Effect of Rising Temperature Due to Ozone Depletion on the Dynamics of a Prey-Predator System: A Mathematical Model

It is well recognized that the greenhouse gas such as Chlorofluoro Carbon (CFC) is responsible directly or indirectly for the increase in the average global temperature of the Earth. The presence of CFC is responsible for the depletion of ozone concentration in the atmosphere due to which the heat accompanied with the sun rays are less absorbed causing increase in the atmospheric temperature of the Earth. The increase in the temperature level directly or indirectly affects the dynamics of interacting species systems. Therefore, in this paper a mathematical model is proposed and analyzed using stability theory to asses the effects of increasing temperature due to the greenhouse gas CFC on the survival or extinction of populations in a prey-predator system. A threshold value in terms of a stress parameter is obtained which determines the extinction or existence of populations in the underlying system

Effect of Toxic Metal on Root and Shoot Biomass of a Plant A Mathematical Model

In this paper, a mathematical model is proposed to study the impact of toxic metals on plant growth dynamics due to transfer of the toxic metal in plant tissues. In the model, it is assumed that the plant uptakes the metal from the soil through the roots and then it is transfered in the plant tissues and cells by transport mechanisms. It is observed experimently that when toxic (heavy) metals combines with the nutrient they form a complex compound due to which nutrient loses its inherent properties and the natural charaterstics of the nutrient are damaged. It is noticed that due to the presence of toxic (heavy) metal in the plant tissues and loss of inherent properties of nutrient due to reaction with the toxic metal, the growth rate of the plant decreases. In order to understand the impact on plant growth dynamics, we have studied two models: One model for a plant growth with no toxic effect and the other model for plant growth with toxic effect. From the analysis of the models the criteria for plant growth with and without toxic effects are derived. The numerical simulation to support the analytical results is done using MathLab

INTRINSIC MECHANISM FOR ENTROPY CHANGE IN CLASSICAL AND QUANTUM EVOLUTION

It is shown that the existence of a time operator in the Liouville space representation of both classical and quantum evolution provides a mechanism for effective entropy change of physical states. In particular, an initially effectively pure state can evolve under the usual unitary evolution to an effectively mixed state.Comment: 20 pages. For more information or comments contact E. Eisenberg at [email protected] (internet)

Zeno Dynamics in Quantum Statistical Mechanics

We study the quantum Zeno effect in quantum statistical mechanics within the operator algebraic framework. We formulate a condition for the appearance of the effect in W*-dynamical systems, in terms of the short-time behaviour of the dynamics. Examples of quantum spin systems show that this condition can be effectively applied to quantum statistical mechanical models. Further, we derive an explicit form of the Zeno generator, and use it to construct Gibbs equilibrium states for the Zeno dynamics. As a concrete example, we consider the X-Y model, for which we show that a frequent measurement at a microscopic level, e.g. a single lattice site, can produce a macroscopic effect in changing the global equilibrium.Comment: 15 pages, AMSLaTeX; typos corrected, references updated and added, acknowledgements added, style polished; revised version contains corrections from published corrigend

Modelling Effect of Toxic Metal on the Individual Plant Growth: A Two Compartment Model

Abstract A two co mpart ment mathematical model for the ind ividual plant growth under the stress of toxic metal is studied. In the model it is assumed that the uptake of to xic metal adsorbed on the surface of soil by the plant is through root compart ment thereby decreasing the root dry weight and shoot dry weight due to decrease in nutrient concentration in each compart ment. In order to visualize the effect of to xic metal on p lant growth, we have studied two models that is, mod el for plant growth with no toxic effect and model fo r plant growth with toxic effect. Fro m the analysis of the models the criteria for plant growth with and without toxic effects are derived. The numerical simu lation is done using Matlab to support the analytical results

Compositional Verification and Optimization of Interactive Markov Chains

Interactive Markov chains (IMC) are compositional behavioural models extending labelled transition systems and continuous-time Markov chains. We provide a framework and algorithms for compositional verification and optimization of IMC with respect to time-bounded properties. Firstly, we give a specification formalism for IMC. Secondly, given a time-bounded property, an IMC component and the assumption that its unknown environment satisfies a given specification, we synthesize a scheduler for the component optimizing the probability that the property is satisfied in any such environment

Mobile-Bayesian Diagnostic System for Childhood Infectious Diseases

About 5.9 million children under the age of 5 died in 2015, Preterm birth, delivery complications and infections source a great number of neonatal deaths. the Sustainable Development goals (SDGs) 3.2 is to end preventable deaths of newborns and children under 5 years of age, with a target to reduce neonatal mortality to at least 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births in all countries. However quality and accessible healthcare service is essential to achieve this goal whereas most undeveloped and developing countries still have poor access to quality healthcare. with the emergences on mobile computing and telemedicine, this work provide diagnostics alternative for childhood infectious diseases using Naïve Bayesian classier which has been proven to be efficient in handling uncertainty as regards learning of incomplete data. In this research, sample data was collected from hospitals to model a pediatric system using Naïve Bayes classifier, which produce a 70% accuracy level suitable for a decision support system. The model was also integrated into a SMS platform to enable ease of usage

Assessment of Machine Learning Classifiers for Heart Diseases Discovery

Heart disease (HD) is one of the utmost serious illnesses that afflict humanity. The ability to anticipate cardiac illness permits physicians to deliver better knowledgeable choices about their patient’s wellbeing. Utilizing machine learning (ML) to minimize and realize the symptoms of cardiac illness is a worthwhile decision. Therefore, this study aims to analyze the effectiveness of some supervised ML procedures for detecting heart disease in respect to their accuracy, precision, f1-score, sensitivity, specificity, and false-positive rate (FPR). The outcomes, which were obtained using python programming language were compared. The data employed in this investigation came from an open database of the National Health Service (NHS) heart disease which originated in 2013. Through the machine learning (ML) technique, a dimensionality reduction technique and five classifiers were employed and a performance evaluation between the three classifiers- principal component analysis (PCA), decision tree (DT), random forest (RF), and support vector machine (SVM). The NHS database contains 299 observations. The system was evaluated using confusion matrix measures like accuracy, precision, f1-score, sensitivity (TPR), specificity, and FPR. It is concluded that ML techniques reinforce the true positive rate (TPR) of traditional regression approaches with a TPR of 98.71% and f-measure value of 68.12%. The true positives rate which is the same as the sensitivity was used to evaluate the accuracy of the classifiers and it was deduced that the PCA + DT outperformed that of the other two with a sensitivity of 98.71% and since the value is on the high side, this implies that the classifier will be able to accurately detect a patient with HD in his or her body

High-efficiency quantum interrogation measurements via the quantum Zeno effect

The phenomenon of quantum interrogation allows one to optically detect the presence of an absorbing object, without the measuring light interacting with it. In an application of the quantum Zeno effect, the object inhibits the otherwise coherent evolution of the light, such that the probability that an interrogating photon is absorbed can in principle be arbitrarily small. We have implemented this technique, demonstrating efficiencies exceeding the 50% theoretical-maximum of the original ``interaction-free'' measurement proposal. We have also predicted and experimentally verified a previously unsuspected dependence on loss; efficiencies of up to 73% were observed and the feasibility of efficiencies up to 85% was demonstrated.Comment: 4 pages, 3 postscript figures. To appear in Phys. Rev. Lett; submitted June 11, 199