Showing papers by "Universal College published in 2011"

Cardiac strain of confectionery worker in relation to heat exposure during regular work shift.

Abstract: Context: In India, a wide variety of occupations are performed in adverse indoor working environment. Work physiological studies in these jobs are scanty as compared to investigations done on more arduous outdoor occupations. Aims and Objectives: Physiological strain of workers engaged in sweet making activities was assessed in terms of cardiac strain indices in relation to heat stress. Setting and Design: 33 full-time workers from eastern India were compared for cardiac strain profile obtained during summer and winter during their regular work shift. A comparison was also done in between younger (n=12) and older (n=16) subjects. Materials and Methods : Working heart rate (WHR) was recorded continuously during work. The pulse deceleration index (PDI) was obtained from recovery heart rate (RHR). Net cardiac cost (NCC) and relative cardiac cost (RCC) were the main indices used to evaluate physical strain. Thermal stress assessed from wet bulb globe temperature (WBGT). Statistical Analysis: Students' t-test and Wilcoxon signed rank paired tests were used for comparing physiological responses. Values were considered significant when P < 0.05. Results: Work load was significantly higher in summer for all workers. RCC of younger workers in winter and summer ranged between 18% and 26%. This was significantly lower as compared to the RCC of older workers which was 27% in winter and 30% in summer. The physiological workload appeared to be moderate in nature. The WBGT index was above the recommended range in summer for both the groups. Older workers showed a no recovery pattern in terms of recovery pulse that indicated toward a cumulative stress which may be attributed to a combined effect of heat and work in summer and extra amount of work performed in winter season. Conclusion: The physical workload is aggravated with various ergonomic stressors present in the work place. An ergonomic intervention has been indicated as further scope of this study.

Rough set anlaysis for uncertain data classification

Abstract: Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, out-dated sources and sampling errors As a result there is a need for tools and techniques for mining and managing uncertain data These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong In this paper proposes a Rough Set method for handling data uncertainty Rough set is a mathematical theory for dealing with uncertainty A pair of crisp sets, called the lower and upper approximations of the set, represents a rough set In this extension if the data point is in the lower approximation, we are sure that it is in the set If it is not in the upper approximation, we are sure that it is not in the set Uncertainty implies inconsistencies, which are taken into account, so that the produced are categorized into certain and possible with the help of rough set theory Experimental results show that proposed model exhibits reasonable accuracy performance in classification on uncertain data