Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model

The remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam–Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6‐LISS III) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud‐free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three‐layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.     

Antimicrobial action of propolis and some of its components: the effects on growth, membrane potential and motility of bacteria

Vertebrates are able to perceive the pitch of a series of harmonics, even when the fundamental frequency has been removed from the acoustic stimulus. Neural periodicity responses corresponding to the "missing fundamental" frequency of sonic stimuli have been observed in the auditory system of several animal species, including our own. This paper examines periodic cochlear neural responses of the gerbil. Periodicity responses to both sonic and ultrasonic stimuli originate within the cochlea of this animal. Acoustic stimuli, consisting of 2-12 successive harmonic frequencies, were used to generate an ensemble cochlear nerve periodicity response that was recorded from the round window of the cochlea. This response had a frequency equal to that of the missing fundamental, and not to those of the harmonic stimuli. Forward masking of the stimuli used to produce the periodicity response was used to generate sharp tuning curves, with tip frequencies corresponding to the harmonics and not to the periodicities. The sharpness of these functions increased as the frequencies of the harmonics increased, up to at least 38 kHz. This property could be related to reception of ultrasonic vocalizations utilized by many rodent species.     

Evaluation of performance of water quality models for a tropical lake system

The ability to predict water quality is a major requirement for planning and execution of developmental projects. It helps entrepreneurs to effectively plan and implement pollution control measures. This study evaluates the ability of different water quality models (statistical; remote sensing; mathematical) to predict salinity in Akkulam–Veli Lake, a tropical lake system. The performance of these three water quality models was evaluated. Prediction of salinity was made accurately with the mathematical model (WASP), compared to the other models. WASP facilitates prediction of daily water quality variations, which is not possible with the other models. A limitation of this model, however, was its ability to predict only a few water quality parameters. The statistical methods are reliable when the number of sampling sites and frequency of sample collection are high, making this method exhaustive and expensive. Remote sensing techniques proved to be less tedious, but are suitable only under specific situations, and not able to produce a high level of accuracy. Nevertheless, this method provides a continuous picture of spatial variations of different water quality parameters to a reasonable level of accuracy. The choice of the ‘best’ model varies on the basis of climatic and field conditions of the lake system of concern. Thus, a combination of water quality models was found to be the most ideal approach for analysing water quality data.     

Effects of water quality on mangroves in an urban lake system

This study assessed the extent of anthropogenic stresses on two mangrove species (Sonneratia caseolaris and Cerbera odollam) in a tropical coastal lake system. An anaerobic factor, organic pollution and salt factors were identified as being responsible for the risks to mangroves. A flora risk tolerance index was developed to ascertain the spatial and temporal risk of plants to water pollution. This study revealed the risk to the Cerbera odollam species is high, compared to Sonneratia caseolaris. Further, the flora risk tolerance index is a good index for assessing the risks to the mangrove species.     

Production of antisera to synthetic benzyloxycarbonyl-C-peptide of human proinsulin

Antisera to the C-peptide of human proinsulin were obtained by immunizing guinea pigs with synthetic benzyloxycarbonyl-C-peptide conjugated to human albumin with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide. In three series of 10, the animals were injected with C-peptide conjugated to albumin in the molar ratio of 23 : 1, 15 : 1, and 4 : 1, respectively. Antibodies to human C-peptide were present in all the surviving 25 animals. Fifteen of the antisera were suitable for measuring C-peptide concentrations lower than 0.10 pmol/ml. The antisera demonstrated an increasing immunogenicity with increasing molar ratio of C-peptide to albumin in the conjugate. In the fourth series, ten guinea pigs immunized with benzyloxycarbonyl-C-peptide ionically bound to QAE-Sephadex A-25 did not produce detectable antibodies to C-peptide. A qualitative evaluation of the radioimmunoassay by use of the antiserum with the highest titer and sensitivity, "M 1230", revealed a mean intra-assay and inter-assay coefficient of variance of 3.2 and 9.6%, respectively.     

Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks

This paper introduces the concept and practice of Neural Network architectures for wind speed prediction in wind farms. The wind speed prediction method has been analyzed by using back propagation network and radial basis function network. Artificial neural network is used to develop suitable architecture for predicting wind speed in wind farms. The key of wind speed prediction is rational selection of forecasting model and effective optimization of model performance. To verify the effectiveness of neural network architecture, simulations were conducted on real time wind data with different heights of wind mill. Due to fluctuation and nonlinearity of wind speed, accurate wind speed prediction plays a major role in the operational control of wind farms. The key advantages of Radial Basis Function Network include higher accuracy, reduction of training time and minimal error. The experimental results show that compared to existing approaches, proposed radial basis function network performs better in terms of minimization of errors.     

Optimization of the process parameters for the removal of boron from drinking water by electrocoagulation—a clean technology

BACKGROUND: There are a number of articles related to removal of boron by electrocoagulation using aluminium electrodes, but there are fewer articles describing the use of magnesium as the anode material. The main disadvantage of aluminium electrodes is the residual aluminium present in the treated water due to cathodic dissolution, which can create health problems. In the case of magnesium electrodes, there is no such disadvantage. This paper presents the results of studies on the removal of boron using magnesium and stainless steel as anode and cathode, respectively. RESULTS: Results show that a maximum removal efficiency of 86.32% was achieved at a current density of 0.2 A dm^?2 and pH of 7 using magnesium as the anode and stainless steel as the cathode. The adsorption of boron fitted the Langmuir adsorption isotherm, suggesting monolayer coverage of adsorbed molecules. The adsorption process follows second‐order kinetics. Temperature studies showed that adsorption was endothermic and spontaneous in nature. CONCLUSIONS: The magnesium hydroxide generated in the cell remove the boron present in the water and reduced to a permissible level and making it drinkable. The process scale up results was consistent with the results obtained from the laboratory scale, showing the robustness of the process. Copyright © 2010 Society of Chemical Industry     

Bio-assisted synthesis and characterization of nanostructured bismuth (III) sulphide using Clostridium acetobutylicum

Nanostructured bismuth (III) sulphide is synthesized at room temperature using a hydrogen sulphide producing microorganism namely Clostridium acetobutylicum. On contrary to chemical routes involving both the high and room temperature methods, the present experimental procedure involves a bio-assisted approach. This method is free from the usage of toxic and hazardous chemicals making it an environment friendly route. The synthesized bismuth sulphide is characterized using transmission electron microscope (TEM), powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry (CV). From our experiments we find that bismuth sulphide produced using this bio-assisted approach exhibits a hexagonal shaped plate-like structures and is stabilized by the extracellular proteins present in the culture medium.