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Design and Optimization of Lightweight Porous Damping Treatments
Get PDFHere, it is shown that properly-designed limp porous materials such as fibrous layers can provide damping equivalent to conventional viscoelastic dampers while providing advantages such as light weight and effective sound absorption. This then allows porous layers to be used as multi-functional noise and vibration control solutions in automotive and aerospace applications. It has also been found that the addition of bulk elasticity to the solid phase of the porous medium is beneficial since it improves damping performance compared to equivalent limp treatments. In this study, porous media, such as fibers and foams, were designed to serve as treatments for various vibrating structures to examine their damping effectiveness. Both analytical modeling and numerical simulation based on finite element methods were involved depending on the complexity of the structure. Specifically, a Fourier transform-based computational method was introduced as the key step to realize the accurate prediction of a panel’s spatial response based on its wavenumber-frequency spectrum. Then, parametric studies were conducted on a porous layer to identify the optimal bulk properties that would allow the layer to provide the largest possible damping within the target frequency region. Finally, design concepts for achieving the maximum damping potential of porous layers are summarized
A Transfer-Matrix-Based Approach to Predicting Acoustic Properties of a Layered System in a General, Efficient, and Stable Way
Get PDFLayered materials are one of the most commonly used acoustical treatments in the automotive industry, and have gained increased attention, especially owing to the popularity of electric vehicles. Here, a method to model and couple layered systems with various layer types (i.e., poro-elastic layers, solid-elastic layers, stiff panels, and fluid layers) is derived that makes it possible to stably predict their acoustical properties. In contrast with most existing methods, in which an equation system is constructed for the whole structure, the present method involves only the topmost layer and its boundary conditions at two interfaces at a time, which are further simplified into an equivalent interface. As a result, for a multi-layered system, the proposed method splits a complicated system into several smaller systems and so becomes computationally less expensive. Moreover, traditional modeling methods can lose stability when there is a large disparity between the magnitudes of the waves within the layers (e.g., at higher frequencies, for a thick layer, or for extreme parameter values). In those situations, the contribution of the most attenuated wave can be masked by numerical errors, hence inducing instability when inverting the system. Here, the accuracy of the wave attenuation terms is ensured by decomposing each layer’s transfer matrix analytically and reformulating the equation system. Therefore, this method can produce a stable prediction of acoustical properties over a large frequency and parameter region. The fact that the proposed method can couple different layer types in a general, efficient, convenient, and stable way is beneficial, for example, when numerically optimizing the design of the acoustical treatments. The predicted acoustic properties of layered systems calculated using the proposed method have been validated by comparison with those predicted by previously existing methods. Further, an optimal design exercise is performed to find a lightweight layered dash panel treatment
WHAT DRIVES SATISFACTION AND DISSATISFACTION OF HOTEL GUESTS? AN EXPLORATORY ANALYSIS BASED ON TRIPADVISOR
Get PDFUnderstanding the drivers of hotel guest satisfaction and dissatisfaction is basic for hotel managers. In this research, we use a large database of more than 30,000 reviews that were posted on TripAdvisor to investigate this issue. We use Power BI to assign a sentiment score to each review and to extract the main phrases from them. This allows us to create two datasets of words and phrases (one derived from the reviews of satisfied guests and one derived from the reviews of dissatisfied guests) in order to compare them. In order to compare both datasets, we perform a quantitative analysis. We find that some of the concepts are mentioned almost equally by both types of guests (the staff, the room, the food options and the infrastructure of the hotel, mainly the pool area), while others appear more predominantly in one of the cases (e.g. the bathroom and prices are mentioned much more frequently by dissatisfied guests). These results allow us to draw some conclusions for hotel managers
Explore the Design and Authoring of Ai-driven Context-aware Augmented Reality Experiences
No full textWith the advents in hardware techniques and mobile computing powers, Augmented Reality (AR) has been promising in various areas of our everyday life and work. By superimposing virtual assets onto the real world, the boundary between the digital and physical spaces has been significantly blurred, which bridges a large amount of digital augmentation and intelligence with the surroundings of the physical reality. Meanwhile, thanks to the increasing developments of Artificial Intelligence (AI) perception algorithms such as object detection, scene reconstruction, and human tracking, the dynamic behaviors of digital AR content have extensively been associated with the physical contexts of both humans and environments. This context-awareness enabled by the emerging techniques enriches the potential interaction modalities of AR experiences and improves the intuitiveness and effectiveness of the digital augmentation delivered to the consumers. Therefore, researchers are gradually motivated to include more contextual information in the AR domain to create novel AR experiences used for augmenting their activities in the physical world.On a broader level, our work in this thesis focuses on novel designs and modalities that combine contextual information with AR content behaviors in context-aware AR experiences. In particular, we design the AR experiences by inspecting different types of contexts from the real world, namely 1) human actions, 2) physical entities, and 3) interactions between humans and physical environments. To this end, we explore 1) software and hardware modules, and conceptual models that perceive and interpret the contexts required by the AR experiences, and 2) supportive authoring tools and interfaces that enable users and designers to define the associations of the AR contents and the interaction modalities leveraging the contextual information. In this thesis, we mainly study the following workflows: 1) designing adaptive AR tutoring systems for human-machine-interactions, 2) customizing human-involved context-aware AR applications, 3) authoring shareable semantic-aware AR experiences, and 4) enabling hand-object-interaction datasets collection for scalable context-aware AR application deployment. We further develop the enabling techniques and algorithms including 1) an adaptation model that adaptively vary the AR tutoring elements based on the real-time learner’s interactions with the physical machines, 2) a customized video-see-through AR headset for pervasive human-activity detecting, 3) a semantic adaptation model that adjusts the spatial relationships of the AR contents according to the semantic understanding of different physical entities and environments, and 4) an AR-based interface that empowers novice users to collect high-quality datasets used for training user- and cite-specific networks in hand-object-interaction-aware AR applications.Takeaways from the research series include 1) the usage of the modern AI modules effectively enlarges both the spatial and contextual scalability of AR experiences, and 2) the design of the authoring systems and interfaces lowers the barrier for end-users and domain experts to leverage AI outputs in the creation of AR experiences that are tailored for target users. We conclude that involving AI techniques in both the creation and implementation stages of AR applications is crucial to building an intelligent, adaptive, and scalable ecosystem of context-aware AR applications
Clinical Applications of Magnetic Resonance Spectroscopy
No full textMagnetic resonance spectroscopy (MRS) is a non-invasive diagnostic technique that provides unique information about the biochemical composition of the human body. By excluding the overwhelming signals from water and fat, clinically relevant biomarkers such as lactate, Nacetyl aspartate, choline, creatine, glutamate/glutamine (Glx), gamma-aminobutyric acid (GABA), glutathione, and myoinositol can be reliably quantified. MRS has diverse applications in investigating the metabolic window of a wide range of biochemical processes.Here, we have utilized MRS to better understand chemical changes associated with neurological disorders and treatment response. We have investigated neurometabolic imbalances in brain regions related to post-traumatic stress disorder (PTSD) symptoms and found that neurometabolic changes are brain region-specific in PTSD population compared to healthy controls. MRS was applied to better understand the neurobiological processes of hyperbaric oxygen therapy in military veterans with clinically diagnosed traumatic brain injury and/or PTSD. From preliminary data, we found largest neurometabolic changes in the insula – a core component of body awareness.With improved specificity and the ability to probe microstructural and chemical changes, MRS represents a powerful tool for investigating various disorders and treatment responses
The Role of Information Systems in Healthcare
No full textFundamental changes have been happening in healthcare organizations and delivery in these decades, including more accessible physician information, the low-cost collection and sharing of clinical records, and decision support systems, among others. Emerging information systems and technologies play a signification role in these transformations. To extend the understanding and the implications of information systems on healthcare, my dissertation investigates the influence of information systems on enhancing healthcare operations. The findings reveal the practical value of digitalization in indicating healthcare providers’ cognitive behaviors, responding to healthcare crises, and improving medical performance.The first essay investigates the unrevealed value of a special type of user-generated content in healthcare operations. In today’s social media world, individuals are willing to express themselves on various online platforms. This user-generated content posted online help readers get easy assess to individuals’ features, including but not limited to personality traits. To study the impact of physicians’ personality traits on medicine behaviours and performance, we take a view from the perspective of user generated content posted by their supplier side as well as using physician statements which have been made available in medical review websites. It has been found that a higher openness score leads to lower mortality rates, reduced lab test costs, shorter time usage in hospitals treated by physicians with greater openness scores. Furthermore, taking these personality traits into consideration in an optimization problem of ED scheduling, the estimation of counterfactual analysis shows an average of 11.4%, 18.4%, and 17.8% reduction in in-hospital mortality rates, lab test expenditures, and lengths of stay, respectively. In future operation of healthcare, physicians’ personalities should be taken into account when healthcare resources are insufficient in times of healthcare pandemics like COVID-19, as our study indicates that health service providers personality is an actual influence on clinical quality.In the second essay, we focus on the influences of the most severe healthcare pandemic in these decades, COVID-19, on digital goods consumption and examine whether digital goods consumption is resilient to an individuals physical restriction induced by the pandemic. Leveraging the enforced quarantine policy during the COVID-19 pandemic as a quasi-experiment, we identify the influence of a specific factor, quarantine policy, on mobile app consumption in every Apple app store category in the short and long terms. In the perspective of better responding in the post-pandemic era, the quantitative findings provide managerial implications to the app industry as well as the stock market for accurately understanding the long-term impact of a significant intervention, quarantine, in the pandemic. Moreover, by using the conditional exogenous quarantine policy to instrument app users daily movement patterns, we are able to further investigate the digital resilience of physical mobility in different app categories and quantify the impact of an individuals physical mobility on human behavior in app usage. For results, we find that the reduction in 10% of ones physical mobility (measured in the radius of gyration) leads to a 2.68% increase in general app usage and a 5.44% rise in app usage time dispersion, suggesting practitioners should consider users physical mobility in future mobile app design, pricing, and marketing.In the third essay, we investigate the role of an emerging AI-based clinical treatment method, robot-assisted surgery (RAS), in transforming the healthcare delivery. As an advanced technique to help diminish the human physical and intellectual limitations in surgeries, RAS is expected to but has not been empirically proven to improve clinical performance. In this work, we first investigate the effect of RAS on clinical outcomes, controlling physicians’ self-selection behavior in choosing whether or not to use RAS treatment methods. In particular, we focus on the accessibility of RAS and explore how physician and patient heterogeneity affect the adoption of the RAS method, including learning RAS and using RAS
Predictors, Mechanisms, and Diversity in Human-Animal Interaction Research
No full textThere has been substantial growth in recent decades in the variety and popularity of roles for dogs assisting humans in professional therapeutic partnerships. Simultaneously, increasingly rigorous research has repeatedly demonstrated the effects of professional human-canine partnerships in remedying important issues of public health among several at-risk populations. Yet, despite these areas of growth, mechanisms of action and predictors of efficacy in the field of human-animal interaction (HAI) remain poorly understood, and the role of human diversity has been rarely discussed. Thus, the present dissertation examines potential mechanisms and diverse predictors in two distinct samples of professional human-canine partnerships, while building the impetus to explore diversity in the HAI field as a whole.For the first three studies (Chapters 2-4), the selected samples of professional human-canine partnerships include military veterans working with psychiatric service dogs to mediate their symptoms of PTSD and healthcare professionals in pediatric hospitals working with facility dogs to benefit their patients. Following the introduction in Chapter 1, the objective of Chapters 2-3 was to examine primary human outcomes in the selected professional canine partnerships. In a cross-sectional study of N=198 military veterans with PTSD, Chapter 2 compared PTSD symptom severity between n=112 veterans with service dogs and n=86 veterans on the waitlist to receive service dogs in the future. Next, in a cross-sectional study of N=130 healthcare professionals in pediatric hospitals, Chapter 3 compared job-related well-being and mental health of n=65 professionals working with facility dogs to n=65 working without. Findings suggested benefits to the mental health and well-being of both military veterans with PTSD and pediatric healthcare professionals, which were significantly associated with their professional canine partnerships.Subsequently, the objective of Chapter 4 was to explore how variances within a specific professional canine partnership may suggest predictors and potential mechanisms for the observed human outcomes. Thus, in a longitudinal study of N=82 veterans with PTSD and their service dogs, Chapter 4 explored associations of veterans’ outcomes with veteran-service dog demographics and interactions. Results suggested components of the human-canine partnership which might explain observed human outcomes, including social connections, a calming influence, and strong human-animal bonds.In pursuing the aim of examining diverse predictors of efficacy in professional human-canine partnerships, vastly homogenous samples in the first three studies (Chapters 2-4) prompted an additional, more targeted look at diversity in the HAI field overall. Thus, the objective of Chapter 5 was to explore diverse representativeness, as well as perceptions about diversity, equity, and inclusion (DEI), among field leaders. Results of this chapter quantified the lack of diversity in the field at present while also indicating that the majority of field leaders find the topic of diversity in HAI to be extremely important. Finally, the necessity and implications of considering these topics in HAI research was described, and proposed strategies for promoting DEI in the field were collated from the existing best practice resources and recommendations of DEI experts.Overall, this research provides an innovative and novel roadmap for examining potential mechanisms of action in professional human-canine partnerships. Further, this work reveals important information about how considerations of DEI and representativeness within the field may inform the study of such human-animal partnerships. Thus, findings contribute to a critical foundation on which the field of human-animal interaction will continue building knowledge of functional processes, predictors of efficacy, and cultural competency
Modeling Wound Healing Mechanobiology
No full textThe mechanical behavior of tissues at the macroscale is tightly coupled to cellular activity at the microscale and tuned by microstructure at the mesoscale. Dermal wound healing is a prominent example of a complex system in which multiscale mechanics regulate restoration of tissue form and function. In cutaneous wound healing, a fibrin matrix is populated by fibroblasts migrating in from a surrounding tissue made mostly out of collagen. Fibroblasts both respond to mechanical cues such as fiber alignment and stiffness as well as exert active stresses needed for wound closureTo model wound healing mechanobiology, we first develop a multiscale model with a twoway coupling between a microscale cell adhesion model and a macroscale tissue mechanics model. Starting from the well-known model of adhesion kinetics proposed by Bell, we extend the formulation to account for nonlinear mechanics of fibrin and collagen and show how this nonlinear response naturally captures stretch-driven mechanosensing. We then embed the new nonlinear adhesion model into a custom finite element implementation of tissue mechanical equilibrium. Strains and stresses at the tissue level are coupled with the solution of the microscale adhesion model at each integration point of the finite element mesh. In addition, solution of the adhesion model is coupled with the active contractile stress of the cell population. The multiscale model successfully captures the mechanical response of biopolymer fibers and gels, contractile stresses generated by fibroblasts, and stress-strain contours observed during wound healing. We anticipate this framework will not only increase our understanding of how mechanical cues guide cellular behavior in cutaneous wound healing, but will also be helpful in the study of mechanobiology, growth, and remodeling in other tissues.Next, we develop another multiscale model with a bidirectional coupling between a microscale cell adhesion model and a mesoscale microstructure mechanics model. By mimicking the generation of fibrous network in experiment, we established a discrete fiber network model to simulate the microstructure of biopolymer gels. We then coupled the cell adhesion model to the discrete model to obtain the solution of microstructure equilibrium. This multiscale model was able to recover the volume loss of fibrous gels and the contraction from cells in the networks observed in experiment. We examined the influence of RVE size, stiffness of single fibers and stretch of the gels. We expect this work will help bridge the activity of cell to the microstructure and then to the tissue mechanics especially in wound healing. We hope this work will provide more rigorous understanding in the study of mechanobiology.At last, we established a computational model to accurately capture the mechanical response of fibrin gels which is a naturally occurring protein network that forms a temporary structure to enable remodeling during wound healing and a common tissue engineering scaffold due to the controllable structural properties. We formulated a strategy to quantify both the macroscale (110 mm) stress-strain response and the deformation of the mesoscale (101000µm) network structure during unidirectional tensile tests. Based on the experimental data, we successfully predict the strain fields that were observed experimentally within heterogenous fibrin gels with spatial variations in material properties by developing a hyperviscoelastic model with non-affined evolution under stretching. This model is also potential to predict the macroscale mechanics and mesoscale network organization of other heterogeneous biological tissues and matrices
