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In this manuscript, we systematically investigate projective difference synchronization between identical generalized Lotka–Volterra biological models of integer order using active control and parameter identification methods. We employ Lyapunov stability theory (LST) to construct the desired controllers, which ensures the global asymptotical convergence of a trajectory following synchronization errors. In addition, simulations were conducted in a MATLAB environment to illustrate the accuracy and efficiency of the proposed techniques. Exceptionally, both experimental and theoretical results are in excellent agreement. Comparative analysis between the considered strategy and previously published research findings is presented. Lastly, we describe an application of our considered combination difference synchronization in secure communication through numerical simulations.

In this paper, the dual combination–combination hybrid synchronization (DCCHS) scheme has been investigated in fractional-order chaotic systems with a distinct dimension applying a scaling matrix. The formulations for the active control have been analyzed numerically using Lyapunov’s stability analysis in order to achieve the proposed DCCHS among the considered systems. With the evolution of time, the error system then converges to zero by applying a suitably designed control function. The proposed synchronization technique depicts a higher degree of complexity in error systems, and therefore, the DCCHS scheme provides higher protection for secure communication. Mathematical simulations are implemented using MATLAB, the results of which confirm that the proposed approach is superior and more effective in comparison to existing chaos literature.

In this paper, we primarily investigate the methodology for the hybrid complex projective synchronization (HCPS) scheme in non-identical complex fractional order chaotic systems via an active complex synchronization technique (ACST). Appropriate controllers of a nonlinear type are designed in view of master–slave composition and Lyapunov’s stability criterion (LSC). The HCPS is an extended version of the previously designed projective synchronization scheme. In the HCPS scheme, by using a complex scale matrix, the system taken as slave system is asymptotically synchronized with another system taken as the master system. By utilizing a complex scale matrix, the unpredictability and security of communication are increased along with image encryption. An efficient computational method has been employed to validate and visualize the HCPS method’s efficacy by performing numerical simulation outcomes in MATLAB (version 2021).

A number of properties for the classes B p − 1 and B p * have been proved. The class B p − 1 characterizes the L p-inequality involving the averaging operator and the class B p * characterizes the Lp -inequality involving the adjoint averaging operator. The reverse inequalities involving the integral operators in L w p ) have also been studied.

In this paper, we obtain the generalized form of Hilbert’s inequality by using series of non-negative terms and convexity, sub-multiplicity of a function on positive real numbers and prove results for integral and discrete forms.

Correction published in volume 11, issue 12, article number 3510, Dec. 9, 2021. DOI: 10.3390/ani11123510

Since December 2019, the novel COVID-19’s spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. They can help predict, screen, and diagnose COVID-19 positive cases. Within this scope, imaging with Computed Tomography (CT) scans and Chest X-rays (CXRs) are widely used in mass triage situations. In the literature, AI-driven tools are limited to one data type either CT scan or CXR to detect COVID-19 positive cases. Integrating multiple data types could possibly provide more information in detecting anomaly patterns due to COVID-19. Therefore, in this paper, we engineered a Convolutional Neural Network (CNN) -tailored Deep Neural Network (DNN) that can collectively train/test both CT scans and CXRs. In our experiments, we achieved an overall accuracy of 96.28% (AUC = 0.9808 and false negative rate = 0.0208). Further, major existing DNNs provided coherent results while integrating CT scans and CXRs to detect COVID-19 positive cases.

Wearable sensors are pioneering devices to monitor health issues that allow the constant monitoring of physical and biological parameters. The immunity towards electromagnetic interference, miniaturization, detection of nano-volumes, integration with fiber, high sensitivity, low cost, usable in harsh environments and corrosion-resistant have made optical wearable sensor an emerging sensing technology in the recent year. This review presents the progress made in the development of novel wearable optical sensors for vital health monitoring systems. The details of different substrates, sensing platforms, and biofluids used for the detection of target molecules are discussed in detail. Wearable technologies could increase the quality of health monitoring systems at a nominal cost and enable continuous and early disease diagnosis. Various optical sensing principles, including surface-enhanced Raman scattering, colorimetric, fluorescence, plasmonic, photoplethysmography, and interferometric-based sensors, are discussed in detail for health monitoring applications. The performance of optical wearable sensors utilizing two-dimensional materials is also discussed. Future challenges associated with the development of optical wearable sensors for point-of-care applications and clinical diagnosis have been thoroughly discussed.