Charging Station for Electric Bikes Powered by Renewable Energy

This report serves the purpose to answer the research problem of developing a charging station for electric bicycles powered by renewable energy allowing rental companies to create a safer and more sustainable way of commuting. The report was divided into a business and a mechanical part to provide a better overview of each area of operations. The business part focuses on the establishment of the company, SolHavn, the analysis of market environment and customers, as well as the creation of a suitable marketing strategy, and the projection of the expected financial positioning of the company. On the other hand, the mechanical part demonstrates the correct and accurate method to dimension and design a solar charging station that capable of charging 10 electric bike according to appropriate mechanical calculation, assumption and analysis realized. The value of the energy consumption was estimated for the off-grid situation primarily and it mainly focus on the worst case scenario season in Barcelona, Spain. Moreover, the consideration of losses that would happen in electrical cabling and other electronic related component like step-down, charge controller and other was taken into account. Finally, the structural design for the station as demonstrated and would be able to support the load of chosen solar panel and other natural loads after detailed calculation. Besides, other simulation which involved other scenario like hybrid and on-grid was considered and analyzed. The comparison between these other scenarios helped in improving the solar station for further project.<br /

Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE

Natural-cause mortality and long-term exposure to particle components : an analysis of 19 European cohorts within the multi-center ESCAPE project

Studies have shown associations between mortality and long-term exposure to particulate matter air pollution. Few cohort studies have estimated the effects of the elemental composition of particulate matter on mortality.; Our aim was to study the association between natural-cause mortality and long-term exposure to elemental components of particulate matter.; Mortality and confounder data from 19 European cohort studies were used. Residential exposure to eight a priori-selected components of particulate matter (PM) was characterized following a strictly standardized protocol. Annual average concentrations of copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM size fractions ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10) were estimated using land-use regression models. Cohort-specific statistical analyses of the associations between mortality and air pollution were conducted using Cox proportional hazards models using a common protocol followed by meta-analysis.; The total study population consisted of 291,816 participants, of whom 25,466 died from a natural cause during follow-up (average time of follow-up, 14.3 years). Hazard ratios were positive for almost all elements and statistically significant for PM2.5 sulfur (1.14; 95% CI: 1.06, 1.23 per 200 ng/m3). In a two-pollutant model, the association with PM2.5 sulfur was robust to adjustment for PM2.5 mass, whereas the association with PM2.5 mass was reduced.; Long-term exposure to PM2.5 sulfur was associated with natural-cause mortality. This association was robust to adjustment for other pollutants and PM2.5