Genetic Analysis in Muskmelon (Cucumis melo L.)

Fifty genotypes of muskmelon (Cucumis melo L.) were evaluated for variability, correlation, path analysis and divergence for yield and its contributing characters. Analysis of variance showed significant variation for all the characters, indicating presence of sufficient variability in the material studied. Genotypic correlations were higher than those of their respective phenotypic correlation coefficients in majority of the cases suggesting, that, genotypic correlations were stronger, reliable and free from environmental influences. Path analysis based on genotypic association revealed that number of fruits per plant and moisture percentage was the main yieldattributing characters in fruit yield of muskmelon. Total soluble solids exhibited positive direct effect on fruit yield per plant. Thus, number of fruits per plant, moisture percentage and total soluble solids may be given more weightage for an effective selection to improve fruit yield in muskmelon. On the basis of relative magnitude of D2 values, all the genotypes were grouped in seven clusters. Maximum genetic distance was observed between clusters II and V, while clusters III and VII displayed the lowest degree of divergence. Total soluble sugars followed by total soluble solids and fruit yield per plant contributed the most towards divergence

Polyurethane Based Inhibition for High Flame Temperature Nitramine Based Composite Modified Double Base propellant

The findings for polypropylene glycol (PPG) and hydroxyl-terminated polybutadiene (HTPB)-based inhibition systems are reported. These findings established that the inhibition system comprising HTPB-IPDI-IDP binder and Sb/sub 2/O/sub 3/-C black filler is most suitable for advanced nitramine-based composite modified double-base propellants in terms of mechanical properties and processibility. The promising composition was characterised for glass-transition behaviour and propellant-inhibition bond strength. Propellant grains inhibited with selected formulations were subjected to static evaluation at extreme temperatures and limited aging studies to obtain data of practical value

On Performance Evaluation of a New Liquid Propellant

A blend of 3-carene and cardanol in 70:30 weight proportion exhibits synergistic hypergolic ignition with red fuming nitric acid (RFNA) as oxidizer. Attempts have been made to evaluate this new propellant by theoretical calculationof performance parameters and verification of the results by static firing of a 10 kg thrust rocket motor around 20 atmosphers of chamber pressure. At an oxidizer-to-fuel weight ratio (O/F) of 3.34 (RFNA used had 21% N204 and 5% by weight of concentrated sulphuric acid as catalyst), the propellant produced a reasonably smooth pressure-time curve with an ignition delay of 35 milliseconds. The theoretical characteristic velocity value matched well with the experimental. No carbon residue was left in the rocket motor after firing. Specific impulse (theoretical) of the propellant has been found to be 223.8 seconds at chamber pressure, 20 atmos and exist pressure, 1 atmos

HAND HYGIENE AND HEALTH: AN EPIDEMIOLOGICAL STUDY OF STUDENTS IN AMRAVATI

Hands may be the most important means by which enteric pathogens are transmitted. Skin hygiene particularly of the hands, has been accepted as a primary mechanism to control the spread of infectious agents. Therefore the present study was undertaken to evaluate the number and type of enteric bacterial pathogens associated with hands. A total of 160 hands swab samples of 80 students of KG, PS, SS, UG, and PG were analyzed. Pathogens were isolated from hands includes Escherichia coli (22%), Pseudomonas aeruginosa (12%), Staphylococcus aureus (15%), Proteus mirabilis (11%), Citrobacter freundii (10%), Enterobacter aerogenes (8%), Streptococcus sp. (7%), Klebsiella sp. (6%), Micrococcus sp. (5%) and Salmonella typhi (4%). The prevalence of the bacterial pathogens was high in students of K.G. and primary than those in secondary schools and colleges. The data indicated that the hands of the female were more contaminated than male and the left hand was more contaminated than the right hand. Thus, the potential risk factors for transmission of enteric pathogens through hands should be investigated in order to improve the general health of the students

Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work

How market structure drives commodity prices

We introduce an agent-based model, in which agents set their prices to maximize profit. At steady state the market self-organizes into three groups: excess producers, consumers and balanced agents, with prices determined by their own resource level and a couple of macroscopic parameters that emerge naturally from the analysis, akin to mean-field parameters in statistical mechanics. When resources are scarce prices rise sharply below a turning point that marks the disappearance of excess producers. To compare the model with real empirical data, we study the relationship between commodity prices and stock-to-use ratios in a range of commodities such as agricultural products and metals. By introducing an elasticity parameter to mitigate noise and long-term changes in commodities data, we confirm the trend of rising prices, provide evidence for turning points, and indicate yield points for less essential commodities

Dopamine Increases a Value-Independent Gambling Propensity

Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine’s role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline (ie, value-independent) gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options—a finding with implications for understanding a range of reward-related psychopathologies including addiction