Effects of curvature and interactions on the dynamics of the deconfinement phase transition

We study the dynamics of first-order cofinement-deconfinement phase transition through nucleation of hadronic bubbles in an expanding quark gluon plasma in the context of heavy ion collisions for interacting quark and hadron gas and by incorporating the effects of curvature energy. We find that the interactions reduce the delay in the phase transition whereas the curvature energy has a mixed behavior. In contrast to the case of early Universe phase transition, here lower values of surface tension increase the supercooling and slow down the hadronization process. Higher values of bag pressure tend to speed up the transition. Another interesting feature is the start of the hadronization process as soon as the QGP is created.Comment: LaTeX, 17 pages including 14 postscript figure

Impact of Water Deficit Condition on Osmoregulation of the Brassica Species

Oilseed rape and mustard are mostly grown on conserved soil water in the Indian sub-continent. These crops generally suffer from water stress at reproductive period of development. Thus, to obtain higher stable yields of Brassica species under routine stress conditions, it is essential to identify and understand the interactions of different morpho-physiological traits responsible for drought resistance. To explicate interaction of traits related to biochemical, physio-morphological factors for sustaining drought resistance in Brassica species. Dry mass production and partition in a plant is important when increased yield are sought. A osmometer like plant cell which allows only selective solutes to pass through elastic membrane, cell wall and the thin layer of cytoplasm and a vacuole containing an aqueous solution. To express drought tolerance turgidity of cells maintained by osmotic adjustments

Computable queries for relational data bases

AbstractThe concept of “reasonable” queries on relational data bases is investigated. We provide an abstract characterization of the class of queries which are computable, and define the completeness of a query language as the property of being precisely powerful enough to express the queries in this class. This definition is then compared with other proposals for measuring the power of query languages. Our main result is the completeness of a simple programming language which can be thought of as consisting of the relational algebra augmented with the power of iteration

Close Formation Flight Missions Using Vision-Based Position Detection System

In this thesis, a formation flight architecture is described along with the implementation and evaluation of a state-of-the-art vision-based algorithm for solving the problem of estimating and tracking a leader vehicle within a close-formation configuration. A vision-based algorithm that uses Darknet architecture and a formation flight control law to track and follow a leader with desired clearance in forward, lateral directions are developed and implemented. The architecture is run on a flight computer that handles the process in real-time while integrating navigation sensors and a stereo camera. Numerical simulations along with indoor and outdoor actual flight tests demonstrate the capabilities of detection and tracking by providing a low cost, compact size and low weight solution for the problem of estimating the location of other cooperative or non-cooperative flying vehicles within a formation architecture

A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel

This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.950C whose desirability is one

A genetic based neuro-fuzzy controller for thermal processes

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.Facultad de Informátic

Production Function and Farmers' Risk Aversion: A Certainty Equivalent-adjusted Production Function

Faced with risky yields and returns, risk-averse farmers require a premium to take risks. In this paper, we estimate individual farmers’ degrees of risk aversion to adjust for the risk premium in returns and to replace the farmers’ realized returns with their certainty equivalent returns in the production function. In that way, the effect of the inputs on returns will automatically be risk-adjusted, i.e., we obtain risk-adjusted marginal effects of inputs, which can be used in decision-making support of farmers’ input choices in production. Using farm-level data from organic basmati rice smallholders in India, we illustrate this method using nonparametric production functions. The results show that the input elasticities and returns-to-scale estimates change when the farmers’ degree of risk aversion is taken into consideration.publishedVersio

A Deep Learning First Approach to Remaining Useful Lifetime Prediction of Filtration System With Improved Response to Changing Operational Parameters Using Parameterized Fully-connected Layer

For the remaining useful lifetime prediction, apart from the normal sensor data which is updated regularly, there are also operational parameters, which do not change during a cycle of operation. Different sets of parameters result in essentially different, but relevant systems and thus require the adaptation from the statistical model for better prediction. We noticed that neural networks could easily overfit into one set of operational parameters and demonstrate constant bias in the prediction for other sets (underfitting). An aspect of major contribution from our work is the use of Parameterized Fully-Connected Layer (PFL). The PFL builds the parameter dependency right into each layer, in this way the parameters act as ”meta-inputs” which adapt the model of neural network models to the different operating conditions. In another aspect of contribution, our work demonstrated that, instead of using feature engineering, convolutional layers could be used to automatically learn the features which are relevant for the prediction. In this way, the deep learning architecture could be reused for different problems or systems. We conduct experiments on the filtration system datasets provided by the Data Challenge 2020 and received results that compare favorably to the prize winners