Effect of pre-treatment on fouling propensity of feed as depicted by the modified fouling index (MFI) and cross-flow sampler-modified fouling index (CFS-MFI)

The effectiveness of different pretreatment on the fouling propensity of the feed was studied using synthetic wastewater. The fouling potential of the feed was characterized by the standard modified fouling index (MFI) and cross-flow sampler modified fouling index (CFS-MFI). In CFS-MFI, a cross-flow sampler was used to simulate the condition of a cross-flow filtration. The results indicated that the pretreatment such as flocculation with an optimum dose of 68 mg/l FeCl3 substantially reduced the fouling propensity of the feed. The standard MFI of flocculated wastewater was reduced by around 99% compared to that of the untreated wastewater. Similarly, the adsorption with powdered activated carbon (PAC) of 1 g/l reduced the standard MFI value to more than 99% compared to that of the untreated wastewater. The CFS-MFI values were lower than the standard MFI values for both treated and untreated wastewater, suggesting that the standard MFI was overestimated. The overestimation of the standard MFI compared to that of the CFS-MFI value was more than 99%. The effect of molecular weight distribution (MWD) of the foulants in the wastewater on the fouling propensity of the feed was investigated. The MWD was correlated with the MFI and CFS-MFI indices. It yielded useful insights in understanding the effect of MW on MFI and CFS-MFI and fouling propensity of the feed. © 2009 Elsevier B.V. All rights reserved

A Risk Management Perspective on Statistical Estimation and Generalized Variational Inference

Generalized variational inference (GVI) provides an optimization-theoretic framework for statistical estimation that encapsulates many traditional estimation procedures. The typical GVI problem is to compute a distribution of parameters that maximizes the expected payoff minus the divergence of the distribution from a specified prior. In this way, GVI enables likelihood-free estimation with the ability to control the influence of the prior by tuning the so-called learning rate. Recently, GVI was shown to outperform traditional Bayesian inference when the model and prior distribution are misspecified. In this paper, we introduce and analyze a new GVI formulation based on utility theory and risk management. Our formulation is to maximize the expected payoff while enforcing constraints on the maximizing distribution. We recover the original GVI distribution by choosing the feasible set to include a constraint on the divergence of the distribution from the prior. In doing so, we automatically determine the learning rate as the Lagrange multiplier for the constraint. In this setting, we are able to transform the infinite-dimensional estimation problem into a two-dimensional convex program. This reformulation further provides an analytic expression for the optimal density of parameters. In addition, we prove asymptotic consistency results for empirical approximations of our optimal distributions. Throughout, we draw connections between our estimation procedure and risk management. In fact, we demonstrate that our estimation procedure is equivalent to evaluating a risk measure. We test our procedure on an estimation problem with a misspecified model and prior distribution, and conclude with some extensions of our approach

Preference of farmers towards private and public extension services

The main purpose of this study was to know the preference of farmers for different services provided by private and public extension agencies. In recent times involvement of private extension agencies has been increased in agricultural sector and up to some extent it has sidelined the public extension agencies, but public extension agencies have potential to do better and to reach farmers at their best. In view of this, present study was undertaken to find out the farmers’ preference towards public and private extension services in Ambala, Kurukshetra, Karnal, Hisar and Fatehabad districts of Haryana state. From each district two blocks were selected randomly and from each block two villages were selected. A manageable size of 10 farmers was selected from each village thus making total sample size of 200 farmers. Various aspects related to agricultural services provided by both public and private agencies were identified and response were obtained by putting a tick mark as per farmers’ preference for private and public agencies. On the basis of statistical tools like rank and mean score, results showed that farmers had great preference for ‘Input supply’ in private extension as compared to public extension followed by ‘Infrastructure facilities’. While for ‘Consultancy and diagnosis services’, ‘Information’ and ‘Technical services’, public extension was preferred as over the private extension

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques

Disease and pest management in apple: Farmers' perception and adoption in J&K state

Diseases and pests are one of the limiting factors for low productivity of the fruit crops in Kashmir valley, India. A study on management of resources with respect to disease and pest management of apple and extent of adoption of recommended plant protection technology was undertaken for increasing apple production in Kashmir valley of J and K State. District Baramulla was selected purposively on the basis of maximum area and production under apple crop. A sample size of 200 apple growers 50 each from 4 villages were selected randomly. The study revealed that the perception index regarding attributes of technology recommended in two diseases viz. San Jose Scale and Apple Scab was 68.88% and 80.76% in respect of profitability (83.97%), simplicity-complexity each 63.57% and 54.27 % for practicability attributes of technology. The data further showed that the farmers adoption level under Chemical control was high at silver tip to green tip stage (80%) and fruit let pea size stage (78%) and medium adoption was observed at pink bloom (bud) stage (74%), petal fall stage (74%) walnut size apple stage (70%) on Apple Scab similarly, the extent of adoption was low (45%) for mechanical and no chemical control measures under clean cultivation. In case of San Jose Scale the farmers adoption level regarding name of chemical, its dose, quantity of water required per acre for preparing spray solution and time of spray at late dormant spray, (feb, March) was high (80%). The findings will help to improve the level of farmers’ knowledge to increase apple production in Kashmir valley

Assessment of the SC (US) performance-based incentive mechanism and economic analysis of the project Revitalizing and Improving Primary Health Care in Battagram District-.

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