Criminal Policy, Security, and Justice in the Time of COVID-19

This study explains the necessary elements in controlling and reducing harmful and incompatible social phenomena with the nature of existence to design correct and challenging social and scientific models using comprehensive approaches to criminal policy and chaos theory.

Development of methods for generation of digital watermarks resistant to distortion

Active attacks and natural impacts can lead to two types of image-container distortions: noise-like and geometric. There are also image processing operations, e.g. scaling, rotation, truncation, pixel permutation which are much more detrimental to digital watermarks (DWM). While ensuring resistance to removal and geometric attacks is a more or less resolved problem, the provision of resistance to local image changes and partial image deletion is still poorly understood. The methods discussed in this paper are aimed at ensuring resistance to attacks resulting in partial image loss or local changes in the image. This study's objective is to develop methods for generating a distortion-resistant digital watermark using the chaos theory. This will improve the resistance of methods of embedding the digital watermark to a particular class of attacks which in turn will allow developers of DWM embedding methods to focus on ensuring the method resistance to other types of attacks. An experimental study of proposed methods was conducted. Histograms of DWMs have shown that the proposed methods provide for the generation of DWM of a random obscure form. However, the method based on a combination of Arnold’s cat maps and Henon maps has noticeable peaks unlike the method based on shuffling the pixels and their bits only with Arnold’s cat maps. This suggests that the method based only on Arnold’s cat maps is more chaotic. This is also evidenced by the value of the coefficient of correlation between adjacent pixels close to zero (0.0109) for color DWMs and 0.030 for black and white images.

An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.

A Review and Classification of the Uncertainties in Projects: The Way Forward

This article aims to summarize existing research on various sources of uncertainty and to classify them based on the determinants and antecedents addressed in projects. A systematic review is conducted using a total of 140 research published in peer-reviewed scientific journals during the last three decades. We classify various uncertainty based on their individual, relational, group, organizational, project-oriented, and managerial characteristics. Additionally, we uncovered a few gaps such as, how uncertainties differ based on the size of the organization, the need for exploring uncertainty from a more cross-disciplinary perspective, differentiating the concept of complexity and risk with uncertainty and the role of chaos theory, that require future study. By leveraging significant findings, this is study contributes from the perspective of theory and practice to academic, project, and industrial management discipline.

Institutional responses to the COVID-19 pandemic: Faculty and administrator experiences

The onset of the COVID-19 pandemic required shifts in operations for institutions of higher education everywhere. Faculty and administrators were asked to adapt to meet the needs of students. We conducted a qualitative content analysis to understand institutional responses and examine the experiences of faculty and administrators during the COVID-19 pandemic through the lens of chaos theory. Institutional responses to the pandemic varied. Participants identified supports and resources deemed helpful, as well as those found to be inadequate or unwanted. We also found that the pandemic resulted in some positive outcomes for faculty and administrators, which led to growth in teaching and self-care. Implications for strategic planning and future directions for research are explored.

SELF-SIMILARITY TECHNIQUES FOR CHAOTIC ATTRACTORS WITH MANY SCROLLS USING STEP SERIES SWITCHING

Highly applied in machining, image compressing, network traffic prediction, biological dynamics, nerve dendrite pattern and so on, self-similarity dynamic represents a part of fractal processes where an object is reproduced exactly or approximately exact to a part of itself. These reproduction processes are also very important and captivating in chaos theory. They occur naturally in our environment in the form of growth spirals, romanesco broccoli, trees and so on. Seeking alternative ways to reproduce self-similarity dynamics has called the attention of many authors working in chaos theory since the range of applications is quite wide. In this paper, three combined notions, namely the step series switching process, the Julia’s technique and the fractal-fractional dynamic are used to create various forms of self-similarity dynamics in chaotic systems of attractors, initially with two, five and seven scrolls. In each case, the solvability of the model is addressed via numerical techniques and related graphical simulations are provided. It appears that the initial systems are able to trigger a self-similarity process that generates the exact or approximately exact copy of itself or part of itself. Moreover, the dynamics of the copies are impacted by some model’s parameters involved in the process. Using mathematical concepts to re-create features that usually occur in a natural way proves to be a prowess as related applications are many for engineers.

Dynamic Chaotic Look-Up Table for MRI Medical Image Encryption

Nowadays, a variety of cryptosystem based on the chaos theory have been proposed. In this paper, we propose a new scheme encryption for Magnetic Resonance Imaging (MRI); medical images, using the chaos theory to define a dynamic chaotic Look-Up Table (LUT). Theoretic analyses and simulation results show that our scheme is secure and efficient. Also, the proposed cryptosystem is resistant to the known plaintext attack.