Students act as civil engineers developing safe railways as a way to strengthen their understanding of parallel and intersecting lines. Using pieces of yarn to visually represent line segments, students lay down "train tracks" on a carpeted floor, and make guesses as to whether these segments are arranged in parallel or non-parallel fashion. Students then test their tracks by running two LEGO® MINDSTORMS® NXT robots to observe the consequences of their track designs, and make safety improvements. Robots on intersecting courses face imminent collision, while robots on parallel courses travel safely.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the transplantation of donor derived stem cells and treats a variety of hematologic and non-hematologic disorders. Allo-HSCT patients exhibit changes in their gut microbiota and experience a range of complications post-treatment, including acute graft-versus-host disease (aGvHD). The potential roles or timing of gut microbiota reestablishment and immunological homeostasis after allo-HSCT are not known. It is also not yet known if the microbiota at other body sites plays a role. Recently, researchers ran an integrated host-microbiota analysis of the gut, oral, and nasal microbiota in children undergoing allo-HSCT. The bacterial diversity decreased in all three sites during the first month. Certain microbial taxa were already different in allo-HSCT patients before transplantation compared to healthy children. Onset of acute GvHD after treatment could be predicted from the pre-treatment microbiota composition at all three body sites..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Background: Activated sludge (AS) of wastewater treatment plants (WWTP) is one of the world’s largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTP performance. However, how to predict its community structure is still unclear. Results: Here, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. The predictive accuracy R21:1 of Shannon-Wiener index reached 60.42%, and the average R21:1 of amplicon sequence variants (ASVs) appearing in at least 10% of samples and core taxa were 35.09% and 42.99%, respectively. We also found that the predictability of ASVs was significantly positively correlated with their relative abundance and occurrence frequency, but significantly negatively correlated with potential migration rate..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

Students learn about the concept of pushing, as well as the relationship between force and mass. Students practice measurement skills using pan scales and rulers to make predictions about mass and distance. A LEGO MINDSTORMS(TM) NXT robot is used to test their hypotheses. By the end of the activity, students have a better understanding of robotics, mass and friction and the concept of predicting.

Students see and learn how crystallization and inhibition occur by making sugar crystals with and without additives in a supersaturation solution, testing to see how the additives may alter crystallization, such as by improving crystal growth by more or larger crystals. After three days, students analyze the differences between the control crystals and those grown with additives, researching and attempting to deduce why certain additives blocked crystallization, showed no change or improved growth. Students relate what they learn from the rock candy experimentation to engineering drug researchers who design medicines for targeted purposes in the human body. Conduct the first half of this activity one day before presenting the associated lesson, Body Full of Crystals. Then conduct the second half of the activity.

Through this activity, students come to understand the environmental design considerations required when generating electricity. The electric power that we use every day at home and work is usually generated by a variety of power plants. Power plants are engineered to utilize the conversion of one form of energy to another. The main components of a power plant are an input source of energy that is used to turn large turbines, and a method to convert the turbine rotation into electricity. The input sources of energy include fossil fuels (coal, natural gas and oil), wind, water, nuclear materials and refuse. This activity focuses on how much energy can be converted to electricity from many of these input sources. It also considers the impact of the by-products associated with using these natural resources, and looks at electricity requirements. To do this, students research and evaluate the electricity needs of their community, the available local resources for generating electricity, and the impact of using those resources.

In this group activity, learners use some common objects and work together to simulate the Coriolis effect. During the challenge, learners make predictions and test different scenarios. This resource includes background information about the Coriolis effect and helpful hints.

Students will predict how many amusement parks are in their state. They will then analyze census data on the numbers of amusement parks in all 50 states in 2016. (Data in this activity do not include the District of Columbia or Puerto Rico.) Then students will write numbers as fractions and create a visual model of the data.