family carers. There was scarce qualitative evidence identified on the lived experience, with a predominant biomedical focus and cross-sectional designs. Methodology In order to gain unique insights into people’s experiences of living with Lewy body dementia a narrative methodology was chosen. A social constructionist approach influenced the research conducted, drawing from the psycho-social discipline and experience-centred narrative theory. The underpinning perspective was that knowledge and reality are socially produced, and humans’ understanding, and interpretations of their world occurs through stories. Method A longitudinal narrative study using three sequential interviews and life story work was completed to gain unique insights into five couples’ experiences of living with Lewy body dementia. Participants were recruited from memory clinics and the ‘Join Dementia Research’ database within the east of England. Narrative data were collected using dyadic narrative interviews with each couple over a six-month period (August 2019 – Februrary 2020). The analysis of the stories was conducted using Murray’s levels of narrative analysis in health psychology. Murray’s anaytical framework enabled stories to be analysed at the personal, interpersonal, positional, and societal level. Findings The main finding from this study was that the overarching narrative of ‘social connectedness’ was found to be important, and this continued over time. In this study, social connectedness represents a stepwise description of how a person is actively involved with others and their surroundings, leading to a sense of comfort and wellbeing. Seven stages of social connectedness were identified: maintaining social connections, developing new connections together, social disconnection, support from adult children, marital disconnection, connecting to health and social care, and emotionally separated but living together. Lack of social connectedness leads to social disconnection. Repeated losses over time resulted in difficulty in maintaining social connections giving rise to a reduced sense of agency. Loss of continence, energy, and independence, together with difficulty managing medications, significantly impacted on couples’ quality of life and ability to remain connected through all stages. Conclusion Maintaining a social life and support network was important for both people living with Lewy body dementia and family carers. The findings contribute to the methodological literature that gives voice to those living with dementia over time. They highlight how physical and personality changes, communication challenges, and behavioural difficulties, undermine established social connections. The stepwise diagrammatic representation of social connectedness provides guidance for more targeted healthcare interventions and management of Lewy body dementia
On the motion planning & control of nonlinear robotic systems
In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance
Application of knowledge management principles to support maintenance strategies in healthcare organisations
Healthcare is a vital service that touches people's lives on a daily basis by providing treatment and
resolving patients' health problems through the staff. Human lives are ultimately dependent on the skilled
hands of the staff and those who manage the infrastructure that supports the daily operations of the
service, making it a compelling reason for a dedicated research study. However, the UK healthcare sector
is undergoing rapid changes, driven by rising costs, technological advancements, changing patient
expectations, and increasing pressure to deliver sustainable healthcare. With the global rise in healthcare
challenges, the need for sustainable healthcare delivery has become imperative. Sustainable healthcare
delivery requires the integration of various practices that enhance the efficiency and effectiveness of
healthcare infrastructural assets. One critical area that requires attention is the management of
healthcare facilities.
Healthcare facilitiesis considered one of the core elements in the delivery of effective healthcare services,
as shortcomings in the provision of facilities management (FM) services in hospitals may have much more
drastic negative effects than in any other general forms of buildings. An essential element in healthcare
FM is linked to the relationship between action and knowledge. With a full sense of understanding of
infrastructural assets, it is possible to improve, manage and make buildings suitable to the needs of users
and to ensure the functionality of the structure and processes.
The premise of FM is that an organisation's effectiveness and efficiency are linked to the physical
environment in which it operates and that improving the environment can result in direct benefits in
operational performance. The goal of healthcare FM is to support the achievement of organisational
mission and goals by designing and managing space and infrastructural assets in the best combination of
suitability, efficiency, and cost. In operational terms, performance refers to how well a building
contributes to fulfilling its intended functions.
Therefore, comprehensive deployment of efficient FM approaches is essential for ensuring quality
healthcare provision while positively impacting overall patient experiences. In this regard, incorporating
knowledge management (KM) principles into hospitals' FM processes contributes significantly to ensuring
sustainable healthcare provision and enhancement of patient experiences. Organisations implementing
KM principles are better positioned to navigate the constantly evolving business ecosystem easily.
Furthermore, KM is vital in processes and service improvement, strategic decision-making, and
organisational adaptation and renewal.
In this regard, KM principles can be applied to improve hospital FM, thereby ensuring sustainable
healthcare delivery. Knowledge management assumes that organisations that manage their
organisational and individual knowledge more effectively will be able to cope more successfully with the challenges of the new business ecosystem. There is also the argument that KM plays a crucial role in
improving processes and services, strategic decision-making, and adapting and renewing an organisation.
The goal of KM is to aid action – providing "a knowledge pull" rather than the information overload most
people experience in healthcare FM. Other motivations for seeking better KM in healthcare FM include
patient safety, evidence-based care, and cost efficiency as the dominant drivers. The most evidence exists
for the success of such approaches at knowledge bottlenecks, such as infection prevention and control,
working safely, compliances, automated systems and reminders, and recall based on best practices. The
ability to cultivate, nurture and maximise knowledge at multiple levels and in multiple contexts is one of
the most significant challenges for those responsible for KM. However, despite the potential benefits,
applying KM principles in hospital facilities is still limited. There is a lack of understanding of how KM can
be effectively applied in this context, and few studies have explored the potential challenges and
opportunities associated with implementing KM principles in hospitals facilities for sustainable healthcare
delivery.
This study explores applying KM principles to support maintenance strategies in healthcare organisations.
The study also explores the challenges and opportunities, for healthcare organisations and FM
practitioners, in operationalising a framework which draws the interconnectedness between healthcare.
The study begins by defining healthcare FM and its importance in the healthcare industry. It then discusses
the concept of KM and the different types of knowledge that are relevant in the healthcare FM sector.
The study also examines the challenges that healthcare FM face in managing knowledge and how the
application of KM principles can help to overcome these challenges. The study then explores the different
KM strategies that can be applied in healthcare FM. The KM benefits include improved patient outcomes,
reduced costs, increased efficiency, and enhanced collaboration among healthcare professionals.
Additionally, issues like creating a culture of innovation, technology, and benchmarking are considered.
In addition, a framework that integrates the essential concepts of KM in healthcare FM will be presented
and discussed.
The field of KM is introduced as a complex adaptive system with numerous possibilities and challenges.
In this context, and in consideration of healthcare FM, five objectives have been formulated to achieve
the research aim. As part of the research, a number of objectives will be evaluated, including appraising
the concept of KM and how knowledge is created, stored, transferred, and utilised in healthcare FM,
evaluating the impact of organisational structure on job satisfaction as well as exploring how cultural
differences impact knowledge sharing and performance in healthcare FM organisations.
This study uses a combination of qualitative methods, such as meetings, observations, document analysis
(internal and external), and semi-structured interviews, to discover the subjective experiences of
healthcare FM employees and to understand the phenomenon within a real-world context and attitudes of healthcare FM as the data collection method, using open questions to allow probing where appropriate
and facilitating KM development in the delivery and practice of healthcare FM.
The study describes the research methodology using the theoretical concept of the "research onion". The
qualitative research was conducted in the NHS acute and non-acute hospitals in Northwest England.
Findings from the research study revealed that while the concept of KM has grown significantly in recent
years, KM in healthcare FM has received little or no attention. The target population was fifty (five FM
directors, five academics, five industry experts, ten managers, ten supervisors, five team leaders and ten
operatives). These seven groups were purposively selected as the target population because they play a
crucial role in KM enhancement in healthcare FM. Face-to-face interviews were conducted with all
participants based on their pre-determined availability. Out of the 50-target population, only 25 were
successfully interviewed to the point of saturation. Data collected from the interview were coded and
analysed using NVivo to identify themes and patterns related to KM in healthcare FM.
The study is divided into eight major sections. First, it discusses literature findings regarding healthcare
FM and KM, including underlying trends in FM, KM in general, and KM in healthcare FM. Second, the
research establishes the study's methodology, introducing the five research objectives, questions and
hypothesis. The chapter introduces the literature on methodology elements, including philosophical views
and inquiry strategies. The interview and data analysis look at the feedback from the interviews. Lastly, a
conclusion and recommendation summarise the research objectives and suggest further research.
Overall, this study highlights the importance of KM in healthcare FM and provides insights for healthcare
FM directors, managers, supervisors, academia, researchers and operatives on effectively leveraging
knowledge to improve patient care and organisational effectiveness
Exponential integrators: tensor structured problems and applications
The solution of stiff systems of Ordinary Differential Equations (ODEs), that typically arise after spatial discretization of many important evolutionary Partial Differential Equations (PDEs), constitutes a topic of wide interest in numerical analysis. A prominent way to numerically integrate such systems involves using exponential integrators. In general, these kinds of schemes do not require the solution of (non)linear systems but rather the action of the matrix exponential and of some specific exponential-like functions (known in the literature as phi-functions). In this PhD thesis we aim at presenting efficient tensor-based tools to approximate such actions, both from a theoretical and from a practical point of view, when the problem has an underlying Kronecker sum structure. Moreover, we investigate the application of exponential integrators to compute numerical solutions of important equations in various fields, such as plasma physics, mean-field optimal control and computational chemistry. In any case, we provide several numerical examples and we perform extensive simulations, eventually exploiting modern hardware architectures such as multi-core Central Processing Units (CPUs) and Graphic Processing Units (GPUs). The results globally show the effectiveness and the superiority of the different approaches proposed
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Complaint Driven Training Data Debugging for Machine Learning Workflows
As the need for machine learning (ML) increases rapidly across all industry sectors, so has theinterest in building ML platforms that manage and automate parts of the ML life-cycle. This has enabled companies to use ML inference as a part of their downstream analytics or their applications. Unfortunately, debugging unexpected outcomes in the result of these ML workflows remains a necessary but difficult task of the ML life-cycle. The challenge of debugging ML workflows is that it requires reasoning about the correctness of the workflow logic, the datasets used for inference and training, the models, and interactions between them. Even if the workflow logic is correct, errors in the data used across the ML workflow can still lead to wrong outcomes. In short, developers are not just debugging the code, but also the data.
We advocate in favor of a complaint driven approach towards specifying and debugging data errors in ML workflows. The approach takes as input user specified complaints specified as constraints over the final or intermediate outputs of workflows that use trained ML models. The approach outputs explanations in the form of specific operator(s) or data subsets, and how they may be changed to address the constraint violations.
In this thesis we make the first steps towards our complaint driven approach to data debugging. As a stepping stone, we focus our attention on complaints specified on top of relational workflows that use ML model inference and whose errors are caused by errors in ML model’s training data. To the best of our knowledge, we contribute the first debugging system for this task, which we call Rain. In response to a user complaint, Rain ranks the ML model’s training examples based on their ability to address the user’s complaint if they were removed. Our experiments show that users can use Rain to debug training data errors by specifying complaints over aggregations of model predictions without having to specify the correct label for each individual prediction.
Unfortunately, Rain’s latency may be prohibitive for use in interactive applications like analytical dashboards or business intelligence tools where users are likely to observe errors and complain. To address Rain’s latency problem when scaling to large ML models and training sets, we propose Rain++. Rain++ pushes the majority of Rain’s computation offline ahead of user interaction, achieving orders of magnitude online latency improvements compared to Rain.
To go beyond Rain’s and Rain++’s approach that evaluates individual training example deletionsindependently we propose MetaRain, a framework for training classifiers that detect training data corruptions in response to user complaints. Thanks to the generality of MetaRain, users can adapt the classifiers chosen to the training corruptions and the complaints they seek to resolve. Our experiments indicate that making use of this ability results in improved debugging outcomes.
Last but not least, we study the problem of updating relational workflow results in response tochanges to the inference ML model used. This can be leveraged by current or future complaint driven debugging systems that repeatedly change the model and reevaluate the relational workflow. We propose FaDE, a compiler that generates efficient code for the workflow update problem by casting it as view maintenance under input tuple deletions. Our experiments indicate that the code generated by FaDE has orders of magnitude lower latency than existing view maintenance systems
Development of an Interpersonally Grounded Construction Management Curriculum Foundation Model
Purpose: Education is the driving force of higher education institution(s) (HEIs) globally and is critical for student employability and practitioner recruitment. Yet, against this backdrop, research in the field is limited and hitherto, investigations into construction management curriculum development are scant. This research presents a foundational design specification for construction management programme curriculum development and aims to engender wider polemic debate as well as stimulate new insight into current higher education employability preparation practice.
Methodology: The overarching epistemology adopts interpretivist, pragmatic and post-positivist philosophical stances to critically analyse extant literature, secondary data and primary data on the foundational skills/competencies of construction management education. Abductive reasoning forms the overarching basis of a new emergent curriculum model that maps interpersonal skills and highlights the critical foundational skills and competencies necessary.
Findings: Research findings illustrate that construction management curriculum development lacks a cohesive community of practice and curriculum agenda. Importantly, it appears that although the modern construction manager needs an appreciation of digital technologies under the umbrella of Industry 4.0, their interpersonal skills and competencies were observed to far outweigh and exceed these. Premised upon these findings, the curriculum foundation model developed delineates the four key interpersonal skills and competencies construction management programmes should utilise for developing their curriculum.
Originality: This novel research unearths the lack of a cohesive curriculum agenda within construction management education, highlights the importance of interpersonal skills and competencies within the role of construction manager and based upon this, presents a curriculum foundation model
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
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