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Based at Thornton Science Park, the new Faculty of Science and Engineering is located in a major research and innovation hub for the North West which is only a 20-minute bus trip from the main Chester Campus. The Faculty offers degrees in engineering and science disciplines using a strongly interdisciplinary teaching philosophy.

### Recent Submissions

• #### Double Bordered Constructions of Self-Dual Codes from Group Rings over Frobenius Rings

In this work, we describe a double bordered construction of self-dual codes from group rings. We show that this construction is effective for groups of order 2p where p is odd, over the rings F2 + uF2 and F4 + uF4. We demonstrate the importance of this new construction by finding many new binary self-dual codes of lengths 64, 68 and 80; the new codes and their corresponding weight enumerators are listed in several tables
• #### De-smokeGCN: Generative Cooperative Networks for Joint Surgical Smoke Detection and Removal

Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images.
• #### Policing the Cyber Threat: Exploring the threat from Cyber Crime and the ability of local Law Enforcement to respond

The landscape in which UK policing operates today is a dynamic one, and growing threats such as the proliferation of cyber crime are increasing the demand on police resources. The response to cyber crime by national and regional law enforcement agencies has been robust, with significant investment in mitigating against, and tackling cyber threats. However, at a local level, police forces have to deal with an unknown demand, whilst trying to come to terms with new crime types, terminology and criminal techniques which are far from traditional. This paper looks to identify the demand from cyber crime in one police force in the United Kingdom, and whether there is consistency in the recording of crime. As well as this, it looks to understand whether the force can deal with cyber crime from the point of view of the Police Officers and Police Staff in the organisation.
• #### Assisting Serious Games Level Design with an Augmented Reality Application and Workflow

With the rise in popularity of serious games there is an increasing demand for virtual environments based on real-world locations. Emergency evacuation or fire safety training are prime examples of serious games that would benefit from accurate location depiction together with any application involving personal space. However, creating digital indoor models of real-world spaces is a difficult task and the results obtained by applying current techniques are often not suitable for use in real-time virtual environments. To address this problem, we have developed an application called LevelEd AR that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability. Finally, we carried out a preliminary case study that demonstrates how LevelEd AR can be successfully used as part of current industry workflows for serious games level design.
• #### Context-Aware Mixed Reality: A Learning-based Framework for Semantic-level Interaction

Mixed Reality (MR) is a powerful interactive technology for new types of user experience. We present a semantic-based interactive MR framework that is beyond current geometry-based approaches, offering a step change in generating high-level context-aware interactions. Our key insight is that by building semantic understanding in MR, we can develop a system that not only greatly enhances user experience through object-specific behaviors, but also it paves the way for solving complex interaction design challenges. In this paper, our proposed framework generates semantic properties of the real-world environment through a dense scene reconstruction and deep image understanding scheme. We demonstrate our approach by developing a material-aware prototype system for context-aware physical interactions between the real and virtual objects. Quantitative and qualitative evaluation results show that the framework delivers accurate and consistent semantic information in an interactive MR environment, providing effective real-time semantic level interactions.
• #### On the behavior of the solutions for linear autonomous mixed type difference equation

A class of linear autonomous mixed type difference equations is considered, and some new results on the asymptotic behavior and the stability are given, via a positive root of the corresponding characteristic equation.
• #### G-codes over Formal Power Series Rings and Finite Chain Rings

In this work, we define $G$-codes over the infinite ring $R_\infty$ as ideals in the group ring $R_\infty G$. We show that the dual of a $G$-code is again a $G$-code in this setting. We study the projections and lifts of $G$-codes over the finite chain rings and over the formal power series rings respectively. We extend known results of constructing $\gamma$-adic codes over $R_\infty$ to $\gamma$-adic $G$-codes over the same ring. We also study $G$-codes over principal ideal rings.
• #### A Modified Bordered Construction for Self-Dual Codes from Group Rings

We describe a bordered construction for self-dual codes coming from group rings. We apply the constructions coming from the cyclic and dihedral groups over several alphabets to obtain extremal binary self-dual codes of various lengths. In particular we find a new extremal binary self-dual code of length 78.
• #### Composite Constructions of Self-Dual Codes from Group Rings and New Extremal Self-Dual Binary Codes of Length 68

We describe eight composite constructions from group rings where the orders of the groups are 4 and 8, which are then applied to find self-dual codes of length 16 over F4. These codes have binary images with parameters [32, 16, 8] or [32, 16, 6]. These are lifted to codes over F4 + uF4, to obtain codes with Gray images extremal self-dual binary codes of length 64. Finally, we use a building-up method over F2 + uF2 to obtain new extremal binary self-dual codes of length 68. We construct 11 new codes via the building-up method and 2 new codes by considering possible neighbors.
• #### A Numerical Feasibility Study of Kinetic Energy Harvesting from Lower Limb Prosthetics

With the advancement trend of lower limb prosthetics headed towards bionics (active ankle and knee) and smart prosthetics (gait and condition monitoring), there is an increasing integration of various sensors (micro-electromechanical system (MEMS) accelerometers, gyroscopes, magnetometers, strain gauges, pressure sensors, etc.), microcontrollers and wireless systems, and power drives including motors and actuators. All of these active elements require electrical power. However, inclusion of a heavy and bulky battery risks to undo the lightweight advancements achieved by the strong and flexible composite materials in the past decades. Kinetic energy harvesting holds the promise to recharge a small on-board battery in order to sustain the active systems without sacrificing weight and size. However, careful design is required in order not to over-burden the user from parasitic effects. This paper presents a feasibility study using measured gait data and numerical simulation in order to predict the available recoverable power. The numerical simulations suggest that, depending on the axis, up to 10s mW average electrical power is recoverable for a walking gait and up to 100s mW average electrical power is achievable during a running gait. This takes into account parasitic losses and only capturing a fraction of the gait cycle to not adversely burden the user. The predicted recoverable power levels are ample to self-sustain wireless communication and smart sensing functionalities to support smart prosthetics, as well as extend the battery life for active actuators in bionic systems. The results here serve as a theoretical foundation to design and develop towards regenerative smart bionic prosthetics.
• #### Numerical methods for solving space fractional partial differential equations by using Hadamard finite-part integral approach

We introduce a novel numerical method for solving two-sided space fractional partial differential equation in two dimensional case. The approximation of the space fractional Riemann-Liouville derivative is based on the approximation of the Hadamard finite-part integral which has the convergence order $O(h^{3- \alpha})$, where $h$ is the space step size and $\alpha\in (1, 2)$ is the order of Riemann-Liouville fractional derivative. Based on this scheme, we introduce a shifted finite difference method for solving space fractional partial differential equation. We obtained the error estimates with the convergence orders $O(\tau +h^{3-\alpha}+ h^{\beta})$, where $\tau$ is the time step size and $\beta >0$ is a parameter which measures the smoothness of the fractional derivatives of the solution of the equation. Unlike the numerical methods for solving space fractional partial differential equation constructed by using the standard shifted Gr\"unwald-Letnikov formula or higher order Lubich'e methods which require the solution of the equation satisfies the homogeneous Dirichlet boundary condition in order to get the first order convergence, the numerical method for solving space fractional partial differential equation constructed by using Hadamard finite-part integral approach does not require the solution of the equation satisfies the Dirichlet homogeneous boundary condition. Numerical results show that the experimentally determined convergence order obtained by using the Hadamard finite-part integral approach for solving space fractional partial differential equation with non-homogeneous Dirichlet boundary conditions is indeed higher than the convergence order obtained by using the numerical methods constructed with the standard shifted Gr\"unwald-Letnikov formula or Lubich's higer order approximation schemes.
• #### The early stages of biofilm formation by Staphylococcus epidermidis studied by XPS and AFM

Staphylococcus epidermidis is an opportunistic bacteria which forms pathogenic biofilms in medical implant environment. Biofilm formation is a complex multistage process within which the initial stages of adhesion are deemed the most critical target for preventing biofilms. This research involves the characterisation of S. epidermidis (ATCC35984 and NCTC13360) by using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM) on model substrates including glass, muscovite mica, silicon (111) wafer, sputter-coated titanium and sputter-coated silver, focusing on the effect of chemical properties of the material on adhesion by using surfaces with minimal roughness. AFM was used to image the surface, from which bacterial coverage can be estimated. AFM was also used to probe adhesion forces and local mechanical properties of all samples through the use of force-distance curves. AFM images were also used to estimate the bacterial coverage. XPS was used to investigate the surface chemistry from the layer thicknesses, the percentage coverage and potential composition of the overlayer. The combination of these techniques allow the relationships between the surface chemistry of the substrate and the bacteria to be correlated with changes in coverage and properties of bacterial films. Data on incubated bacterial samples were compared with those from the reference substrates, both before and after autoclaving, and from samples prepared using protein rich growth medium (tryptic soy broth) in the absence of bacteria as well as a pure bacterial pellet in an assumed non-biofilm forming state. The research indicates the potential differences between biofilm and non-biofilm former strains, with both strains being covered by an organic layer with little influence of the growth media used to incubate the bacteria. This research also shows how XPS and AFM data can be combined and applied to bacterial adhesion.
• #### Numerical Approximation of Stochastic Time-Fractional Diffusion

We develop and analyze a numerical method for stochastic time-fractional diffusion driven by additive fractionally integrated Gaussian noise. The model involves two nonlocal terms in time, i.e., a Caputo fractional derivative of order $\alpha\in(0,1)$, and fractionally integrated Gaussian noise (with a Riemann-Liouville fractional integral of order $\gamma \in[0,1]$ in the front). The numerical scheme approximates the model in space by the standard Galerkin method with continuous piecewise linear finite elements and in time by the classical Gr\"unwald-Letnikov method, and the noise by the $L^2$-projection. Sharp strong and weak convergence rates are established, using suitable nonsmooth data error estimates for the deterministic counterpart. One- and two-dimensional numerical results are presented to support the theoretical findings.
• #### Magnetic cationic liposomal nanocarriers for the efficient drug delivery of a curcumin-based vanadium complex with anticancer potential

In this work novel magnetic cationic liposomal nanoformulations were synthesized for the encapsulation of a crystallographically defined ternary V(IV)-curcumin-bipyridine (VCur) complex with proven bioactivity, as potential anticancer agents. The liposomal vesicles were produced via the thin film hydration method employing N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethylammonium (DOTAP) and egg phosphatidylcholine lipids and were magnetized through the addition of citric acid surface-modified monodispersed magnetite colloidal magnetic nanoparticles. The obtained nanoformulations were evaluated for their structural and textural properties and shown to have exceptional stability and enhanced solubility in physiological media, demonstrated by the entrapment efficiency and loading capacity results and the in vitro release studies of their cargo. Furthermore, the generated liposomal formulations preserved the superparamagnetic behavior of the employed magnetic core maintaining the physicochemical and morphological requirements for targeted drug delivery applications. The novel nanomaterials were further biologically evaluated for their DNA interaction potential and were found to act as intercalators. The findings suggest that the positively charged magnetic liposomal nanoformulations can generate increased concentration of their cargo at the DNA site, offering a further dimension in the importance of cationic liposomes as nanocarriers of hydrophobic anticancer metal ion complexes for the development of new multifunctional pharmaceutical nanomaterials with enhanced bioavailability and targeted antitumor activity.
• #### A discrete mutualism model: analysis and exploration of a financial application

We perform a stability analysis on a discrete analogue of a known, continuous model of mutualism. We illustrate how the introduction of delays affects the asymptotic stability of the system’s positive nontrivial equilibrium point. In the second part of the paper we explore the insights that the model can provide when it is used in relation to interacting financial markets. We also note the limitations of such an approach.
• #### Isolation of a Ferroelectric Intermediate Phase in Antiferroelectric Dense Sodium Niobate Ceramics

Switchable ferroelectric/antiferroelectric ceramics are of significant interest for high power energy storage applications. Grain size control of this switching is an interesting approach to controlling polarization and hence dielectric properties. However, the use of this approach in technologically relevant ceramics is hindered by difficulty in fabricating dense ceramics with small grain sizes. Here an intermediate polar ferroelectric phase (P21ma) has been isolated in dense bulk sodium niobate ceramics by grain size control through spark plasma sintering methods. Our findings, supported by XRD, DSC, P-E (I-E) loops and dielectric characterization, provide evidence that the phase transition from the antiferroelectric (AFE) R-phase, in space group Pnmm, above 300 C, to the AFE P-phase, in space group Pbma, at room temperature, always involves the polar intermediate P21ma phase and that the P21ma to Pbma transition can be suppressed by reducing grain size.
• #### Evaluation of a Micro Gas Turbine With Post-Combustion CO2 Capture for Exhaust Gas Recirculation Potential With Two Experimentally Validated Models

The growing global energy demand is facing concerns raised by increasing greenhouse gas emissions, predominantly CO2. Despite substantial progress in the field of renewable energy in recent years, quick balancing responses and back-up services are still necessary to maintain the grid load and stability, due to increased penetration of intermittent renewable energy sources, such as solar and wind. In a scenario of natural gas availability, gas turbine power may be a substitute for back-up/balancing load. Rapid start-up and shut down, high ramp rate, and low emissions and maintenance have been achieved in commercial gas turbine cycles. This industry still needs innovative cycle configurations, e.g. exhaust gas recirculation (EGR), to achieve higher system performance and lower emissions in the current competitive power generation market. Together with reduced NOx emissions, EGR cycle provides an exhaust gas with higher CO2 concentration compared to the simple gas turbine/combined cycle, favorable for post-combustion carbon capture. This paper presents an evaluation of EGR potential for improved gas turbine cycle performance and integration with a post-combustion CO2 capture process. It also highlights features of two software tools with different capabilities for performance analysis of gas turbine cycles, integrated with post-combustion capture. The study is based on a combined heat and power micro gas turbine (MGT), Turbec T100, of 100kWe output. Detailed models for the baseline MGT and amine capture plant were developed in two software tools, IPSEpro and Aspen Hysys. These models were validated against experimental work conducted at the UK PACT National Core Facilities. Characteristics maps for the compressor and the turbine were used for the MGT modeling. The performance indicators of systems with and without EGR, and when varying the EGR ratio and ambient temperature, were calculated and are presented in this paper.
• #### Magnetic cationic liposomal nanocarriers for the efficient drug delivery of a curcumin-based vanadium complex with anticancer potential.

In this work novel magnetic cationic liposomal nanoformulations were synthesized for the encapsulation of a crystallographically defined ternary V(IV)-curcumin-bipyridine (VCur) complex with proven bioactivity, as potential anticancer agents. The liposomal vesicles were produced via the thin film hydration method employing N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethylammonium (DOTAP) and egg phosphatidylcholine lipids and were magnetized through the addition of citric acid surface-modified monodispersed magnetite colloidal magnetic nanoparticles. The obtained nanoformulations were evaluated for their structural and textural properties and shown to have exceptional stability and enhanced solubility in physiological media, demonstrated by the entrapment efficiency and loading capacity results and the in vitro release studies of their cargo. Furthermore, the generated liposomal formulations preserved the superparamagnetic behavior of the employed magnetic core maintaining the physicochemical and morphological requirements for targeted drug delivery applications. The novel nanomaterials were further biologically evaluated for their DNA interaction potential and were found to act as intercalators. The findings suggest that the positively charged magnetic liposomal nanoformulations can generate increased concentration of their cargo at the DNA site, offering a further dimension in the importance of cationic liposomes as nanocarriers of hydrophobic anticancer metal ion complexes for the development of new multifunctional pharmaceutical nanomaterials with enhanced bioavailability and targeted antitumor activity. [Abstract copyright: Copyright © 2019 Elsevier Inc. All rights reserved.]
• #### Evaporation of liquid nitrogen droplets in superheated immiscible liquids

Liquid nitrogen or other cryogenic liquids have the potential to replace or augment current energy sources in cooling and power applications. This can be done by the rapid evaporation and expansion processes that occur when liquid nitrogen is injected into hotter fluids in mechanical expander systems. In this study, the evaporation process of single liquid nitrogen droplets when submerged into n-propanol, methanol, n-hexane, and n-pentane maintained at 294 K has been investigated experimentally and numerically. The evaporation process is quantified by tracking the growth rate of the resulting nitrogen vapour bubble that has an interface with the bulk liquid. The experimental data suggest that the bubble volume growth is proportional to the time and the bubble growth rate is mainly determined by the initial droplet size. A comparison between the four different bulk liquids indicates that the evaporation rate in n-pentane is the highest, possibly due to its low surface tension. A scaling law based on the pure diffusion-controlled evaporation of droplet in open air environment has been successfully implemented to scale the experimental data. The deviation between the scaling law predictions and the experimental data for 2-propanol, methanol and n-hexane vary between 4% and 30% and the deviation for n-pentane was between 24% and 65%. The more detailed bubble growth rates have been modelled by a heuristic one-dimensional, spherically symmetric quasi-steady-state confined model, which can predict the growth trend well but consistently underestimate the growth rate. A fixed effective thermal conductivity is then introduced to account for the complex dynamics of the droplet inside the bubble and the subsequent convective processes in the surrounding vapour, which leads to a satisfactory quantitative prediction of the growth rate.