Review of the business strategy to facilitate 3-UK to monetise mobile internet and explore new business models to leverage those strategies.

“Mobile Internet Services Booming” (ComScore, April 9, 2009). As of 2007, the number of PC users in the world has exceeded 1 billion and there are 1.5 billion internet users globally. At the same time, there are more than 4 billion mobile subscribers, indicating the vast growth potential for mobile internet, especially considering that 58% of the PCs are in market that account for 15% of the world population. This means mobile will be the internet terminal for majority of the users. Global migration to Third Generation (3G) is also picking up the pace and it leaves a huge growth potential for the mobile internet. According to the analysts, Nielsen Online, rate of mobile internet in the UK is growing 8 times faster than internet use on computers. Hence, most of the mobile operators are strategically positioning themselves to capitalise on this growth of the mobile internet. The core business strategy of 3-UK, a leading mobile operator in the UK, is to mobilise internet. 3-UK is intending to monetise internet access by their existing and new customer base. This project looks at various options where by 3-UK can monetise mobile internet. This project includes a detailed analysis of the existing strategy of 3-UK and recommendations are made. Also, attempts are made to explore any potential new business models for 3-UK which could generate better revenue streams

Review of the business strategy to facilitate 3-UK to monetise mobile internet and explore new business models to leverage those strategies.

“Mobile Internet Services Booming” (ComScore, April 9, 2009). As of 2007, the number of PC users in the world has exceeded 1 billion and there are 1.5 billion internet users globally. At the same time, there are more than 4 billion mobile subscribers, indicating the vast growth potential for mobile internet, especially considering that 58% of the PCs are in market that account for 15% of the world population. This means mobile will be the internet terminal for majority of the users. Global migration to Third Generation (3G) is also picking up the pace and it leaves a huge growth potential for the mobile internet. According to the analysts, Nielsen Online, rate of mobile internet in the UK is growing 8 times faster than internet use on computers. Hence, most of the mobile operators are strategically positioning themselves to capitalise on this growth of the mobile internet. The core business strategy of 3-UK, a leading mobile operator in the UK, is to mobilise internet. 3-UK is intending to monetise internet access by their existing and new customer base. This project looks at various options where by 3-UK can monetise mobile internet. This project includes a detailed analysis of the existing strategy of 3-UK and recommendations are made. Also, attempts are made to explore any potential new business models for 3-UK which could generate better revenue streams

Automated Execution and Optimization of Flow Chemistry on a Robotic Platform with Integrated Analytics

The development, optimization, and characterization of chemical processes for the synthesis of organic compounds, which play a key role in society as medicines and materials, is currently an expensive and laborious enterprise. These inefficiencies are driving efforts to develop automated, data-rich experimentation (DRE) platforms and methods designed to maximize the amount of useful data generated per unit time and raw material expended. In this thesis, a modular robotic platform for continuous flow synthesis was utilized for machine-assisted organic reaction development. An improved version of the platform was built with new capabilities including a Cartesian robot for fast and reliable pick-and-place, integrated process analytical technology (PAT) such as LC-MS and FT-IR spectroscopy for online reaction monitoring, and closed-loop feedback optimization of reaction conditions using a Bayesian optimization algorithm. In the first case study, algorithmic reaction optimization helped partially automate the specification of critical process parameters (both continuous and categorical) for a computer-proposed and human-refined synthetic route. A representative multistep synthesis involving 3 reactions (including a heterogeneous hydrogenation) and 1 separation was chosen. In multistep flow processes where downstream residence times are physically constrained by upstream flow rates, the modular reactor volumes of the robotic platform were leveraged to introduce an independent degree of freedom. Deployment of multiple PAT tools facilitated thorough process understanding and workflow automation helped accelerate and reduce the manual burden during experimentation. In the second case study, the platform’s toolkit was further expanded with the addition of an LED array to perform photochemistry. This new capability enabled the development of two photochemical steps that lead to an important class of drugs. Bayesian optimization aided in optimizing continuous variables including residence time and stoichiometry, and characterizing the effect of critical process parameters. Finally, the design of data-rich dynamic flow experiments, where continuous reactors are operated under controlled transients in input variables, was computationally studied and experimentally validated. Mathematical modeling using transport equations and a parametric analysis helped identify a simple criterion to guide the design of dynamic trajectories. Sinusoidal dynamic experiments designed using the criterion were executed on the robotic platform with two and three simultaneously varying inputs.Ph.D

Performance Modeling of Multi-tiered Web Applications with Varying Service Demands

International audienceMulti-tiered transactional web applications are frequently used in enterprise based systems. Due to their inherent distributed nature, pre-deployment testing for high-availability and varying concurrency are important for post-deployment performance. Accurate performance modeling of such applications can help estimate values for future deployment variations as well as validate experimental results. In order to theoretically model performance of multi-tiered applications, we use queuing networks and Mean Value Analysis (MVA) models. While MVA has been shown to work well with closed queuing networks, there are particular limitations in cases where the service demands vary with concurrency. This is further contrived by the use of multi-server queues in multi-core CPUs, that are not traditionally captured in MVA. We compare performance of a multi-server MVA model along-side actual performance testing measurements and demonstrate this deviation. Using spline interpolation of collected service demands, we show that a modified version of the MVA algorithm (called MVASD) that accepts an array of service demands, can provide superior estimates of maximum throughput and response time. Results are demonstrated over multi-tier vehicle insurance registration and e-commerce web applications. The mean deviations of predicted throughput and response time are shown to be less the 3% and 9%, respectively. Additionally, we analyze the effect of spline interpolation of service demands as a function of throughput on the prediction results

Tomographic features of idiopathic polypoidal choroidal vasculopathy using spectral domain OCT

Purpose: To study the tomographic features of idiopathic polypoidal choroidal vasculopathy (IPCV) diagnosed on indocyanine green (ICG) angiogram using spectral domain optical coherence tomography (SDOCT). Design: This was a retrospective observational case series. Materials and Methods: Spectral domain optical coherence tomography (SD OCT) features of 50 eyes of 50 consecutive patients diagnosed as idiopathic polypoidal choroidal vasculopathy (IPCV) between January 2013 to January 2015 on ICG angiograms were studied. A qualitative analysis based on various tomographic features corresponding to the polypoidal lesions and branching vascular network on Spectrailis OCT was studied. Polyps were localized to subfoveal and juxtafoveal areas. These were later compared with SDOCT features of 15 eyes of 15 consecutive patients newly diagnosed as occult choroidal neovascular membrane (CNVM) on FFA/ICG. Results: Of the 50 eyes, sharp peak pigment epithelial notch (PED) was present in 49 eyes (98%); PED notch in 49 eyes (98%); a visible hyporeflective lumen with hyperreflective lesions adherent to the outer surface of the RPE in 48 eyes (96%), multiple PED in 44 eyes (80%), and diffuse PED in 44 eyes (80%); intraretinal hyperreflective dots representing hard exudates were seen in 44 eyes (88%). Surrounding OCT features such as intraretinal hyperreflective dots represent hard exudates, Cystoid macular edema and subretinal fluid were seen in 44 eyes (88%). Sub-RPE features such as PED with sheaths of internal reflectivity – branching vascular network in 19 eyes (38%) and prominent Bruchs membrane and surfacing of choroidal vessels was seen in 18 eyes (36%). At least 3 of the abovementioned OCT features were seen in all of the eyes diagnosed as PCV. The height of the PED ranged from 138–1300 μ (median = 422.2 μ). Of the 15 eyes, 80% showed presence of FVPED; multiple PED were seen in 33.3%; intraretinal hard exudates in 66.7%, and notch PED in 6.7%. Hyporeflective lumen with hyperreflective lesion under RPE was not seen in any of the eyes. The height of PED ranged from 118–339 μ (median = 164.28 μ). Conclusions: SDOCT-based features mentioned above allows detection of IPCV and differentiate it from occult CNVM. Our results suggest that SDOCT may be a useful noninvasive tool compared to ICG in detecting PCV, especially in places where ICG is not available or is contraindicated

Correlation between the health of the cone outer segment tips line and ellipsoid zone with vision after macular hole surgery

Purpose: To determine whether preoperative cone outer segment tips (COST) line, ellipsoid zone (EZ), and external limiting membrane (ELM) defects is significantly related with postoperative recovery of photoreceptors and vision after type 1 macular hole closure. Materials and Methods: A retrospective observational study was conducted on 28 eyes of 28 patients with surgically closed macular holes. Heidelberg's Spectralis optical coherence tomography (OCT) was used to obtain images of the foveal area, and lengths of COST line defect, EZ defect, and ELM defect were measured preoperatively and at 1 month, 3 months, and 6 months after surgery. The pattern of recovery of the photoreceptor layers was observed as complete recovery, incomplete recovery, or disrupted. Results: It was observed that 13 out of 28 eyes with a mean preoperative length of foveal COST line defect 303.8 microns, showed a significant visual improvement in the range of 0.17 logMAR units (6/9-6/6) with complete or incomplete recovery of the EZ and ELM. Twelve patients with mean preoperative length of foveal COST line defect 632 microns showed only slight visual improvement by one or two lines in the range of 0.47 logMAR units (6/18), with an incomplete pattern of photoreceptor recovery. However, 3 patients with a mean preoperative COST line defect of 815 microns did not show any visual improvement of 0.61 logMAR units (6/36) with EZ and ELM disruptions postoperatively. Conclusion: The length of preoperative COST line defect is predictive of the recovery and arrangement of photoreceptors and best corrected visual acuity after type 1 macular hole closure. Eyes with complete and incomplete recovery of COST, EZ, and ELM were associated with significantly better visual acuity as compared to eyes with a disrupted pattern of photoreceptor recovery

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R2 (-) values higher than 0.8 (-). Considering the bias (Tonnes h−1), the ML predicted the emission fluxes within 6.3%, 3.3%, and 0.3% of WRF predictions for the old mine, new mine, and the pond, respectively