The Politics of Implementation Design

This Article argues that implementation design specified in policy legislation is potentially a major source of conflict and difficulties in the carrying out the legislative policy

Delayed re-laparotomy after total hysterectomy

Background: Since beginning it’s a dilemma whether to remove or preserve the ovaries. In the present study an attempt is made to understand this phenomenon and to have some direction for removal of ovaries. Preservation of the ovaries at the time of hysterectomy does not seem to compromise patient care. Impaired function or failure of the retained ovaries, however, is not uncommon; close post-treatment surveillance is therefore important in terms not only of recurrent disease but of function of the ovaries as well.Methods: This study was done on 37 patients in duration of 3 years from June 2009 to May 2012. It is a retrospective statistical hospital based study of re-laparotomy done in post hysterectomised patients.Results: The most common pathology in these patients was a simple ovarian cyst (45.95%), followed by endometriotic cyst (21.62%), mucinous adenoma (8.10%), serous cyst adenoma (5.40%), serous cyst adenocarcinoma (2.70%) and poorly differentiated adenocarcinoma (2.70%).Conclusions: Emergence of pelvic mass after hysterectomy poses diagnostic and therapeutic challenge to gynecologists. In future, as the patients become more aware and the clinicians more enlightened on the long term benefits and risks of hormone replacement therapy, decisions might be easier for the patients and the clinicians alike

Synthesis of 1,2-dimethyl-6-(2' -hydroxyisopropyl)-7 -methoxynaphthalene, an isomer of emmotin-G methyl ether

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Senslide: a distributed landslide prediction system

We describe the design, implementation, and current status of Senslide, a distributed sensor system aimed at predicting landslides in the hilly regions of western India. Landslides in this region occur during the monsoon rains and cause significant damage to property and lives. Unlike existing solutions that detect landslides in this region, our goal is to predict them before they occur. Also, unlike previous efforts that use a few but expensive sensors to measure slope stability, our solution uses a large number of inexpensive sensor nodes inter-connected by a wireless network. Our system software is designed to tolerate the increased failures such inexpensive components may entail. We have implemented our design in the small on a laboratory testbed of 65 sensor nodes, and present results from that testbed as well as simulation results for larger systems up to 400 sensor nodes. Our results are sufficiently encouraging that we intend to do a field test of the system during the monsoon season in India

Potassium phosphate catalyzed highly efficient synthesis of structurally diverse thioethers at ambient temperature

154-158Commercially available potassium phosphate has been demonstrated to be a highly efficient catalyst for the synthesis of thioethers employing two different routes viz. alkylation of thiols with alkyl/aralkyl halides and by Michael addition of thiols to conjugated alkenes

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and Bi-Directional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatial-spectral-temporal characteristics for water stress classification

Modelling and application of stochastic processes

The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realiza­ tion problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically ef­ ficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side, chapters on use of Markov random fields for modelling and analyzing image signals, use of complementary models for the smoothing problem with missing data, and nonlinear estimation are included. Chapter 1 by Klein and Dickinson develops the nested orthogonal state space realization for ARMA processes. As suggested by the name, nested orthogonal realizations possess two key properties; (i) the state variables are orthogonal, and (ii) the system matrices for the (n + l)st order realization contain as their "upper" n-th order blocks the system matrices from the n-th order realization (nesting property)