Active Search with a Cost for Switching Actions

Active Sequential Hypothesis Testing (ASHT) is an extension of the classical sequential hypothesis testing problem with controls. Chernoff (Ann. Math. Statist., 1959) proposed a policy called Procedure A and showed its asymptotic optimality as the cost of sampling was driven to zero. In this paper we study a further extension where we introduce costs for switching of actions. We show that a modification of Chernoff's Procedure A, one that we call Sluggish Procedure A, is asymptotically optimal even with switching costs. The growth rate of the total cost, as the probability of false detection is driven to zero, and as a switching parameter of the Sluggish Procedure A is driven down to zero, is the same as that without switching costs.Comment: 8 pages. Presented at 2015 Information Theory and Applications Worksho

Green Synthesis Of Silver Nanoparticles Using Calendula Officinalis And Its Anti-Bacterial Studies

Green synthesis of Silver nanoparticles was carried out using Calendula officinalis (marigold flower) extract without use of any organic solvent and heat. The biosynthesis of Ag nanoparticles was confirmed by UV-Vis Spectrophotometer and XRD. The surface morphology and size of the prepared nanoparticles were confirmed by TEM. The anti-bacterial properties of the prepared NPs were tested against E. coli bacteria by growth inhibitory zone plate well method

Vaillant’s Contribution To Research And Theory Of Adult Development

Vaillant’s recent synthesis of the findings of three longitudinal studies on aging adds new insights to our theories of adult development. These insights provide us with new sets of variables for quasi-experimental and even descriptive studies of successful aging. His frame of reference is fundamentally Erikson’s to which he adds two stages, Career Consolidation and Keeper of the Meaning. He arrived at his model inductively over years of qualitative and quantitative longitudinal observations

അറിയാം നമ്മുടെ ഉഷ്ണഗ്രഹത്തെ (Know Your Warming Planet-ClimEd Series:1C)

This instructional material "Know Your Warming Planet" has been developed as a part of the Belmont funded project titled "Global Understanding and Learning for Local solutions: Reducing Vulnerability of marine dependent coastal communities" as a means to create awareness and impart climate change knowledge across the target populace

Analytical Method Development and Validation of Lumefantrine by RP-HPLC

is considered accurate if the average recovery is not less than 98% and not more than 102%. Precision parameter RSD of six replicate injections should be NMT 2%. The linearity range of Lumefantrine was found to be 100-500 μg/ml in HPLC. Linear regression was not more than 0.999.the values of %RSD was <2 Robustness of assay method is demonstrated by changing the flow rate for 1.4ml/min and 1.8ml/min instead of 1.6ml/min by injecting the 6 replicate injections of standard in 1.4ml/min and 1.8ml/min flow rate and found that system suitability parameters are passed. By changing the column temperature for 30 �c and 40 �c instead of 35 �c by injecting the 6 replicate injections of standard in 30 �c and 40 �c temperature and found that system suitability parameters are passed Ruggedness parameter RSD for the assay values of 6 sample preparations of same batch should not be more than2.0%. The LOD are calculated from the calibration curve by using the formulas LOD = 3 x SD/ bWhere, SD- the estimate is the standard deviation of the peak area of the drugs. b -is slope of the corresponding calibration curvepassed.The LOQ are calculated from the calibration curve by using the formulas LOQ = 10 x SD/ where,limits. These results shows the method is accurate, precise, sensitive, economic & rugged. The HPLC method is more rapid. The proposed method is successfully applied to the bulk dosage form. The method was found to be having suitable application in routine laboratory analysis with high degree of accuracy and precision

Identification of interstitial lung diseases using deep learning

The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet

Design and Characterization of Floating Tablets of Ranolazine.

The proposed formulation is developed by considering the drawbacks associated with the conventional controlled drug delivery system. The formulation may remained in the stomach and / or upper part of GIT for prolonged period of time thereby giving sufficient time for drug candidate to achieve maximum bioavailability and reduce unwanted side effects by minimizing or avoiding drug release at unfavorable site.From the above study it can be concluded that promising controlled release by gastro retentive floating tablets of Ranolazine was developed using a combination of HPMC K15M and Chitosan. The floating tablet of Ranolazine was capable of maintaining plasma drug concentration through 12 hrs. The release rate of the drug from the floating tablets was significantly influenced by the proportion as well as viscosity of the polymer used. The formulation RF8 was selected as an optimized formulation because it gave the best result in terms of the required in-vitro buoyancy study, good floating integrity and drug release in sustained release manner. The release profile of the optimized formula, fitted best to Korsmeyer- Peppas model with R2 value of 0.975.As the n value for the Korsmeyer-Peppas model was found to be less than 0.89, it follows case-2 transport.In-vivo radiographic studies revealed that the tablets remain in the stomach for 300±10 mins which indicates the increase in the gastric residence time. Short-term stability studies indicated no appreciable changes in the drug content and In-vitro drug release rates of formulation RF8. The aim of the present dissertation work is to sustain the release of drug due to floating in stomach at acidic pH 1.2 up to 12 hrs