A field survey on abundance of biofuel plant species in Alur Taluk of Hassan District, Karnataka, India

There are more than 150 species of plants that could be used for biofuel production. Important among them in Karnataka are Honge (Pongamia pinnata), Neem (Azadiracta indica), Hippe (Maduca latifolia), Jatropha (Jatropha curcas) and Simarouba (Simarouba glauca). A field survey was conducted in Alur taluk of Hassan District, (75º 9´ to 12º 9´) Karnataka, to know the abundance of biofuel plant species and to estimate resource availability for extraction of oil and production of Biodiesel. The number of Households in the study villages ranged from 120 to 600 with population ranging from 60 to 2,500 and number of productive Honge trees varied from 3 to 2,000 trees/village yielding on an average around 2 to 10 kg seeds per tree with very few Neem and Hippe trees but Jatropha plants were present in every village with very low yielding potential (50-100 grams per plant). Majority of the biofuel plants present were in vegetative stage and few were yielding. The yield of biofuel plant species is quite promising and the process of seed for oil extraction is possible and provides employment to the rural youth in the taluk. The substantial demand has been noticed in Alur taluk for Honge and Neem oil cakes and the availability is meager. Substantial scope is available for growing of biofuel trees, seed collection, processing and marketing providing additional employment to rural people. There is an increased demand for the biofuels and by utilizing the available resources the rural youth can start their own enterprise

Tuberculosis Prediction using KNN Algorithm

In this paper, a machine learning model is used to develop a model that is used for tuberculosis prediction. Tuberculosis is known to be one of the top reasons for death from an infectious agent that affects the lungs and continues to threaten the human population on a wider basis. According to WHO, tuberculosis is a serious threat to the human population after HIV/AIDS. It is estimated by the World Health Organization (WHO) that 1/3rd of the global population is infected with TB and that seven to eight million new cases of TB occur each year across the globe Because the disease is difficult to differentiate between the common cold, it takes a long time to decide the patient is affected by the disease. So we use the detection of tuberculosis by utilizing the K-NN algorithm method for classification and HOG as feature extraction. K-NN abbreviated as K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on the Supervised Learning technique. The data provided K-NN model should be labeled one. Then these datasets are given to a training model where the training process of the model is being undergone. Once the training is completed, the next step is to predict the output. For this process, we have to provide new data that may or may not belong to the dataset, so that the model can predict the output of it. If the prediction is wrong, again the training is done until we get the actual output matching with the desired output given by the designer for verification purposes. This is the basic working process under the K-NN algorithm. The data that is used for this separation is a Tuberculosis dataset that contains various information about the different symptoms that are helpful in detecting tuberculosis effectively. Here it is used in the early detection of tuberculosis which helps save millions of people which might otherwise lead to death because of lack of detection. ML model helps to improve the efficiency in detecting by considering various symptoms. ML models are more accurate at differentiating even the slightest difference that deviates from the data that was used to train the model. Unlike the manpower we fail to detect the slightest as we notice the symptoms only after they become more severe. The accuracy of this model was found to be 98%. The following model uses a dataset consisting of data that contrasts between males and females and the various symptoms are shown in them. It also contrasts the severity of these two

INFLUÊNCIA DO ANELAMENTO E ESTIOLAMENTO DE RAMOS NA PROPAGAÇÃO DA LARANJEIRA VALÊNCIA (Citrus sinensis Osbeck) ATRAVÉS DE ESTACAS

Objetivando estudar a influência do condicionamento de ramos, através do anelamento e estiolamento parcial, sobre o enraizamento de estacas de laranjeira cv. Valência, conduziu-se o experimento no período de fevereiro a dezembro de 1993. Para o anelamento foi utilizado arame fino de cobre com o qual provocou-se um estrangulamento na base do ramo. No estiolamento, utilizou-se fita preta de polietileno, que foi enrolada na base das brotações. De cada ramo condicionado, obteve-se apenas uma estaca com 15 cm de comprimento, diâmetro médio de 0,5 cm e um par de folhas apicais, cortadas pela metade. O plantio das estacas foi realizado em 12.04.93, em substrato de areia lavada e peneirada. Adotou-se o delineamento inteiramente casualizado, em esquema fatorial 3x3, resultante das combinações entre os períodos de zero, 30 e 60 dias dos fatores anelamento e estiolamento. Verificou-se que não ocorreu enraizamento nas estacas que permaneceram três meses no substrato e variou de 0 até 26,04% para aquelas que permaneceram oito meses. O período de estiolamento influenciou significativamente o percentual de estacas enraizadas.<br>With the aim of studying the effect of branch conditioning through girdling and banding on Valência orange cuttings a rooting trial was carried out from February to December of 1993. The girdling was performed with a thin cooper wire tightly tied at the branch base reaching the log, and the banding with black poliethylene tape, covering the branch at the base of the sprouts. Each conditioned branch gave one cutting of 15 cm length, 0.5 cm diameter and a pair of half cut apical leaves. Cuttings were planted on Dec.04, 93, in washed and sieved sand substrate. The experimental design was completely randomized, with a factorial 3 x 3 from 0, 30 and 60 day periods, girdling and banding. There was no rooting in cuttings that remained three months in the substrate and there was up to 26.04% rooting for those ones that lasted for eight months in the substrate. The banding period significantly affected the cuttings rooting percentage