Characteristic of silicon doped diamond like carbon thin films on surface properties and human serum albumin adsorption

Diamond-like carbon (DLC) coatings are useful for creating biocompatible surfaces for medical implants. DLC and silicon doped DLC have been synthesised using plasma enhanced chemical vapour deposition (PECVD). The effects of surface morphology on the interaction of human serum albumin (HSA) with doped and undoped DLC films have been investigated using a range of surface analysis techniques using Raman spectroscopy and atomic force microscopy (AFM). Raman spectra of doped DLC show that silicon doped DLC reduces the growth range of the ID/IG ratio, with a significant red-shift of the G peak position. Following exposure to protein, for undoped DLC the peaks at 1664 cm−1 and around 1241 cm−1 can be attributed to amide I and III, respectively, with an increase in the surface morphology of the surfaces giving some indication of the protein structure on the surfaces. Results indicate that HSA exhibit the majority of β-sheet during the adsorption on the surfaces. The results showed that the silicon incorporation DLC tends to increase of surface roughness and the adsorbed level of HSA is higher with higher levels of silicon doping of the DLC. Therefore, doping DLC may provide a method of controlling the adsorption of protein

Replacing conventional energy sources of electricity with solar energy in the UK and Iraq using statistical inference with hypothesis testing

Solar power represents a vast resource which could, in principle meet the world’s needs for lowcarbon power generation many times over. Recent growth in the use of photovoltaic (PV) technology (of around 40% per year) and rapid reduction in its cost (20% per doubling of capacity) has demonstrated the potential of solar power to deliver on a large scale. Energy is a vital resource required for the operation of any business. Currently, the vast majority of businesses use electricity derived for non-renewable fossil fuels, which are expected to run out at its current rate of expenditure and causing substantial environmental damage threatening the future generations. In the UK and Iraq if the current energy source used by small and medium enterprises (SME’s) could be replaced by solar energy then damage to the environment can be prevented. Solar cells involve harnessing the energy from the sun to generate electricity and as such the amount of sunlight hours or solar insulation available in the country is of utmost importance. In this study a methodology has been developed to compare a model micro-business in the UK and Northern Iraq. The comparison shows that using statistically inference the different regions (latitudinally) in Northern Iraq have a reasonable constant supply of solar insulation compared with the U.K which shows that there is more variation and less solar insulation in the more northern regions of the country. Therefore, it is more feasible to replace the existing non-renewable fossil fuel sources with solar cells in all regions of Iraq than the U.K which requires further cost benefit considerations

Collaborative Learning in Computer Vision

The science of designing machines to extract meaningful information from digital images, videos, and other visual inputs is known as Computer Vision (CV). Deep learning algorithms cope CV problems by automatically learning task-specific features. Especially, Deep Neural Networks (DNNs) have become an essential component in CV solutions due to their ability to encode large amounts of data and capacity to manipulate billions of model parameters. Unlike machines, humans learn by rapidly constructing abstract models. This is undoubtedly due to the fact that good teachers supply their students with much more than just the correct answer; they also provide intuitive comments, comparisons, and explanations. In deep learning, the availability of such auxiliary information at training time (but not at test time) is referred to as learning by Privileged Information (PI). Typically, predictions (e.g., soft labels) produced by a bigger and better network teacher are used as structured knowledge to supervise the training of a smaller network student, helping the student network to generalize better than that trained from scratch. This dissertation focuses on the category of deep learning systems known as Collaborative Learning, where one DNN model helps other models or several models help each other during training to achieve strong generalization and thus high performance. The question we address here is thus the following: how can we take advantage of PI for training a deep learning model, knowing that, at test time, such PI might be missing? In this context, we introduce new methods to tackle several challenging real-world computer vision problems. First, we propose a method for model compression that leverages PI in a teacher-student framework along with customizable block-wise optimization for learning a target-specific lightweight structure of the neural network. In particular, the proposed resource-aware optimization is employed on suitable parts of the student network while respecting the expected resource budget (e.g., floating-point operations per inference and model parameters). In addition, soft predictions produced by the teacher network are leveraged as a source of PI, forcing the student to preserve baseline performance during network structure optimization. Second, we propose a multiple-model learning method for action recognition, specifically devised for challenging video footages in which actions are not explicitly visualized, but rather, only implicitly referred. We use such videos as stimuli and involve a large sample of subjects to collect a high-definition EEG and video dataset. Next, we employ collaborative learning in a multi-modal setting i.e., the EEG (teacher) model helps the video (student) model by distilling the knowledge (implicit meaning of visual stimuli) to it, sharply boosting the recognition performance. The goal of Unsupervised Domain Adaptation (UDA) methods is to use the labeled source together with the unlabeled target domain data to train a model that generalizes well on the target domain. In contrast, we cast UDA as a pseudo-label refinery problem in the challenging source-free scenario i.e., in cases where the source domain data is inaccessible during training. We propose Negative Ensemble Learning (NEL) technique, a unified method for adaptive noise filtering and progressive pseudo-label refinement. In particular, the ensemble members collaboratively learn with a Disjoint Set of Residual Labels, an outcome of the output prediction consensus, to refine the challenging noise associated with the inferred pseudo-labels. A single model trained with the refined pseudo-labels leads to superior performance on the target domain, without using source data samples at all. We conclude this dissertation with a method extending our previous study by incorporating Continual Learning in the Source-Free UDA. Our new method comprises of two stages: a Source-Free UDA pipeline based on pseudo-label refinement, and a procedure for extracting class-conditioned source-style images by leveraging the pre-trained source model. While stage 1 holds the same collaborative peculiarities, in stage 2, the collaboration exists in an indirect manner i.e., it is the source model that provides the only possibility to generate source-style synthetic images which eventually helps the final model in preserving good performance on both source and target domains. In each study, we consider heterogeneous CV tasks. Nevertheless, with an extensive pool of experiments on various benchmarks carrying diverse complexities and challenges, we show that the collaborative learning framework outperforms the related state-of-the-art methods by a considerable margin

Design and Advanced Model Predictive Control of Wide Bandgap Based Power Converters

The field of power electronics (PE) is experiencing a revolution by harnessing the superior technical characteristics of wide-band gap (WBG) materials, namely Silicone Carbide (SiC) and Gallium Nitride (GaN). Semiconductor devices devised using WBG materials enable high temperature operation at reduced footprint, offer higher blocking voltages, and operate at much higher switching frequencies compared to conventional Silicon (Si) based counterpart. These characteristics are highly desirable as they allow converter designs for challenging applications such as more-electric-aircraft (MEA), electric vehicle (EV) power train, and the like. This dissertation presents designs of a WBG based power converters for a 1 MW, 1 MHz ultra-fast offboard EV charger, and 250 kW integrated modular motor drive (IMMD) for a MEA application. The goal of these designs is to demonstrate the superior power density and efficiency that are achievable by leveraging the power of SiC and GaN semiconductors. Ultra-fast EV charging is expected to alleviate the challenge of range anxiety , which is currently hindering the mass adoption of EVs in automotive market. The power converter design presented in the dissertation utilizes SiC MOSFETs embedded in a topology that is a modification of the conventional three-level (3L) active neutral-point clamped (ANPC) converter. A novel phase-shifted modulation scheme presented alongside the design allows converter operation at switching frequency of 1 MHz, thereby miniaturizing the grid-side filter to enhance the power density. IMMDs combine the power electronic drive and the electric machine into a single unit, and thus is an efficient solution to realize the electrification of aircraft. The IMMD design presented in the dissertation uses GaN devices embedded in a stacked modular full-bridge converter topology to individually drive each of the motor coils. Various issues and solutions, pertaining to paralleling of GaN devices to meet the high current requirements are also addressed in the thesis. Experimental prototypes of the SiC ultra-fast EV charger and GaN IMMD were built, and the results confirm the efficacy of the proposed designs. Model predictive control (MPC) is a nonlinear control technique that has been widely investigated for various power electronic applications in the past decade. MPC exploits the discrete nature of power converters to make control decisions using a cost function. The controller offers various advantages over, e.g., linear PI controllers in terms of fast dynamic response, identical performance at a reduced switching frequency, and ease of applicability to MIMO applications. This dissertation also investigates MPC for key power electronic applications, such as, grid-tied VSC with an LCL filter and multilevel VSI with an LC filter. By implementing high performance MPC controllers on WBG based power converters, it is possible to formulate designs capable of fast dynamic tracking, high power operation at reduced THD, and increased power density

RESOURCE USE AND FARM PRODUCTIVITY UNDER CONJUNCTIVE WATER MANAGEMENT IN PAKISTAN

The paper describes a study of canal and supplemental ground water used by 544 farmers for wheat crop in the Rechna Doab catchment of Pakistan. The main objective was to assess the on-farm financial gains through conjunctive water use. For econometric analysis, a linear relationship between the wheat production and different determinant variables was assumed. The results highlighted the problem of increased use of tubewells water in the saline zones that had resulted in the deterioration of the groundwater quality and led to the problem of permanent upconing of saline groundwater. Conjunctive water management increased the farm income by about Rs. 1000 and 5000 per hectare compared to only using the canal and tubewell water, respectively The results of financial analysis show that the net-gains were 30 percent higher on the farms using conjunctive water management as compared to the farms using only tubewell irrigation.Environmental Economics and Policy, Resource /Energy Economics and Policy,

Managerial Gaps in e-Banking Quality Drivers: An Empirical Assessment

Providing quality service to the customer is a main issue for e-banking. The extant literature on e-services has preferentially examined quality factors as perceived by customers. On the other hand, quality depends on the managerial perceptions about quality drivers and the decisions that would follow from these perceptions. According to SERVQUAL - the most known service quality model - any gaps between management’s and customers’ perceptions would affect the experienced quality and then the customer satisfaction. The aim of this paper is to explore how bank managers perceive quality drivers for e-banking through a preliminary empirical survey

Prediction of fretting wear in spline couplings

The original contribution of this work is modeling of fretting wear in aero-engine spline couplings widely used in aero-industry to transfer power and torque. Their safe operation is very critical with respect to flight safety. They consist of two components namely hub and shaft. As they are of light weight, usually it is difficult to realize a perfect alignment. To allow for misalignment, their teeth are designed to be of crowned shape. The crowing allows a degree of misalignment without concentration of stresses which is otherwise inevitable if a misalignment is introduced in case of straight teeth. However, crowing results in another problem of fretting wear and fretting fatigue owing to kinematic constraints imposed as a result of misalignment. The focus of this work is development of mathematical models for prediction of fretting wear and not fretting fatigue. The spline couplings under consideration are industrial scale and made up of nitrogen hardened 42CrMo4. The aero industry requires a reliable method to model and predict fretting wear to be able to optimize the design of spline coupling and reduce the maintenance costs. Wear tests on crowned spline couplings on a dedicated test bench have been conducted and analyzed. Empirical, artificial neural network based and analytical models have been de- veloped to analyse, predict and formulate fretting wear in spline couplings. The empirical and artificial neural netwrok based models are specific to the given case of spline couplings and tribological conditions. However, the analytical model developed has been found to be quite general. Incremental fretting wear formulation both in terms of wear volume and wear depth has been realized. Some novel findings regarding effect of roughness parameters in conjunction with applied torque and misalignment angles with respect to fretting wear are also reported. It has been observed that the evolution of wear depth accelerates with increased applied torque or misalignment angle. Changes in roughness parameters are also found to be increasing with torque and misalignment angle in most of the cases. Preliminary tests for frequency effects on fretting wear have also been conducted