Towards Determining the Optimum Process Mean using an Exponential Distribution

Manufacturers are often faced with the problem of selecting the optimum process mean. Wen and Mergen (1999) used the unbalanced step loss function for measuring the cost of the non-conforming item and adopted a trade-off model for determining the optimum process mean. They assumed that the quality characteristic is normally distributed, the process variance constant and the process mean is unknown. This paper presents the modified Wen and Mergen model with a step loss function and piecewise function using an exponential distribution. The proposed model is a generalization of Wen and Mergens model. Keywords: step-loss function, piecewise linear loss function, exponential distributio

Advanced techniques in environmental monitoring (NIAS Report R39-2016)

Our planet is witness to incidents and events of different scales-some benign, some catastrophic, some due to natural causes and some due to man-made interventions. In order to anticipate, forecast, control and manage events and their effects, information and knowledge are key. And sophisticated sensors and techniques are the key to such information and knowledge, India, within a network of seismic sensors and good remote sensing capabilities already in place, has a certain level of environmental monitoring capability. The theme of the workshop was to collate this capability, compare it with contemporary international trends and highlight measures needed for India to attain and maintain state-of-the-art capability in Advanced Environment Monitoring Technique

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Not AvailableHigh temperature has detrimental effects on growth and yield of any crops. A total of 62 two eggplant genotypes were evaluated for growth and yield traits at two growing season during summer season (March-July) and kharif season (July-November). Average of three replications was used for each trait in statistical analysis involving one way analysis of variance (ANOVA), Principal Component Analysis (PCA) and Agglomerative Hierarchial Clustering (AHC) using SAS ver. 9.3 and R package. Analysis of variance indicated high variability for all the traits among the eggplant genotypes. Highest yield per plant was recorded in Guhala Chatua Local (1.8 kg) in summer season whereas in kharif season, yield per plant was maximum in Swarnamani Black (5.97 Kg). The mean yield per plant (1.83 kg) was more in kharif season as compared to summer season (0.09 kg). The percentage yield reduction in summer season was more than 90% in almost all the genotypes and the lowest yield reduction (22.11%) was found in Guhala Chatua Local followed by DBL-21 and DBL-08. In summer season, the first principal component (PC1) and second principal component (PC2) could explain 48% and 14% of total variance, respectively where yield per plant contributed positively to PC1. In kharif season, first principal component (PC1) and second principal component (PC2) could explain 33% and 20% of the variance, respectively where total number of fruit weight per plant, fruit diameter and yield per plant traits contributed positively to PC1. The Hierarchical cluster analysis revealed five clusters based on the similarities among the genotypes in both summer and kharif season but the clustering pattern was different among season. Based on the study, promising heat-tolerant genotypes (Guhala Chatua Local, DBL-21 and DBL-08) have been identified which could be novel source for heat tolerance gene (s) for utilizing in breeding programme.Not Availabl