1,054 research outputs found
A Theistic Critique of Secular Moral Nonnaturalism
This dissertation is an exercise in Theistic moral apologetics. It will be developing both a critique of secular nonnaturalist moral theory (moral Platonism) at the level of metaethics, as well as a positive form of the moral argument for the existence of God that follows from this critique. The critique will focus on the work of five prominent metaethical theorists of secular moral non-naturalism: David Enoch, Eric Wielenberg, Russ Shafer-Landau, Michael Huemer, and Christopher Kulp. Each of these thinkers will be critically examined. Following this critique, the positive moral argument for the existence of God will be developed, combining a cumulative, abductive argument that follows from filling in the content of a succinct apagogic argument. The cumulative abductive argument and the apagogic argument together, with a transcendental and modal component, will be presented to make the case that Theism is the best explanation for the kind of moral, rational beings we are and the kind of universe in which we live, a rational intelligible universe
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between âdrug likeâ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study
Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the methodâs value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits
IMAGINING, GUIDING, PLAYING INTIMACY: - A Theory of Character Intimacy Games -
Within the landscape of Japanese media production, and video game production in particular, there is a niche comprising video games centered around establishing, developing, and fulfilling imagined intimate relationships with anime-manga characters. Such niche, although very significant in production volume and lifespan, is left unexplored or underexplored. When it is not, it is subsumed within the scope of wider anime-manga media. This obscures the nature of such video games, alternatively identified with descriptors including but not limited to âvisual novelâ, âdating simulatorâ and âadult computer gameâ.
As games centered around developing intimacy with characters, they present specific ensembles of narrative content, aesthetics and software mechanics. These ensembles are aimed at eliciting in users what are, by all intents and purposes, parasocial phenomena towards the gameâs characters. In other words, these software products encourage players to develop affective and bodily responses towards characters. They are set in a way that is coherent with shared, circulating scripts for sexual and intimate interaction to guide player imaginative action. This study defines games such as the above as âcharacter intimacy gamesâ, video game software where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters. To do so, however, player must recognize themselves as playing that type of game, and to be looking to develop that kind of response towards the gameâs characters. Character Intimacy Games are contingent upon player developing affective and bodily responses, and thus presume that players are, at the very least, non-hostile towards their development. This study approaches Japanese character intimacy games as its corpus, and operates at the intersection of studies of communication, AMO studies and games studies.
The study articulates a research approach based on the double need of approaching single works of significance amidst a general scarcity of scholarly background on the subject. It juxtaposes data-driven approaches derived from fan-curated databases â The Visual Novel Database and Erogescape -ErogÄ HyĆron KĆ«kan â with a purpose-created ludo-hermeneutic process. By deploying an observation of character intimacy games through fan-curated data and building ludo-hermeneutics on the resulting ontology, this study argues that character intimacy games are video games where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters and recognizing themselves as doing so. To produce such conditions, the assemblage of software mechanics and narrative content in such games facilitates intimacy between player and characters. This is, ultimately, conductive to the emergence of parasocial phenomena. Parasocial phenomena, in turn, are deployed as an integral assumption regarding player activity within the gameâs wider assemblage of narrative content and software mechanics
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstÞtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er Ä designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede lÞsningen er fokusert pÄ forbedring av fysisk aktivitet. Prototypen bruker bÊrbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for Ä utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen pÄ teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Designing adaptivity in educational games to improve learning
The study of pedagogy has shown that students have different ways of learning and processing information. Students in a classroom learn best when being taught by a teacher who is able to adapt and/or change the pedagogical model being used, to better suit said students and/or the subject being taught. When considering other teaching mediums such as computer-assisted learning systems or educational video games, research also identified the benefits of adapting educational features to better teach players. However, effective methods for adaptation in educational video games are less well researched.This study addresses four points regarding adaptivity within educational games. Firstly, a framework for making any game adaptive was extracted from the literature. Secondly, an algorithm capable of monitoring, modelling and executing adaptations was developed and explained using the framework. Thirdly, the algorithm's effect on learning gains in players was evaluated using a customised version of Minecraft as the educational game and topics from critical thinking as the educational content. Lastly, a methodology explaining the process of utilising the algorithm with any educational game and the evaluation of said methodology were detailed
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospitalâs new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Advances in automatic terminology processing: methodology and applications in focus
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The information and knowledge era, in which we are living, creates challenges in many fields, and terminology is not an exception. The challenges include an exponential growth in the number of specialised documents that are available, in which terms are presented, and the number of newly introduced concepts and terms, which are already beyond our (manual) capacity. A promising solution to this âinformation overloadâ would be to employ automatic or semi-automatic procedures to enable individuals and/or small groups to efficiently build high quality terminologies from their own resources which closely reflect their individual objectives and viewpoints. Automatic terminology processing (ATP) techniques have already proved to be quite reliable, and can save human time in terminology processing. However, they are not without weaknesses, one of which is that these techniques often consider terms to be independent lexical units satisfying some criteria, when terms are, in fact, integral parts of a coherent system (a terminology). This observation is supported by the discussion of the notion of terms and terminology and the review of existing approaches in ATP presented in this thesis. In order to overcome the aforementioned weakness, we propose a novel methodology in ATP which is able to extract a terminology as a whole. The proposed methodology is based on knowledge patterns automatically extracted from glossaries, which we considered to be valuable, but overlooked resources. These automatically identified knowledge patterns are used to extract terms, their relations and descriptions from corpora. The extracted information can facilitate the construction of a terminology as a coherent system. The study also aims to discuss applications of ATP, and describes an experiment in which ATP is integrated into a new NLP application: multiplechoice test item generation. The successful integration of the system shows that ATP is a viable technology, and should be exploited more by other NLP applications
Machine learning NLP-based recommendation system on production issues
The techniques related to Natural Language Processing (NLP) as information extraction are increasingly popular in media, E-commerce, and online games. However, the application with such techniques is yet to be established for production quality control in the manufacturing industry.
The goal of this research is to build a recommendation system based on production issue descriptions in a textual format. The data was extracted from a manufacturing control system where it has been collected in Finnish on a relatively good scale for years. Five different NLP methods (TF-IDF, Word2Vec, spaCy, Sentence Transformers and SBERT) are used for modelling, converting hu-man digital written texts into numerical feature vectors. The most relevant issue cases could be retrieved by calculating the cosine distance between the query sentence vector and corpus embed matrix which represents the whole dataset. Turku NLP-based Sentence Transformer achieves the best result with Mean Average Precision @10 equal to 0.67, inferring that the initial dataset is large enough using deep learning algorithms competing with machine learning methods. Even though a categorical variable were chosen as a target variable to compute evaluation metrics, this research is not a classification problem with single variable for model training. Additionally, the metric selected for performance evaluation measures for every issue case. Therefore, it is not necessary to balance and split the dataset.
This research work achieves a relatively good result with less data available compared to the size of data used for other businesses. The recommendation system can be optimized by feeding more data and implementing online testing. It also has the possibility to transform into collaborative filtering to find patterns of users instead of simply focusing on items, in the condition of comprehensive user information included
The Impact of Attribution Theory on Information Technology Professionalsâ Perceptions of Glass Ceilings
As of 2021, women comprised almost half of the United States workforce, nearly 47%. Despite this, women represent only 24% of top earning officers and only 6% of chief executive officer positions. Glass ceilings are a phenomenon that represent an invisible barrier that prevents professional advancement for minority populations, including women, in business. Glass ceilings can impact several minority groups, but mostly appear to be a distinctive gender phenomenon. The challenges for women are well documented, but less understood are the attributional causes of glass ceilings as perceived by information technology (IT) professionals. The framework for this dissertation is the attribution theory, which explains how glass-ceiling viewpoints are formed by gender using mental and cognitive observations. The Career Pathways Survey (CPS), designed to examine employeesâ views on the causes of glass ceilings, was the measurement tool used. The first purpose of this dissertation was to analyze the differences of gender perceptions about which CPS subscale was most strongly associated with glass-ceiling beliefs for professionals in the IT sector. A secondary purpose was to understand the associations between the CPS subscales and the demographic variables in the study. Discriminant analysis findings support external attributional views for both men and women in the IT industry. The findings also show a positive relationship between CPS subscales and the demographic variables examined. The findings from this dissertation should encourage U.S. corporations to increase their investment in the development and advancement of female IT professionals, strengthening corporate cultures and promoting inclusion and diversity in leadership roles
- âŠ