Study of strange quark density fluctuations in Au+Au Collisions at sNN\sqrt{s_{NN}} = 7.7-200 GeV from AMPT Model

The strangeness production is an important observable to study the QCD phase diagram. The yield ratios of strange quark can be helpful to search for the QCD critical point and/or first order phase transition. In this work, we studied the production of $K^{\pm}$, $\Xi^-(\bar{\Xi}^{+})$, $\phi$ and $\Lambda (\bar \Lambda)$ in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4 and 200 GeV from A Multi-Phase Transport model with string melting version (AMPT-SM). We calculated the invariant yield of these strange hadrons using a different set of parameters reported in earlier studies and also by varying the hadronic cascade time ($t_{max}$) in the AMPT-SM model. We also calculated the yield ratios, $\mathcal{O}_{K^{\pm}-\Xi^{-}(\bar \Xi^{+})-\phi-\Lambda (\bar \Lambda)}$ which are sensitive to the strange quark density fluctuations and found that the AMPT-SM model fails to describe the non-monotonic trend observed by the STAR experiment. The negative particle ratio are found to be higher than the ratio of positive particles which is consistent with the experimental data. A significant effect is also seen on these ratios by varying the $t_{max}$. This study based on the transport model can be helpful to provide possible constraints as well as reference for the search of CEP in future heavy-ion experiments. Our findings suggest that the ongoing Beam Energy Scan program at RHIC and the future heavy-ion experiments will be able to find/locate the possible CEP in the QCD phase diagram which results large quark density fluctuations.Comment: 7 pages, 2 figure

A Mosaic of Risk Factors for Female Infertility in Pakistan

Background: To identify different risk factors for female infertility including hormonal imbalance (FSH, LH and Prolactin) Methods: Infertile women were enrolled in this prospective study. A questionnaire was designed to collect information regarding socio-demographic and clinical characteristics of the study participants. Serum FSH, LH and Prolactin levels were estimated between 1-5 days of post menstrual period. Independent sample t- test, Spearman correlation and multivariate logistic regression were performed to find the association of different risk factors with female infertility. Results: Highest percentage (57.7%) of infertile females was in the age bracket of 26 to 35 years. The prevalence of primary infertility was 60.4% . Mean levels of LH and prolactin were significantly higher in women with primary infertility compared to those with secondary infertility. No significant difference was observed in the mean level of FSH . A significant positive correlation was found between infertility and age , marital history and infertility duration. On multivariate logistic regression analysis women with secondary infertility were more likely to be hypertensive(OR=2.126,95%CI:1.020-4.474, p-value0.044), using contraceptive OR = 5.876, 95% CI: 2.491–13.86, p-value .001),have hyperprolactenemia (OR=1.289,95%CI:0.960-1.996,p-value0.001) and have marital history of more than 16 years OR=12.166,95%CI:5.048-29.322, p-value0.001). Conclusion:Highest prevalence of infertility was seen in the age group of 26-35 years. Advanced age, hypertension, hyperprolactemia, use of contraceptive and marital history of more than 16 years are significantly associated with female infertilit

Effect of hadronic cascade time on freeze-out properties of Identified Hadrons in Au+Au Collisions at sNN\sqrt{s_{NN}} = 7.7-39 GeV from AMPT Model

We report the transverse momentum $p_T$ spectra of identified hadrons ($\pi^\pm$, $K^\pm$ and $p(\bar p)$) in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 39 GeV from A Multi Phase Transport Model with string melting effect (AMPT-SM). During this study, a new set of parameters are explored to study the effect of hadronic cascade by varying hadronic cascade time $t_{max}$ = 30 $f$m/$c$ and 0.4 $f$m/$c$. No significant effect of this change is observed in the $p_T$ spectra of light hadrons and the AMPT-SM model reasonably reproduces the experimental data. To investigate the kinetic freeze-out properties the blast wave fit is performed to the $p_T$ spectra and it is found that the blast wave model describes the AMPT-SM simulations well. We additionally observe that the kinetic freeze-out temperature ($T_{kin}$) increases from central to peripheral collisions, which is consistent with the argument of short-lived fireball in peripheral collisions. Whereas the transverse flow velocity, shows a decreasing trend from central to peripheral collisions indicating a more rapid expansion in the central collisions. Both, $T_{kin}$ and show a weak dependence on the collision energy at most energies. We also observe a strong anti-correlation between $T_{kin}$ and $$. The extracted freeze-out parameters from the AMPT-SM simulations agree with the experimental data as opposed to earlier studies that reported some discrepancies. Whereas, no significant effect is found on the freeze-out parameters by varying the $t_{max}$. We also report the $p_T$ spectra of light hadrons and their freeze-out parameters by AMPT-SM simulations at $\sqrt{s_{NN}}$ = 14.5 GeV, where no experimental data is available for comparison. Overall, the set of parameters used in this study well describes the experimental data at BES energies.Comment: 12 pages, 7 figures, 2 table

Study of Baryon number transport using model simulations in pppp collisions at LHC Energies

We report on the excitation function of anti-baryon to baryon ratios ($\overline{p}/p$, {\alam /\lam} and {\axi / \xim}) in $pp$ collisions at {\sqrts} = 0.9, 2.76, 7 TeV from DPMJET-III, Pythia~8, EPOS~1.99, and EPOS-LHC model simulations. To study the predictions of these models at {\sqrts} = 13.6 TeV. The anti-baryon to baryon ratios are extremely important for the study of baryon number transport mechanisms. These ratios help determine the carriers of the baryon number and in the extraction of baryon structure information. Even though all models show a good agreement between model simulations and data, the ratios extracted from DPMJET-III model closely describes data at all energies. It is observed that these ratios converge to unity for various model predictions. This convergence also indicates that the anti-baryon to baryon ratios follow the mass hierarchy, such that the hyperon specie containing more strange quarks ({\alam /\lam} and {\axi / \xim}) approaches unity faster than specie containing fewer strange quarks ($\overline{p}/p$). It is also observed that the $\overline{B}/B$ ratio approaches unity more rapidly with the increase in {\sqrts} energy. At lower energies we observe an excess production of baryons over anti-baryons. However, this effect vanishes at higher energies due to the baryon-anti-baryon pair production and the baryon-anti-baryon yield becomes equal. Using model simulations, we additionally compute the asymmetry, (A\equiv\frac{N_{p}-N_{\bar{p}}}N_{p}+N_{\bar{p}}}) for protons. The asymmetry shows a decreasing trend with increase in energy from 0.9 to 7 TeV for all energies. This asymmetry trend is confirmed by model predictions at {\sqrts} = 13.6 TeV which will help to put possible constraints on model calculations at {\sqrts} = 13.6 TeV once the Run-III data for LHC becomes available.Comment: 14 pages, 8 figures, 2 table

A Novel Image Encryption Scheme Based on Elliptic Curves over Finite Rings

Image encryption based on elliptic curves (ECs) is emerging as a new trend in cryptography because it provides high security with a relatively smaller key size when compared with well-known cryptosystems. Recently, it has been shown that the cryptosystems based on ECs over finite rings may provide better security because they require the computational cost for solving the factorization problem and the discrete logarithm problem. Motivated by this fact, we proposed a novel image encryption scheme based on ECs over finite rings. There are three main steps in our scheme, where, in the first step, we mask the plain image using points of an EC over a finite ring. In step two, we create diffusion in the masked image with a mapping from the EC over the finite ring to the EC over the finite field. To create high confusion in the plain text, we generated a substitution box (S-box) based on the ordered EC, which is then used to permute the pixels of the diffused image to obtain a cipher image. With computational experiments, we showed that the proposed cryptosystem has higher security against linear, differential, and statistical attacks than the existing cryptosystems. Furthermore, the average encryption time for color images is lower than other existing schemes

Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart Patients Using Multiple Machine Learning Models

About 26 million people worldwide experience its effects each year. Both cardiologists and surgeons have a tough time determining when heart failure will occur. Classification and prediction models applied to medical data allow for enhanced insight. Improved heart failure projection is a major goal of the research team using the heart disease dataset. The probability of heart failure is predicted using data mined from a medical database and processed by machine learning methods. It has been shown, through the use of this study and a comparative analysis, that heart disease may be predicted with high precision. In this study, researchers developed a machine learning model to improve the accuracy with which diseases like heart failure (HF) may be predicted. To rank the accuracy of linear models, we find that logistic regression (82.76 percent), SVM (67.24 percent), KNN (60.34 percent), GNB (79.31 percent), and MNB (72.41) perform best. These models are all examples of ensemble learning, with the most accurate being ET (70.31%), RF (87.03%), and GBC (86.21%). DT (ensemble learning models) achieves the highest degree of precision. CatBoost outperforms LGBM, HGBC, and XGB, all of which achieve 84.48% accuracy or better, while XGB achieves 84.48% accuracy using a gradient-based gradient method (GBG). LGBM has the highest accuracy rate (86.21 percent) (hypertuned ensemble learning models). A statistical analysis of all available algorithms found that CatBoost, random forests, and gradient boosting provided the most reliable results for predicting future heart attacks