Modelling atmospheric ozone concentration using machine learning algorithms

Air quality monitoring is one of several important tasks carried out in the area of environmental science and engineering. Accordingly, the development of air quality predictive models can be very useful as such models can provide early warnings of pollution levels increasing to unsatisfactory levels. The literature review conducted within the research context of this thesis revealed that only a limited number of widely used machine learning algorithms have been employed for the modelling of the concentrations of atmospheric gases such as ozone, nitrogen oxides etc. Despite this observation the research and technology area of machine learning has recently advanced significantly with the introduction of ensemble learning techniques, convolutional and deep neural networks etc. Given these observations the research presented in this thesis aims to investigate the effective use of ensemble learning algorithms with optimised algorithmic settings and the appropriate choice of base layer algorithms to create effective and efficient models for the prediction and forecasting of specifically, ground level ozone (O3). Three main research contributions have been made by this thesis in the application area of modelling O3 concentrations. As the first contribution, the performance of several ensemble learning (Homogeneous and Heterogonous) algorithms were investigated and compared with all popular and widely used single base learning algorithms. The results have showed impressive prediction performance improvement obtainable by using meta learning (Bagging, Stacking, and Voting) algorithms. The performances of the three investigated meta learning algorithms were similar in nature giving an average 0.91 correlation coefficient, in prediction accuracy. Thus as a second contribution, the effective use of feature selection and parameter based optimisation was carried out in conjunction with the application of Multilayer Perceptron, Support Vector Machines, Random Forest and Bagging based learning techniques providing significant improvements in prediction accuracy. The third contribution of research presented in this thesis includes the univariate and multivariate forecasting of ozone concentrations based of optimised Ensemble Learning algorithms. The results reported supersedes the accuracy levels reported in forecasting Ozone concentration variations based on widely used, single base learning algorithms. In summary the research conducted within this thesis bridges an existing research gap in big data analytics related to environment pollution modelling, prediction and forecasting where present research is largely limited to using standard learning algorithms such as Artificial Neural Networks and Support Vector Machines often available within popular commercial software packages

Surgical Antimicrobial Prophylaxis : Challenges in translating evidence to practice

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Enhanced gas condensate recovery by CO2 injection

Perhaps no other single theme offers such potential for the petroleum industry and yet is never fully embraced as enhanced hydrocarbon recovery. Thomas et al. (2009, p. 1) concluded their review article with “it appears that gas condensate reservoirs are becoming more important throughout the world. Many international petroleum societies are beginning to have conferences specifically oriented to gas condensate reservoirs and discussing all parameters germane to such systems.” Gas condensate reservoirs however, usually experience retrograde thermodynamic conditions when the pressure falls below the dewpoint pressure. Condensate liquid saturation builds up near the wellbore first and then propagates radially away along with the pressure drop. This liquid saturation throttles the flow of gas and thus reduces the productivity of a well by a factor of two to four (Afidick et al., 1994; Barnum et al., 1995; Smits et al., 2001; Ayyalasomayajulla et al., 2005). The severity of this decline is to a large extent related to fluid phase behaviour, flow regime (Darcy or non-Darcy), interfacial forces between fluids, capillary number, basic rock and fluid properties, wettability, gravitational forces as well as well type (well inclination, fractured or non-fractured).Thomas et al. (2009, p. 4) added “... for gas condensate systems which exhibit high interfacial tensions where the pore throats are very small, which may correspond either to low permeability rocks or high permeability rocks but with very large coordination number, the success of flowing the liquid from the rock, once it has condensed, will be limited. In such cases, vaporisation (lean gas cycling) or injection of interfacial tension reducing agents (CO2) may be the only option to enhance the performance.” In their comparison of several EOR mechanisms, Ollivier and Magot (2005, p. 217) reported “since large changes in viscous forces are only possible for the recovery of heavy oil, the reduction (or entire elimination) of interfacial forces by solvents such as injection gases seems to be a practical way to achieve large changes in capillary number.” While the majority of the state of the art publications cover sensational aspects of gas condensate reservoirs such as phase couplings and mass transfer between original reservoir components, very little has been reported on fluid dynamics and interfacial interactions of CO2 injection into such systems. This, along with the conceptual frameworks discussed above, serves as the motive for this research work.High pressure high temperature experimental laboratories that simulate reservoir static and thermodynamic conditions have been established to evaluate the: (1) effectiveness of CO2 injection into gas condensate reservoirs through interfacial tension (IFT) and spreading coefficients measurements at various reservoir conditions, (2) efficiency of the process through recovery performance and mobility ratio measurements; with special emphasis on the rate-dependent, IFT-dependent, and injection gas composition-dependant relative permeabilities, and (3) the behaviour of CO2 injection into gas condensate reservoirs on a field scale through numerical simulations in heterogeneous, anisotropic, fractured and faulted systems. The study also investigates the performance of various reservoir fluid thermodynamic conditions, injection design variables, and economic recovery factors associated with CO2 injection.Condensate recovery was found to be a strong function of CO2 injection pressure (and thus IFT), displacement flow rate, injection gas composition as well as phase behaviour and fluid properties. These parameters control the orientation and continuity of the fluid phases, solubility, gravity segregation, mobility ratio, and the ultimate recovery efficiency. Simulation analysis also suggests that developments of fractured gas condensate reservoirs depend to a large extent on initial reservoir thermodynamic conditions (initial pore pressure and fluid composition) as well as on production operations (natural depletion, waterflooding, continuous CO2 injection, gas injection after waterflooding GAW, or water alternating gas WAG).Much like the interrelation between accuracy and precision in science and engineering statistics, this research work draws a link between the effectiveness (quality metric through IFT measurements) and the efficiency (productivity metric through coreflooding experiments) of CO2 injection into gas condensate reservoirs. The data reported in this research work should help reservoir engineers better characterise gas condensate systems. The results can also aid the engineering design of CO2-EOR and CO2 sequestration projects

Health and usage monitoring system for military vehicles

© Cranfield University, 2019The aircraft industry has been able to adopt improved maintenance and logistics planning as a result of the technological advances in Integrated Modular Avionics (IMA) and Equipment Health Monitoring (EHM). Same cannot be said about the land system. In the land environment, military vehicles are well behind in achieving the same abilities and hence, the problem of inefficiency in the maintenance and logistics for land based system needs to be addressed. To address this and assess the viability of integrating HUMS and Autonomic Logistics on military land vehicles, this project was proposed. Three main contributions from this research which adds to the knowledge are: (1) assessment of some real system failure which could lead to a poor operational readiness, (2) evaluation of how HUMS can improve the availability and operational readiness and reduction in maintenance cost that leads to the development of cost model and (3) a use of case studies to evaluate degradation of systems under consideration and how their continuous monitoring can help reduce the maintenance cost. A cost modelling study presented a simple and effective method to analyse the financial implication of integrating HUMS system was proposed for military land vehicles. The model provides logical steps to estimate the yearly repair costs, operational availability and the overall costs to understand the financial implication of HUMS integration over the whole service life. The model was also used to assess the financial viability of integrating HUMS in other military platforms e.g. light armoured vehicle, Piranha and Main Battle Tank, Challenger 2. In both the cases, the analysis showed significant financial savings in the long term. A case study was conducted on two different military vehicles to identify the frequency of different systems and sub-systems failures. The 20 challenger 2 and 40 Piranha were studied over the period of 10 years of service time. Study has found that cooling-, lubrication- and the suspension- system were the mostly affected systems in those particular vehicles. An experimental protocol was developed to study the failure detection techniques for the suspension system. The frequency response function was used to identify the failure of the damper and hence the suspension system. The study has observed the changes in the resonance frequency of the failed suspension system with different excitation magnitudes. Effect of vibration waveform was observed to be negligible. However, the small changes in the resonance frequencies using different magnitudes of base excitation seems to suggest the excitation magnitude has the potential to identify the failure based on the frequency response function.Another experimental protocol was developed to examine the failure detection technique for the cooling system of the military vehicle. When the failure was introduced to the cooling system, the significant variations in the temperature were observed for all the engine running conditions at the lab as well as the test with the vehicle running in the field. The variations observed in the temperature measured in different locations in the cooling system could be used to diagnose an early stage of failure in the cooling system, and it can be used to take a preventive action before the actual failure occurs

Harmonics Temporal Profile in High-Voltage Networks: Case Study

This chapter presents a case study about harmonics measurements in high-voltage networks. Measurements were conducted at two locations in the main interconnected system (MIS) of Oman. Voltage and current THDs were recorded for a period of 1 week. The power quality analyzer was set to record required data for a period of 1 week, and the observation period for each recorded value is 10 minutes. At the first location, the grid station (132/33) is feeding industrial as well as other customers. The second grid station (220/132/33 kV) is dedicated to large industrial customers including arc furnaces and rolling mills. The power quality analyzer was installed at the 132 kV side of power transformers at both locations. Recorded data are analyzed, and temporal harmonics profiles are studied. A clear temporal variation of harmonics similar to that of aggregate load and local voltage profiles was observed at the grid station feeding mixed residential and industrial loads. However, this correlation between system load and harmonics profile diminishes at the grid station dedicated for heavy industrial loads

Ebola preparedness in Oman: An experience from the Middle East

LETTER TO THE EDITOR

Evaluation of the Intestinal Bacterial Community of Local Omani and Cobb 500 Broiler Chickens Raised in an Open-Sided House Using 16S rDNA-Based Analysis

Little is known about how the intestinal bacterial microbiota differs among different strains of chickens raised in an open sided house, predominantly those with lower growth rates, such as Indigenous chickens. Ninety-one-day-old chicks of each strain of chickens were raised in an open-sided house system and fed a conventional corn-soybean meal diet from Day 0–35 days of age. The objective of this study was to assess the relative abundance of bacteria microbiota identified in the intestinal tract of local Omani and Cobb 500 broiler chickens raised in an open-sided house system using 16S rDNA-based analysis. The results obtained showed the diversity of bacterial populations in different intestinal regions of two chicken strains. Bacilli were found in higher numbers and reached 98.8% of the bacteria in the duodenum on Day 5 in Cobb 500 versus 72.5% in the Omani chickens. Local Omani chickens had significantly higher numbers of Clostridia at an early age period. On Day 5 Clostridia comprised 13.1% of the bacteria in the duodenum of local Omani chickens, versus only 0.062% in the Cobb 500. The relative abundance of the bacterial microbiota differed significantly (p <0.05) across different intestinal segments of the two strains of chickens, suggesting that each region generated its bacterial community with different relative abundances

A RADIOLOGICAL PROFILE OF FUNGAL SINUSITIS

  Objectives: To create a radiological profile of fungal sinusitis and determine the radiological differences between fungal and nonfungal sinusitis based on the presence of hyperattenuation, bony erosion, neo-osteogenesis, air-fluid level, and extrasinus extension.Methods: This is a retrospective, single-blind, case-control study involving the analysis of 119 computed tomography (CT) scans of the paranasal sinuses. Based on the histopathology, they were divided into cases comprising fungal sinusitis and controls of nonfungal sinusitis. Benign and malignant tumors and previously operated cases of fungal sinusitis were excluded from the study. The principal investigators were blinded to the diagnosis. The comparison parameters were hyperattenuation, the presence of air-fluid level, bone erosion, neo-osteogenesis, and extrasinus extension. Data was analyzed by Chi-square and Fischer exact t-test using SPSS 14.0 software and a p < 0.05 was considered significant.Results: Our study showed the presence of hyperattenuation, neo-osteogenesis, bone erosion, air-fluid level, extrasinus extension in 75.2%, 48.3%, 25.9%, 36.2%, and 6.9% of the cases and 13.1%, 16.4%, 6.6%, 9.8%, and 0 controls, respectively. All the parameters were statistically significant in cases when compared to controls.Conclusion: Hyperattenuation, neo-osteogenesis, air-fluid level, bone erosion, and extrasinus extension are the parameters on CT imaging that will help routinely assess and differentiate fungal sinusitis from nonfungal sinusitis with considerable accuracy, although, there is an overlap with malignancy when the parameter of bone erosion is considered as a differential diagnosis of chronic invasive fungal sinusitis. It reiterates the fact that history, clinical examination, and laboratory evaluation hold an important role in provisional diagnosis

Outlook For Graphene-Based Desalination Membranes

We discuss here next-generation membranes based on graphene for water desalination, based on the results of molecular simulations, application of nanofabrication technologies, and experiments. The potential of graphene to serve as a key material for advanced membranes comes from two major possible advantages of this atomically thin two-dimensional material: permeability and selectivity. Graphene-based membranes are also hypothetically attractive based on concentration polarization and fouling, and graphene's chemical and physical stability. Further research is needed to fully achieve these theoretical benefits, however. In addition, improvement in the design and manufacturing processes, so to produce performance and cost-effective graphene-based desalination devices, is still an open question. Finally, membranes are only one part of desalination systems, and current processes are not optimized to take full advantage of the higher selectivity and permeability of graphene. New desalination processes are, therefore, needed to unlock the full benefits of graphene