The objective of this research is detection of outliers in multivariate data employing various distance measure, particularly using robust regression diagnosis technique. Several classical outlier identification methods are based on the sample mean and covariance matrix in general. But they do not always yield better result, as they themselves are affected by the outliers. Sometimes one outlier point has hide the other outliers. To identify them, methods which have masking effect with outlier points are being used. An appropriate method is adopted to identify the unmasking outliers and also to compare the various distance measures.

Identifying anomalous values in the realworld database is important both for improving the quality of original data and for reducing the impact of anomalous values in the process of knowledge discovery in databases. Such anomalous values give useful information to the data analyst in discovering useful patterns. Through isolation, these data may be separated and analyzed. The analysis of outliers and influential points is an important step of the regression diagnostics. In this paper, our aim is to detect the points which are very different from the others points. They do not seem to belong to a particular population and behave differently. If these influential points are to be removed it will lead to a different model. Distinction between these points is not always obvious and clear. Hence several indicators are used for identifying and analyzing outliers. Existing methods of outlier detection are based on manual inspection of graphically represented data. In this paper, we present a new approach in automating the process of detecting and isolating outliers. Impact of anomalous values on the dataset has been established by using two indicators DFFITS and Cook'sD. The process is based on modeling the human perception of exceptional values by using multiple linear regression analysis.

Climate change plays an important role in agricultural production. Agricultural productivity is highly affected by a number of factors including precipitation, temperature. This paper examines the relationship between the yield of two major rice crops (e.g., Kharif and Rabi) and three main climate variables (e.g., maximum temperature, minimum temperature and rainfall). The dynamic relationships among the variables considered for observing the impact of climate change on rice yields are examined based on Vector Autoregression (VAR) model with the use of Granger causality test, impulse response functions and variance decomposition for the data. Maximum and Minimum temperature have significant effects, meanwhile rainfall has negative impact on Kharif rice yield. Adverse effects on Rabi rice yield are observed by maximum temperature and rainfall, whereas minimum temperature affect yield positively. Appropriate adaptive techniques are recommended to overcome this emerging hazard of climate change on rice production.

ABSTRACT:The new machine vision that is emerging is already generating millions of dollars per year in thousands of successful applications. Machine vision is becoming established as a successful tool for industrial automation where 100 % inspection of manufactured parts during production is becoming a reality. Two-thirds of applications of machine vision are found in quality control in which objects are categorized using its dimensions in metric measurements that are extracted from its 2D-pixel image. Building a machine vision system requires careful selection of appropriate sensor, lens, extraction and integration of information from the available cues, sensed data and evaluation of system performance and robustness. In this paper, it is proposed to provide a roadmap with an overview of Imaging system, Radiometric and Geometric Modeling to design a machine vision system with the illustration of design of automated visual inspection system to image new bottles on a conveyor line at a distance of 100 cm from the camera in order to ensure 30 mm diameter of the bottle neck.

No abstract

Objectives: Clinical empathy is an important predictor of patient outcomes. Several factors affect physician’s empathy and client perceptions. We aimed to assess the association between physician and client perception of clinical empathy, accounting for client, physician, and health system factors. Methods: We conducted a hospital-based cross-sectional study in 3 departments (family medicine, internal medicine, and surgery) of King Saud Medical City in Riyadh, Saudi Arabia. We interviewed 30 physicians and 390 clients from 3 departments. Physicians completed the Jefferson Scale of Empathy (JSE) and the clients responded to the Jefferson Scale of Patient Perceptions of Physician Empathy (JSPPPE). We used a hierarchical multilevel generalized structural equation approach to model factors associated with JSE and JSPPPE and their inter-relationship. Results: Mean (SD) score of client-rated physician empathy was 26.6 (6) and that of physician self-rated was 111 (12.8). We found no association between the 2 ( b  =  0.06; 95% confidence intervals CI: −0.1, 0.21), even after adjusting for client, physician, and health system factors. Physician's nationality (0.49; 0.12, 0.85), adequate consultation time (1.05; 0.72, 1.38), and trust (1.33; 0.9, 1.75) were positively associated whereas chronic disease (−0.32; −0.56, −0.07) and higher waiting times (−0.26; −0.47, −0.05) were negatively associated. Conclusion: A physician's self-assessed empathy does not correlate with clients’ perception. We recommend training and monitoring to enhance clinical empathy.

Let G be a countable exact discrete group. We show that G has the approximation property if and only ifG) ⊗ S for any Hilbert space H and closed subspace S ⊆ H, we have where C * u (G) is the uniform Roe algebra. This answers a question of J. Zacharias.

Let G be a countable exact discrete group. G has the strong invariant approximation property(SIAP) if and only if⊗ S for any Hilbert space H and closed subspace S ⊆ H. We shall use results of Haagerup and Kraus on the approximation property (AP) to investigate some permanence properties of the SIAP for discrete groups. This can be done most efficiently for exact groups. In this paper we describe that the stability properties of the SIAP property pass to semi direct products, and extensions for discrete exact groups.