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- Creator:
- Abboud, Ibrahim
- Description:
- One of the issues that the world is facing is that we are running out of the fossil energy resources, and the use of renewable ones is currently limited. Recently, researchers have put lots of efforts to find a way to use biomass as one of the possible renewable sources. They realized that it is possible to convert this biomass material into organic compounds that are close to the characteristics of petrochemicals. One of the reactions that help in this process is the catalytic deoxydehydration (DODH), which is a reaction that removes two adjacent hydroxyl groups (-OH) from vicinal diols to produce alkenes. The DODH reaction requires a catalyst and a reducing agent that help in completing the reaction. According to the article " Deoxydehydration (DODH) of Biomass-Derived Molecules", the first catalytic deoxydehydration was demonstrated by Andrews and Cook using PPh3 as the reducing agent and Cp*ReO3 as the catalyst (1). Subsequently, researchers started synthesizing catalysts with different metals (such as rhenium, ruthenium, vanadium, or molybdenum) adding various reductants such as phosphines, sulfite, alcohols while changing the other parameters such as temperature and reaction time. In this research paper, molybdenum-based catalysts for DODH are discussed.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Lewis, Grace, Retherford, Margaux, and Caudle, Mitchell
- Description:
- The integration of unmanned aerial vehicles (UAVs) into the national airspace system presents itself with a myriad of technical problems. One of the key requirements for this integration is the human equivalent level of safety, which requires the ability to detect and avoid other aircraft/obstacles in their flight path so that the UAVs complete their mission without any loss or damage to other aircraft or property. This presentation talks about the use of ADS-B (Automatic Dependent Surveillance- Broadcast) transponders for detection of collision of other similarly equipped aircraft/UAVs. ADS-B transponders can receive and broadcast global position and velocities among other pertinent information in a 100-nautical mile radius. This research used Ping-2020 ADS-B transponders for the collision detection. The collision detection and avoidance is first tested in software-in-the-loop simulation, which also uses the flight controller, Ardupilot, and ADS-B transponders in the loop. MAVproxoy, a UAV ground station software package, is used to communicate between in the autopilot and simulation environment via MAVLink. FlightGear flight simulator is used to visualize the motion of the UAVs. The research uses two fixed-wing aircraft equipped with Pixhawk autopilots, which allow autonomous waypoint navigation. The collision avoidance algorithms use a three-step system of detect, predict, and avoid. The algorithm calculates and sends the waypoints for collision avoidance to the autopilot. Using the kinematic equations, the UAV velocities can be calculated from the information received from GPS sensors, and future positions can be predicted. The collision avoidance algorithm is tested using the incoming information from real-time aircraft.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Raman, Isabella
- Description:
- In the present study, we plan to examine the neural correlates of the conscious recollection of long-term memories (LTM). This process is defined as the retrieval of qualitative or associative information during recognition, and a wealth of studies have shown it is dissociable from "familiarity", which is a strength-based type of recognition. However, fewer studies have focused on whether the cognitive and/or neural basis of recollection differs as a function of the type of association that is retrieved. We take up this question in the present study and hypothesize that the electroencephalographic (EEG) activity and event-related potentials (ERPs) associated with recollection following item-feature vs. item-item associative encoding differs. Following the performance of two mental imagery encoding tasks (separation imagery and interactive imagery), designed to promote dissociable levels of recollection of item-feature vs. item-item, respectively, yet comparable levels of item recollection, we will record EEG while participants perform a recognition memory task sensitive to the contribution of recollection or familiarity. This will permit us to investigate whether the specific ERP associated with recollection, the "P600 old/new effect", differs as a function of encoded association. We will examine whether overall oscillatory activity differs as well. of the brain that occurs during item recollection. Overall, we believe that our results will help inform neurocognitive models of recognition LTM.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Agunos, Darwin
- Description:
- The Force Concept Inventory (FCI) is an important diagnostic tool widely utilized within the PER (Physics Education Research) community. While students' performance on the FCI has been extensively studied, relatively less work was done in primarily undergraduate institutions (PUI) and minority-serving institutions. As part of an effort to provide evidence for the reproducibility of educational studies for a variety of student body as well exploring possible gender or racial gaps in student's performance, data was collected over a year-long period for a number of introductory physics courses at Cal Poly Pomona (both a PUI and Hispanic serving institution) to understand factors that affect students' performance on the FCI. In this work, we discuss background variables that predict students' FCI scores at the end of the term. This baseline measure can be used for any future studies conducted at our institution to evaluate the effectiveness of any pedagogical reforms. We will also explore possible gender or racial gaps for our student body.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Kupsik, Jakub
- Description:
- Traffic congestion on the freeways is one of the worst issues in California. During peak hours, traffic can slow down to a stop-and-go crawl. A significant cause of this behavior is driving habits: human beings have a tendency to drive closer as they slow down, which makes merging difficult and exacerbates the slowness. The Tail Gator app uses machine vision to, when placed on the dashboard of a normal car, collect and relay information to the driver in a hands-free manner regarding the vehicle in front of them. It would tell the driver the distance and relative speed of the car in front of them, which the driver can then use to build good, anti-congestion driving habits and stop tailgating in traffic. The technology behind this has further applications: it could be used to recommend to drivers optimal speed and acceleration based on the traffic situation, which would also be useful in guiding the behaviors of self driving cars to better the road for everyone.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Osuna, Ivan
- Description:
- Human comfort in regards to clothing systems depends upon the ability of the fabrics and their construction to transmit perspiration away from the body. If the clothing system is inefficient in this regard, the wearer will experience a great deal of discomfort and physiological distress. It is critical, therefore, for the clothing to be "breathable" This project was an attempt to quantify the effect of environmental factors on the creation and transport of moisture/moisture vapor through different fabrics. The physics of the phenomenon will be described. The design and construction of a custom-made apparatus will be reported. The apparatus utilizes a controlled water heater, a humidity/temperature sensor, a fan, and a weighing scale. The results will be reported and discussed.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Tasci, Amy
- Description:
- Butanol is a gasoline substitute and naturally derived, making the production of butanol of increasing importance. Butanol is produced through acetone, butanol, ethanol (ABE) fermentation pathway in some bacteria, including the Clostridium species. Because of the increasing interest in butanol production, understanding the metabolic flux through the ABE pathway is important for optimizing the recovery of the biofuel. Global profiling of the ABE pathway intermediates is difficult due to the volatile nature of the metabolites and their structural similarity. Our goal is to develop a procedure for the quantification of butanol and butyric acid using Nuclear Magnetic Resonance Spectroscopy (NMR). Due to the complexity of the spectra, traditional 1H-NMR is insufficient for quantification, so we developed 2D approaches for quantification that involve J-resolved spectroscopy (JRES) and Total Correlation Spectroscopy (TOCSY) through the application of a calibration curve. We created a calibration curve with standards and then tested the correlation of the expected concentration of an analyte with the known concentration, and found that they were in high agreement (R2 = 0.97). Overall, there were 4 successful reproducible trails. Although the 1H approach was the most accurate, we are working on improving the 2D approaches so that butanol and butyric acid can be quantified in a complex sample. Once these tests work on known sample, they can be moved onto real samples from Dr. Wei-Jen Lin's lab from Biological Sciences.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Singh, Parveena
- Description:
- Fanfiction allows people from various communities to celebrate their favorite fandoms, characters, and relationships together. Over the past decade, fanfiction has developed into a center for activism through the racebending movement in online communities like Tumblr. The following research focuses on how racebending in fanfiction provides women of color with a space where they can have creative power and a voice for their unique narratives by recasting white female characters with women of color instead. There is primarily a focus on women of color due to how studies show women having lower self-esteem with increased media exposure as opposed to men, and how stereotyped representations of female minorities on screen have a profound effect on girls of color. The research shows why racebending is of importance for women of color by highlighting the lack of central characters played by them in movies and television, as well as the lack of these women in creatives roles such as writers and directors. The deficiency of women of color both onscreen and off screen directly leads to the homogenous nature of mainstream media and the occurrence of whitewashing, which is detrimental because as Dr. Lori Kido Lopez presents in "Fan-Activists and the Politics of Race in The Last Airbender" it supports the idea that whiteness can stand in for all racial difference. Fanfiction counters this idea through racebending, where women of color can see positive representations of themselves by being producers of their own media instead of simply consumers of it.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Brown, Aaron, Khatri, Hemisha, and Santana, Anthony
- Description:
- West Nile virus (WNV) is a neurotropic flavivirus capable of causing irreparable neuronal injury and encephalitis which can potentially result in lethal disease. In order to control WNV replication and any associated neurologic disease, it is vital that anti-viral T cells enter the central nervous system (CNS) and become fully activated. Dendritic cells (DCs) are powerful APCs responsible for activating T cells and regulating their effector immune responses, however, their role within the CNS during WNV encephalitis remains uncertain. Previously, we detected the presence of DEC-205-expressing DCs within the perivascular spaces of the CNS during WNV encephalitis. DEC-205 is an endocytic receptor expressed on a subset of DCs which have been shown to be effective at cross-presentation and initiating anti-viral immunity. We hypothesize that DEC-205 expressing DCs are critical in the immune defense against WNV encephalitis via the activation of anti-viral T cell immunity. In this study, the ability of DEC-205 expressing DCs to limit viral infection, replication, and neuronal injury will be examined utilizing a well-established mouse model of WNV encephalitis in DEC-205 expressing wild-type (WT) mice and DEC-205 gene knockout (KO) mice following WNV infection. In future studies, utilizing the same mouse model, the mechanisms by which DEC-205 expressing DCs are able to induce protection against WNV encephalitis will be determined by investigating the a
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona
- Creator:
- Oliver, Pi Raymond
- Description:
- Advancements in machine learning over the past several decades have provoked a sharp increase in humanity's ability to gather and analyze bulk data at a deep level of understanding - and use that data to make informed decisions and solve critical issues. Artificial Neural Networks (ANN's) have been used to handle analysis of various high-dimensional data types - including audio, video, visual, and numerical data. Medical Infrared Thermography (IT) utilizes infrared light to produce surface-temperature measurements of thermal images which may be used for medical diagnosis and analysis. Surface temperature thermograms give insight into the subsurface blood flow and tissue properties in a medically non-invasive and cost-effective manner. Image analysis of thermograms with ANN's have already been used to diagnose conditions and identify patterns in medical imagery for humans and many other species - but in particular - there are not many examples in the literature of its use in dogs (Canis lupus familiaris). Dogs have varying thermal surface temperature patterns based on breed/mix, coat type, relative size, and other characteristics. Our research seeks to identify a normal thermal pattern/range of dogs and determine how machine learning technologies may be applied to diagnose medical phenomena therein. We are developing a Convolutional Neural Network that seeks to analyze paired thermal/visual images of dogs alongside numerical data and determine if the dog has a normal or abnormal thermal pattern. Further classification, training, and network optimization may lead to increased diagnostic capability.
- Resource Type:
- Student Research, Presentation, Poster, and Abstract
- Campus Tesim:
- Pomona