Matching Items (3)
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Description
The atmosphere contains a substantial amount of water soluble organic material, yet despite years of efforts, little is known on the structure, composition and properties of this organic matter. Aqueous phase processing by fogs and clouds of the gas and particulate organic material is poorly understood despite the importance for

The atmosphere contains a substantial amount of water soluble organic material, yet despite years of efforts, little is known on the structure, composition and properties of this organic matter. Aqueous phase processing by fogs and clouds of the gas and particulate organic material is poorly understood despite the importance for air pollution and climate. On one hand, gas phase species can be processed by fog/cloud droplets to form lower volatility species, which upon droplet evaporation lead to new aerosol mass, while on the other hand larger nonvolatile material can be degraded by in cloud oxidation to smaller molecular weight compounds and eventually CO2.

In this work High Performance Size Exclusion Chromatography coupled with inline organic carbon detection (SEC-DOC), Diffusion-Ordered Nuclear Magnetic Resonance spectroscopy (DOSY-NMR) and Fluorescence Excitation-Emission Matrices (EEM) were used to characterize molecular weight distribution, functionality and optical properties of atmospheric organic matter. Fogs, aerosols and clouds were studied in a variety of environments including Central Valley of California (Fresno, Davis), Pennsylvania (Selinsgrove), British Columbia (Whistler) and three locations in Norway. The molecular weight distributions using SEC-DOC showed smaller molecular sizes for atmospheric organic matter compared to surface waters and a smaller material in fogs and clouds compared to aerosol particles, which is consistent with a substantial fraction of small volatile gases that partition into the aqueous phase. Both, cloud and aerosol samples presented a significant fraction (up to 21% of DOC) of biogenic nanoscale material. The results obtained by SEC-DOC were consistent with DOSY-NMR observations.

Cloud processing of organic matter has also been investigated by combining field observations (sample time series) with laboratory experiments under controlled conditions. Observations revealed no significant effect of aqueous phase chemistry on molecular weight distributions overall although during cloud events, substantial differences were apparent between organic material activated into clouds compared to interstitial material. Optical properties on the other hand showed significant changes including photobleaching and an increased humidification of atmospheric material by photochemical aging. Overall any changes to atmospheric organic matter during cloud processing were small in terms of bulk carbon properties, consistent with recent reports suggesting fogs and clouds are too dilute to substantially impact composition.
ContributorsWang, Youliang (Author) / Herckes, Pierre (Thesis advisor) / Fraser, Matthew (Committee member) / Anbar, Ariel (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The purpose of our research was to develop recommendations and/or strategies for Company A's data center group in the context of the server CPU chip industry. We used data collected from the International Data Corporation (IDC) that was provided by our team coaches, and data that is accessible on the

The purpose of our research was to develop recommendations and/or strategies for Company A's data center group in the context of the server CPU chip industry. We used data collected from the International Data Corporation (IDC) that was provided by our team coaches, and data that is accessible on the internet. As the server CPU industry expands and transitions to cloud computing, Company A's Data Center Group will need to expand their server CPU chip product mix to meet new demands of the cloud industry and to maintain high market share. Company A boasts leading performance with their x86 server chips and 95% market segment share. The cloud industry is dominated by seven companies Company A calls "The Super 7." These seven companies include: Amazon, Google, Microsoft, Facebook, Alibaba, Tencent, and Baidu. In the long run, the growing market share of the Super 7 could give them substantial buying power over Company A, which could lead to discounts and margin compression for Company A's main growth engine. Additionally, in the long-run, the substantial growth of the Super 7 could fuel the development of their own design teams and work towards making their own server chips internally, which would be detrimental to Company A's data center revenue. We first researched the server industry and key terminology relevant to our project. We narrowed our scope by focusing most on the cloud computing aspect of the server industry. We then researched what Company A has already been doing in the context of cloud computing and what they are currently doing to address the problem. Next, using our market analysis, we identified key areas we think Company A's data center group should focus on. Using the information available to us, we developed our strategies and recommendations that we think will help Company A's Data Center Group position themselves well in an extremely fast growing cloud computing industry.
ContributorsJurgenson, Alex (Co-author) / Nguyen, Duy (Co-author) / Kolder, Sean (Co-author) / Wang, Chenxi (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Management (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
Many researchers aspire to create robotics systems that assist humans in common office tasks, especially by taking over delivery and messaging tasks. For meaningful interactions to take place, a mobile robot must be able to identify the humans it interacts with and communicate successfully with them. It must also be

Many researchers aspire to create robotics systems that assist humans in common office tasks, especially by taking over delivery and messaging tasks. For meaningful interactions to take place, a mobile robot must be able to identify the humans it interacts with and communicate successfully with them. It must also be able to successfully navigate the office environment. While mobile robots are well suited for navigating and interacting with elements inside a deterministic office environment, attempting to interact with human beings in an office environment remains a challenge due to the limits on the amount of cost-efficient compute power onboard the robot. In this work, I propose the use of remote cloud services to offload intensive interaction tasks. I detail the interactions required in an office environment and discuss the challenges faced when implementing a human-robot interaction platform in a stochastic office environment. I also experiment with cloud services for facial recognition, speech recognition, and environment navigation and discuss my results. As part of my thesis, I have implemented a human-robot interaction system utilizing cloud APIs into a mobile robot, enabling it to navigate the office environment, identify humans within the environment, and communicate with these humans.
Created2017-05