Design of Multifunctional Janus Metasurface Based on Subwavelength Grating

In this paper, a Janus metasurface is designed by breaking the structural symmetry based on the polarization selection property of subwavelength grating. The structure comprises three layers: a top layer having a metallic nanostructure, a dielectric spacer, and a bottom layer having subwavelength grating. For a forward incidence, the metal-insulator-metal (MIM) structure operates as a gap plasmonic cavity if the linearly polarized (LP) component is parallel to the grating wires. It also acts as a high-efficiency dual-layer grating polarizer for the orthogonal LP component. For the backward incidence, the high reflectance of the grating blocks the function of the gap plasmonic cavity, leading to its pure functioning as a polarizer. A bifunctional Janus metasurface for 45 degrees beam deflector and polarizer, with a transmission of 0.87 and extinction ratio of 3840, is designed at 1.55 μm and is investigated to prove the validity of the proposed strategy. Moreover, the proposed metasurface can be cascaded to achieve more flexible functions since these functions are independent in terms of operational mechanism and structural parameters. A trifunctional Janus metasurface that acts as a focusing lens, as a reflector, and as a polarizer is designed based on this strategy. The proposed metasurface and the design strategy provide convenience and flexibility in the design of multifunctional, miniaturized, and integrated optical components for polarization-related analysis and for detection systems.

A Well-Organized Safeguarded Access on Key Propagation by Malleable Optimization in Blend With Double Permutation

With the rapid developments occurring in cloud computing and services, there has been a growing trend of using the cloud for large-scale data storage. This has led to a major security dispute on data handling. Thus, the process can be overcome by utilizing an efficient shielded access on a key propagation (ESAKP) technique along with an adaptive optimization algorithm for password generation and performing double permutation. The password generation is done by adaptive ant lion optimization (AALO) which tackles the problem of ineffiency. This build has stronger security which needs an efficient selection property by eliminating the worst fit in each iteration. The optimized password is utilized by an adaptive vignere cipher for efficient key generation in which adaptiveness is employed to prevent the dilemma of choosing the first letter of alphabet which in turn reduces the computation time and improves the security. Additionally, there is a need to encrypte the symmetric key asymmetrically with a Elliptic Curve-Diffie Hellman algorithm (EC-DH) with a double stage permutation which produces a scrambling form of data adding security to the data.

A Well-Organized Safeguarded Access on Key Propagation by Malleable Optimization in Blend With Double Permutation

With the rapid developments occurring in cloud computing and services, there has been a growing trend of using the cloud for large-scale data storage. This has led to a major security dispute on data handling. Thus, the process can be overcome by utilizing an efficient shielded access on a key propagation (ESAKP) technique along with an adaptive optimization algorithm for password generation and performing double permutation. The password generation is done by adaptive ant lion optimization (AALO) which tackles the problem of ineffiency. This build has stronger security which needs an efficient selection property by eliminating the worst fit in each iteration. The optimized password is utilized by an adaptive vignere cipher for efficient key generation in which adaptiveness is employed to prevent the dilemma of choosing the first letter of alphabet which in turn reduces the computation time and improves the security. Additionally, there is a need to encrypte the symmetric key asymmetrically with a Elliptic Curve-Diffie Hellman algorithm (EC-DH) with a double stage permutation which produces a scrambling form of data adding security to the data.

Bayesian Lasso Tobit regression

In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.

Policy Iterations Without Selection Property

In this paper, we propose a modified policy iterations algorithm which does not rely on the selection property. The selection property is the key argument to make improvements during policy iterations. Indeed, a new policy is computed as an optimal solution of a minimization problem. However, in some cases, it might be difficult to prove that an optimal solution exists. To overcome this issue, the new policy is computed as a guaranteed sub-optimal solution of the minimization problem. The good choice of the perturbation parameters preserves the advantages of the original policy iterations algorithm such as the computation of a post-fixed point at each step and the convergence to a fixed point.