Assessment of sustainable vermiconversion of water hyacinth by Eudrilus eugeniae and Eisenia fetida

The present work has assessed sustainable vermiconversion of aquatic weed water hyacinth (Eichornia crassipes). The garden soil, water hyacinth and cow dung were taken in the following the combinations of 1: 2 : 1, 2: 1: 1 and 1 : 1: 2. Two species of earthworms Eudrilus eugeniae and Eisenia fetida was used for the experiment. The total nitrogen (0.18% in control and 1.68% in earthworm treated) and phosphate (0.63 % in control and 1.64 % in earthworm treated) levels were increased and toxic heavy metals zinc (7.66 ppm in control and 2.58 ppm in earthworm treated) and copper (6.68 ppm in control and 1.15 ppm in earthworm treated) were significantly decreased. The earthworm enriches the compost with various nutrients for plant and microbial growth. Plant growth studies were conducted in all the combination of water hyacinth, maximum growth of root length (8.9cm and 7.2 in control) and shoot length (21.6cm and 16.2 in control) observed compare to control. Gut microbial analysis revealed that Bacillus cereus, Micrococcus luteus were predominantly present in the earthworm. The study recommended that the aquatic weed compost was suitable of agricultural usage

Performance analysis of bio-signal processing in ocean environment using soft computing techniques

Wireless communication has become an essential technology in our day-to-day life both in air and water medium. To monitor the health parameter of human begins, advancement techniques like internet of things is evolved. But to analyze underwater living organisms health parameters, researchers finding difficulties to do so. The reason behind is underwater channels has drawbacks like signal degradation due to multipath propagation, severe ambient noise and Attenuation by bottom and surface loss. In this paper Artificial Neural Networks (ANN) is used to perform data transfer in water medium. A sample EEG signal is generated and trained with 2 and 20 hidden layers. Simulation result showed that error free communication is achieved with 20 hidden layers at 10th iteration. The proposed algorithm is validated using a real time watermark toolbox. Two different modulation scheme was applied along with ANN. In the first scenario, the EEG signal is modulated using convolution code and decoded by Viterbi Algorithm. Multiplexing technique is applied in the second scenario. It is observed that energy level in the order of 40 dB is required for least error rate. It is also evident from simulation result that maximum of 5% CP can be maintained to attain the least Mean Square Error

Potential of distillery effluents for safe water through vermifiltration

Vermifiltration of wastewater using waste eater earthworms is a newly conceived novel technology. The present study evaluated BOD, COD and TS showing significant variation in decrease by 95%, 90% and 80% respectively through vermifiltration of distillery effluents. The nutrient contents (TN, TP, TK, TCa and TMg) in the vermicasts had increase (1.82 % in TN, 0.24% in TP, 2.15% in TK, 2.07% in TCa and 2.86 % in TMg) in the range of fold than the control level. The morphology of the control and experimental vermicast samples were analyzed with SEM and the image showed significant variation. The FT-IR spectrum analysis showed reduction of aliphatic/aromatic (C=C and OH) compounds in the vermicompost. Thus, the present study significantly highlights the vermifiltration technology in treating distillery effluent

Effect of Data Preprocessing in the Detection of Epilepsy using Machine Learning Techniques

1066-1077Epilepsy is the one of the most neurological disorder in our day to day life. It affects more than seventy million people throughout the world and becomes second neurological diseases after migraine. Manual inspection of seizures is time consuming and laborious task. Nowadays automated techniques are evolved for detection of seizures by means of signal processing or through machine learning techniques. In this article, supervised learning algorithms are applied to the EEG dataset and performance are measured in terms of Accuracy, precision and few more. Machine learning algorithm plays a vital role in classification and regression problem in the past few decades. The most important reason for this is a large set of signal or data are trained and the test signals are evaluated using training network. To get the better accuracy, the input data are first normalized carefully. The various normalization techniques applied in this article are Z-Score, Min-Max, Logarithmic and Square Root Normalization. For simulation purpose, Electroencephalography (EEG) signal from UCI Machine Learning Respiratory are used. Dataset consists of 11500 patient details with 5 different cases and each signal are recorded for the duration of 23 seconds. Spider chart is used to show the metric value in detail. It is observed from the result that supervised learning algorithm yields a better result compared to logistic and KNN (K-Nearest Neighbor) algorithm at high iteration

Susceptibility baselines for the invasive mealybugs Phenacoccus manihoti and Paracoccus marginatus (Hemiptera: Pseudococcidae) in cassava ecosystem against selected neonicotinoid insecticides

In recent years, an invasive cassava mealybug Phenacoccus manihoti has been threatening cassava cultivation alongside another invasive papaya mealybug Paracoccus marginatus which invaded the country more than a decade ago. In order to evaluate their responses against the commonly used neonicotinoid insecticides: thiamethoxam 25 WG and imidacloprid 17.8 SL,  acute toxicity experiments to determine the susceptibility baselines in populations of two invasive mealybugs in the cassava agro-ecosystem, namely, cassava mealybug P. manihoti and papaya mealybug P. marginatus were performed upto 15 generations. A systemic uptake method was used for the bioassay. The LC50 values of thiamethoxam for F1 generation were 3.298 ppm whereas it was 1.066 ppm for F15 in cassava mealybug. The LC50 values of F1 generation were 2.014 ppm and that of F15 generation was 1.384 ppm when tested with imidacloprid. In the case of papaya mealybug, the LC50 values ranged from 6.138 ppm (F1) to 2.503 ppm (F15) for thiamethoxam and 7.457 ppm (F1) to 3.231 ppm (F15) for imidacloprid. All the susceptibility indices calculated were less than threefold. The rate of resistance development was negative in all cases showing that none of the tested populations harboured any resistance without insecticidal selection pressure. Tentative discriminating doses were fixed for both chemicals with the help of LC95 values obtained from the bioassay experiments, namely five ppm for both thiamethoxam and imidacloprid in the case of cassava mealybug and 10 ppm and 15 ppm, respectively, for thiamethoxam and imidacloprid in the case of papaya mealybug.          

New limit for the half-life of double beta decay of 94^{94}Zr to the first excited state of 94^{94}Mo

Neutrinoless Double Beta Decay is a phenomenon of fundamental interest in particle physics. The decay rates of double beta decay transitions to the excited states can provide input for Nuclear Transition Matrix Element calculations for the relevant two neutrino double beta decay process. It can be useful as supplementary information for the calculation of Nuclear Transition Matrix Element for the neutrinoless double beta decay process. In the present work, double beta decay of $^{94}$Zr to the $2^{+}_{1}$ excited state of $^{94}$Mo at 871.1 keV is studied using a low background $\sim$ 230 cm$^3$ HPGe detector. No evidence of this decay was found with a 232 g.y exposure of natural Zirconium. The lower half-life limit obtained for the double beta decay of $\rm^{94}Zr$ to the $2^{+}_{1}$ excited state of $\rm^{94}Mo$ is $T_{1/2} (0\nu + 2\nu)> 3.4 \times 10^{19}$ y at 90% C.L., an improvement by a factor of $\sim$ 4 over the existing experimental limit at 90\% C.L. The sensitivity is estimated to be $T_{1/2} (0\nu + 2\nu) > 2.0\times10^{19}$ y at 90% C.L. using the Feldman-Cousins method.Comment: 11 pages, 7 figures, Accepted in Eur. Phys. J.

Theoretical and experimental studies of molecular interactions between engineered graphene and phosphate ions for graphene-based phosphate sensing

Fundamental understanding of the interactions of nanoscale materials with molecules of interest is essential for the development of electronic devices, such as sensors. In particular, structures and molecular interaction properties of engineered graphenes are still largely unexplored, despite these materials’ great potential to be used as molecular sensors. As an example of end user application, the detection of phosphorus in the form of phosphate in a soil environment is important for soil fertility and plant growth. However, due to the lack of an affordable technology, it is currently hard to measure the amount of phosphate directly in the soil; therefore, suitable sensor technologies need to be developed for phosphate sensors. In this work, pristine graphene and several modified graphene materials (oxygenated graphene, graphene with vacancies, and curved graphene) were studied as candidates for phosphate sensor materials using density functional theory (DFT) calculations. Our calculations showed that both pristine graphene and functionalized graphene were able to adsorb phosphate species strongly. In addition, these graphene nanomaterials showed selectivity of adsorption of phosphate with respect to nitrate, with stronger adsorption energies for phosphate. Furthermore, our calculations showed significant changes in electrical conductivities of pristine graphene and functionalized graphenes after phosphate species adsorption, in particular, on graphene with oxygen (hydroxyl and epoxide) functional groups. Experimental measurements of electrical resistivity of graphene before and after adsorption of dihydrogen phosphate showed an increase in resistivity upon adsorption of phosphate, consistent with the theoretical predictions. Our results recommend graphene and functionalized graphene-based nanomaterials as good candidates for the development of phosphate sensors

Transition metal saccharide chemistry and biology: syntheses, characterization, solution stability and putative bio-relevant studies of iron-saccharide complexes

A number of Fe(III) complexes of saccharides and their derivatives, and those of ascorbic acid were synthesized, and characterized by a variety of analytical, spectral (FT-IR, UV-Vis, EPR, Mossbauer and EXAFS), magnetic and electrochemical techniques. Results obtained from various methods have shown good correlations. Data obtained from EPR, magnetic susceptibility and EXAFS techniques could be fitted well with the mono-, di- and trinuclear nature of the complexes. The solution stability of these complexes has been established using UV-Vis absorption and cyclic voltammetric techniques as a function of pH of the solution. Mixed valent, Fe(II,III) ascorbate complexes have also been synthesized and characterized. Reductive release of Fe(II) from the complexes using sodium dithionite has been addressed. In vitro absorption of Fe(III)-glucose complex has been studied using everted sacs of rat intestines and the results have been compared with that of simple ferric chloride. Fe(III)-saccharide complexes have shown regular protein synthesis even in hemin-deficient rabbit reticulocyte lysate indicating that these complexes play a role that is equivalent to that played by hemin in order to restore the normal synthesis of protein. These complexes have exhibited enhanced DNA cleavage properties in the presence of hydrogen peroxide with pUC-18 DNA plasmid

Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting

The coefficient of determination, known as R2, is commonly used as a goodness-of-fit criterion for fitting linear models. R2 is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fitting a parametric distribution to a data set using, say, the maximum likelihood estimation method. A general approach to measure the goodness-of-fit of the fitted parameters, which is advocated herein, is to use a non- parametric measure for comparison between the empirical distribution, comprising the raw data, and the fitted model. In particular, for this purpose we put forward the Survi- val Jensen-Shannon divergence (SJS) and its empirical counterpart (ESJS) as a metric which is bounded, and is a natural generalisation of the Jensen-Shannon divergence. We demonstrate, via a straightforward procedure making use of the ESJS, that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution. Furthermore, we show the validity of the proposed method with simulated data, and three empirical data sets