121 research outputs found

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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    A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

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    Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels

    ATHENA Research Book, Volume 2

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    ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers

    Technology, Science and Culture: A Global Vision, Volume IV

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    The Ecology and Evolution of Human Reproductive Behavior

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    The complexity of human reproductive behavior has necessitated its examination through a variety of scientific disciplines, each focusing on specific elements of our biology, behavior, and society. However, this complexity also necessitates that we reintegrate the information learned from each discipline into a single framework, one rooted in the evolutionary principles that have shaped the development of all life on earth. In this dissertation, I use this framework to explore human reproductive behavior, with a particular focus on sexual coercion and fertility-mediated sexual behavior. In Chapter 1, I introduce the approach taken in this document, identify several key limitations, and outline the general structure. In Chapter 2, I conduct a comprehensive and interdisciplinary review that includes the fundamentals of sexual conflict and reproductive strategies; the evolution of human reproductive characteristics in response to socio-cognitive demands; the aspects of human sociality expected to influence reproductive behavior; the identified trends in human mating behavior; the proposed pressures behind concealed ovulation in primates; the essentials of the menstrual cycle; and the existing evidence for behavioral fertility in humans. In Chapter 3, I use a game-theory model to investigate the emergence of sexually coercive behavior across a variety of species, including humans, in which male coercion is a non-developmentally-determined reproductive strategy to identify several ecological and behavioral characteristics that predict the emergence of coercive behavior generally consistent with observed trends. In Chapter 4, I use face-trait research to investigate the degree to which women recognize and discriminate between images of men with personality traits associated with different male reproductive strategies as well as how these preferences might be mediated by her relationship and fertility status. In Chapter 5, explore the intersection of fertility, fertility belief, and sexuality, specifically testing the hypothesis that a woman’s sexual interest shifts in response to her fertility while taking into consideration her beliefs regarding her fertility. Finally, in Chapter 6, I review the primary take-home messages of this work and recommend that future research take these into consideration as they move forward. By taking an interdisciplinary approach rooted in evolutionary biology, this work reveals the need for an understanding of human reproductive behavior that incorporates a wider view of reproductive ecology. In doing so, we can gain a more accurate, comprehensive, and nuanced understanding of human reproductive behavior

    Advances in the Diagnosis and Treatment of Thyroid Carcinoma

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    This reprint is related to the latest research in the field of thyroid surgery, including molecular and imaging diagnosis, surgical treatment, and the treatment of recurrent disease and advanced thyroid carcinoma

    Risk Stratification of Thyroid Nodule: From Ultrasound Features to TIRADS

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    Since the 1990s, ultrasound (US) has played a major role in the assessment of thyroid nodules and their risk of malignancy. Over the last decade, the most eminent international societies have published US-based systems for the risk stratification of thyroid lesions, namely, Thyroid Imaging Reporting And Data Systems (TIRADSs). The introduction of TIRADSs into clinical practice has significantly increased the diagnostic power of US to a level approaching that of fine-needle aspiration cytology (FNAC). At present, we are probably approaching a new era in which US could be the primary tool to diagnose thyroid cancer. However, before using US in this new dominant role, we need further proof. This Special Issue, which includes reviews and original articles, aims to pave the way for the future in the field of thyroid US. Highly experienced thyroidologists focused on US are asked to contribute to achieve this goal
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