Developing Swarm Intelligence with Flying Robots for Life Saving
Purpose of the research I had a bicycle accident eight months ago and I suffered an opened humeral fracture. I wasn’t able to get up on my feet, I had to suffer on the ground while a couple accidentally walked there, and they called the emergency services, I lost a lot of blood and the doctors had to cut a small piece of muscle out of my biceps. Now this shouldn’t have happened if there were a faster method on making first aid. Example an intelligent flying robot could easily handle this situation. Procedures There is a hexcopter and a quadcopter. Both of them has cameras with live image transmissions in Full HD and they also have GPS. They are equipped with a lot of sensors (image recognition with Raspberry Pi, 6 axis distance sensors, long range communication modules) and with these they are able to solve problems just a bird or a human does. These drones can communicate up to 1.8 kilometers, they communicate with the main server, they share their knowledge with each other and this way they can learn about the world and about the terrain that surrounds them. In a very basic way they are able to learn… Each time they make their flying techniques smoother and their database is growing. These drones can cooperate and save lives faster than humans do. They are also able to fly in non-accessible zones by themselves with no human help. For example with their sensors they can fly in and map buildings that are inaccessible for people. They can also do artificial farming with their object recognizing features, like recognizing plants and colors. (Right now they are calibrated to look for survivors alone in the mountains/forests.) They go to a given GPS coordinate then they are flying in a spiral form to find the injured people in less than a half hour. I really hope that one day these flying intelligent machines will save lives! Note: (I probably can only take one drone with myself to Taiwan, because of the traveling restrictions on planes.)
Anaerobic Respiration: A Novel Bioelectrochemical Copper Recovery System?
Increasing concentrations of copper in discharged effluents pose hazards to aquatic food chains. This project aimed to develop a self-sustained copper remediation system based on electrical and microbiological principles. The production of electrons during yeast fermentation was investigated to catalyze the reduction reaction of dissolved copper ions. An electrical circuit was designed to harness electrons produced from either a pure or mixed culture of yeast, and were compared for voltage outputs. This system utilized a combination of carbon cloth and copper wire as the electrodes, and a magnesium sulfate based electrolyte. The better-performing cell was subjected to copper reduction analysis, in which various initial concentrations of copper were examined. Further data analysis was carried out on the voltage outputs achieved with both the mixed and pure cultures of yeast, in which an average base line was established and voltage flunctuations were compared to that of the base line. In this way, it was possible to determine the amount and severity of each voltage flunctuation — thus demonstrating whether mixed or pure cultures of yeast produced more stable outputs. Throughout the experiment, self-constructed equipment, including arduino microcontroller moderated incubators and drip-feed systems were implemented to maintain an optimum yeast growth rate. It was found that mixed yeast cultures produced smoother electrical potential outputs in response to feeding and stress intervals. The copper recovery experiment was therefore conducted using the mixed culture. Through a series of conductivity measurements indicative of copper concentrations, metal recovery was successfully demonstrated. Trend line analysis indicated similar flunctuations between voltage output and copper recovery rates, demonstrating how copper was recovered as a result of electrons harnessed from the yeast culture. These findings can be applied to the development of an energy efficient and cost-effective copper remediation system for contaminated water effluents.
Discovery, Cloning and Recombinant Expression of a Coral Peptide with anti-Bacteria activity
Inflammatory Bowel Disease (IBD) is a prevalent disease of the West which pathogenesis is driven by a combination interaction between bacteria and inflammatory cells. In this study, two Kazal domain peptide from Palythoa Caribaeorum were identified. They were found to exhibit serine protease inhibitory, anti-bacterial effects and low toxicity, making them ideal candidates for IBD treatment due to their ability to inhibit inflammatory cell migration and bacterial load. We amplified their coding DNA sequences via PCR and ligated the resulting PCR product into pGEX-4T3 vector. The recombinant plasmid was verified by sequencing, and restriction digest before being transformed into competent E.coli cells. Following transformation, we induced target peptides expression by IPTG to confirmed successful transformation and peptide production. Selected transformed bacterial colonies were expanded in LB broth before mixing with glycerol and frozen in -80°C freezer to complete the process of cell bank production.
Random number generators and their applications in Computer Science with the Monte Carlo Method
Monte Carlo methods are non-parametric algorithms that use random numbers and theorems of probability theory to approximate values that are not random. The purpose of my research was to approximate the surface of different geographical areas that can be easily approximated to polygons (e.g. lakes, glaciers, deserts) with Monte Carlo simulations starting from either Cartesian coordinates or pictures. Computer science would not exist without math, and this research project showed me the importance of a deep understanding of probability theory in the world of simulations and, more generally, the importance of developing new theorems and algorithms. The results of my research could be developed in different ways: it would be interesting to produce software that allows one to approximate areas from pictures taken from a smartphone; as well, the theorem I found has to be proven, and also Monte Carlo methods as a means of random number generation can always be improved. There are still many possibilities.
Graphene Nanoplatelet-Embedded Acrylic Paint for Low Cost Waterproof Paintable Capacitive Sensors and Free Standing Supercapacitors
Modern capacitive touch input and proximity sensing technologies are rigid and limited to flat substrates making it impossible to apply them onto objects with irregular geometries like textiles or car handles. Furthermore, the high cost restricts the applications to small surfaces and cannot be scaled up to be applied on large surfaces such as walls. Therefore, a paint-on scheme would broaden the applications of capacitive touch input and proximity sensing devices. Paintable capacitive sensors are an emerging technology hindered by the high cost and lackluster properties of conductive paints. Existing conductive paints utilize expensive filler materials such as silver and gold to achieve high conductivity but suffer from low surface area. High surface area is critical for capacitive proximity sensors to detect objects from far distances and for overall sensitivity. Carbonaceous alternatives using micronized graphite exhibit low conductivity, require high loadings and most disintegrate when in contact with water. Multilayer graphene nanoplatelets are investigated for their high conductivity, high surface area, low cost, flexibility and eco friendliness. A waterproof acrylic latex is combined with multilayer graphene and dispersed via bath sonication. The optimal time of sonication and optimal graphene loading is determined through systematic testing. An Arduino Uno is loaded with a CapSense library and the graphene based paint is utilized as the interface to sense both touch and proximity.