HandExo
Stroke is a very common disease, almost a national disease. In terms of stroke frequency, 匈牙利 ranks second in the world. Every year, 40-50 thousand people become paralyzed or permanently injured as a result of cerebrovascular disorders. This number is three to four times higher than in developed countries. Almost every Hungarian family is affected! Of course, saving the life of someone who has a stroke is the most important thing, but rehabilitation is also very important, since only with the help of a physiotherapist will the patient be able to live a full life.
SAFE_MEDICATION - A STUDY OF USING ARTIFICIAL INTELLIGENCE TO RECOGNISE MEDICATION ERRORS
Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.
Non-invasive study of the electrical activity of the brain of various chordate animals
In clinical practice, EEG is used to diagnose a number of neurological diseases and to diagnose epilepsy. But at present, the question of the nature of EEG has not been completely resolved and is of great scientific interest. There have been no studies at all on the non-invasive study of the electrical activity of the brain of the shark superorder, which belongs to the class of cartilaginous fish. By studying the electrical activity of the brain of various gnathostomes, it is possible to obtain an answer to the question of the emergence of rhythms from the point of view of phylogenesis and evolution, and by comparing their EEG with the human EEG, one can identify similar patterns that help in the study of reactions to various influences. During the work, for the first time, EEG indicators of spotted cat sharks, ECG, heart rate and respiratory rate of cat sharks and toads were obtained. In the future, it is planned to assemble a smaller neuroheadset for non-invasive studies of the electrical activity of the brain of small animals (sharks, toads, monitor lizards). This data can be used for evolutionary and medical research. *No animals were harmed during or after the experiments.
Upcycling of Abandoned Beehives!!
Upcycling abandoned beehives to make new products can reuse the useful materials in old beehives and produce less trash. As known that bees leave their beehive in these following situations like insufficient replenishment, frequent unboxing and environmental issues. Then the beehive will be abandoned and will have no use left. In this project, a piece of honeycomb was collected from abandoned beehive and melted in order to extract beeswax. The potential of the extracted beeswax for replacing plastic to produce fillers of 3D pens was studied. Natural materials like seashell, rosin, soy bean and coffee ground were tested as ingredients of 3D printing materials. Finally, the potential of using extracted beeswax in 3D printing was confirmed. Beeswax has a low melting point at around 64°C and solidify quickly at room temperature. The high plasticity of this natural wax fulfills the criteria of 3D printing materials. Biodegradable wastes, like coffee grounds and soy bean grounds were tested as additives for reducing the beeswax content. Sea shell grounds were eliminated from the tested list as its filaments broke into small pieces of brittle fragments during the production process. 5% and 10% of these additives were the optimal formula for making long filaments. Yet, the thin filaments made by pure beeswax were not strong enough, filaments of selected beeswax-soy bean grounds were further strengthened by mixing with 5% or 10% rosin. Among the four different ratios of Beeswax: Soy bean grounds: Rosin (9:1:0.5 / 9:1:1 / 9.5:0.5:0.5 / 9.5:0.5:1), filaments in the ratio 9.5:0.5:0.5 demonstrated better flexibility, higher tensile strength and compressive strength, thus B9.5:S0.5:R0.5 was the final formula of biodegradable beeswax 3D filament.