Aircraft down-range profiles formed from simulated and compact-range radar backscatter

Compact-range radar backscatter measurements are taken of aircraft scale models. In addition, computer software is used to predict the RCS of the aircraft. Synthetic down-range profiles formed from the two sources of backscatter data are compared and visualized in an innovative manner. Similar discrimination rates between the two aircraft are obtained on data from both source

Down-regulation of cerebellar 5-HT2C receptors in pilocarpine-induced epilepsy in rats: Therapeutic role of Bacopa monnieri extract

Epilepsy is a syndrome of episodic brain dysfunction characterized by recurrent unpredictable, spontaneous seizures. Cerebellar dysfunction is a recognized complication of temporal lobe epilepsy and it is associated with seizure generation, motor deficits and memory impairment. Serotonin is known to exert a modulatory action on cerebellar function through 5HT2C receptors. 5-HT2C receptors are novel targets for developing anticonvulsant drugs. In the present study, we investigated the changes in the 5-HT2C receptors binding and gene expression in the cerebellum of control, epileptic and Bacopa monnieri treated epileptic rats. There was a significant down regulation of the 5-HT content (pb0.001), 5-HT2C gene expression (pb0.001) and 5-HT2C receptor binding (pb0.001) with an increased affinity (pb0.001). Carbamazepine and B. monnieri treatments to epileptic rats reversed the down regulated 5-HT content (pb0.01), 5-HT2C receptor binding (pb0.001) and gene expression (pb0.01) to near control level. Also, the Rotarod test confirms the motor dysfunction and recovery by B. monnieri treatment. These data suggest the neuroprotective role of B. monnieri through the upregulation of 5-HT2C receptor in epileptic rats. This has clinical significance in the management of epilepsy

Influence Of Additives On The Drain Down Characteristics Of Stone Matrix Asphalt Mixtures

In Kerala highways, where traditional dense graded mixtures are used for the surface courses, major distress is due to moisture induced damages. Development of stabilized Stone Matrix Asphalt (SMA) mixtures for improved pavement performance has been the focus of research all over the world for the past few decades. Many successful attempts are made to stabilize SMA mixtures with synthetic fibres and polymers. India, being an agricultural economy produces fairly huge quantity of natural fibres such as coconut, sisal, banana, sugar cane, jute etc.. Now- a -days the disposal of waste plastics is a major concern for an eco- friendly sustainable environment. This paper focuses on the influence of additives like coir, sisal, banana fibres (natural fibres), waste plastics (waste material) and polypropylene (polymer) on the drain down characteristics of SMA mixtures. A preliminary investigation is conducted to characterize the materials used in this study. Drain down sensitivity tests are conducted to study the bleeding phenomena and drain down of SMA mixtures. Based on the drain down characteristics of the various stabilized mixtures it is inferred that the optimum fibre content is 0.3% by weight of mixture for all fibre mixtures irrespective of the type of fibre. For waste plastics and polypropylene stabilized SMA mixtures, the optimum additive contents are respectively 7% and 5% by weight of mixture. Due to the absorptive nature of fibres, fibre stabilizers are found to be more effective in reducing the drain down of the SMA mixture. The drain values for the waste plastics mix is within the required specification range. The coir fibre additive is the best among the fibres investigated. Sisal and banana fibre mixtures showed almost the same characteristics on stabilization.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems