A Study of Some Transportation Networks from a Complex Network Perspective Chakrabarthy, Pooja.

Complex networks are ubiquitous; consider, for example, road networks, protein-protein interaction networks, metabolic networks, power networks etc. They can be applied to a wide range of areas like mathematics, computer science, social, and biological sciences. Random graphs can aid in observing the topological features in various data sources and give insights on their respective real-world networks. In this thesis, we study the particular case of small-world networks and compare them with random and regular networks. We start by explaining the complexity of networks and how graph theory can be applied to complex networks to understand their topological properties. Using which, we can tell how nodes and edges are arranged in a complex networks. We study some of the network models based on this work, elaborating how we can use these models to explain their topological properties. Finally, we conduct an empirical study on Transportation networks to demonstrate the efficacy of the proposed framework. Due to urbanization, more than half of the world population live in cities currently. To overcome the rapid urbanization in a sustainable manner, transit systems all around the world are likely to grow. By studying transportation networks as a complex network, this thesis identifies the properties and effects of road network designs using a graph theory approach. We observe that our network model follows small world properties and, finally, some future works are proposed in this area of research

A Study of Some Transportation Networks from a Complex Network Perspective Chakrabarthy, Pooja.

Complex networks are ubiquitous; consider, for example, road networks, protein-protein interaction networks, metabolic networks, power networks etc. They can be applied to a wide range of areas like mathematics, computer science, social, and biological sciences. Random graphs can aid in observing the topological features in various data sources and give insights on their respective real-world networks. In this thesis, we study the particular case of small-world networks and compare them with random and regular networks. We start by explaining the complexity of networks and how graph theory can be applied to complex networks to understand their topological properties. Using which, we can tell how nodes and edges are arranged in a complex networks. We study some of the network models based on this work, elaborating how we can use these models to explain their topological properties. Finally, we conduct an empirical study on Transportation networks to demonstrate the efficacy of the proposed framework. Due to urbanization, more than half of the world population live in cities currently. To overcome the rapid urbanization in a sustainable manner, transit systems all around the world are likely to grow. By studying transportation networks as a complex network, this thesis identifies the properties and effects of road network designs using a graph theory approach. We observe that our network model follows small world properties and, finally, some future works are proposed in this area of research

Management of multidrug resistant tuberculosis (MDR-TB) – Monitoring is the key to successful outcome

AbstractContextTreatment of multidrug resistant tuberculosis (MDR-TB) is challenging. In India, standard treatment regimen is established by Revised National Tuberculosis Control Programme (RNTCP). Adequate follow-up of patients during the treatment period is a challenging task under programmatic conditions. We did a retrospective analysis of patients enrolled and treated under the national programme to study the outcome.AimsTo study the treatment outcome of MDR-TB and the factors affecting it.Settings and designRetrospective analysis of 69 patients treated with standard regimen for MDR-TB, as per RNTCP guidelines.Methods and materialRetrospective analysis of 69 MDR-TB patients for the clinical and demographic profile. Treatment outcome is defined as cure rate, default rate, death rate and failure. The factors affecting this outcome are also studied.ResultsSputum culture conversion rate was 33.9% and 62.5% at 3rd and 6th month of treatment respectively. Cure rate was 47.8%, death rate 27.5%, default rate 14.5% and failure 7.3%.ConclusionsThe major hindrance in achieving a good cure-rate was a high death rate and default. Early diagnosis of MDR-TB and adequate clinical monitoring during treatment is essential. Identifying adverse drug reactions, other co morbidities and their optimal management is the key to success