Chlorella vulgaris chlorophyll a fluorescence as a potential indicator for zinc and nickel detection
Heavy metals contaminate many bodies of water, posing a health risk to not only organisms that live and use the water in these areas, but also to the humans that live nearby. Chlorella vulgaris, a microalga, is one organism whose chlorophyll a fluorescence can indicate the presence of these substances, detecting any changes in concentrations using fluorescence microscopy and other fluorescence devices. The study explores the sensitivity of C. vulgaris to the heavy metal zinc where the algae was exposed to five concentrations of zinc: 0 ppm, 5 ppm, 10 ppm, 50 ppm, and 100 ppm. The fluorescence of the samples was observed with a fluorescence microscope on days 0, 4, 7, and 12, where the algal samples were adapted to the dark for 5 minutes, then exposed to light for 90 seconds. The values of the minimal and maximal fluorescence of the samples in the dark were noted. There is a significant difference in the values of the minimal fluorescence, maximal fluorescence, and maximum quantum yield, a value derived from the minimal and maximal fluorescence, at the highest concentration, 100 ppm, from the other treatments for the entirety of the experiment. The significantly low values at 100 ppm and the calculated EC50 of 75.70 ppm indicate that C. vulgaris is indeed a viable indicator for zinc detection at this and higher concentrations of zinc.
Automated Inflation and Pressure Regulation for Recreational and Professional Cyclists
Cycling is a very popular mode of transport as well as a famous sport around the world. Many people enjoy this sport either professionally or recreationally. Cycling in the UK alone has grown up to 200% since lockdown in 2020. (Chandler, 2020) Cyclists make use of a broad selection of products to enhance their performance. Those products range from wireless gear shifting, advanced geometry, smart suspension. This project is aimed to indicate the importance of tire pressure and to introduce a product which will be able to adjust tire pressure while cycling. This product will give cyclist an advantage on different terrains as well as eliminate some common problems amongst cyclists. Flat tires are one of these problems. It occurs commonly amongst cyclists and can happen due to a variety of reasons. Another problem is wrongly inflated tires. This causes unnecessary loss in a cyclist’s power and speeds due to the high rolling resistance between the tires and the surface. This then results in losing time whether racing or commuting. In an article published in 2014 in Velonews.com, Lennard Zinn states: “Whether on tarmac or singletrack, a tire with lower rolling resistance reduces the power required to move forward while also providing a better quality ride. The tire absorbs small bumps by not transferring them into the bicycle and rider, resulting in a smoother ride, faster speeds, and better cornering." (Zinn, 2014) Taking this in consideration it becomes clear that it is important to develop a system which is able to control tire pressure.
A Person Re-identification based Misidentification-proof Person Following Service Robot
Two years ago, I attended a robot contest, in which one of the missions required the robot to follow the pedestrian to complete the task. At that time, I used their demo program to complete the task. Not long after, I found two main issues: 1. The program follows the closest point read by the depth camera, which if I walk close to a wall next to, the robot may likely ‘follow’ the wall. 2. Not to mention if another pedestrian crosses between the robot and the target. Regarding these two issues, I decided to improve it. We’ve designed a procedure of using YOLO Object Detection and Person re-identification to re-identify the target for continuous following.
A Person Re-identification based Misidentification-proof Person Following Service Robot
Two years ago, I attended a robot contest, in which one of the missions required the robot to follow the pedestrian to complete the task. At that time, I used their demo program to complete the task. Not long after, I found two main issues: 1. The program follows the closest point read by the depth camera, which if I walk close to a wall next to, the robot may likely ‘follow’ the wall. 2. Not to mention if another pedestrian crosses between the robot and the target. Regarding these two issues, I decided to improve it. We’ve designed a procedure of using YOLO Object Detection and Person re-identification to re-identify the target for continuous following.