Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

The application of process mining to care pathway analysis in the NHS

Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

2010 - 2011 University Catalog

Volume 100, Number 1, July 2010 Published once a year, July 2010https://scholarsrepository.llu.edu/univcatalog/1006/thumbnail.jp

Scientific Advances in STEM

Following a previous topic (Scientific advances in STEM: from professors to students; https://www.mdpi.com/topics/advances_stem), this new topic aims to highlight the importance of establishing collaborations among research groups from different disciplines, combining the scientific knowledge from basic to applied research as well as taking advantage of different research facilities. Fundamental science helps us to understand phenomenological basics, while applied science focuses on products and technology developments, highlighting the need to perform a transference of knowledge to society and the industrial sector

Evolution of the human oral microbiome and resource development for ancient metagenomics

The microbes that live in and on our bodies play major roles in health and disease due to their symbiotic relationship with the host. Understanding how these communities adapt to changes in their environment - either by natural or anthropological forces - is currently a critical area of research for improving holistic healthcare. The aim of this thesis was to demonstrate the potential of large-scale shotgun-sequenced ancient dental calculus to study the wider diversity of the oral microbiome. In Manuscript A, I have shown that ancient dental calculus can be used to improve the understanding of past human oral microbiome diversity, after analysing the largest and oldest ancient dental calculus dataset to date. In this manuscript I also present new tools to help improve authentication of ancient microbiomes. Manuscript B describes the repository AncientMetagenomeDir, a community-level resource that lists all public ancient metagenomic sequencing datasets. The resource will allow researchers to efficiently re-use public data to ensure the robusticity and improve the statistical power of future studies. Manuscript C presents an entirely rewritten user-friendly palaeogenomics pipeline following latest software development and bioinformatics best practices. The pipeline nf-core/eager, has been developed in a way that allows for easy integration with large scale computing infrastructure required for such analyses. Importantly, I have extended this genomics pipeline to have in-parallel metagenomic profiling and screening of ancient DNA characteristics. These manuscripts have contributed new insights into the biology and evolution of oral biofilms, but also introduced new open-source and sustainable tools and resources that will allow further investigation of ancient microbiomes