428 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Artificial intelligence in wind speed forecasting: a review

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    Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Machine learning based anomaly detection for industry 4.0 systems.

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    223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

    Automated Design of Metaheuristic Algorithms: A Survey

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    Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field

    Differential evolution of non-coding DNA across eukaryotes and its close relationship with complex multicellularity on Earth

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    Here, I elaborate on the hypothesis that complex multicellularity (CM, sensu Knoll) is a major evolutionary transition (sensu Szathmary), which has convergently evolved a few times in Eukarya only: within red and brown algae, plants, animals, and fungi. Paradoxically, CM seems to correlate with the expansion of non-coding DNA (ncDNA) in the genome rather than with genome size or the total number of genes. Thus, I investigated the correlation between genome and organismal complexities across 461 eukaryotes under a phylogenetically controlled framework. To that end, I introduce the first formal definitions and criteria to distinguish ‘unicellularity’, ‘simple’ (SM) and ‘complex’ multicellularity. Rather than using the limited available estimations of unique cell types, the 461 species were classified according to our criteria by reviewing their life cycle and body plan development from literature. Then, I investigated the evolutionary association between genome size and 35 genome-wide features (introns and exons from protein-coding genes, repeats and intergenic regions) describing the coding and ncDNA complexities of the 461 genomes. To that end, I developed ‘GenomeContent’, a program that systematically retrieves massive multidimensional datasets from gene annotations and calculates over 100 genome-wide statistics. R-scripts coupled to parallel computing were created to calculate >260,000 phylogenetic controlled pairwise correlations. As previously reported, both repetitive and non-repetitive DNA are found to be scaling strongly and positively with genome size across most eukaryotic lineages. Contrasting previous studies, I demonstrate that changes in the length and repeat composition of introns are only weakly or moderately associated with changes in genome size at the global phylogenetic scale, while changes in intron abundance (within and across genes) are either not or only very weakly associated with changes in genome size. Our evolutionary correlations are robust to: different phylogenetic regression methods, uncertainties in the tree of eukaryotes, variations in genome size estimates, and randomly reduced datasets. Then, I investigated the correlation between the 35 genome-wide features and the cellular complexity of the 461 eukaryotes with phylogenetic Principal Component Analyses. Our results endorse a genetic distinction between SM and CM in Archaeplastida and Metazoa, but not so clearly in Fungi. Remarkably, complex multicellular organisms and their closest ancestral relatives are characterized by high intron-richness, regardless of genome size. Finally, I argue why and how a vast expansion of non-coding RNA (ncRNA) regulators rather than of novel protein regulators can promote the emergence of CM in Eukarya. As a proof of concept, I co-developed a novel ‘ceRNA-motif pipeline’ for the prediction of “competing endogenous” ncRNAs (ceRNAs) that regulate microRNAs in plants. We identified three candidate ceRNAs motifs: MIM166, MIM171 and MIM159/319, which were found to be conserved across land plants and be potentially involved in diverse developmental processes and stress responses. Collectively, the findings of this dissertation support our hypothesis that CM on Earth is a major evolutionary transition promoted by the expansion of two major ncDNA classes, introns and regulatory ncRNAs, which might have boosted the irreversible commitment of cell types in certain lineages by canalizing the timing and kinetics of the eukaryotic transcriptome.:Cover page Abstract Acknowledgements Index 1. The structure of this thesis 1.1. Structure of this PhD dissertation 1.2. Publications of this PhD dissertation 1.3. Computational infrastructure and resources 1.4. Disclosure of financial support and information use 1.5. Acknowledgements 1.6. Author contributions and use of impersonal and personal pronouns 2. Biological background 2.1. The complexity of the eukaryotic genome 2.2. The problem of counting and defining “genes” in eukaryotes 2.3. The “function” concept for genes and “dark matter” 2.4. Increases of organismal complexity on Earth through multicellularity 2.5. Multicellularity is a “fitness transition” in individuality 2.6. The complexity of cell differentiation in multicellularity 3. Technical background 3.1. The Phylogenetic Comparative Method (PCM) 3.2. RNA secondary structure prediction 3.3. Some standards for genome and gene annotation 4. What is in a eukaryotic genome? GenomeContent provides a good answer 4.1. Background 4.2. Motivation: an interoperable tool for data retrieval of gene annotations 4.3. Methods 4.4. Results 4.5. Discussion 5. The evolutionary correlation between genome size and ncDNA 5.1. Background 5.2. Motivation: estimating the relationship between genome size and ncDNA 5.3. Methods 5.4. Results 5.5. Discussion 6. The relationship between non-coding DNA and Complex Multicellularity 6.1. Background 6.2. Motivation: How to define and measure complex multicellularity across eukaryotes? 6.3. Methods 6.4. Results 6.5. Discussion 7. The ceRNA motif pipeline: regulation of microRNAs by target mimics 7.1. Background 7.2. A revisited protocol for the computational analysis of Target Mimics 7.3. Motivation: a novel pipeline for ceRNA motif discovery 7.4. Methods 7.5. Results 7.6. Discussion 8. Conclusions and outlook 8.1. Contributions and lessons for the bioinformatics of large-scale comparative analyses 8.2. Intron features are evolutionarily decoupled among themselves and from genome size throughout Eukarya 8.3. “Complex multicellularity” is a major evolutionary transition 8.4. Role of RNA throughout the evolution of life and complex multicellularity on Earth 9. Supplementary Data Bibliography Curriculum Scientiae Selbständigkeitserklärung (declaration of authorship

    Value Loss of Activities Propelled by Digital Transformation: Theoretical Evaluation and Empirical Modelling to Identify Efficiency Potentials to Maximize Value in the Field of Marketing & Sales.

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    Digital transformation of firms and the adoption of digital technologies is progressing inexorably. Decision-makers are preoccupied with the endeavor to identify the potentials of existing as well as newly emerging technologies and underutilize the entailed profits. This research study proposes a newly developed conceptualization and model to compute efficiency potentials in the field of marketing and sales, a business function with an intense consumer focus. While this conjoint business unit mainly fosters and propels the performance measure of effectiveness, the full exploitation of internal workforce efficiency stays neglected and barely treated by practice and science. By employing expert interviews with managers in this field, a tailored efficiency determination model is created with in total eight efficiency potentials allocated to three digital technology effects, acceleration, automation, and outsourcing. The efficiency coefficient of time weights the human labor input while the additive connection with digital technologies as input factor engenders either a complementary, substitutional, or no effect. With a sequential mixed-methods research approach, a further quantitative study with 251 employees in the field of marketing and sales uses the qualitative model to determine the efficiency potential based on individual task assessments, including the identification of task values. While distinguishing between office and customer interaction-related work, the study finds that 45 percent of the working time underlies an efficiency potential by utilizing the ONET database, which contains 214 individual tasks in the career cluster marketing and professional sales.Administración y Dirección de Empresa

    FUNCTIONAL LIVER TESTS IN LIVER DISEASE

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    The liver is subject to several insults, whether from ingested chemicals, pathogens or genetic diseases. Many of these are transient and full function returns. However, chronic injury may lead to scarring, the volume and effect of which may be viewed along a spectrum inclusive of fibrosis, cirrhosis and eventually death. Several complications may arise from this process, including decompensation and hepatocellular carcinoma. Recognising this damage in a timely, objectively reproducible and, most importantly, safe manner is clinically critical and great strides in this regard have recently been made. Several non-invasive technologies have been produced, the most ubiquitous being transient elastography (TE), for detecting fibrosis. To measure the actual function of the remaining liver, indocyanine green excretion allows for direct and minimally-invasive testing. This thesis focusses on the use of non-invasive testing to identify significant liver disease in a variety of different disorders. I have studied clinically relevant changes in liver fibrosis and function and related these to conventional liver function tests in an attempt to develop clinically useful markers of liver function that can be used by clinicians to risk stratify patients at risk of liver disease. Hepatitis C treatment has recently been revolutionised with the introduction of direct-acting antivirals, not only allowing a greater level of treatment success but also widening those patients we could treat to include those with cirrhosis. However, the benefits of therapy to those with advanced fibrosis are unclear and it is not established which patients are likely to undergo functional recovery following viral clearance nor is it clear which patients are at greatest risk of liver cancer. We studied liver fibrosis and indocyanine green excretion in a cohort of patients with advanced fibrosis and here I show that patients (n=43) with mild functional impairment (ICGR15<20.9%) are likely to recover but others are unlikely to undergo significant functional recovery. I studied the massive HCV Research UK database to identify risk factors for post-treatment development of HCC and I showed that virological treatment failure and pre-existing liver lesions predispose to future development of liver cancer. Recent medical advances have improved the prognosis for patients with cystic fibrosis and sickle cell disease. This improved life expectancy has unveiled new long-term complications of these disorders, including liver disease. To investigate the prevalence of liver disease in adults with these genetic disorders I conducted two clinical audits using non-invasive testing and here I show that the use of a TE in a sickle cell disease clinic adds value and identifies people with liver fibrosis. However, in an adult cystic fibrosis clinic the use of TE was of limited value as some patients with evident fibrosis were not detected by this technique and other diagnostic criteria need to be applied. In summary this work has identified prognostic factors for patients with hepatitis C and cirrhosis who have undergone effective antiviral therapy and demonstrated the value of non-invasive testing in haematological conditions whilst identifying weaknesses in the use of TE in those with cystic fibrosis
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