Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

Non-Destructive Classification of Diversely Stained Capsicum annuum Seed Specimens of Different Cultivars Using Near-Infrared Imaging Based Optical Intensity Detection

Abstract: The non-destructive classification of plant materials using optical inspection techniques has been gaining much recent attention in the field of agriculture research. Among them, a near-infrared (NIR) imaging method called optical coherence tomography (OCT) has become a well-known agricultural inspection tool since the last decade. Here we investigated the non-destructive identification capability of OCT to classify diversely stained (with various staining agents) Capsicum annuum seed specimens of different cultivars. A swept source (SS-OCT) system with a spectral band of 1310 nm was used to image unstained control C. annuum seeds along with diversely stained Capsicum seeds, belonging to different cultivar varieties, such as C. annuum cv. PR Ppareum, C. annuum cv. PR Yeol, and C. annuum cv. Asia Jeombo. The obtained cross-sectional images were further analyzed for the changes in the intensity of back-scattered light (resulting due to dye pigment material and internal morphological variations) using a depth scan profiling technique to identify the difference among each seed category. The graphically acquired depth scan profiling results revealed that the control specimens exhibit less back-scattered light intensity in depth scan profiles when compared to the stained seed specimens. Furthermore, a significant back-scattered light intensity difference among each different cultivar group can be identified as well. Thus, the potential capability of OCT based depth scan profiling technique for non-destructive classification of diversely stained C. annum seed specimens of different cultivars can be sufficiently confirmed through the proposed scheme. Hence, when compared to conventional seed sorting techniques, OCT can offer multipurpose advantages by performing sorting of seeds in respective to the dye staining and provides internal structural images non-destructively.

Sexual Selection, Signaling and Facial Hair: US and India Ratings of Variable Male Facial Hair

Abstract: The objective of this study was to address the putative ancestral social signaling value of male facial hair, in concert with variable cultural meaning. The ability to grow facial hair might have served as an honest ancestral signal of male age, social dominance, strength and health. Male facial hair may also have had signaling value for attractiveness, though these might be less strong than effects tied to male-male competition. Male facial hair can also be modified, giving rise to cultural variation in its potential signaling function. We surveyed N = 252 US men and women and N = 280 Indian men and women, ages 18–25, about sociodemographics and attitudes toward male facial hair. Participants rated a randomized series of nine images of a composite male model with facial hair with respect to: preferred style, estimated age, attractive to potential partners, assertive, physically strong, friendly, and healthy. Types of facial hair were group into three categories: clean shaven, partial (e.g., Van Dyke, soul patch, stubble) and beard. Supporting hypothesized differences, results show that more male facial hair was positively associated with age estimates and negatively with friendliness, and positively related to assertiveness and physical strength. Supporting hypotheses, women preferred less facial hair and rated less facial hair as more attractive. Some sample differences arose, such as Indian participants perceiving greater age range estimates than US respondents. These data indicate patterned variation in evaluations of male facial hair that can be situated within an evolutionary and culturally evolved signaling framework.

The effect of servant leadership on ad hoc schoolteachers’ affective commitment and psychological well-being: The mediating role of psychological capital

Abstract: Progress on the fourth United Nations Sustainable Development Goal (SDG 4), which strives to “ensure inclusive and equitable quality education”, can only be made with teachers whose levels of job satisfaction and dedication to teaching are high. The authors of this article conducted a survey among ad hoc teachers (also referred to as para teachers) in rural India. The purpose of their study was to find out the extent to which being led by principals who practised a management style termed servant leadership positively impacted respondents’ affective commitment and psychological well-being. A servant leader seeks to serve by developing the followers’ selfhood in various relational, ethical, emotional and spiritual contexts. This has the effect of encouraging the followers to become the best version of themselves. Data collection involved the completion of a questionnaire by a sample of 1,120 (840 female, 280 male) para teachers from 17 non-formal community learning centres and 10 schools in the Indian state of Jharkhand. The results of the survey revealed that there is an indirect effect of servant leadership on affective commitment and psychological well-being through a set of three elements, hope, efficacy and resilience, which together amount to a para teacher’s personal resource of psychological capital. Relying on the findings of their research, the authors suggest that it will be beneficial for Jharkhand’s Department of Education to implement interventional teacher training programmes which nurture servant leadership among school principals and educational officers and thereby foster psychological capital among para teachers.

Prominent label identification and multi-label classification for cancer prognosis prediction

Abstract: Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels from cancer databases and use them in a multi-class environment. The implementation consist of three phases namely, pre-processing, prominent label identification and multi-label classification. Breast, Colorectal and Respiratory Cancer Data sets have been used for the experimentation. Also random samples from all three data sets are generated to form a mixed cancer data. Patient survival, number of primaries and age at diagnosis are the prominent labels identified from others using the Decision tree, Naive Bayes and KNN algorithms. The three prominent labels have been tested using multi-label RAkEL algorithm to find the relations between them. The results of the empirical study are comparatively better than the traditional way of cancer prediction.

Prediction of Material Removal Rate and Surface Roughness in Hot Air Assisted Hybrid Machining on Soda-Lime-Silica Glass using Regression Analysis and Artificial Neural Network

Abstract: Hybrid machining is a combination of conventional with the non-conventional process or two non-conventional processes. In the present work, an attempt has been made to combine hot air with a conventional cutting tool to form a novel Hot Air Assisted Hybrid Machining (HAAHM) for the machining of soda-lime-silica glass. The mathematical model for the Material Removal Rate (MRR) and Surface Roughness (Ra) using Regression Analysis (RA) and the Artificial Neural Network (ANN) models has been developed for the grooving process. The deviation of 8.24% and 7.70% were found in the prediction of MRR and Ra by regression analysis and the deviation of 1.89% and 1.70% for MRR and Ra using an artificial neural network model. The deviation between the predicted and the experimental results of both the models are found to be within the permissible limit. Higher predictive capabilities were observed in ANN model than the regression model. However, both models demonstrated good agreement with the MRR of soda-lime-silica glass by this hybrid machining process.