Software Porting of a 3D Reconstruction Algorithm to Razorcam Embedded System on Chip

A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a UAV with tight constraints of size, weight, and power. The potential impacts will be seen through the continuation of this research in the Smart ES lab at University of Arkansas

Bearing Failure On A Boiler Feed Water Pump

Case Stud

Homelessness in Richmond

Capstone presentation for the University of Richmond SSIR (Sophomore Scholars in Residence) Program.https://scholarship.richmond.edu/ssir-presentations-2017/1002/thumbnail.jp

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.</p

Text mining of veterinary forums for epidemiological surveillance supplementation

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns

Site-Specific Immunomodulator: A Novel Treatment for Crohn\u27s Disease

We investigated the mechanism of action, safety, and efficacy of the Site-Specific Immunomodulator (SSI) QBECO, a novel immunotherapy for Crohn’s disease (CD). Using human monocytic THP-1 cells, we demonstrate that SSI QBECO (derived from the common colon bacteria E. coli) activates macrophages to an M1 phenotype (associated with enhanced capacity to eliminate bacteria and activate innate immune responses). We assessed SSI QBECO in a compassionate use protocol of ten adult patients with active CD. Patients with moderate to severe clinical symptoms receiving conventional CD treatments and/or complementary therapies were included, except patients receiving anti-TNF medications. SSI QBECO was self-administered subcutaneously every second day, for a minimum of 2.5 months and a maximum of 11 months. All 10 patients reported improvement of symptoms while on the SSI QBECO treatment. Seven patients reported full resolution of clinical symptoms during a course of SSI QBECO of at least three months. Three patients have experienced ongoing sustained clinical remission after discontinuing all medications, including SSI treatment. The longest case of clinical remission is still ongoing (\u3e4 years). No serious severe adverse clinical events were reported. Collectively, we conclude that treatment with the immunoactive SSI QBECO was well tolerated and effective for treatment of Crohn’s disease in this case series