Bold:Bio-inspired optimized leader election for multiple drones

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm

Computational design of biopolymer aerogels and predictive modelling of their nanostructure and mechanical behaviour

To address the challenge of reconstructing or designing the three-dimensional microstructure of nanoporous materials, we develop a computational approach by combining the random closed packing of polydisperse spheres together with the Laguerre–Voronoi tessellation. Open-porous cellular network structures that adhere to the real pore-size distributions of the nanoporous materials are generated. As an example, κ-carrageenan aerogels are considered. The mechanical structure–property relationships are further explored by means of finite elements. Here we show that one can predict the macroscopic stress–strain curve of the bulk porous material if only the pore-size distributions, solid fractions, and Young’s modulus of the pore-wall fibres are known a priori. The objective of such reconstruction and predictive modelling is to reverse engineer the parameters of their synthesis process for tailored applications. Structural and mechanical property predictions of the proposed modelling approach are shown to be in good agreement with the available experimental data. The presented approach is free of parameter-fitting and is capable of generating dispersed Voronoi structures

An evaluation of crude palm oil (CPO) and tocotrienol rich fraction (TRF) of palm oil as percutaneous permeation enhancers using full-thickness human skin

The drawbacks associated with chemical skin permeation enhancers such as skin irritation and toxicity necessitated the research to focus on potential permeation enhancers with a perceived lower toxicity. Crude palm oil (CPO) is obtained by direct compression of the mesocarp of the fruit of the oil palm belonging to the genus Elaeis. In this research, CPO and tocotrienol-rich fraction (TRF) of palm oil were evaluated for the first time as skin permeation enhancers using full-thickness human skin. The in vitro permeation experiments were conducted using excised human skin mounted in static upright ‘Franz-type’ diffusion cells. The drugs selected to evaluate the enhancing effects of these palm oil derivatives were 5-fluorouracil, lidocaine and ibuprofen: compounds covering a wide range of Log p values. It was demonstrated that CPO and TRF were capable of enhancing the percutaneous permeation of drugs across full-thickness human skin in vitro. Both TRF and CPO were shown to significantly enhance the permeation of ibuprofen with flux values of 30.6 µg/cm2 h and 23.0 µg/cm2 h respectively, compared to the control with a flux of 16.2 µg/cm2 h. The outcome of this research opens further scope for investigation on the transdermal penetration enhancement activity of pure compounds derived from palm oil

SoK: Contemporary Issues and Challenges to Enable Cyber Situational Awareness for Network Security

Cyber situational awareness is an essential part of cyber defense that allows the cybersecurity operators to cope with the complexity of today's networks and threat landscape. Perceiving and comprehending the situation allow the operator to project upcoming events and make strategic decisions. In this paper, we recapitulate the fundamentals of cyber situational awareness and highlight its unique characteristics in comparison to generic situational awareness known from other fields. Subsequently, we provide an overview of existing research and trends in publishing on the topic, introduce front research groups, and highlight the impact of cyber situational awareness research. Further, we propose an updated taxonomy and enumeration of the components used for achieving cyber situational awareness. The updated taxonomy conforms to the widely-accepted three-level definition of cyber situational awareness and newly includes the projection level. Finally, we identify and discuss contemporary research and operational challenges, such as the need to cope with rising volume, velocity, and variety of cybersecurity data and the need to provide cybersecurity operators with the right data at the right time and increase their value through visualization