Crowdsourcing is a way to gain knowledge from a crowd. To lower the cost, companies nowadays apply crowdsourcing systems as an alternative to using a consulting company or hiring a group of people from within the company. Amazon Mechanical Turk is a new online crowdsourcing platform. The purpose of this research is to give recommendations to Amazon Mechanical Turk in order to increase the user’s experience and the quality of the platform. We applied two methods with five different machine learning methods to determine the efficiency of using crowdsourcing. The first method is comparing the significant variables and MSE values from each machine learning methods. The machine learning methods include Linear Regression, Random Forest, and LASSO. The second method is using the PCA method to see the relationship in groups for the variables in the dataset. Both methods helped us analyze public available data in order to better understand the relationship between costs and quality of the online crowdsourcing system. As a result of the two methods, we categorized the significant variables into four groups which are cost, conditions, quality of measurement, and advertising strategy. By analyzing the relationship between cost and quality in these four groups, we concluded that the Amazon Mechanical Turk platform could benefit from addition of a discount feature, a rubric system, a rating system, and a trend system. Our research results could also be applied on other manufacturing field in the future.

The English language Wikipedia is notable for its large number of articles. However, 288 other active language editions of Wikipedia have also developed through the intricate interactions of contributing editors. While the editor interactions in the English Wikipedia have been researched extensively, these other language editions remain understudied. To understand how editors currently come to consensus in article building in the Spanish language, a team of researchers has leveraged an existing English framework that depicts how power and policies play a role in mass collaboration. Using this English language framework, we are using qualitative coding methods to build a unique model of the editor interactions on the Spanish language Wikipedia. The results of this study will help contribute to a deeper understanding of how a framework in a different language edition of Wikipedia varies from the English. Our preliminary results show that policy plays a large role in justifying editor decisions for the edits they make on various articles. Furthermore, our research findings have expanded our knowledge of the issues surrounding replication of an English framework in a different language platform.

Makerspaces are an emerging tool in the engineering education field. Compared to the current standard of formal, class-based education, makerspaces provide a multitude of resources meant to support students through more informal, project-based learning. This study is part of a larger project exploring supporting design learning through design decision making. Here we investigate how students make material decisions when pursuing projects in a university makerspace. What kinds of questions, options, and criteria do they explore, and what rationale do they use to make their final choice? How does this change in time, across different projects, and across different students? In previous work, 7 undergraduate students completed a self-driven project while documenting their process and anything else they felt was relevant. During this study, 6 researchers analyzed written traces of the students’ project progress. Material and tool decisions were identified, and coded to present questions, options, and criteria over time using the Design Space Analysis framework. Trends were identified across students, time, and different materials or tools. Through our analysis, we discovered the following results. Though students are pursuing different projects, they all deal with similar decisions around material and tool choice during their processes. For most decisions, students consider very few options, although there are some where more are contemplated. Regarding criteria, students consider cost, aesthetics, and availability, but often not specific functionality requirements. Students naturally provide design rationale as part of their process, but it is not very well developed. The results from this study will allow us to gain greater understanding on what students tend to consider, and develop methodology to make a greater number of potential options more visible to students during their project processes.

Water is the substance that exists everywhere in our lives ranging from drinking water to the blood in the body. It is well known that there are 3 phases of water: gas, liquid and solid. Yet, in Dr. Gerald Pollack’s lab, we conduct researches on the Exclusion Zone (EZ) water, which we term as the ‘Fourth Phase of Water.’ Dr. Pollack’s lab centers largely on the identification of EZ water and many applications in nature and technology. Among the natural applications, the lab emphasizes on the role of EZ water in human health, including cell biology since cells are filled with EZ water and cannot function without enough EZ water. Dr. Pollack’s lab conducts the research uncovering the nature’s hidden secret that has tremendous potentials to be applied to different bioengineering products. I am currently conducting a project on how Wi-Fi impacts EZ water as an external source of disturbances. Humans are exposed to Wi-Fi signals constantly in our everyday lives. As human body cells are filled with EZ water, we predict the Wi-Fi signals could alter our bodily functions through changes in EZ water properties such as amount of EZ water. EZ water develops around hydrophilic substances, and we uesed blood vessel-like tube to observe EZ water development. Then, we measured the amount of EZ water and analyzed the data. For this project, we compare the difference in the amount of EZ water built with and without presence of Wi-Fi. We currently have statistically significant results that Wi-Fi decreases amount of EZ water developed by ~18%. As a further step in the future, we are investigating on how different types of disturbances such as cell phone impact on EZ water for further health care.

Ongoing research has been conducted to design optimal routes for vaccine distribution to health centers in Mozambique. Distance between health centers is needed in order to construct routes, but is not easily available. My objective is to easily generate distances between health centers, given their geographic coordinates, and to visualize the optimal routes on a map. Using a conglomeration of tools including Python, API, OpenRouteService and Excel, I was able to read an Excel file of latitudes and longitudes for health centers, separated into provinces and districts, and produce an Excel matrix of distances from health center to health center as well as an API map visualizing the routes to and from the given health centers. My Python code utilizes OpenRouteService with an API that provides data including distance, travel time, direction, maps, etc. OpenRouteService also allows the user to specify their route preferences. We can choose the road type, the speed limit, the time of travel, the mode of transportation, etc. The created distance matrix and map will be used in an interactive route optimization tool. The route optimization tool allows end users to efficiently distribute vaccines using available vehicles. The distance matrix gives the tool correct distances between health centers and the map gives visualizations of the distances. The optimization tool provides routes for efficient distribution and my Python code maps the routes for easy interpretation of the delivery system. Moving forward, we want to make the code more user-friendly, so anyone can create a new and improved version.