STEM-COMD Interdisciplinary Approach to Illustrating STEM Manuscripts: In this project Communication Design students get together to work on one project throughout the semester. Communication Design students’ goal is to create a creative solution for college students to help them to engage more with a STEM manual book. Students and faculty decided to execute the project as a manual with fun illustrations to be incorporated with the informative text. In the process students learn new information on soil mechanics while experiencing the similarity challenges of real world scenarios on collaboration work with the team and the client.
This continuation of our research focuses on approaches to mitigation solutions for severe acute respiratory syndrome SARS-CoV-2 in HVAC design, to mitigate the further spread of aerosol transmission indoors. We investigated source control and operational control methods by examining displacement ventilation, demand control ventilation, UV light treatment, and MERV13-16 rated filters. They are efficient ventilation, filtration, and air purification means in limiting indoor air transmission of Covid-19. Future study will give more understanding in how effective ventilation, filtration, and air purification systems will be, once combined. The goal is to understand how their incorporation will effectively combat Covid-19 indoor transmission.
Everyone has been affected by the COVID-19 pandemic, especially students of all ages, who have been forced to adapt to this abrupt change. Have you ever thought about how the change from in person learning to distance learning has affected students, especially students who require hands on experience? That is precisely what our project evaluates. Through surveys, using a Likert type scale, we are measuring the thoughts and concerns of distance learning on Radiologic Technology students. Juniors and seniors are experiencing different parts of the curriculum so different surveys were used. The concerns between the students are different because seniors are currently at clinical sites while juniors are only taking academic courses. The results for the Spring 2021 semester were interesting as students have varying opinions on the assignments and different degrees of motivation. We also measured the students desire to take the vaccine and quite a few are against being vaccinated. We will continue to survey and monitor how the students are adapting to the current educational environment under the COVID-19 umbrella.
The objective of the project is to research and plan a compact and low-cost smart IoT device which can help to reduce the hot-car death of infants. We will build a prototype system that can detect if a child is left alone in a closed vehicle. When such an event is detected, the device will send alarms to the child’s parents or caregiver through multiple means. If the parents or caregiver do not provide any response after a short period, the device will alert the first responder for immediate action. We will use a compact physical computing platform that will connect to a Cloud that will not require active management of users. By the research done so far, we created a
preliminary system design that includes multiple channels of sensors. In the next step of this research project, we will develop, implement, and test our system. We will also further add more features to our system and implement them.
Green hybrid renewable energy systems that create a smart electrical grid and sustainable power, are necessary for NYC to facilitate its carbon footprint reduction initiative by 2050. Energy efficient hybrid solar-wind systems, such as ENLIL smart solar VAWT and electric vehicles, creates an efficient solution for urban environments where space and pollution are problematic. Two design deficits of conventional (HAWT) wind turbines are their large sizes and continuous vibrations impact soil-structure stability—resulting in pressure uplift of the foundation. Based on technical and geoscience research, we found a modified compact design of vertically aligned turbine blades, and reinforced cone-shape concrete foundation, can reduce the resonance of soil-structure interaction.
Our research project on the Effects of Late Spring Frost on Forest and Landscape Health of the Black Rock Forest, New York, focused on the potential impacts of late Spring frost events. In this study, we analyzed a recent freeze event at Black Rock Forest and came up with information on any damage done to the hardwood in the area affected. we made a comparison between the frost year to previous years and then determine the forest health and recovery. We used NDVI (Normalized Difference Vegetation Index) and LST (land surface temperature) analysis. We discovered where there was no frost event, there was consistency in the rise of forest greenness from early May to the beginning of June. NDVI images of 2020 before the frost event show similarities in greenness with 2019. Mean NDVI values show a steady level of forest greenness from early 2020 until 3 May 2020, with a mean NDVI value of 0.4. NDVI values significantly decrease right after the frost. The project was able to verify that the frost event had a meaningful toll on forest life.
This research project was conducted by Naved Khan (Mechanical Engineering Technology) and Ruben Vecino (Computer Systems) from New York City College of Technology under the mentorship of Abdou Bah from the CUNY Graduate Center. In this project, a GOES-R satellite image of land surface temperatures over an urban environment is downscaled from a 2-kilometer resolution to a 30-meter resolution image with the support of a Landsat 8 satellite image. The GOES-R data with a coarse resolution of 2-kilometers was obtained every 5 minutes while the Landsat 8 data with a finer resolution of 30-meters was obtained every 16 days. This was done by applying matrix mathematics using MATLAB to superimpose temperature variances between the GOES-R data and the Landsat 8 data. As a result, we can achieve finer surface temperature readings more frequently in urban environments and observe temperature anomalies more accurately.
“Bedford-Stuyvesant, Brooklyn, New York was shown through satellite analysis to be a heat vulnerable community in New York City. Additionally, a linkage between redlining and the urban heat island was studied by examining socioeconomic and sociodemographic factors in the area. To increase community engagement in this study a community survey, interactive maps and websites were developed to connect with the community. “
Incorrect weather forecasts can be deadly and very costly; therefore, it is important to explore where and why weather forecasting makes errors. In this work, we analyze the distribution of errors when making predictions further in advance. As a case study, we computed the differences between the MOS forecasts and GHCN observations of US stations for the year 2019. By analyzing this data with K-means clustering we will see what errors are similar, and if they are localized we can compute the bias. We can then use this computation to bias correct the predictions. This method can possibly help us to tune the model for more accuracy in its predictions.
Affected by anthropogenic climate change, lakes may consist of a relatively small percentage of global water bodies, their impact is nevertheless significant on their surrounding communities. In this project, 507 global lakes were studied using Moderate Resolution Imaging Spectroradiometer (MODIS). Our research suggests that Lake Surface Water Temperature (LSWT) is an indicator, which was compared with Land Surface Temperature and other factors; the results for which were analyzed in MATLAB and R. Curiously, 41.15% of the lakes were shown to be warming, with 51% cooling; and 62.53% were shown to be shrinking, while 28.35% were shown growing. It is our hope that understanding these phenomena will allow us to help those communities dependent on these lakes, for the good of our planet.
Streamflow is the flow of water in streams, rivers, and other channels. Changes in streamflow can influence the amount of water available for crops, the generation of electricity, fishery, many plants, and animals. The target of my research project is to predict daily streamflow using streamflow and precipitation data of six locations of Lower Cosumnes in California. We are using deep learning models as Recurrent Neural Network, and Machine Learning models such as Support Vector Regression, Boosting, ARIMA, and Linear Regression. We are using these methods with different windows of 7, 10, 14, and 30 days and two preprocessing methods (Z-score and normalization) for each of the models. Our ultimate goal is to find the model with the least error, the most accurate prediction, and at the same time predicting the peaks correctly.