Anti-FER monoclonal antibody, clone 6E3D5

A firm's performance depends on efficient management of economic resources. Performance is usually a function of firm-specific economic factors and macroeconomic factors. In other words, an efficient allocation, management and manipulation of these factors is required to enhance profitability. This study attempts to identify some factors to understand how and to what extent these factors influence the profitability of the Indian cement industry. Seven hypotheses were framed. Panel data of 146 firms were gathered over a period of 22 years, spanning from 1996 to 2017. Thereafter, a Fixed Effect Regression (FER) model was fitted to the data. Results suggested that both firm-specific (e.g. size of firm; age of firm; fixed asset turnover) and macroeconomic variables (e.g. GDP; inflation; export intensity) made a significant impact on profitability of this industry. The findings of this study should assist managers as well as policy makers to frame sustainable policies of this mature industry. This research fulfills a need for a study that shows the degree of contribution of firm-specific and macroeconomic factors to profitability of the Indian cement industry.

Face expression recognition is a key-subject of machine learning, and the primary issue on it is lack of accuracy. This paper proposes a novel face expression recognition method using Decision Based Rule-Oriented Median Filter (DBROMF) and Multi-Directional Triangles Pattern (FER-MDTP). The DBROMF is a novel noise reduction method which removes the impulse noise from the facial images. This FER method enriched with a new image descriptor (MDTP) which is structured via multi-directional triangle pattern to provide a superior image description. An unparalleled novel algorithm to locate the human face organ viz. lip and eyeball stuffed in this research by getting assistance with fuzzy edge strength and abbreviated as an MDTP-FES method. These landmarks of features extracted, and the histogram oriented features tailored with the classification division. The support vector neural network classifier (SVNN) is integrated to conduct the classification job. The JAFFE, CK, TFEID, and ADFES databases are linked to perform the simulation which telescoped with six face expressions. The proposed method lifts the accuracy to a significant range than the existing state-of-the-art methods.