A clinico-pathological study of benign breast diseases in rural population

Background:Objective of current study was to study the pattern of benign breast diseases in females in our society.Methods:One hundred females who were treated in the department of surgery at Agartala government medical college & G B P Hospital, Agartala, Tripura (West) with various forms of benign breast diseases during the period from January 2013 to December 2013, were studied. Diagnosis were made by a combination of clinical assessment, radiological imaging and tissue biopsy so - called triple assessment.Results:The commonest presentation of benign breast diseases was breast lump followed by nodularity of breast. Fibroadnomas are the commonest benign breast disease and fibrocystic changes form the second most common lesion. The common age group of benign breast diseases range from 21 years to 40 years. This might be associated with certain environmental, regional, dietary or hormonal factors.Conclusion:The result of this study showed that benign breast diseases in females of our society are fibroadenomas followed by fibrocystic diseases. The actual factors responsible for this change needs further research and study

Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement

Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems

Cyber-Physical Power System Layers: Classification, Characterization, and Interactions

This paper provides a strategy to identify layers and sub-layers of cyber-physical power systems (CPPS) and characterize their inter- and intra-actions. The physical layer usually consists of the power grid and protection devices whereas the cyber layer consists of communication, and computation and control components. Combining components of the cyber layer in one layer complicates the process of modeling intra-actions because each component has different failure modes. On the other hand, dividing the cyber layers into a large number of sub-layers may unnecessarily increase the number of system states and increase the computational burden. In this paper, we classify system layers based on their common, coupled, and shared functions. Also, interactions between the classified layers are identified, characterized, and clustered based on their impact on the system. Furthermore, based on the overall function of each layer and types of its components, intra-actions within layers are characterized. The strategies developed in this paper for comprehensive classification of system layers and characterization of their inter- and intra-actions contribute toward the goal of accurate and detailed modeling of state transition and failure and attack propagation in CPPS, which can be used for various reliability assessment studies.Comment: Accepted in Texas Power and Energy Conference (TPEC) 202

Determinants of smoking and chewing habits among rural school children in Bankura district of West Bengal, India

Objectives The present study was undertaken to assess the prevalence of smoking and chewing habits and causes of addiction among the school children of rural areas.Methods This cross-sectional study was conducted in four secondary schools from rural areas of Bankura District, West Bengal during August 2012 to September 2012. Total 1674 students studying in 5th to 10th standard (age group of 10-15 years) were enrolled in the present study. A self-administered questionnaire was applied for data collection.Results The study showed that 18.45%, 27.95% and 67.56% of the students were smokers, chewer and non-addicted, respectively. Considerable number of boys were addicted with smoking (boys 32.3% vs. 4.33girls %) and chewing habits (boys 43.53% vs 12.15girls %). In case of boys, these habits were increased with advancement of ages. Students were more attracted to bidi and tobacco with pan-masala among different types of smoking and chewing agents. The most familiar reasons for tobacco user were: influenced by friends (22.88%), influenced by family members (16.32%) and stress relief (10.88%). Conclusion This study indicated that smoking and chewing habits among school children in rural areas is looming public health issue. Adverse health effect of tobacco use may be incorporated in school secondary curriculum to change the attraction with tobacco among the young generation