2,639 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

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    Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

    Robust interventions in network epidemiology

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    Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective function. Moreover, the tools to make reliable inferences---on the dynamical parameters, in particular---remain limited due to computational problems and issues of identifiability. Given these challenges, models thus remain more useful for building intuition than for designing actual interventions. This thesis seeks to elevate complex dynamical models from intuition-building tools to methods for the practical design of interventions. First, we circumvent the inference problem by searching for robust decisions that are insensitive to model misspecification.If these robust solutions work well across a broad range of structural and dynamic contexts, the issues associated with accurately specifying the problem inputs are largely moot. We explore the existence of these solutions across three facets of dynamic importance common in network epidemiology. Second, we introduce a method for analytically calculating the expected outcome of a spreading process under various interventions. Our method is based on message passing, a technique from statistical physics that has received attention in a variety of contexts, from epidemiology to statistical inference.We combine several facets of the message-passing literature for network epidemiology.Our method allows us to test general probabilistic, temporal intervention strategies (such as seeding or vaccination). Furthermore, the method works on arbitrary networks without requiring the network to be locally tree-like .This method has the potential to improve our ability to discriminate between possible intervention outcomes. Overall, our work builds intuition about the decision landscape of designing interventions in spreading dynamics. This work also suggests a way forward for probing the decision-making landscape of other intervention contexts. More broadly, we provide a framework for exploring the boundaries of designing robust interventions with complex systems modeling tools

    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

    Predicting students' performance using machine learning algorithms and educational data mining (a case study of Shahed University)

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    The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA

    Taking Politics at Face Value: How Features Expose Ideology

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    Previous studies using computer vision neural networks to analyze facial images have uncovered patterns in the feature extracted output that are indicative of individual dispositions. For example, Wang and Kosinski (2018) were able to predict the sexual orientation of a target from his or her facial image with surprising accuracy, while Kosinski (2021) was able to do the same in regards to political orientation. These studies suggest that computer vision neural networks can be used to classify people into categories using only their facial images.However, there is some ambiguity in regards to the degree to which these features extracted from facial images incorporate facial morphology when used to make predictions. Critics have suggested that a subject’s transient facial features, such as using makeup, having a tan, donning a beard, or wearing glasses, might be subtly indicative of group belonging (Agüera y Arcas et al., 2018). Further, previous research in this domain has found that accurate image categorization can occur without utilizing facial morphology at all, instead relying upon image brightness, color dominance, or the background of the image to make successful classifications (Leuner, 2019; Wang, 2022). This dissertation seeks to bring some clarity to this domain. Using an application programming interface (API) for the popular social networking site Twitter, a sample of nearly a quarter million images of ideological organization followers was created. These images were followers of organizations supportive of, or oppositional to, the polarizing political issues of gun control and immigration. Through a series of strong comparisons, this research tests for the influence of facial morphology in image categorization. Facial images were converted into point and mesh coordinate representations of the subjects’ faces, thus eliminating the influence of transient facial features. Images were able to be classified using facial morphology alone at rates well above chance (64% accuracy across all models utilizing only facial points, 62% using facial mesh). These results provide the strongest evidence to date that images can be categorized into social categories by their facial morphology alone

    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

    Understanding U.S. Customers\u27 Intention to Adopt Robo-Advisor Technology

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    Finance and information technology scholars wrote that there is a literature gap on what factors drive investors in Western financial markets to use a Robo-advisor to manage their investments. The purpose of this qualitative, single case study with embedded units is to understand the adoption intentions of retail investors in U.S. markets to use a Robo-advisor instead of a human advisor. A single case study design addressed the literature gap, and qualitative data from seven semi=structured interviews, reflective field notes, and archival data were triangulated to answer the research question. This study was grounded in a theoretical framework that includes the theory of planned behavior, the technology acceptance model, the unified theory of acceptance, and the use of technology. Thematic analysis revealed nine themes of the study: a) awareness of Robo-advisory systems, (b) perceptions of risk connected to customer’s financial literacy, (c) data security risk lowers acceptance of Robo-advisor technology, (d) Robo-advisor is filtering out emotional customer biases, (e) customer ambivalence on Robo-advisor capabilities, (f) perceived ease of use, (g) trust in the Robo-advisor, (h) customer ambivalence on adoption intention, and (i) low adoption intention for customers with low financial literacy. This study’s results indicated that financial institutions must still earn customers’ trust by protecting their data through secure platforms and processes and customizing Robo advisor services, products, and offers, to their needs. By further understanding retail investors’ adoption intentions in using a Robo-advisor, this study’s results may drive positive social change by offering pathways to very low-cost, automated financial management advice to a broader segment of new and intermediate investors

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
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