917 research outputs found
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
A qualitative study exploring whether emotion work conducted by health visitors has an influence on their assessment and identification of children in need of care and protection?
There is an increased understanding that experiencing adversity in childhood can have a significantly negative impact on the long-term developmental wellbeing of children and young people, as well as their families and communities. Political and societal ambition is that such adverse experiences and their consequences are eradicated through preventative and early intervention measures taken by health, education, and social care practitioners on the identification of a child(ren) who requires support.
Professionals working with children have become increasingly proficient in this type of work however no professional is infallible. As a result, many children and young people living with adverse circumstances can go unnoticed. For some this includes experiencing harm which often only comes to light when they have been significantly or fatally injured.
Every child living in the United Kingdom is aligned with the universal health visiting service following birth to school entry. Health visitors play an essential role in âsearching for health needsâ through the âsurveillance and assessment of the populationâs health and wellbeingâ (Nursing & Midwifery Council [NMC] 2004, page 11) . Such universal contact based on these core principles mean that health visitors are ideally positioned to identify children living in challenging situations but, like others, they can find this difficult on occasions.
The purpose of this study is to explore whether health visitors view the emotion work they carry out as part of their role has an influence on their ability to assess, identify, and respond to children in need of care and protection.
STUDY â METHOD:
The study has been progressed qualitatively, using a reflexive ethnographic approach to interviews as the main data collection and analytic method with short periods of office-based observation. 16 health visitors who managed caseloads of between 100-450 pre-school children were observed and interviewed to understand their experiences, values, and beliefs. Geeâs (2014) toolkit was used to critically analyse the discourse shared during the interviews.
FINDINGS:
The emergent findings demonstrate that health visitors can be conceptualised as âapplied clinical anthropologistsâ in the way they develop relationships with families to gain access to their home environments. The approach taken is to gather information to the depth required for a social, bioecological assessment (Bronfenbrenner 2005) of a child in the context of their family and community system. Health visitors are welcomed by most families and are often successful in assessing and responding to child need. However, at times, the level of engagement necessary can be overwhelming for both the health visitor and parent/carer. This influences the level of child centred assessment obtained.
The study has demonstrated that the influences on the work of the health visitor can be interpreted through a complex interplay of theoretical concepts. Firstly, Bourdieuâs âtheory of practiceâ (Bourdieu & Wacquant 1992, page 4) provides the basis on which to understand why challenges and barriers arise during the relational work of the health visitor with the child and family. Secondly, Grossâ (2014) Emotion Regulation Framework and Hochschildâs (1983) theory of Emotional Labour, are utilised to consider how health visitors and families respond emotionally to these challenges. The study then goes on to demonstrate what impact these responses can have on the assessment of children.
RECOMMENDATIONS:
Implications for practice are that health visitors require increased rates of supervision. This should include an observational element. Educational programmes for health visitors, require a focus on promoting professional wellbeing with learning sessions on unconscious bias. Research and learning developments are suggested to influence assessment and decision-making practice. Research with other professional groups and children & families is recommended to build on the findings of this study in order to influence future safeguarding policy and practice to protect children
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
An optimal approach to the design of experiments for the automatic characterisation of biosystems
The Design-Build-Test-Learn cycle is the main approach of synthetic biology to re-design and create new biological parts and systems, targeting the solution for complex and challenging paramount problems. The applications of the novel designs range from biosensing and bioremediation of water pollutants (e.g. heavy metals) to drug discovery and delivery (e.g. cancer treatment) or biofuel production (e.g. butanol and ethanol), amongst others. Standardisation, predictability and automation are crucial elements for synthetic biology to efficiently attain these objectives. Mathematical modelling is a powerful tool that allows us to understand, predict, and control these systems, as shown in many other disciplines such as particle physics, chemical engineering, epidemiology and economics. Yet, the inherent difficulties of using mathematical models substantially slowed their adoption by the synthetic biology community.
Researchers might develop different competing model alternatives in absence of in-depth knowledge of a system, consequently being left with the burden of with having to find the best one. Models also come with unknown and difficult to measure parameters that need to be inferred from experimental data. Moreover, the varying informative content of different experiments hampers the solution of these model selection and parameter identification problems, adding to the scarcity and noisiness of laborious-to-obtain data. The difficulty to solve these non-linear optimisation problems limited the widespread use of advantageous mathematical models in synthetic biology, broadening the gap between computational and experimental scientists. In this work, I present the solutions to the problems of parameter identification, model selection and experimental design, validating them with in vivo data. First, I use Bayesian inference to estimate model parameters, relaxing the traditional noise assumptions associated with this problem. I also apply information-theoretic approaches to evaluate the amount of information extracted from experiments (entropy gain). Next, I define methodologies to quantify the informative content of tentative experiments planned for model selection (distance between predictions of competing models) and parameter inference (model prediction uncertainty). Then, I use the two methods to define efficient platforms for optimal experimental design and use a synthetic gene circuit (the genetic toggle switch) to substantiate the results, computationally and experimentally. I also expand strategies to optimally design experiments for parameter identification to update parameter information and input designs during the execution of these (on-line optimal experimental design) using microfluidics. Finally, I developed an open-source and easy-to-use Julia package, BOMBs.jl, automating all the above functionalities to facilitate their dissemination and use amongst the synthetic biology community
Machine learning in portfolio management
Financial markets are difficult learning environments. The data generation process is time-varying,
returns exhibit heavy tails and signal-to-noise ratio tends to be low. These contribute to the challenge
of applying sophisticated, high capacity learning models in financial markets. Driven by recent
advances of deep learning in other fields, we focus on applying deep learning in a portfolio
management context. This thesis contains three distinct but related contributions to literature. First,
we consider the problem of neural network training in a time-varying context. This results in a neural
network that can adapt to a data generation process that changes over time. Second, we consider
the problem of learning in noisy environments. We propose to regularise the neural network using a
supervised autoencoder and show that this improves the generalisation performance of the neural
network. Third, we consider the problem of quantifying forecast uncertainty in time-series with
volatility clustering. We propose a unified framework for the quantification of forecast uncertainty that results in uncertainty estimates that closely match actual realised forecast errors in cryptocurrencies
and U.S. stocks
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21â22 September 2023
Drawing Exact Samples: Rejection Sampling, Density Fusion and Constrained Disaggregation
Sampling is an important topic in the area of computational statistics. Being able to draw samples from a designated distribution allows one to numerically compute various statistics without the need to solve for solutions analytically.
A popular branch of the sampling method generates samples by evolving a stationary Markov chain that admits the target distribution as its stationary distribution. The problem, however, is that one does not have a universal criterion to assess whether the chain is stationary.
On the other hand, exact simulation methods, being the focus of this thesis, always produce samples that precisely follow the target distribution. We first begin with the path-space rejection sampling for the exact simulation of diffusion bridges and show how this rejection scheme can be further set up into an exact simulation method for sampling product densities. We provide guidance on how to tune the algorithm parameters in order to attain a near-optimal performance and introduce the construction of an importance sampler/particle filter based on the same theoretical result for better efficiency. Finally, we show a variant of the sampler that deals with linear constraints which render most of the target distributions intractable.
Two application studies are conducted in the end to demonstrate the effectiveness of the algorithm
A scalable formulation of joint modelling for longitudinal and time to event data and its application on large electronic health record data of diabetes complications
INTRODUCTION:
Clinical decision-making in the management of diabetes and other chronic diseases depends upon individualised risk predictions of progression of the disease or complica- tions of disease. With sequential measurements of biomarkers, it should be possible to make dynamic predictions that are updated as new data arrive. Since the 1990s, methods have been developed to jointly model longitudinal measurements of biomarkers and time-to-event data, aiming to facilitate predictions in various fields.
These methods offer a comprehensive approach to analyse both the longitudinal changes in biomarkers, and the occurrence of events, allowing for a more integrated understanding of the underlying processes and improved predictive capabilities. The aim of this thesis is to investigate whether established methods for joint modelling are able to scale to large-scale electronic health record datasets with multiple biomarkers measured asynchronously, and evaluates the performance of a novel approach that overcomes the limitations of existing methods.
METHODS:
The epidemiological study design utilised in this research is a retrospective observa- tional study. The data used for these analyses were obtained from a registry encompassing all individuals with type 1 diabetes in Scotland, which is delivered by the Scottish Care Information - Diabetes Collaboration platform. The two outcomes studied were time to cardiovascular disease (CVD) and time to end-stage renal disease (ESRD) from T1D diag- nosis. The longitudinal biomarkers examined in the study were glycosylated haemoglobin (HbA1c) and estimated glomerular filtration rate (eGFR). These biomarkers and endpoints were selected based on their prevalence in the T1D population and the established association between these biomarkers and the outcomes.
As a state-of-the-art method for joint modelling, Brillemanâs stan_jm() function was evaluated. This is an implementation of a shared parameter joint model for longitudinal and time-to- event data in Stan contributed to the rstanarm package. This was compared with a novel approach based on sequential Bayesian updating of a continuous-time state-space model for the biomarkers, with predictions generated by a Kalman filter algorithm using the ctsem package fed into a Poisson time-splitting regression model for the events. In contrast to the standard joint modelling approach that can only fit a linear mixed model to the biomarkers, the ctsem package is able to fit a broader family of models that include terms for autoregressive drift and diffusion. As a baseline for comparison, a last-observation-carried-forward model was evaluated to predict time-to-event.
RESULTS:
The analyses were conducted using renal replacement therapy outcome data regarding 29764 individuals and cardiovascular disease outcome data on 29479 individuals in Scotland (as per the 2019 national registry extract). The CVD dataset was reduced to 24779 individuals with both HbA1c and eGFR data measured on the same date; a limitation of the modelling function itself. The datasets include 799 events of renal replacement therapy (RRT) or death due to renal failure (6.71 years average follow-up) and 2274 CVD events (7.54 years average follow-up) respectively. The standard approach to joint modelling using quadrature to integrate over the trajectories of the latent biomarker states, implemented in rstanarm, was found to be too slow to use even with moderate-sized datasets, e.g. 17.5 hours for a subset
of 2633 subjects, 35.9 hours for 5265 subjects, and more than 68 hours for 10532 subjects. The sequential Bayesian updating approach was much faster, as it was able to analyse a dataset of 29121 individuals over 225598.3 person-years in 19 hours. Comparison of the fit of different longitudinal biomarker submodels showed that the fit of models that also included a drift and diffusion term was much better (AIC 51139 deviance units lower) than models that included only a linear mixed model slope term. Despite this, the improvement in predictive performance was slight for CVD (C-statistic 0.680 to 0.696 for 2112 individuals) and only moderate for end-stage renal disease (C-statistic 0.88 to 0.91 for 2000 individuals) by adding terms for diffusion and drift. The predictive performance of joint modelling in these datasets was only slightly better than using last-observation-carried-forward in the Poisson regression model (C-statistic 0.819 over 8625 person-years).
CONCLUSIONS:
I have demonstrated that unlike the standard approach to joint modelling, implemented in rstanarm, the time-splitting joint modelling approach based on sequential Bayesian updating can scale to a large dataset and allows biomarker trajectories to be modelled with a wider family of models that have better fit than simple linear mixed models. However, in this application, where the only biomarkers were HbA1c and eGFR, and the outcomes were time-to-CVD and end-stage renal disease, the increment in the predictive performance of joint modelling compared with last-observation-carried forward was slight. For other outcomes, where the ability to predict time-to-event depends upon modelling latent biomarker trajectories rather than just using the last-observation-carried-forward, the advantages of joint modelling may be greater.
This thesis proceeds as follows. The first two chapters serve as an introduction to the joint modelling of longitudinal and time-to-event data and its relation to other methods for clinical risk prediction. Briefly, this part explores the rationale for utilising such an approach to manage chronic diseases, such as T1D, better. The methodological chapters of this thesis describe the mathematical formulation of a multivariate shared-parameter joint model and introduce its application and performance on a subset of individuals with T1D and data pertaining to CVD and ESRD outcomes.
Additionally, the mathematical formulation of an alternative time-splitting approach is demonstrated and compared to a conventional method for estimating longitudinal trajectories of clinical biomarkers used in risk prediction. Also, the key features of the pipeline required to implement this approach are outlined. The final chapters of the thesis present an applied example that demonstrates the estimation and evaluation of the alternative modelling approach and explores the types of inferences that can be obtained for a subset of individuals with T1D that might progress to ESRD. Finally, this thesis highlights the strengths and weaknesses of applying and scaling up more complex modelling approaches to facilitate dynamic risk prediction for precision medicine
Impact of Social Media Marketing on Consumer Purchase Action: A Case Study of SME consumers in Bangladesh
This study examines the influence of Impact of Social Media Marketing on Consumer Purchase Action. A Case Study of SME consumers in Bangladesh. A survey questionnaire was used to conduct the study on Bangladeshi SME customers who utilise online social media sites. The purpose of the research was to examine the significance of social media marketing in Bangladesh and how it influences customer purchase action. In the study's first phase, relevant literature research was conducted to thoroughly comprehend social media marketing techniques. After analysing previous research in the subject matter, the conceptual framework of the study was developed. This framework studied both the causes and consequences of social media marketing on consumer engagement. In the second phase, a cross-sectional quantitative data survey was constructed to evaluate the research framework and hypotheses.
In a pilot test, 16 valid survey questionnaires were distributed to determine how social media is embedded in various situations and locations. The assumptions of the extended model were then verified with 329 valid surveys. The data were analysed through structural equation modelling (SEM). The research found a positive correlation between customer engagement and the social media marketing efforts of SMEs. Moreover, the statistically significant mediating influences of trust, perceived value, and social media antecedents on this connection were discovered.
Furthermore, there was a substantial correlation between customer engagement and client acquisition, indicating that SMEs in Bangladesh might strengthen their customer interactions by using a social media marketing approach. This study also examines how social media marketing influences consumer behaviour, customer engagement, and consumer purchase action in Bangladesh's small and medium-sized enterprises. This study could help Bangladesh's SMEs interact with consumers on social media platforms and establish the framework for future research on the moderating influence of online consumer behaviour
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