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- Language: English
- Creators: Ira A. Fulton Schools of Engineering
The combined use of methamphetamine and opioids has been reported to be on the rise throughout the United States (U.S.). However, our knowledge of this phenomenon is largely based upon reported overdoses and overdose-related deaths, law enforcement seizures, and drug treatment records; data that are often slow, restricted, and only track a portion of the population participating in drug consumption activities. As an alternative, wastewater-based epidemiology (WBE) has the capability to track licit and illicit drug trends within an entire community, at a low cost and in near real-time, while providing anonymity to those contributing to the sewer shed. In this study, wastewater was collected from two Midwestern U.S. cities (2017-2019) and analyzed for the prevalence of methamphetamine and the opioids oxycodone, codeine, fentanyl, tramadol, hydrocodone, and hydromorphone. Monthly 24-hour time-weighted composite samples (n = 48) from each city were analyzed using isotope dilution liquid chromatography tandem mass spectrometry. Results showed that methamphetamine and total opioid consumption (milligram morphine equivalents) in City 1 were strongly correlated only in 2017 (Spearman rank order correlation coefficient, ρ = 0.78), the relationship driven by fentanyl, hydrocodone, and hydromorphone. For City 2, methamphetamine and total opioid consumption were strongly positively correlated during the entire study (ρ = 0.54), with the correlations driven by hydrocodone and hydromorphone. In both cities, hydrocodone and hydromorphone mass loads were highly correlated, suggesting a parent and metabolite relationship. WBE provides important insights into licit and illicit drug consumption patterns in near real-time as they evolve; important information for community stakeholders in municipalities across the U.S.
Accessible STEAM (Science, Technology, Engineering, Art, and Mathematics) education is imperative in creating the future innovators of the world. This business proposal is for a K-8 STEAM Museum to be built in the Novus Innovation Corridor on Arizona State University (ASU)’s Tempe campus. The museum will host dynamic spaces that are constantly growing and evolving as exhibits are built by interdisciplinary capstone student groups- creating an internal capstone project pipeline. The intention of the museum is to create an interactive environment that fosters curiosity and creativity while acting as supplemental learning material to Arizona K-8 curriculum. The space intends to serve the greater Phoenix area community and will cater to underrepresented audiences through the development of accessible education rooted in equality and inclusivity.
Technology has managed to seamlessly grow into every industry fathomable without much resistance. This could be due to the fact that the majority of industries that have integrated technology have lacked insurmountable barriers which could hold back strategic innovations. Even with a wide array of industries applying technology to their framework, some haven’t managed to reach the true capability of technological advances. One industry that has both taken wide advantage of technology while also barely scraping the surface of the depth behind its potential has been politics. Electronic voting booths, targeted online marketing campaigns, and live streamed debates have been integral parts of our modern-day political environment, however, approval rating-based forecasting for elections has been an area that isn’t commonly referenced by both large political players.
In an age of information where data can be extracted just about anywhere and interpolated using extensive statistical processing, the fact that systems modeling isn’t a pillar of campaign efforts seems ludicrous. A field that is heavily dependent on pivoting concern based on lack of support would make sense to heavily depend on a modeling system that can accurately predict future points of interest.
This report aims to lay the foundation that can be built upon through providing pitfalls in potential modeling, importance of a modeling system, and a barebones skeleton model in AnyLogic with a scheme of how the model would work. I hope this report can serve political interests by providing context on which modeling can accurately provide insight.
GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
Project management is the crucial component for managing and mitigating the inherent risks associated with changes in technology and innovation. The procedures to track the schedule, budget, and scope of various projects in the standard worlds of engineering, manufacturing, construction, etc., are essential elements to the success of the project. Cost overruns, schedule changes, and other natural risks must be managed effectively. But what happens when a project manager is tasked with delivering an attraction that needs to withstand harsh weather conditions, and millions of people enjoying it every year, for a company with arguably the highest standards for quality and guest satisfaction? This would describe the project managers at Walt Disney Imagineering (WDI) and the projects they oversee have tight budgets, aggressive schedules and require a bit more pixie dust than other engineering projects. However, the universal truth is that no matter the size or the scope of the endeavor, project management processes are absolutely essential to ensuring that every team member can effectively collaborate to deliver the best product.
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
