19,328 research outputs found

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

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    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Biocontrol as a key component to manage brown rot on cherry

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    Brown rot, caused by Monilinia spp., is one of the most important diseases in stone fruits worldwide. Brown rot can cause blossom wilts and fruit rots in the orchard as well as latent infections of fruit, leading to post-harvest fruit decaying. Current control methods rely on scheduled spraying of fungicides. However, the continuing pressure to reduce fungicide use has seen an increase in research into alternative management methods, such as biological control. NIAB EMR recently identified two microbes that significantly reduced sporulation of Monilinia laxa under laboratory conditions. These two isolates were a bacterial species Bacillus subtilis (B91) and yeast-like fungus Aureobasidium pullulans (Y126) and are currently being formulated into commercial products. We are investigating how to optimise the use of these two potential biocontrol products in practice, in terms of suppressing Monilinia sporulation on overwintered mummies and preventing infection of blossoms and fruits. When applied to mummified fruits in winter Y126’s population was stable through the winter but at a low concentration. The B91 survived a little longer with the population reaching that of the control group by week 4. Neither Biological control (BCA) treatments had an affected the population of M. laxa when compared to the control treatment of sterile distilled water. The interaction time between the BCAs and M. laxa showed the longer the interaction time the lower the spore count of M. laxa. Another study was performed looking into the ability of our BCAs to colonise and survive on blossoms. B91 did not survive well on blossoms but could survive on fruits. However, its antagonistic compounds need to be in relatively high concentration to be effective against M. laxa. Therefore, it is best used as a fungicide, ensuring the antagonistic compounds are at a high concentration when applied in the field. Y126 can persist throughout the season and was marginally, though not statistically significantly, more effective at long term reduction in M. laxa. This could be because Y126 works through competition, therefore the interaction time with the pathogen could be important for efficacy and something worth investigating further. The difference between the BCAs highlights the need to understand each BCA’s ecology to ensure maximum efficacy. In a latent infection experiment, we inoculated trees with M. laxa and then treated them with the two biocontrol isolates two weeks before harvest. Post-harvest disease development was assessed after four days of storage in 2019 and two weeks in 2020. There was a significant reduction in rot incidence (p < 0.001) of 29% (Y126) and 27% (B91) in 2019 and 62 % (Y126) and 80 % (B91) in 2020 when the harvested fruit was stored at cold store levels. With new products to be introduced into the environment, it's important to understand the effects they may have on the plant's microbiome. Using next-generation sequencing techniques, we looked at the impact B91 and Y126 has on the blossom and cherry microbiomes. There was a treatment effect in both the bacterial and fungal communities on the blossom and ripe cherry. But the biggest variability was between blocks (Geographical effect) and between the years in which we experimented (p < 0.0001). This research will assist in the development of management strategies, especially spray timings for brown rot on stone fruit, integrating BCAs with other management practices

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    NFT-Related Companies: Token Sale Returns

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    Non-fungible tokens (NFTs) have emerged as a new means of digital asset ownership and many companies are building projects that revolve around the technology. These companies are blockchain-based and raise capital for their projects through cryptocurrency token sales, which have become a new mechanism of entrepreneurial finance. In a sample of 62 NFT-related companies, I examine which company, fundraising, and token sale process characteristics are associated with the performance of 7-day and 60-day market returns after a token’s public listing. A multivariate regression analysis finds that the total amount of capital raised before a token launch has a negative relationship with the 7-day and 60-day market returns. Ethereum returns, the length of the team token lock-up period and the presence of a vesting schedule have positive relationships with 60-day token returns

    Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

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    In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

    How Students use the Services available from Lindenwood Universities Library

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    Student Assessment Scholars took on the Library Services stakeholder proposal. Their goal was to find how students use the service available from Lindenwood Universities Library. Through evidence, it was found that students have a positive sentiment towards the Lindenwood’s Library Services. That the Students want longer hours, better marketing, and find one of the best services being the building itself. Lindenwood Universities Library Services are similar to the other schools in the athletic conference. Lindenwood University houses a Maker Lab, Career Services, and technology rentals like the other universities, but Lindenwood students were not aware of them. Overall, students were happy with the library\u27s services and wanted their ability to use the services to be extended. The LARC is currently open 78 hours a week and the average of the other school’s library hours is 92. To compete with other schools in the athletic conference and increase students use and satisfaction of Library Services, they should first work towards extending their hours

    How to Be a God

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    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods

    Balancing the urban stomach: public health, food selling and consumption in London, c. 1558-1640

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    Until recently, public health histories have been predominantly shaped by medical and scientific perspectives, to the neglect of their wider social, economic and political contexts. These medically-minded studies have tended to present broad, sweeping narratives of health policy's explicit successes or failures, often focusing on extraordinary periods of epidemic disease viewed from a national context. This approach is problematic, particularly in studies of public health practice prior to 1800. Before the rise of modern scientific medicine, public health policies were more often influenced by shared social, cultural, economic and religious values which favoured maintaining hierarchy, stability and concern for 'the common good'. These values have frequently been overlooked by modern researchers. This has yielded pessimistic assessments of contemporary sanitation, implying that local authorities did not care about or prioritise the health of populations. Overly medicalised perspectives have further restricted historians' investigation and use of source material, their interpretation of multifaceted and sometimes contested cultural practices such as fasting, and their examination of habitual - and not just extraordinary - health actions. These perspectives have encouraged a focus on reactive - rather than preventative - measures. This thesis contributes to a growing body of research that expands our restrictive understandings of pre-modern public health. It focuses on how public health practices were regulated, monitored and expanded in later Tudor and early Stuart London, with a particular focus on consumption and food-selling. Acknowledging the fundamental public health value of maintaining urban foodways, it investigates how contemporaries sought to manage consumption, food production waste, and vending practices in the early modern City's wards and parishes. It delineates the practical and political distinctions between food and medicine, broadly investigates the activities, reputations of and correlations between London's guild and itinerant food vendors and licensed and irregular medical practitioners, traces the directions in which different kinds of public health policy filtered up or down, and explores how policies were enacted at a national and local level. Finally, it compares and contrasts habitual and extraordinary public health regulations, with a particular focus on how perceptions of and actual food shortages, paired with the omnipresent threat of disease, impacted broader aspects of civic life
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