Assessment of Traffic Noise Pollution in Burla Town,India;An InclusiveAnnoyance Study

Noise pollution is one of the major public health problems in urban areas throughout the world.Noise is unwanted sound which produces undesirable problems in day to day life of human being(e.g.,physiological and psychological problems).Rapid increase of the industrialization,urbanization,infrastructure,volume of motor vehicles,and increase in the road networks brought noise pollution to the highest level of disaster in a current situation.In urban areas,road traffic noise plays commanding role among all noise sources and affects the exposed inhabitants.The present work is done to evaluate and assess the traffic noise and its effects in Burla town.Burla,Vidyanagari of Odisha,is an emerging town in India,as it hosts national level of teaching and research institutions like IIM Sambalpur,a medical college-cum-hospital(VIMSAR),a technical university(VSSUT)and Sambalpur University.In last two decade,the road traffic volume has been increased and is facing severe noise pollution to its inhabitants.Noise pollution assessment was made at different locations of the town.This study unveiled the dismal state of noise pollution in the town.Noise contour maps were drawn to visualize the noise level at the traffic and its surroundings.The numbers of hearing impaired patients in different hospitals of the locality are increasing.That shows grim picture of the situation.Regression equations were established taking noise levels with percentage of highly annoyed people during study indicates strong correlation.

Reducing the radiation dose with the adaptive statistical iterative reconstruction technique for chest CT in adults： a parameter study

Background Currently there is a trend towards reducing radiation dose while maintaining image quality during computer tomography （CT） examination.This results from the concerns about radiation exposure from CT and the potential increase in the incidence of radiation induced carcinogenesis.This study aimed to investigate the lowest radiation dose for maintaining good image quality in adult chest scanning using GE CT equipment.Methods Seventy-two adult patients were examined by Gemstone Spectral CT.They were randomly divided into six groups.We set up a different value of noise index （NI） when evaluating each group every other number from 13.0 to 23.0.The original images were acquired with a slice of 5 mm thickness.For each group,several image series were reconstructed using different levels of adaptive statistical iterative reconstruction （ASIR） （30％,50％,and 70％）.We got a total of 18 image sequences of different combinations of NI and ASIR percentage.On one hand,quantitative indicators,such as CT value and standard deviation （SD）,were assessed at the region of interest.The signal-to-noise ratio （SNR） and contrast-to-noise ratio （CNR） were calculated.The volume CT dose index （CTDI） and dose length product （DLP） were recorded.On the other hand,two radiologists with ＞5 years of experience blindly reviewed the subjective image quality using the standards we had previously set.Results The different combinations of noise index and ASIR were assessed.There was no significant difference in CT values among the 18 image sequences.The SD value was reduced with the noise index＇s reduction or ASIR＇s increase.There was a trend towards gradually lower SNR and CNR with an NI increase.The CTDI and DLP were diminishing as the NI increased.The scores from subjective image quality evaluation were reduced in all groups as the ASIR increased.Conclusions Increasing NI can reduce radiation dose.With the premise of maintaining the same image quality,using a suitable percentage of ASIR can increase the value of NI.To assure image quality,we concluded that when the NI was set at 17.0 and ASlR was 50％,the image quality could be optimal for not only satisfying the requirements of clinical diagnosis,but also achieving the purpose of low-dose scanning.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.