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The security of a broad family of coding‐based cryptographic techniques relies on the hardness of the Syndrome Decoding Problem (SDP). In this problem, the aim is to find a word with a given syndrome and of Hamming weight smaller than a prefixed bound. If this last condition is replaced by "of minimum weight," then we have the Coset Leader Problem (CLP), being Finding Low Weight Codewords (FLWC) a particular case (when the zero syndrome is considered). An algorithm that has been proposed in order to obtain approximate solutions of problems of these kind (NP‐complete) is the Quantum Approximate Optimization Algorithm (QAOA), a variational hybrid quantum‐classical algorithm. In this paper, we apply the QAOA to the CLP for binary linear codes. We model the problem, make the theoretical analysis the case of the first level, and introduce some experiments to test its performance. The experiments have been carried out on quantum computer simulators with codes of different lengths and QAOA of different depth. [ABSTRACT FROM AUTHOR]

Many problems of modern laser physics are governed by equations or sets of equations in an unbounded domain. For solving these problems using the computer simulation, it is necessary to introduce the bounded domain, which size should be extended significantly to avoid the spurious wave reflection from the domain boundaries. Alternatively, the artificial (non‐reflective or transparent) boundary conditions should be stated. This approach is also effective for enhancing computation performance at the numerical solution of the nonlinear partial differential equations (PDEs). In the current paper, we investigate the laser pulse propagation in a semiconductor, governed by the Schrödinger equation, under the appearance of spatio‐temporal contrast structures of semiconductor characteristics. Their evolution is described by a set of PDEs. The optical pulse is partly reflected from the boundaries of these structures. Consequently, even a little reflection of the optical pulse from the artificial boundaries can essentially distort the numerical solution. Thus, these artificial boundary conditions must possess a high quality to minimize their reflection coefficients. With this aim, we propose the method for constructing adaptive artificial boundary conditions and discuss their advantages. [ABSTRACT FROM AUTHOR]

Leukemia is a malignant cancer of the blood. It is the most common type of cancer in children. In this paper, a mathematical model of leukemia in terms of ordinary differential equations has been developed. We propose a model to study the spread of leukemia by considering the effect of adoptive T cell therapy. The disease dynamics are given by a system of ordinary nonlinear differential equations that describe the interaction among susceptible blood cells, infected blood cells, cancer cells, and immune cells. The model is analyzed by using the stability theory of nonlinear differential equations and numerical simulations. A major goal of this work is to determine the spread of leukemia after applying the adoptive T cell therapy. We have observed that the system is stable locally and globally if stimulation rate or antigenicity rate of immune cells is greater than a threshold value dependent on the density of immune cells in the blood. We have also observed that the external reinfusion of immune cells by adoptive T cell therapy reduces the concentration of cancer cells and infected cells in the blood, which implies that immune cells kill cancer cells after being stimulated in the body. [ABSTRACT FROM AUTHOR]

This paper is devoted to study the nonlinear diffusion parabolic partial differential equations, which describes the invasion of cancer cells into the normal tissues. We prove the existence of a weak solution of the considered model using the Faedo‐Galerkin approximation method, a priori estimates, and compactness arguments. Then, the considered model is discretized in space with the standard Galerkin finite elements and in time with the Crank‐Nicolson method. Furthermore, nonlinear terms in the proposed model is treated semi‐implicitly in the finite element scheme. Numerical results are validated by convergence study and existing numerical results of similar problems. Finally, the influence of nonlinear diffusion is studied to understand the effect in the cancer invasion model. [ABSTRACT FROM AUTHOR]

Several standard distributions can be used to model lifetime data. Nevertheless, a number of these datasets from diverse fields such as engineering, finance, the environment, biological sciences, and others may not fit the standard distributions. As a result, there is a need to develop new distributions that incorporate a high degree of skewness and kurtosis while improving the degree of goodness-of-fit in empirical distributions. In this study, by applying the T-X method, we proposed a new flexible generated family, the Ramos-Louzada Generator (RL-G) with some relevant statistical properties such as quantile function, raw moments, incomplete moments, measures of inequality, entropy, mean and median deviations, and the reliability parameter. The RL-G family has the ability to model "right," "left," and "symmetric" data as well as different shapes of the hazard function. The maximum likelihood estimation (MLE) method has been used to estimate the parameters of the RL-G. The asymptotic performance of the MLE is assessed by simulation analysis. Finally, the flexibility of the RL-G family is demonstrated through the application of three real complete datasets from rainfall, breaking stress of carbon fibers, and survival times of hypertension patients, and it is evident that the RL-Weibull, which is a special case of the RL-G family, outperformed its submodels and other distributions. [ABSTRACT FROM AUTHOR]

In Debnath et al. (2019), a tritrophic food chain model subject to a Allee effect on the prey growth and Crowley–Martin senses functional response between intermediate predator and top predator, with a top predator of sexually reproductive type is considered. It is claimed that under certain restrictions on the parameter space, the model has bounded solutions for all positive initial conditions, and is dissipative. We show that this is not true. In particular, solutions to the model can blow‐up in finite time, even under the restrictions derived in Debnath et al. (2019), for sufficiently chosen initial data. We derive a new extinction boundary for the system. We also conjecture on the effect of the Allee threshold on the blow‐up dynamics in the model. All of our results are validated via numerical simulations. [ABSTRACT FROM AUTHOR]