Curvature measures on manifolds: minimal immersions

The purpose of this work is to give some results on the various curvature measures on manifolds and also have a brief look at minimal immersions of manifolds in Riemannian spaces. With regard to the former, the first chapter deals with the 1th TAC as defined by Chen [9]. In Chapter II we look at minimal immersions of compact manifolds in Riemannian spaces and in particular at pseudo-umbilical immersions - the term first introduced by Otsuki. The two more familiar curvatures are the scalar curvature, and the mean curvature, and in Chapter III we define the ɑ(^th) scalar curvature. Finally we look at submanifolds with constant mean curvature. Lastly, in Chapter IV, a differential equation is derived for "stable hypersurfaces". A hypersurface is said to be 'stable' if б│(_M(^n)) (^n/2) * 1 = 0 for any normal variation of the integral. A particular case of this problem, (i.e. for surfaces in E(^3)) was first considered by Hombu. A bibliography follows Chapter IV

Update on Membranoproliferative GN

Membranoproliferative GN represents a pattern of injury seen on light microscopy. Historically, findings on electron microscopy have been used to further subclassify this pathologic entity. Recent advances in understanding of the underlying pathobiology have led to a proposed classification scheme based on immunofluorescence findings. Dysregulation of the complement system has been shown to be a major risk factor for the development of a membranoproliferative GN pattern of injury on kidney biopsy. Evaluation and treatment of this complex disorder rest on defining the underlying mechanisms

Prevalence, pattern and perceptions of self-medication in medical students

Background: The use of self-medication is highly prevalent in the community more so amongst the medical students. Self-medication can be defined as the use of drugs to treat self-diagnosed disorders or symptoms, or the intermittent or continued use of a prescribed drug for chronic or recurrent disease or symptoms.Aims and Objectives: To study the prevalence and pattern of use of self-medication among medical students from first year to internship.Methods: This cross sectional study was carried out among under graduate medical students including interns of Smt. NHL Municipal Medical College, Ahmedabad during the period of March 2010 to May 2010. Results: Out of 747 students and interns enrolled, 685 responded (91.7%). Out of 685 respondents 564 (82.3%) reported self-medication within one year of recall period. Most common conditions/symptoms for self-medication in students were fever (72.7%), headache (69.1%), upper respiratory tract infections (64.1%) followed by others like body-ache, abdominal pain, diarrhoea etc. Over the counter drugs (84.2%) was the most common category of drugs used by all the students except first year students who used prescription only drugs more frequently (48.5%). Herbal and Ayurvedic drugs were also used as self-medication (17.8%); most frequently by the first year students (22.7%).Conclusion: The pattern of self-medication practice changes with time and advancement of knowledge

Improving effectiveness of honeypots: predicting targeted destination port numbers during attacks using J48 algorithm

During recent years, there has been an increase in cyber-crime and cybercriminal activities around the world and as countermeasures, effective attack prevention and detection mechanisms are needed. A popular tool to augment existing attack detection mechanisms is the Honeypot. It serves as a decoy for luring attackers, with the purpose to accumulate essential details about the intruder and techniques used to compromise systems. In this endeavor, such tools need to effectively listen and keep track of ports on hosts such as servers and computers within networks. This paper investigates, analyzes and predicts destination port numbers targeted by attackers in order to improve the effectiveness of honeypots. To achieve the purpose of this paper, the J48 decision tree classifier was applied on a database containing information on cyber-attacks. Results revealed insightful information on key destination port numbers targeted by attackers, in addition to how these targeted ports vary within different regions around the world

Intelligent robotics harvesting system process for fruits grasping prediction.

This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system's performance. The research findings suggest that the proposed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%