Jain Buddhist Virtue in Purananuru

The Indian philosophy is based on the Vedas, and based on these Vedas, philosophies are divided into Vedic-accepting and Vedic-rejecting philosophies. These are called Vedic and Avedic philosophies respectively.  It is a constant argument of scholars that Indian philosophies based on the Vedas are antiquated. Sangyam, Yogam, Nyayam, Vaisedigam, Purvamimamsa, Utiramimamsa, Buddhism, Jainism, Asivakam are the Avedic philosophies. This article is to find out how these anti-vedic philosophies and Jain Buddhist religious values have been influenced in purananuru

BIOINFORMATIC STUDY OF AN ANTITUMOR PROTEIN, AZURIN

Objective: The main objective of this study is to analyze the structure and function of an antitumor protein, azurin, thereby giving validation to the protein structure and existing physicochemical properties in the anticancer protein which are responsible for the anticancer activity.Methods: Protein sequence analysis was done using Basic Local Alignment Search Tool (BLAST) with ten different randomly selected species of Pseudomonas obtained from GenBank. The physicochemical properties, prediction of secondary structure, identification of motifs and domains, three-dimensional (3-D) structure of the antitumor protein, validation through Ramachandran plot, multiple sequence alignment (MSA), and phylogenetic analysis were studied and functional property was confirmed through in silico docking.Results: The similarity search (BLAST-P analysis) for the primary sequence from GenBank carried out showed 86% similarity to the second sequence, azurin (Pseudomonas nitroreducens). The ProtParam, ExPASy tool server indicated the presence of essential physicochemical properties in azurin. Secondary structure prediction revealed random coil, extended strand, alpha helix, and beta turn. The study on domains indicated the presence of one domain in azurin responsible for the anticancer activity. The 3-D structural analysis revealed azurin as metalloprotein, of length-128, and polymer-1 with α-helices, β-sheets, and β-barrels. The validation carried out through Ramachandran plot showed the presence of two outliers (phi and psi). The biological relationship between the input sequences was studied through MSA and phylogenetic analysis. Further, azurin docked against the target protein (p53 tumor suppressor) showed the maximum binding affinity confirming its functional property of causing apoptosis.Conclusion: All the properties analyzed in the present study revealed that the azurin protein can act as a very good anticancer agent, and through the phylogenetic analysis, it was identified that Pseudomonas nitroreducens was closely related to the test organism Pseudomonas aeruginosa

Probabilistic analysis of time to recruit and recruitment time in manpower system with two groups,

Abstract: In this paper, we consider the Manpower System of an organization with two groups. Breakdown occurs in the two groups of the Manpower System due to attrition process. Group A consists of employees other than top management level executives; group B consists of top management level executives. Two models are studied in this paper. In Model-1, group A is exposed to Cumulative Shortage Process (CSP) due to attrition and group B has an exponential life distribution. In Model-2, both the groups A and B are exposed to CSP and have exponential thresholds. Inter occurrence time of shortages to them have exponential distribution. In each model, two cases are discussed. In one case, after the threshold, recruitment policy to compensate the shortages one by one is followed. Joint Laplace transforms of Time to Recruit and Recruitment time have been found. In the second case, recruitment policy of filling vacancies simultaneously is followed. Here, marginal Recruitment time distributions have been obtained

EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)

In present scenario, in machine learning technology, computer vision technology and image processing have attained a massive growth. Amongst many branches of image processing and classification, Object Detection (OD) is the major research domain. In several domains such as face detection, self-driving cars, pedestrian detection, and security surveillance systems, object detection (OD) and classification have experienced a significant surge in popularity in recent years. The conventional techniques for object detection, such as background removal, Gaussian Mixture Model (GMM), and Support Vector Machine (SVM), exhibit limitations such as object overlap, distortion caused by environmental factors including smoke, fog, and varying lighting conditions.Though there are several methods developed for OD, the respective field still stumbles upon many confrontations at the real-time implementations. Detecting objects from the undefined background is the major problem to be considered. Hence, machine learning techniques are incorporated for detecting the objects accurately, when the Neural Networks are effectively trained. With that note, this paper develops a new model, called Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN). For producing appropriate results, sensitivity Measurement is carried out based on brightness, saturation, contrast, Gaussian blur, Gaussian Noise and sharpness. Following this, FRCNN is trained for OD and the results are obtained. The model evaluations are carried out based on some evaluation factors with the acquired dataset images. The obtained results are compared with CNN, YOLO. The result shows that the model exemplifies the other compared works in terms of efficiency and accuracy

EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)

In present scenario, in machine learning technology, computer vision technology and image processing have attained a massive growth. Amongst many branches of image processing and classification, Object Detection (OD) is the major research domain. In several domains such as face detection, self-driving cars, pedestrian detection, and security surveillance systems, object detection (OD) and classification have experienced a significant surge in popularity in recent years. The conventional techniques for object detection, such as background removal, Gaussian Mixture Model (GMM), and Support Vector Machine (SVM), exhibit limitations such as object overlap, distortion caused by environmental factors including smoke, fog, and varying lighting conditions.Though there are several methods developed for OD, the respective field still stumbles upon many confrontations at the real-time implementations. Detecting objects from the undefined background is the major problem to be considered. Hence, machine learning techniques are incorporated for detecting the objects accurately, when the Neural Networks are effectively trained. With that note, this paper develops a new model, called Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN). For producing appropriate results, sensitivity Measurement is carried out based on brightness, saturation, contrast, Gaussian blur, Gaussian Noise and sharpness. Following this, FRCNN is trained for OD and the results are obtained. The model evaluations are carried out based on some evaluation factors with the acquired dataset images. The obtained results are compared with CNN, YOLO. The result shows that the model exemplifies the other compared works in terms of efficiency and accuracy

A Study of Lipid Profile in Nondiabetics with Stroke

AIM OF THE STUDY: The aim is to study the serum lipid profile in non diabetics with stroke and to determine significant correlation between them. OBJECTIVE: The objective is to know about the association between dyslipidemia and non diabetic stroke patients which can help modify our prevention and treatment strategies. Type of Study: Cross Sectional study. Place of Study: Study was conducted on patients of Kilpauk Medical College Hospital for six months. Patients and controls were tested for fasting lipid profile 12 hours after overnight fast. Participants: Participants were 60 patients of non diabetic stroke and 60 controls. Among the 60 patients 37 were males and 23 were females. In controls there were 37 males and 23 females. Age and sex matched controls were selected. Stroke patients with infarct or haemorrage in CT brain were included in the study. RESULTS: In this study total cholesterol, LDL cholesterol and triglycerides were significantly associated with risk of stroke. In this study 56.7% of patients had HDL200 mg/dl, 65% of them had LDL cholesterol > 100 mg/dl and 43.3 % of patients had VLDL >30 mg/dl. CONCLUSION: Our study was conducted on 60 non-diabetic stroke patients and 60 controls. Exclusion was done because diabetes is associated with hyperlipidemia and atherosclerosis. This study showed significant association of total cholesterol, triglycerides, LDL cholesterol in non-diabetics with stroke. High levels of total cholesterol, triglycerides, LDL cholesterol are associated with higher risk of stroke. Lowered HDL cholesterol levels were not significantly associated with stroke. The ratio of HDL/LDL Cholesterol, TG/HDL cholesterol was calculated. Dyslipidemia is a tip in iceberg. Dyslipidemia if properly treated being a modifiable risk factor for stroke it decreasing the incidence of stroke due to dyslipidemia. This leads to decreased morbidity and mortality leading to a healthier society

THERMAL DEGRADATION AND XRD STUDIES OF VEGETABLE OIL BASED NOVOLAC SCAFFOLDS FOR THE FORMULATION OF RESINS

Biomaterials, chemicals and energy from renewable resources have been the object of considerable interest in recent years. Vegetable oils are one of the cheapest and most abundant biological sources available in large quantities and their use as starting materials has numerous advantages such as low toxicity, inherent biodegradability and high purity. They are considered to be one of the most important classes of renewable resources for the production of bio-based thermosets. As a substitute to the use of conventional reinforcing synthetic resins, biobased resins were synthesized from cardanol, renewable and low cost industrial grade oil obtained by vacuum distillation of Cashew Nut Shell Liquid (CNSL), an abundant agricultural byproduct of cashew industry. On the other hand to further expand the field of application, cardanol-based novolac scaffolds, used in the formulation of thermosetting resins by blending with a conventional epoxy resin, especially designed to be compatible with conventional bisphenol- A epoxy resins. In the present study resins have been synthesized by condensing diazotized p-anisidine cardanol dye with urea, resorcinol and furfural as condensing agent.. The resins have been characterised by FT-IR, 1H-NMR and XRD studies. Thermal behavior of the resins has been studied by Thermogravimetric Analysis (TGA) and Differential thermal analysis (DTA). The DTA, SEM and XRD data indicated the percentage of crystallinity associated with the thermal stability of the resins

Quorum sensing - a promising tool for degradation of industrial waste containing persistent organic pollutants

Restoring an environment contaminated with persistent organic pollutants (POPs) is highly challenging. Biodegradation by biofilm-forming bacteria through quorum sensing (QS) is a promising treatment process to remove these pollutants and promotes eco-restoration. QS plays an important role in biofilm formation, solubilization, and biotransformation of pollutants. QS is a density-based communication between microbial cells via signalling molecules, which coordinates specific characters and helps bacteria to acclimatize against stress conditions. Genetic diversification of a biofilm offers excellent opportunities for horizontal gene transfer, improves resistance against stress, and provides a suitable environment for the metabolism of POPs. To develop this technology in industrial scale, it is important to understand the fundamentals and ubiquitous nature of QS bacteria and appreciate the role of QS in the degradation of POPs. Currently, there are knowledge gaps regarding the environmental niche, abundance, and population of QS bacteria in wastewater treatment systems. This review aims to present up-to-date and state-of-the-art information on the roles of QS and QS-mediated strategies in industrial waste treatment including biological treatments (such as activated sludge), highlighting their potentials using examples from the pulp and paper mill industry, hydrocarbon remediation and phytoremediation. The information will help to provide a throughout understanding of the potential of QS to degrade POPs and advance the use of this technology. Current knowledge of QS strategies is limited to laboratory studies, full-scale applications remain challenging and more research is need to explore QS gene expression and test in full-scale reactors for wastewater treatment

Pilot scale microbial production and optimization of Serratia peptidase from Serratia marcescens

Serratia peptidase is active proteolytic enzyme which has the potential of cleaving peptide bond.  Present investigation deals about the Microbial production of serratia peptidase using Serratia marcescens in small scale fermentor. Batch fermentor has been run continuously throughout the night to analyze the production of protein as well as kinetics. Culture broth was maintained at 150rpm for 72 hrs. Protein sample was isolated by centrifuging at 3000rpm for 10mints. The result revealed that Serratia marcescens showed the enormous production of protein in fed batch fermentor compared to the small scale level.  Different substrates were been used for the production of enzyme. Among all cysteine showed the better activity as 2 units/ml of enzyme. Enzymatic assay of Serratia peptidase was done at different time interval of crude broth. Enzyme activity showed that maximum at 40ºC for 72hrs. It was observed that 0.65 units/ml of enzyme. Fed batch pilot scale production of Serratia peptidase was done at 0.5%cystein and 700rpm for 48hrs of run time.Â

Analysis of Manpower System with Alert Human Resource Personnel

Abstract Manpower planning is concerned with matching the supply of people with the jobs available in any organization. Every year, during the months of appraisal, organizations record high rates of employee turnover. Due to various reasons, manpower employed leave the system periodically. Loss of manpower also occurs due to dismissal and death of employees. This loss of manpower has to be compensated by suitable recruitment. But, recruitment cannot be made frequently since it involves cost. Also recruitment of new employees and giving them 4028 S. Mythili, R. Ramanarayanan and S. Srinivasa Raghavan training to suit the needs of the organization works out to be costlier than retaining the available employees. Hence the Human Resource Department has to be alert and avoid manpower loss due to resignations. There is a maximum amount of loss of man power that can be permitted in the organization which is called the threshold beyond which the manpower system of the organization reaches a point of break down. In this paper we introduce the concept of Human Resource Department alertness and find the joint Laplace Stieltjes transform of time to recruit (T) and recruitment time (R). Mathematics Subject Classification: 90B0