Supervision aspects in District Tuberculosis Programme with special reference to Short Course Chemotherapy

The National Tuberculosis Control Programme has been introduced with objectives to implement the curative and preventive strategies of tuberculosis. It is necessary that proper supervision should be exercised to achieve the goals of the, programme in order to have a major impact on the control of tuberculosis. The National Tuberculosis Programme (NTP) has been implemented in almost all parts of India, with a few exceptions, integrated with the Primary Health Care (PHC) System. Hence, it is important that the infrastructure of the PHC has to be sound enough for effective implementation and efficient functioning of the programme. This calls for good health care management skills from the personnel in charge of the Health Care Delivery Systems. Such manegerial skills should be made available and exercised et every stage by proper supervision of the system with added training component of the staff involved at the grass-root level. This paper describes one facet of the management

Methods of Spat Collection in the Culture of Shellfishes

Mariculture, the practice of farming marine invertebrates, vertebrates and algae for increasing the production of seafood, is of comparatively recent interest in India. In the culture practice, collection of 'seed' is one of the essential prerequistes

Two generalizations of the PRV conjecture

Let G be a complex connected reductive group. The PRV conjecture, which was proved independently by S. Kumar and O. Mathieu in 1989, gives explicit irreducible submodules of the tensor product of two irreducible G-modules. This paper has three aims. First, we simplify the proof of the PRV conjecture, then we generalize it to other branching problems. Finally, we find other irreducible components of the tensor product of two irreducible G-modules that appear for "the same reason" as the PRV ones

Control of Quantized Multi-Agent Systems with Linear Nearest Neighbor Rules: A Finite Field Approach

We study the problem of controlling a multi-agent system where each agent is only allowed to be in a discrete and finite set of states. Each agent is capable of updating its state based on the states of its neighbors, and there is a leader agent in the network that is allowed to update its state in arbitrary ways (within the discrete set) in order to put all agents in a desired state. We present a novel solution to this problem by viewing the discrete states of the system as elements of a finite field. Specifically, we develop a theory of structured linear systems over finite fields, and show that such systems will be controllable provided that the size of the finite field is sufficiently large, and that the graph associated with the system satisfies certain properties. We then use these results to show that a multi-agent system with a leader node is controllable via a linear nearest-neighbor update as long as there is a path from the leader to every node, and that the number of discrete states for each node is large enough

On the Time Complexity of Information Dissemination via Linear Iterative Strategies

Given an arbitrary network of interconnected nodes, each with an initial value, we study the number of timesteps required for some (or all) of the nodes to gather all of the initial values via a linear iterative strategy. At each time-step in this strategy, each node in the network transmits a weighted linear combination of its previous transmission and the most recent transmissions of its neighbors. We show that for almost any choice of real-valued weights in the linear iteration (i.e., for all but a set of measure zero), the number of time-steps required for any node to accumulate all of the initial values is upper-bounded by the size of the largest tree in a certain subgraph of the network; we use this fact to show that the linear iterative strategy is time-optimal for information dissemination in certain networks. In the process of deriving our results, we also obtain a characterization of the observability index for a class of linear structured systems

An Optimization of Energy Saving in Cloud Environment

Cloud computing is a technology in distributed computing which facilitates pay per model based on user demand and requirement. Cloud can be defined as a collection of virtual machines. This includes both computational and storage facility. The goal of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud Computing is developing day by day and faces many challenges; one of them is i) Load Balancing and ii) Task scheduling. Load balancing is defined as division of the amount of work that a system has to do between two or more systems so that more work gets done in the same amount of time and all users get served faster. Load balancing can be implemented with hardware, software, or a combination of both. Load balancing is mainly used for server clustering. Task Scheduling is a set of policies to control the work order to be performed by a system. It is also a technique which is used to improve the overall execution time of the job. Task Scheduling is responsible for selection of best suitable resources for task execution, by taking some parameters into consideration. A good task scheduler adapts its scheduling strategy according to the changing environment and the type of task. In this paper, the Energy Saving Load Balancing (ESLB) Algorithm and Energy Saving Task Scheduling (ESTS) algorithm was proposed. The various scheduling algorithms (FCFS, RR, PRIORITY, and SJF) are reviewed and compared. The ESLB algorithm and ESTS algorithm was tested in cloudsim toolkit and the result shows better performance

Wind Speed Intervals Prediction using Meta-cognitive Approach

© 2018 The Authors. Published by Elsevier Ltd. In this paper, an interval type-2 neural fuzzy inference system and its meta-cognitive learning algorithm for wind speed prediction is proposed. Interval type-2 neuro-fuzzy system is capable of handling uncertainty associated with the data and meta-cognition employs self-regulation mechanism for learning. The proposed system realizes Takagi-Sugeno-Kang inference mechanism and adopts a fast data-driven interval-reduction method. Meta-cognitive learning enables the network structure to evolve automatically based on the knowledge in data. The parameters are updated based on an extended Kalman filter algorithm. In addition, the proposed network is able to construct prediction intervals to quantify uncertainty associated with forecasts. For performance evaluation, a real-world wind speed prediction problem is utilized. Using historical data, the model provides short-term prediction intervals of wind speed. The performance of proposed algorithm is compared with existing state-of-the art fuzzy inference system approaches and the results clearly indicate its advantages in forecasting problems