The effect of quantum memory on quantum games

We study quantum games with correlated noise through a generalized quantization scheme. We investigate the effects of memory on quantum games, such as Prisoner's Dilemma, Battle of the Sexes and Chicken, through three prototype quantum-correlated channels. It is shown that the quantum player enjoys an advantage over the classical player for all nine cases considered in this paper for the maximally entangled case. However, the quantum player can also outperform the classical player for subsequent cases that can be noted in the case of the Battle of the Sexes game. It can be seen that the Nash equilibria do not change for all the three games under the effect of memory.Comment: 26 pages, 7 ps figure

Efficacy of fertilizing method for different potash sources in cotton (Gossypium hirsutum L.) nutrition under arid climatic conditions

Precise choice of potassium (K) source and application method does matter for its cost-effectiveness. This study was aimed to evaluate the best source and method of K fertilizer application to improve cotton productivity and profitability under an arid climate. Three different K sources (KNO3, K2SO4 and KCl) were applied at 100 kg ha-1 by four methods, i.e. a) basal application, b) side dressing, c) fertigation and d) foliar application of 2% K2SO4. The highest productivity and profitability were recorded with K2SO4 applied as foliar application. Total boll weight per plant was similar in foliar applied K2SO4 and basal application of KNO3. Better boll opening in foliar applied K2SO4, perhaps, played decisive role for increased seed-cotton yield. For basal application and side dressing, KNO3 produced the highest seed-cotton yield, but the benefit cost ratio was better for foliar applied K2SO4. In crux, foliar application of K2SO4 might be opted to improve the seed cotton yield, fiber quality and net returns under the arid climate. However, soil K application through K2SO4 and/or KNO3 is essential to balance the K removal from soil

Throughput and energy efficiency of two-tier cellular networks: Massive MIMO overlay for small cells

In this paper, the downlink performance of twotier heterogenous network is investigated. We consider a scenario where the macro-tier is empowered by massive antennaarray thus allowing for Massive multiple-input multiple-output (MIMO) transmission scheduling. The small cellular network complements the macro-tier capacity. We propose a novel channel allocation mechanism which optimally splits the spectral resources to maximize network level throughput and energy efficiency. Our proposed channel allocation mechanism is robust to the topological and channel variations. More specifically, the proposed scheme is designed by capturing the random locations of the users in both tiers by a Poisson Point Process (PPP). The channel uncertainty is captured by considering Rayleigh fading complemented by large scale power law path-loss. Our analysis shows that there exists an optimal split which maximizes the network wide throughput and energy efficiency. We also demonstrate that there exists an optimal transmit power which maximizes the energy efficiency for the network. Under different scenarios, massive MIMO plays a vital role in improving sum rate capacity as compared to single antenna femtocells. Finally, using implementation parameters, we obtain the optimal configurations that improve system capacity and energy efficienc

Mutual prodrug of cephazolin and benzydamin: 3-[(1-benzyl-1H-indazol-3-yl)­oxy]-N,N-dimethyl­propan-1-aminium 3-{[(5-methyl-1,3,4-thia­diazol-2-yl)sulfan­yl]meth­yl}-8-oxo-7-[(1H-tetra­zol-1-yl)acetamido]-5-thia-1-aza­bicyclo­[4.2.0]octane-2-carboxyl­ate (benzydaminium cephazolinate)

In the crystal of the title mol­ecular salt, C19H24N3O+·C14H13N8O4S3 −, the cations and anions are linked by N—H⋯O hydrogen bonds. Short intra­molecular C—H⋯O contacts occur within the anion and inter­molecular C—H⋯O and C—H⋯π bonds help to establish the packing

Cell visco-elasticity measured with AFM and optical trapping at sub-micrometer deformations

The measurement of the elastic properties of cells is widely used as an indicator for cellular changes during differentiation, upon drug treatment, or resulting from the interaction with the supporting matrix. Elasticity is routinely quantified by indenting the cell with a probe of an AFM while applying nano-Newton forces. Because the resulting deformations are in the micrometer range, the measurements will be affected by the finite thickness of the cell, viscous effects and even cell damage induced by the experiment itself. Here, we have analyzed the response of single 3T3 fibroblasts that were indented with a micrometer-sized bead attached to an AFM cantilever at forces from 30–600 pN, resulting in indentations ranging from 0.2 to 1.2 micrometer. To investigate the cellular response at lower forces up to 10 pN, we developed an optical trap to indent the cell in vertical direction, normal to the plane of the coverslip. Deformations of up to two hundred nanometers achieved at forces of up to 30 pN showed a reversible, thus truly elastic response that was independent on the rate of deformation. We found that at such small deformations, the elastic modulus of 100 Pa is largely determined by the presence of the actin cortex. At higher indentations, viscous effects led to an increase of the apparent elastic modulus. This viscous contribution that followed a weak power law, increased at larger cell indentations. Both AFM and optical trapping indentation experiments give consistent results for the cell elasticity. Optical trapping has the benefit of a lower force noise, which allows a more accurate determination of the absolute indentation. The combination of both techniques allows the investigation of single cells at small and large indentations and enables the separation of their viscous and elastic components

Enhancing Breast Cancer Prediction through Deep Learning and Comparative Analysis of Gene Expression and DNA Methylation Data using Convolutional Neural Networks

Recent advances in the production of statistics have resulted in an exponential increase in the number of facts, ushering in a whole new era dominated by very large facts. Conventional machine-learning algorithms are unable to handle the most recent aspects of huge data. This is a fact.  In order to make an accurate prognosis of breast cancer, researchers employ and evaluate three distinct computer programmes called Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). Within the context of huge statistics, we explore the question of how breast cancer may be predicted in this particular research. Gene expression and DNA methylation are both taken into consideration as part of the analysis (GE and DM, respectively). The purpose of the work that we are doing is to increase the capacity of the Deep Learning algorithms that are now being used for typing by applying each dataset individually and together. As a result of this decision, the platform of choice is MATLAB. In the process of breast cancer prediction, the Convolutional Neural Network (CNN) algorithm is used. Comparisons of GE, DM, and GE and DM are carried out with the help of this method. The results of the CNN algorithm are compared to those of the RF algorithm. According to findings of the experiments, the scaled system that was presented works better than the other classifiers. This is due to the fact that using the GE dataset; it acquired the best accuracy at the lowest cost