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Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH): Physiological, Cognitive, and Biological Markers of Performance Decline in an Extreme Environment
Get PDFSuccess in extreme environments comes with a cost of subtle performance decrements that if not mitigated properly can lead to lifethreatening consequences. Identification and prediction of performance decline could alleviate deleterious consequences and enhance success in challenging and high-risk operations. The Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH) project was designed to examine the cognitive, physiological, and biological markers of performance decline in the extreme environment of the Grand Canyon Rim-to-Rim (R2R) hike. The study utilized commercial off-the-shelf cognitive and physiological monitoring techniques, along with subjective self-assessments and hematologic measurements to determine subject performance and changes across the hike. The multiyear effort collected these multiple data streams in parallel on a large sample of participants hiking the R2R, leading to a rich and complex data set. This article describes the methodology and its evolution as devices and measurements were assessed after each data collection event. It also highlights a subset of the patterns of results found across the data streams. Subsequent work will draw on this data set to focus on building more sophisticated, predictive statistical models and dive deeper into specific analyses (such as the physiological and biological profiles of hikers who were left behind by their hiking partners)
Drag Cuisines: The Queer Ontology of Veganism
No full textDrag Cuisines is an interdisciplinary study of the cultural, social, and historical interconnectedness of veganism, queerness, and animality. To interrogate these links requires mixed methods such as the collection of oral histories with self-identified queer vegans, analysis of animal themes in queer film and literature, social media analysis, and analysis of food cultures and restaurant rhetorics. Following work by prominent American Studies scholars, this project posits that the practice of veganism embodies queer performativity in how queerness and animality are ontologically linked
Identification of Web Security Threats to Online Business Models
No full textOnline business models have become increasingly popular in recent years, providing new opportunities for entrepreneurs and established companies alike. However, along with these opportunities come new risks, particularly in the realm of web security. While traditional threats typically affect the backend systems that provide web services, attackers nowadays can also target the actual business model itself to make financial damage. The threats are becoming more difficult to discover because of the wide-scaled and complex web ecosystem that involves multiple parties.In this dissertation, we present proposals to identify web security threats to online business models. Specifically, we first introduce a novel ad budget draining attack, AdBudgetKiller, in order to demonstrate a possible attack scenario with real-world cases and to come up with prevention methods. AdBudgetKiller automatically discloses a targeting strategy of an advertiser, then fabricate browsing profiles to dispatch advertisements from the targeted advertiser.We also present a testing-based approach to automatically identify client-side business flow tampering vulnerabilities. In particular, our method systematically analyzes websites to gather potential tampering locations by using dynamic execution data collection. We then test the websites with tampering proposals to identify any business flow tampering vulnerabilities. Further, we present an enhanced detection method for digital content services that detects business flow tampering vulnerabilities. We perform differential analysis on collected execution traces to determine how the business flow begins to differ. Then we test if the divergence points can be tampered with
Impact of Engineers Without Borders USA Experiences on Professional Preparation
No full textEngineering graduates are called on by society to work with others to address wicked problems which incorporate a wide range of socio-technical considerations. One promising approach to more wholistically prepare students for the demands of engineering-related work and positively contributing as citizens is community-engaged learning. To help this pedagogy more closely meet its full potential, this study used the context of Engineers Without Borders USA (EWB-USA), as viewed through the lens of its alums in professional practice. It also explored individuals’ differentiated outcomes produced by the many types of variation inherent in the EWB-USA model. The goal of the project was to inform best practices for how community-engaged engineering programs can be implemented to support students’ professional preparation. This study took a QUAN QUAL explanatory sequential mixed-methods approach. The survey instrument (n = 268) led to non-parametric tests for group comparisons which were conducted on scores generated through exploratory factor analysis. Inductive thematic analysis was then used on the semi-structured interview transcripts (n = 29). EWB-USA was shown to support the transition between schooling and work through authentic experiential learning, which incorporated inherently-complex projects truly intended for implementation to meaningfully benefit end-users and engaging with a wide range of diverse stakeholders. It especially bolstered the development of competencies in project management, design and project processes, communication, diverse teaming, contextualization, addressing challenges and new situations, and functioning as a connected element of larger complex socio-technical systems. These gains were reflected in the alums’ perceived advantage in career outcomes, demonstrating their long-lasting transferability to professional practice. The results of this study also showed that while limited variations were found based on participant demographics, differences in personal experience within EWB-USA had a greater effect on outcomes. The differences found based on demographic groupings consisted of women reporting greater benefits to their confidence and sense of community. Impactful individual experience differences identified included length of time involved with EWB-USA, mentor engagement, leadership opportunities, repeating phases on different projects, seeing a project from start-to-finish, and number of trips taken to the community partner site. Across the competencies developed from the program, alums often reported perceiving greater benefits from their EWB-USA experiences once they had an opportunity to apply their learnings in professional practice
Implementation of Superabsorbent Polymers for Internally Cured Concrete
No full textHydrated portland cement provides the solid adhesive matrix necessary to bind aggregate (sand and gravel) into concrete. The hydration reaction requires water, however the products of the reaction limit further diffusion of water to unreacted cement. Superabsorbent polymer (SAP) hydrogel particles absorb mixing water, then subsequently desorb when the relative humidity drops, serving as internal water reservoirs within the cement matrix to shorten diffusion distances and promote the hydration reaction in a process called internal curing. Internally cured cementitious mixtures exhibit an increased degree of hydration and reduced shrinkage and cracking, which can increase concrete service life. Increased service life can, in turn, reduce overall demand for portland cement production, thereby lowering CO2 emissions. This dissertation addresses practical implementation questions key to the translation of SAP hydrogel internal curing technology to from the benchtop to the field in transportation applications, including: (1) What effects do mix design adjustments made to increase mixture flow when using SAP have on cementitious mixture properties? and (2) What effect do cementitious binder characteristics have on SAP performance? The addition of SAP to a cementitious mixture changes the mixture’s flow behavior. Flow behavior is an important aspect of concrete workability and sufficient flow is necessary to place well consolidated and molded samples. Often, additional water is added to mixtures using SAP to account for the absorbed water, however cementitious mixture workability is often tuned using high range water reducing admixtures (e.g., polycarboxylate ester-based dispersants). Fresh and hardened properties of mortars were characterized with respect to flow modification method (using the mortar flow table test; compressive strength at 3, 7, and 28 days; flexural strength at 7 and 28 days; and microstructural characterization of 28-day mortars). At typical doses, it was found that the addition of extra water lowers the resulting compressive and flexural strength, while high range water reducing admixtures administered at doses to achieve sufficient mortar flow did not compromise compressive or flexural strength. The SAPs used in cement are generally poly(acrylamide-acrylic acid) hydrogels and are not chemically inert in high ionic-load environments, such as cement mixtures. The behavior of an industrial SAP formulation with characterized across five different cement binder compositions with respect the cement hydration reaction (using isothermal calorimetry, thermogravimetric analysis of hydration product fraction, and scanning electron microscopy (SEM)/energy dispersive x-ray spectroscopy (EDS) microstructural analysis), the absorption behavior of the SAP, and the fresh and hardened properties of SAP-cement composites (mortar flow and compressive and flexural strength). The change in properties induced by the addition of SAP was similar across ASTM Type I cements from three manufacturing sources, suggesting that SAP internal curing can be implemented predictably over time and geography. Excitingly, in analysis of cement systems meeting different ASTM standards (Type III and Type I with 30% replacement by mass with ground blast furnace slag), synergistic and mitigating reaction behaviors were observed, respectively, in Type III and slag cement, suggesting that further study of SAP with these cement systems could be of particular interest
Application of Machine Learning Algorithms on Consumer Choices
No full textIn recent years, machine learning algorithms have been widely used in marketing to address the challenges posed by increasingly complex marketing contexts and large-scale unstructured data. Marketing researchers also have emphasized the need to prioritize accurate estimates of causal effects using machine learning algorithms. This dissertation aims to extend the application of machine learning algorithms to consumer choices, incorporating the two themes outlined above. In the first essay, we employ an advanced machine learning algorithm in the causal inference-generalized synthetic control (GSC) method to investigate the impact of household store choices on household sustainability. Specifically, we examine the effect of shopping at club stores on a household\u27s food carbon footprint. Using a process-based Life Cycle Assessment (LCA) model and Nielsen Consumer Panel data, we calculate the household-level food carbon footprint between 2007 and 2017. Using GSC and Diff-in-Diff approaches, we find a consistent average treatment effect indicating that households generate 7-9% more per capita carbon emissions after purchasing groceries at club stores. We also observe a heterogeneous treatment effect that shows a larger increase for low-income and small households. Furthermore, we find that larger package sizes in club stores have a more significant impact on increasing the food carbon footprint. Our study is the first in the marketing field to introduce a carbon emissions calculation model and explore the relationship between retail formats and household sustainability. These findings have significant implications for policymakers and managers. In the second essay, we apply text mining algorithms to understand users\u27 choices in the crowdfunding market using unstructured data. Specifically, we investigate how changes in platform specialization in terms of platform size and backers\u27 composition (i.e., donors vs. buyers) influence platform participant behaviors and campaign outcomes in the event of a crowdfunding platform split-up. Our results show a higher probability of reaching funding goals for campaigns on a reward-based platform (main platform) after the launch of a donation-based platform. This is due to fewer campaigns being launched on the main platform after the split, and creators providing more visual campaign information (i.e., images and videos) to mitigate information asymmetry that is more of a concern after the platform split-up, as the increased proportion of buyers, who are more sensitive to such visual information than donors. Our findings support the notion that potential backers\u27 motivations for supporting a campaign drive creators\u27 information disclosure strategies. The study provides significant managerial implications for platforms and participants
Out-of-Distribution Representation Learning for Network System Forecasting
No full textRepresentation learning algorithms, as the cutting edge of modern AIs, has shown their ability to automatically solve complex tasks in diverse fields including computer vision, speech recognition, autonomous driving, biology. Unsurprisingly, representation learning applications in computer networking domains, such as network management, video streaming, traffic forecasting, are enjoying increasing interests in recent years. However, the success of representation learning algorithms is based on consistency between training and test data distribution, which can not be guaranteed in some scenario due to resource limitation, privacy or other infrastructure reasons. Caused by distribution shift in training and test data, representation learning algorithms have to apply tuned models into environments whose data distribution are solidly different from the model training. This issue is addressed as Out-Of-Distribution (OOD) Generalization, and is still an open topic in machine learning. In this dissertation, I present solutions for OOD cases found in cloud services which will be beneficial to improve user experience. First, I implement Infinity SGD which can extrapolate from light-load server log to predict server performance under heavy-load. Infinity SGD builds the bridge between light-load and heavy-load server status through modeling server status under different loads by an unified Continuous Time Markov Chain (CTMC) of same parameters. I show that Infinity SGD can perform extrapolations that no precedent works can do on real-world testbed and synthetic experiments. Next, I propose Veritas, a framework to answer what will be the user experience if a different ABR, a kind of video streaming data transfer algorithm, was used with the same server, client and connection status. Veritas strictly follows Structural Causal Model (SCM) which guarantees its power to answer what-if counterfactual and interventional questions for video streaming. I showcase that Veritas can accurately answer confounders for what-if questions on real-world emulations where on existing works can. Finally, I propose time-then-graph, a provable more expressive temporal graph neural network (TGNN) than precedent works. We empirically show that time-then-graph is a more efficient and accurate framework on forecasting traffic on network data which will serve as an essential input data for Infinity SGD. Besides, paralleling with this dissertation, I formalize Knowledge Graph (KG) as doubly exchangeable attributed graph. I propose a doubly exchangeable representation blueprint based on the formalization which enables a complex logical reasoning task with no precedent works. This work may also find potential traffic classification applications in networking field
DNA Self-Assembly on Surface
No full textDNA nanotechnology has rendered programmable, bottom-up self-assembly of nanostructures in various morphology, versatile functionalization, and atomic level precision over the last forty years. DNA nanostructures are usually assembled in solution by the thermodynamic process in a specific solution. In recent years, DNA two-dimensional (2D) structures on the surface have been widely applied in semiconductors, electronic devices, and biomedical studies. My research mainly focused on novel DNA nanostructures assembly on the surface and their applications. I have developed an equilibrium-enabled flexibly curved DNA homopolymer. I have further developed a novel method to determine the interhelical angle of DNA secondary structure by DNA 2D-array. In this thesis, I have envisioned a strategy to prepare DNA linear polymers with flexible curvature and further assembled them into spiral or concentric rings on the surface. In DNA double crossover-like (DXL) homopolymers, an aromatic chemical group was introduced to the 3\u27-end in each strand. The planar group could stack into the DNA homopolymer, which increases the length on one side of the DXL polymer and further bend the structure by uneven-length stress. Moreover, the stacking in is under the equilibrium with flipping out, endowing the dynamic change and flexibility to the curvature of the DNA homopolymer, which could be a benefit in the surface-assisted construction of spirals or concentric rings. With the appropriate design, DNA could be self-assembled on the surface into 2D crystals in a certain periodicity. Such a structure could be applied in nucleic acid secondary structure determination as a crystallography-like method. In this work, I have successfully incorporated the 10-23 DNAzyme, a common-used RNA-cleavage DNA sequence into DNA 2D arrays. In the brick-wall-like DNA 2D arrays, the repeating distance determined the interhelical angle of the 10-23 DNAzyme flanks. By 2D fast Fourier transform (FFT), this repeating distance could be measured and calibrated, following the deduction of the target angle. This approach had been validated with well-known DNA secondary structures
The Future of iO Kids: Establishing Brushing Habit in Early Childhood
No full textThe design project aims to produce a toothbrush that will make it easier for young children to develop good brushing habits. Previous research shows us that children need consistency and parental guidance to establish healthy habits successfully. Without parent guidance, professional intervention, and other important variables, children are less likely to create an oral hygiene routine that they understand and can keep up with in their future years. What if we could start young to improve and build sustainable oral hygiene habits? The goal of this project is to imagine the future of children’s toothbrushes so that these changes last across the lifespan. Through the role of parents, rewards, and the collaboration with Oral-B at Procter & Gamble, this technology and conceptual design came to life. The final outcome of this project is a habit-forming toothbrush system that helps parents to establish brushing habits in themselves and their children through a fun and addicting toothbrushing journey