Enhanced disturbance mitigation in nonlinear time delay systems with applications to double boost converters in microgrid environments.

This study proposes an enhanced Smith Predictor (ESP) based Modified repetitive control (MRC) technique to develop a precise tracking performance of the input delayed double boost converter model, which is highly influenced by disturbances and saturation nonlinearity. A group of inequalities is derived on the basis of Lyapunov stability concept, which are in the form of linear-matrix-inequalities (LMIs) to guarantee the asymptotic stability and tracking performance of the considered delayed double boost converter. To be more specific, the proposed Lyapunov method not only provides the asymptotic stability of the system but also design the controller parameters when the feasibility of the derived LMIs are analyzed.

Empowering drones in vehicular network through fog computing and blockchain technology.

The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems.

Unveiling Cannabinoids and Terpenes Diversity in Cannabis sativa L. from Northern India for Future Breeding Strategies.

Cannabis sativa L. is an important medicinal plant with high commercial value. In recent years, the research interest in cannabidiol (CBD) and terpene-rich cannabis has been rapidly expanding due to their high therapeutic potential.

Retraction Note: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm.

[This retracts the article DOI: 10.1007/s00500-020-05275-y.].

SyncGenie: A programmable event synchronization device for neuroscience research.

In neuroscience, accurately correlating brain activity with stimuli and other events requires precise synchronization between neural data and event timing. To achieve this, purpose-built synchronization devices are often used to detect events. This paper introduces SyncGenie, a programmable synchronization device designed for a range of uses in neuroscience research-primarily as a "trigger box" to align neurophysiological data with physical stimulus events, among other possibilities.