Quantification of the abundance and diversity of predatory spiders in rice ecosystem of Rajendranagar, Telangana, India

The effective prey searching ability and polyphagy of spiders makes them important predators of crop pests. 19 species of spiders have been recorded in rice ecosystem (Rajeswaran et al., 2005). There is now a growing need to conserve all species and not only the large vertebrates (Samways, 1990) and contribute to the natural biological process. However, literature pertaining to their abundance and diversity in rice crop in Rajendranagar area is scant. Hence, the p resent study was conducted to understand their abundance and diversity. Spider samples were collected from rice fields of Rajendranagar in kharif and rabi seasons of 2011-12 and 2012-13. A total of 2,094 individuals collected in kharif represented eight families with a density of 12.48/sq.m. Members of Tetragnathidae were recorded most abundantly in kharif (46.32% of the Arachnid population) followed by Lycosids (26.22%). In rabi 1,095 spiders of seven families were collected with a density of 6.38/sq.m. Tetragnathidae andLycosidae were the most abundantly found species in rabi also comprising 27.85% and 26.12% of Arachnid population respectively. Study of guild composition was also carried out. A t-test between indices of richness, diversity, effective no.of species and species evenness of kharif and rabi seasons revealed that there were no significant differences with respect to these parameters (p>0.05) indicating that spider diversity of rice in Rajendranagar was more or less same between kharif and rabi seasons. This is the first study on the spider diversity of rice ecosystem of Rajendranagar, Hyderabad, India

Efficacy of antibacterial activity of garlic cloves from Tamil Nadu and Jowai region

Background:The development of antibiotic resistance has become a global health challenge which is causing ineffectiveness of the available antibacterial agents leading to increase in diseases and death rate. Therefore this study intends to investigate the antibacterial action of Aqueous Garlic Extract (AGE) against 9 multidrug-resistant gram-positive and gram-negative bacterial isolates, including Staphylococcus aureus, Enterococcus species, ß hemolytic streptococcus, Pseudomonas aeruginosa, Proteus mirabilis, Klebsiella pneumoniae, Escherichia coli, Vibrio cholerae and Serretia marscenes.Methods:Antibacterial activity of different concentrations of AGE by well-diffusion method was recorded by measuring the diameter of zone of inhibition. The Tamil Nadu garlic cloves as well as Jowai region garlic cloves showed antibacterial activity against both GPC and GNR.Results: The maximum zone of inhibition was observed in Tamil Nadu garlic than that of Jowai region, but the only bacteria which showed a better zone of inhibition with Jowai region than Tamil Nadu garlic was Pseudomonas aeruginosa.Conclusion:Thus our study reveals that garlic not only makes the food more spicy & edible with its flavour but can also be used as an effective antibacterial agents against MDR gram positive & gram negative bacteria.

Cassava Leaf Disease Identification and Detection Using Deep Learning Approach

Agriculture is the primary source of livelihood for about 60% of the world's total population according to the Food and Agricultural Organization (FAO). The economy of the developing countries is solely dependent on agriculture commodities. As the world population is increasing at faster pace, the demand for food is also escalating tremendously. In recent days, agriculture is experiencing an automation revolution. Hence the introduction of disruptive technologies like Artificial Intelligence plays a major role in increasing agricultural productivity. AI enabled approaches would help in overcoming the traditional challenges faced in agriculture practices, by automating various agriculture related tasks. Nowadays, farmers adopt precision farming which uses AI techniques namely in crop health monitoring, weed detection, plant disease identification and detection, and forecast weather, commodity prices to increase the yield. As there is scarcity of manpower in agriculture sector, AI based equipment like bots and drones are used widely. Crop diseases are a major threat to food security and the manual identification of the diseases with the help of experts will incur more cost and time, especially for larger farms. The machine-vision based techniques provide image based automatic process control, inspection, and robot guidance for pest and disease control. It provides automated process in agriculture, paving way for improved efficiency and profitability. Various factors contribute for plant diseases, which includes soil health, climatic conditions, species and pests. The proposed chapter elaborates on the use of deep learning techniques in the leaf disease detection of Cassava plants. The chapter initially describes the evolution of various neural network techniques used in classification and prediction. It describes the significance of using Convolutional Neural Network (CNN) over deep neural networks. The chapter focuses on classification of leaf disease in Cassava plants using images acquired real time and from Kaggle dataset. In the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance metric

Experimental Investigation on performance of silica fumes as a soil stabilizer for oil contaminated strata

Oil leakage is an environmental issue unnoticed in the present time. The problem of oil leakage and oil contamination is main concern for petroleum harvesting countries. Oil contamination in soil creates health issues in the area surrounding it. The nutrients in the soil get reduced significantly due to oil contamination which makes the land not suitable for cultivation. The oil produces hydrocarbons which makes the civil structures weak and out at risk. The most harmful effects of oil contamination are excessive settlement of structures, breakage of underground pipes, etc. In this project, we are trying to study the effects of oil contamination in the soil and also to find a sustainable solution for it. The soil is contaminated in the percentage from 0 to 20% and the tests on index and engineering properties have been conducted to find the effect of engine oil. In order to stabilize the oil contaminated soil, we use silica fumes as a stabilizing agent. The optimum percentage of silica fume is chosen based on the tests of Index and Engineering properties conducted on the soil with silica fumes. The percentage of oil where the soil properties need stabilization is known and the soil is stabilized with the optimum silica fume percentage

Creation of FRIENDSPERK Platform for Social Network

Social Networking. It's the way the 21st century imparts now. Person to person communication is the gathering of people into particular gatherings, similar to little rustic groups or an area subdivision. Albeit long range interpersonal communication is conceivable in individual, particularly in the work environment, colleges, and secondary schools, it is most well-known on the web. This is on the grounds that not at all like most secondary schools, universities, or work environments, the web is loaded with a great many people why should looking meet other individuals

Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy

In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%

Microelectromechnical Systems Inertial Measurement Unit Error Modelling and Error Analysis for Low-cost Strapdown Inertial Navigation System

This paper presents error modelling and error analysis of microelectromechnical systems (MEMS) inertial measurement unit (IMU) for a low-cost strapdown inertial navigation system (INS). The INS consists of IMU and navigation processor. The IMU provides acceleration and angular rate of the vehicle in all the three axes. In this paper, errors that affect the MEMS IMU, which is of low cost and less volume, are stochastically modelled and analysed using Allan variance. Wavelet decomposition has been introduced to remove the high frequency noise that affects the sensors to obtain the original values of angular rates and accelerations with less noise. This increases the accuracy of the strapdown INS. The results show the effect of errors in the output of sensors, easy interpretation of random errors by Allan variance, the increase in the accuracy when wavelet decomposition is used for denoising inertial sensor raw data.Defence Science Journal, 2009, 59(6), pp.650-658, DOI:http://dx.doi.org/10.14429/dsj.59.157