Traditional robots, while capable of being efficient and effective for the task they were designed, are dangerous when operating in unmodeled environments or around humans. The field of soft robotics attempts to increase the safety of robots thus enabling them to operate in environments where traditional robots should not operate. Because of this, soft robots were developed with different goals in mind than traditional robots and as such the traditional metrics used to evaluate standard robots are not effective for evaluating soft robots. New metrics need to be developed for soft robots so that effective comparison and evaluations can be made. This dissertation attempts to lay the groundwork for that process through a survey on soft robot metrics. Additionally we propose six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. New models, control methods and estimation methods also need to be developed for soft robots. Many of the traditional methods and assumptions for modeling and controlling robotic systems are not able to provide the fidelity that is needed for soft robots to effectively complete useful tasks. This dissertation presents specific developments in each of these areas of soft robot metrics, modeling, control and estimation. We show several incremental improvements to soft robot dynamic models as well as how they were used in control methods for more precise control. We also demonstrate a method for linearizing high degree of freedom models so it can be simplified for use in faster control methods for better performance. Lastly, we present an improved continuum joint configuration estimation method that uses a linear combination of length measurements. All these developments combine to help build the "fundamental engineering framework" that is needed for soft robotics as well as helping to move robots out of their confined spaces and bring them into new unmodeled/unstructured environments.

Staying current on literature related to antimicrobial stewardship can be challenging given the ever-increasing number of published articles. The Southeastern Research Group Endeavor (SERGE-45) identified antimicrobial stewardship–related peer-reviewed literature that detailed an actionable intervention for 2019. The top 13 publications were selected using a modified Delphi technique. These manuscripts were reviewed to highlight the actionable intervention used by antimicrobial stewardship programs to provide key stewardship literature for teaching and training and to identify potential intervention opportunities within one’s institution.

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Systems created within the aspect-oriented paradigm (AOP) are difficult for programmers to understand fully. AOP suggests moving crosscutting concerns scattered throughout class code into an individual module, known as an aspect. The process of aspect weaving injects the crosscutting concern back into class code at specific locations, known as joinpoints. A side effect of the weaving process is aspect interference-when aspect code creates unexpected results at a joinpoint. Therefore, developing an understanding of locations that could either cause or exhibit aspect interference problems is essential to developing an interference-free AOP system. This study used the interference potential (IP) and interference causality potential (ICP) metrics, and derived a new metric called total interference potential (TIP), to classify areas of potential interference problems. In addition, the project performs a hierarchical agglomerative clustering using the three metrics. Experiments conducted on two AOP systems identified clusters within each program that could cause or exhibit aspect interference problems. Results showed the merit of using clustering analysis as a technique to locate portions of a system to review or alter to prevent interference problems.

Indiana University-Purdue University Indianapolis (IUPUI) / In the present work, we consider a class of Finsler metrics using the warped product notion introduced by B. Chen, Z. Shen and L. Zhao (2018), with another “warping”, one that is consistent with the form of metrics modeling static spacetimes and simplified by spherical symmetry over spatial coordinates, which emerged from the Schwarzschild metric in isotropic coordinates. We will give the PDE characterization for the proposed metrics to be Ricci-flat and construct explicit examples. Whenever possible, we describe both positive-definite solutions and solutions with Lorentz signature. For the latter, the 4-dimensional metrics may also be studied as Finsler spacetimes.

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The way a multi-agent modeller represents an agent not only affects its ability to reason about agents but also the interpretability of its representation space as well as its efficacy on future downstream tasks. We utilize and repurpose metrics from the field of representation learning to specifically analyze and compare multi-agent modellers that build real-valued vector representations of the agents they model. By generating two datasets and analyzing the representations of multiple LSTM- or transformer-based modellers with various embedding sizes, we demonstrate that representation metrics provide a more complete and nuanced picture of a modeller's representation space than an analysis based only on performance. We also provide insights regarding LSTM- and transformer-based representations. Our proposed metrics are general enough to work on a wide variety of modellers and datasets.

In the dynamic domain of Development, Security, and Operations (DevSecOps), a quantitative approach is critical, with the usage of metrics being a key method to realize this goal. However, there is a notable absence of a set of metrics and assessment specifically for Cloud-Based Web Microservices (CBWMs) within a DevSecOps framework. This study seeks to fill this void by developing a quantitative assessment framework designed for CBWMs in the context of DevSecOps. Utilizing a Multi-Vocal Literature Review (MLR) methodology, we gathered and analyzed 92 documents from 2018 to 2023, sourced from IEEE Xplore, Springer, and Google, to select twelve effective metrics for CBWM assessment within DevSecOps. These metrics, categorized by scale and interrelationships, were chosen due to the tools available in the market for obtaining them, their general applicability across various CBWMs, and their clearly defined measurements and criteria. Each metric is supported by academic and industry literature, providing a comprehensive basis for their selection. Leveraging the Constructive Cost Model (COCOMO) II and the stages of Development and Operations (DevOps) – Plan, Code, Build, Test, Release, Deploy, Operate, and Monitor – our framework outlines an assessment flow that segments into three phases - Development, Integration, and Post-Deployment, aligning with the iterative Software Development Life Cycle (SDLC). This structure facilitates continuous improvement of CBWMs within a DevSecOps framework using these carefully selected metrics. / 2025-01-08T00:00:00Z

In today’s world, ontologies are being widely used for data integration tasks and solving information heterogeneity problems on the web because of their capability in providing explicit meaning to the information. The growing need to resolve the heterogeneities between different information systems within a domain of interest has led to the rapid development of individual ontologies by different organizations. These ontologies designed for a particular task could be a unique representation of their project needs. Thus, integrating distributed and heterogeneous ontologies by finding semantic correspondences between their concepts has become the key point to achieve interoperability among different representations. In this thesis, an advanced instance-based ontology matching algorithm has been proposed to enable data integration tasks in ocean sensor networks, whose data are highly heterogeneous in syntax, structure, and semantics. This provides a solution to the ontology mapping problem in such systems based on machine-learning methods and string-based methods.

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