2,871 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

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    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

    Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward

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    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Análisis global y local de la predicción mediante Potenciales Relacionados con Eventos. Aplicación al Trastorno del Espectro Autista

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    Bajo la perspectiva teórica de la codificación predictiva, el cerebro humano funciona como una sofisticada máquina que está constantemente evaluando las características de los estímulos percibidos y la relación existente entre ellos, con el objetivo de extraer nuevos patrones de diferentes niveles de abstracción para anticipar el siguiente evento. Esta dinámica automática de generación o modificación de las predicciones con el fin de dirigir la atención y ajustar la respuesta, es considerada uno de los procesos psicofisiológicos más fundamentales que existen, por lo que su alteración está relacionada con diversas patologías. El presente trabajo describe dos estudios desarrollados mediante EEG, en los que fueron analizados (i) la habituación y discriminación auditiva preatencional mostrada por un grupo de niños con TEA en comparación con uno control, y (ii) el establecimiento y actualización de las predicciones desarrolladas por un grupo de adultos sanos en paradigmas de diferente complejidad. Para conseguir estos objetivos, todos los sujetos registrados en ambos estudios recibieron una estimulación auditiva pasiva similar a la del tipo Oddball, aunque las características y la organización de los estímulos fueron diferentes en ambos experimentos. La estimulación auditiva presentada en el estudio de TEA estuvo compuesta por tonos generados electrónicamente o producidos por una cantante profesional, con el objetivo de investigar si la alteración en el desarrollo del lenguaje, típicamente descrita para estos niños, es causada por un déficit selectivo al procesar las características internas de la voz humana. Los componentes analizados para el estudio del TEA fueron el P1 y la MMN. Los niños con TEA presentaron una menor amplitud para los componentes P1 y MMN en comparación con el grupo control, lo cual sugiere una habituación y discriminación auditivas reducidas tanto para el sonido electrónico como el humano. Dado que la MMN también se ha relacionado con la codificación predictiva, los sujetos con TEA tendrían disminuida esta capacidad. El diseño experimental presentado al grupo adulto sano estuvo compuesto por dos paradigmas experimentales diferentes según el nivel de abstracción requerido para extraer un patrón: en un caso, la predicción dependía de las características físicas de los estímulos presentados (las frecuencias de los tonos) mientras que, en el otro caso, dependía de su organización (la dirección ascendente o descendente de las secuencias de tonos). Un componente similar a la MMN (“MMN-like”) y uno lento (posiblemente una Postimperative Negative Variation, PINV) fueron analizados con el objetivo de comprobar la hipótesis de la codificación predictiva. Los resultados obtenidos son detallados en dos artículos. Estos componentes registrados en población adulta fueron, primero, propuestos como una MMN y una Slow Preceding Negativity (SPN) y, posteriormente, reconceptualizados en el segundo artículo como un N1 tardío y una PINV. Ambos componentes se desarrollaban en la latencia de la MMN y el intervalo entre ensayos, respectivamente. Los resultados obtenidos en la investigación realizada en el grupo adulto, mostraron una mayor amplitud ante los ensayos desviantes y el paradigma más complejo, en ambos componentes analizados, lo que sugiere que están involucrados en el establecimiento y actualización de las predicciones basadas no solo en las características físicas de los estímulos, sino también en reglas abstractas. Además, tanto el N1 tardío como la PINV presentaron respuestas de diferente amplitud dependiendo del ensayo previamente presentado, lo que sugeriría una actualización continua de los ensayos, hasta donde sabemos, no descrita para estos componentes en la literatura previa

    Exploring new avenues for the meta-analysis method in personality and social psychology research

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    This dissertation addresses theoretical validity and bias in meta-analytic research in personality and social psychology research. The conceptual starting point of the dissertation is research on ego depletion (Baumeister et al., 1998). In this line of research, hundreds of studies documented an experimental effect that probably does not exist, as was later revealed by extensive replication work (Hagger et al., 2010, 2016). This debacle has presumably been caused by dysfunctional structures and procedures in psychological science, such as widespread publication bias (Carter & McCullough, 2014). Unfortunately, these dysfunctionalities were (and in some cases still are) also prevalent in other areas of psychological research beside ego depletion (Ferguson & Brannick, 2012; Open Science Collaboration, 2015). Because extensive replication research is too costly to be repeated for all past work, it has been a contentious question what to do with research data that has been generated during an era of questionable research practices: should this research be abandoned or can some of it be salvaged? In four research papers, this dissertation project attempts to address these questions. In part I of the dissertation project, two papers highlight and analyze challenges when summarizing past research in social psychology and personality research. Paper 1 (Friese et al., 2017) attempted to find summary evidence for the effectiveness of self-control training, a research field related to ego depletion, but came to a sobering conclusion: The summary effect was small, likely inflated by publication bias, and could not be attributed beyond doubt to a theoretical mechanism. Paper 2 (Friese & Frankenbach, 2020) reported on a simulation study that showed how multiple sources of bias (publication bias, p-hacking) can interact with contextual factors and each other to create significant meta-analytic evidence from very small or even zero true effects. Part II of the dissertation project is an attempt to advance social-psychological and personality theory with meta-scientific work despite an unknowable risk of bias in the literature. In part II, two papers (Frankenbach et al., 2020, 2022) make use of one key idea: Re-using existing raw research data to test novel theoretical ideas in secondary (meta-)analyses. Results revealed that this idea helps towards both goals of the dissertation project, that is, advancing theory while reducing risk-of-bias. The general discussion analyses promises and limitations of such secondary data analyses in more detail and attempts to situate the idea more broadly in the psychological research toolkit by contrasting integrative versus innovative research. Further discussion covers how conceptual and technological innovations may facilitate more secondary data analyses in the future, and how such advances may pave the way for a slower, more incremental, but truly valid and cumulative psychological science.Die vorliegende Dissertation behandelt theoretischen Validität und Verzerrung (Bias) von meta-analytischer Forschung in der Persönlichkeits- und Sozialpsychologie. Der konzeptuelle Ausgangspunkt der Dissertation ist die Forschung zu „Ego Depletion“ (Baumeister et al., 1998). In dieser Forschungslinie haben hunderte von Studien einen Effekt belegt, der, wie sich später durch umfangreiche Replikationsarbeiten (Hagger et al., 2010, 2016) herausstellte, vermutlich nicht existiert. Dieses Debakel wurde mutmaßlich mitverursacht durch dysfunktionale Strukturen und Prozesse in der psychologischen Forschung, insbesondere Publikationsbias („publication bias“). Unglücklicherweise lagen (und liegen) diese Dysfunktionalitäten neben Ego Depletion auch in anderen psychologischen Forschungsbereichen vor (Ferguson & Brannick, 2012; Open Science Collaboration, 2015). Da aus Kostengründen nicht alle Forschungsarbeiten der Vergangenheit repliziert werden können, ergibt sich eine kritische Frage: Wie soll mit psychologischer Forschung umgegangen werden, die unter mutmaßlich verzerrenden Bedingungen generiert wurde? Sollte diese Forschung ad acta gelegt werden oder können Teile davon weiterverwendet werden? Das vorliegende Dissertationsprojekt versucht im Rahmen von vier Forschungsbeiträgen sich diesen Fragen anzunähern. Im ersten Teil der Dissertation beleuchten und analysieren zwei Forschungsbeiträge Probleme und Herausforderungen, die sich bei der Zusammenfassung von bestehender Forschung der Sozial- und Persönlichkeitspsychologie ergeben. Der erste Beitrag (Friese et al., 2017) versucht in einer Meta-Analyse Evidenz für die Wirksamkeit von Selbstkontrolltrainings zu finden, aber kommt zu einem ernüchternden Ergebnis: Die Gesamteffekte sind klein, mutmaßlich durch Publikationsbias fälschlich überhöht und können überdies nicht zweifelsfrei einem theoretischen Kausalmechanismus zugeordnet werden. Der zweite Beitrag (Friese & Frankenbach, 2020) umfasst eine Simulationsstudie, die aufzeigt, wie verschiedene Formen von Bias (Publikationsbias und sog. „p-hacking“) miteinander und mit Kontextfaktoren interagieren können, wodurch signifikante, meta-analytische Effekte aus sehr kleinen wahren Effekten oder sogar Nulleffekten entstehen können. Der zweite Teil der Dissertation versucht, trotz eines unbestimmbaren Bias-Risikos, Fortschritte in der sozial- und persönlichkeitspsychologischen Theorie zu erzielen. Zu diesem Zweck wird in zwei Forschungsbeiträgen (Frankenbach et al., 2020, 2022) auf eine Schlüssel-Idee zurückgegriffen: Die Testung von neuen theoretischen Hypothesen unter Wiederverwendung von existierenden Forschungsdaten in Sekundärdatenanalysen. Die Ergebnisse zeigen, dass dieser Ansatz tatsächlich dazu beitragen kann, theoretische Fortschritte mit vermindertem Verzerrungsrisiko zu machen. Die anschließende, übergreifende Diskussion behandelt Möglichkeiten und Limitationen solcher Sekundärdatenanalysen und versucht, den Ansatz in einer Gegenüberstellung von integrativer und innovativer Forschung übergreifender in die psychologische Forschungsmethodik einzuordnen. Im Weiteren wird diskutiert, wie konzeptuelle und technologische Entwicklungen in der Zukunft Sekundärdatenanalysen erleichtern könnten und wie solche Fortschritte den Weg ebnen könnten für eine langsamere, inkrementelle, aber wahrhaft valide und kumulative psychologische Wissenschaft.German Research Foundation (DFG): "Die Rolle mentaler Anstrengung bei Ego Depletion

    A Behavioural Decision-Making Framework For Agent-Based Models

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    In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making

    Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding

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    Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI? The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise

    Development and Applications of Similarity Measures for Spatial-Temporal Event and Setting Sequences

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    Similarity or distance measures between data objects are applied frequently in many fields or domains such as geography, environmental science, biology, economics, computer science, linguistics, logic, business analytics, and statistics, among others. One area where similarity measures are particularly important is in the analysis of spatiotemporal event sequences and associated environs or settings. This dissertation focuses on developing a framework of modeling, representation, and new similarity measure construction for sequences of spatiotemporal events and corresponding settings, which can be applied to different event data types and used in different areas of data science. The first core part of this dissertation presents a matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This framework supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the modified Jaccard index with temporal order constraints and accommodates different event data types. This approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. These similarity measures are incorporated into a clustering method and successfully demonstrate the usefulness in a case study analysis of event sequences extracted from space time series of a water quality monitoring system. This dissertation further proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been considered. While modeling event setting sequences, spatial and temporal scales are considered to define the bounds of the setting and incorporate dynamic variables along with static variables. Using a matrix-based representation and an extended Jaccard index, new similarity measures are developed to allow for the use of all variable data types. With these similarity measures coupled with other multivariate statistical analysis approaches, results from a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, support the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. To test the scalability of STES similarity measure in a larger dataset and an extended application in different fields, this dissertation compares and contrasts the prospective space-time scan statistic with the STES similarity approach for identifying COVID-19 hotspots. The COVID-19 pandemic has highlighted the importance of detecting hotspots or clusters of COVID-19 to provide decision makers at various levels with better information for managing distribution of human and technical resources as the outbreak in the USA continues to grow. The prospective space-time scan statistic has been used to help identify emerging disease clusters yet results from this approach can encounter strategic limitations imposed by the spatial constraints of the scanning window. The STES-based approach adapted for this pandemic context computes the similarity of evolving normalized COVID-19 daily cases by county and clusters these to identify counties with similarly evolving COVID-19 case histories. This dissertation analyzes the spread of COVID-19 within the continental US through four periods beginning from late January 2020 using the COVID-19 datasets maintained by John Hopkins University, Center for Systems Science and Engineering (CSSE). Results of the two approaches can complement with each other and taken together can aid in tracking the progression of the pandemic. Overall, the dissertation highlights the importance of developing similarity measures for analyzing spatiotemporal event sequences and associated settings, which can be applied to different event data types and used for data mining, sequence classification, and clustering
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