Electrochemically Induced Mesomorphism Switching in a Chlorpromazine Hydrochloride Lyotropic Liquid Crystal

The discovery of electrochemical switching of the Lα phase of chlorpromazine hydrochloride in water is reported. The phase is characterized using polarizing microscopy, X-ray scattering, rheological measurements, and microelectrode voltammetry. Fast, heterogeneous oxidation of the lyotropic liquid crystal is shown to cause a phase change resulting from the disordering of the structural order in a stepwise process. The underlying molecular dynamics is considered to be a cooperative effect of both increasing electrostatic interactions and an unfolding of the monomers from "butterfly"-shaped in the reduced form to planar in the oxidized form

Energy efficient processing in opportunistic vehicular edge clouds

The Cisco Visual Networking Index of 2019 reports that more than six billion Machine-to-Machine (M2M) connections were added in 2017 and the number of connections is expected to grow by more than 50% by 2022. The proliferation of connected devices is accompanied by rapid growth in the generated traffic between the edge layer and data centres, and therefore is expected to lead to significant increase in the power consumption of the network infrastructure. This calls for new architectural designs capable of reducing the traffic congestion and power consumption in the network. At the same time, vehicles are going through a huge revolution in term of their on-board units and processing capabilities producing a new promising framework concept. This concept is a consequence of the integration of vehicles and cloud computing, referred to as Vehicular Edge Cloud (VEC). This thesis investigates distributed processing in VECs, where a group of vehicles in a car park, at a charging station or at a road traffic intersection, cluster and form a temporary vehicular cloud by combining their computational resources in the cluster. We investigated the problem of energy efficient processing task allocation in VEC by developing a Mixed Integer Linear Programming (MILP) model to minimise power consumption by optimising the allocation of different processing tasks to the available network resources, cloud resources, fog resources and vehicular processing nodes resources. Three dimensions of processing allocation were investigated. The first dimension compared centralised processing (in the central cloud) to distributed processing (in the multi-layer fog nodes). The second dimension introduced opportunistic processing in the vehicular nodes with low and high vehicular node density. The third dimension considered non-splittable tasks (single allocation) versus splittable tasks (distributed allocation), representing real-time versus non real-time applications respectively. The results revealed that a power savings up to 70% can be achieved by allocating processing to the vehicles. However, many factors have an impact on the power saving percentage such the vehicle capacities, vehicles density, workload size, and the number of generated tasks. It was observed that the power saving is improved by exploiting the flexibility of task splitting among the available vehicles. In addition to the processing allocation problem, this thesis investigated the software matching problem in VEC. The vehicles involved may not be equipped with the full set of software needed to process the tasks requested. Moreover, as vehicles in VEC represent processing at the edge layer, we studied the impact of edge processing on the propagation and queuing delay in a joint optimisation modelling intended to minimise both power consumption and delay. Our investigation showed a significant impact on the processing allocation decision and therefore, the power consumption, attributed to the location of the processing node and the service rate of the network controller

Classification of Firewall Log Data Using Multiclass Machine Learning Models

These days, we are witnessing unprecedented challenges to network security. This indeed confirms that network security has become increasingly important. Firewall logs are important sources of evidence, but they are still difficult to analyze. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have emerged as effective in developing robust security measures due to the fact that they have the capability to deal with complex cyberattacks in a timely manner. This work aims to tackle the difficulty of analyzing firewall logs using ML and DL by building multiclass ML and DL models that can analyze firewall logs and classify the actions to be taken in response to received sessions as “Allow”, “Drop”, “Deny”, or “Reset-both”. Two sets of empirical evaluations were conducted in order to assess the performance of the produced models. Different features set were used in each set of the empirical evaluation. Further, two extra features, namely, application and category, were proposed to enhance the performance of the proposed models. Several ML and DL algorithms were used for the evaluation purposes, namely, K-Nearest Neighbor (KNN), Naïve Bayas (NB), J48, Random Forest (RF) and Artificial Neural Network (ANN). One interesting reading in the experimental results is that the RF produced the highest accuracy of 99.11% and 99.64% in the first and the second experiments respectively. Yet, all other algorithms have also produced high accuracy rates which confirm that the proposed features played a significant role in improving the firewall classification rate

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population