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.
AGRO-GUARD:Machine Learning-Driven Plant Real-Time Disease Detection,Clustering and Community Notifications
Agro-guard aims to revolutionize disease identification and community-based projects in the field of agriculture. Integrating Machine learning, Computer vision, clustering, and community-based technology, this project helped farmers to detect their plant disease with their solution and for early warning of plant disease which was spreading in their community which helped in crop management. The research project is divided into three parts.First,Integrating Machine learning to detect and classify plant disease with their solutions.Second,Integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN),to identify disease and analyze the pattern within agricultural regions.Third,Establishing notification system to notify real-time alerts to farmers about disease spreading in particular region.The research is crucial because it solve one of the crucial problem of our community which is untimely detection of disease.The finding of the research highlight the effectiveness of Agro-Guard framework in early disease detection and community detection.The machine learning models achieved high accuracy in identifying common plant disease and clustering results the pattern in diseases that were very important for notifying the community.The significance of these findings is that it can build powerful system which will overall grow the production of crops and plants due to timely update of the disease prevailing in the community.It contributes in sustainability production of crops and plants which ultimately ensure the good livelihood of farmer.
Evaluation of the Effect of Different Nutrients' Concentration and Composition on Hydroponically Grown Plant
As the world population grows, the demand of food products grows as well and there will be an expected food crisis in the coming years. To prevent those crises, alternative food farming methods must be used. This paper studied two farming systems in different conditions, to compare and find the best, natural and cost-effective system that will cover the current and future demand. The system which can also be used in those areas where soil is less cultivated with insufficient aeration. The first system is the soil-based system (traditional), and the other is hydroponic system. Hydroponic is a technique of growing plants in nutrient solutions with or without the use of an inert medium. Two types of seeds; peas and spinach were observed in both systems over a period of 25 days. In hydroponic plants coco peat was used in place of soil along with the Aegis nutrient. 8 plants were seeded for both types of plants in different systems, conditions, concentrations and pH to conclude the best condition. Growth parameters of all plants including root, shoot and leaf length were observed and recorded daily. On the uprooting, their weight (g), no. of root hairs and used nutrient’s volume(ml) were also recorded. Fungus and insects were seen in the soil plants. The results executed that the growth was maximum in spinach having normal manufacturer nutrient’s spray concentration(1.25ml/625ml) with pH 6 and in peas having normal supplier concentration (5ml/625ml) with pH 4. So, it can be concluded that hydroponic spinach, which is a green leafy plant, can ideally grow at the pH of 6 and peas in slightly acidic condition. Hydroponic planting system has a better growth effect than traditional soil system and this system don’t need any fertilizer, insecticide, pesticide, fungicide and herbicide. While soil plants’ growth was adversely affected by fungus and insects in the absence of these chemicals which can contaminate our food and make it less hygienic for our health. This result achieves the aim of this paper which is finding a planting system and its conditions that can increase the productivity to cover the food demand.
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.