The effects of language on judgments of universal facial expressions of emotion

Because of the close connection between culture and language, a number of writers have suggested that bilinguals will differ in their behavior because of differences in the degree of assimilation of different cultures in the same individual. We tested this notion by obtaining data from bilingual (English and Hindi) college students in India using a well-studied cross-cultural research paradigm involving emotional perception. Subjects judged universal facial expressions of emotion in two separate sessions, one conducted entirely in English, the other in Hindi. In each session, they judged which emotion was being portrayed, and how intensely. Subjects recognized anger, fear, and sadness more accurately in English than in Hindi. They also attributed greater intensity to female photos of anger when rating in Hindi, but attributed greater intensity to female photos of sadness when rating in English. These findings were discussed in relation to the theoretical connection between culture and language.     

Green supply chain management: the case of the construction sector in the United Arab Emirates (UAE)

Restraining the negative environmental impacts of the construction sector constitutes one of the major challenges of the twenty-first century. However, efforts to address it have been largely fragmented. With environmental consequences of a construction project typically dispersed across its life cycle, i.e. from design through to end-of-life, greening this sector requires a supply chain wide focus inclusive of all key stages and stakeholders; also, all relevant aspects such as the nature of green practices implemented and associated drivers, barriers and performance implications need to be considered. This forms the focus of the present study where a comprehensive, green supply chain management oriented understanding of the construction sector is developed through the context of the UAE construction sector, and incorporating inputs from all key stakeholders, i.e. Developers, Architects/Consultants, Contractors and (material) Suppliers. The study contributes to improving the efficiency and effectiveness of greening of the construction sector.     

Experimental designs and their efficiencies for spatially correlated observations in two dimensions

Optimality of experimental designs for spatially correlated observations is investigated.come two dimensional correlation structures are discussed and an attempt has been made to find optimal or nearly optimal design for each sitution.The solution lend to designs similar to that used for repeated measurements.The relative efficiency of the proposed designs in comparison to randomized latin square designs is tabulated for some cases.     

When Precaution Creates Misunderstandings: The Unintended Effects of Precautionary Information on Perceived Risks,the EMF Case

In the past decade, growing public concern about novel technologies with uncertain potential long‐term impacts on the environment and human health has moved risk policies toward a more precautionary approach. Focusing on mobile telephony, the effects of precautionary information on risk perception were analyzed. A pooled multinational experimental study based on a 5 × 2 × 2 factorial design was conducted in nine countries. The first factor refers to whether or not information on different types of precautionary measures was present, the second factor to the framing of the precautionary information, and the third factor to the order in which cell phones and base stations were rated by the study participants. The data analysis on the country level indicates different effects. The main hypothesis that informing about precautionary measures results in increased risk perceptions found only partial support in the data. The effects are weaker, both in terms of the effect size and the frequency of significant effects, across the various precautionary information formats used in the experiment. Nevertheless, our findings do not support the assumption that informing people about implemented precautionary measures will decrease public concerns.     

Inventory model for convertible item with deterioration

Abstract

In this article, we developed a model for a convertible item (or product) where initial form of the item converts into another product by consuming conversion cost and time both. After duration, it converts again into a new product of a different nature. It is a sequential-type conversion from initial into two other products over states. The demand pattern and deterioration rate differ at each converted state. An inventory model is developed for such a kind of sequential convertible item. Expressions for total cost and other related costs (as per states) are derived and optimal time to convert the product in different states are calculated under model assumptions. A numerical example is incorporated in support of the theoretical findings and it validates the strength of the model.     

A study of physical-health and neuroticism among women engaged in the home-based production of chikan embroidery

The present study examined physical health and neuroticism among women (n=200) engaged in the unorganized home-based production of chikan embroidery. Physical health of women chikan workers was examined with the help of an abbreviated version of CMI, and neuroticism, with PGI Health Questionnaire N-2 in easy paced, face to face personal interviews. The results indicated that women chikan workers in general were facing some serious health problems. Significant variations were also obtained across groups of women associated with different centres of chikan production. The study points towards the stressful nature of environment in which these women chikan workers live and work.     

A noise-based stabilizer for convolutional neural networks

Overfitting occurs when one tries to train a large model on small amount of data. Regularizing a neural network using prior knowledge remains a topic of research as it is not concluded how much prior information can be given to the neural network. In this paper, a novel algorithm is introduced which uses regularization to train a neural network without increasing the dataset. A trivial prior information of a class label is supplied to the model while training. Laplace noise is introduced to the intermediate layer for more generalization. The results show significant improvement in accuracy on the standard datasets for a simple Convolutional Neural Network (CNN). While the proposed method outperforms previous regularization techniques like dropout and batch normalization, it can also be applied with them for further improvement in the performance. On the variants of MNIST, proposed algorithm achieved an average 48% increment in the test accuracy.     

Edge Estimation in the Population of a Binary Tree Using Node-Sampling

Suppose a finite population of several vertices, each connected to single or multiple edges. This constitutes a structure of graphical population of vertices and edges. As a special case, the graphical population like a binary tree having only two child vertices associated to parent vertex is taken into consideration. The entire binary tree is divided into two sub-graphs such as a group of left-nodes and a group of right-nodes. This paper takes into account a mixture of graph structured and population sampling theory together and presents a methodology for mean-edge-length estimation of left sub-graph using right edge sub-graph as an auxiliary source of information. A node-sampling procedure is developed for this purpose and a class of estimators is proposed containing several good estimators. Mathematical conditions for minimum bias and optimum mean squared error of the class are derived and theoretical results are numerically supported with a test of 99% confidence intervals. It is shown that suggested class has a sub-class of optimum estimators, and sample-based estimates are closer to the true value of the population parameter.     

A general class of non parametric tests for comparing scale parameters

In this paper, a general class of non parametric tests is proposed for the two-sample scale problem. Testing of the scale parameter is very useful in real-life situations commonly faced in engineering, trade, cultivation, industries, medicine, etc. In all these fields, one will prefer the method that gives more consistent results. Thus, it is worthwhile to test the equality of scale parameters. The distribution of the proposed test is established. To assess the performance of the proposed test, the asymptotic efficacies are studied for some underlying distributions and the results are interpreted with useful information. To see the working of the proposed test, an illustrative example for the real-life data set is provided. The simulation study is also carried out to find the asymptotic power of the proposed test. An extension of the general class of tests to the multiple-sample problem is also discussed.