A rare case of omental torsion - a surprise diagnosis of acute pelvic pain

Omental torsion is a rare cause of acute abdominal or pelvic pain. It is rarely diagnosed pre-operatively. It poses a great challenge as it closely mimics the other conditions causing acute surgical abdomen, such as appendicitis, ovarian torsion, ruptured ovarian cyst or ectopic pregnancy etc. Hence, often it presents as a surprise intra-operative diagnosis. Though exploratory laparotomy represents the diagnostic and definitive therapeutic procedure, presently Laparoscopy is the first choice procedure

PHOTOCATALYTIC DEGRADATION OF PHARMACEUTICAL DRUG ZIDOVUDINE BY UNDOPED AND 5 % BARIUM DOPED ZINC OXIDE NANOPARTICLES DURING WATER TREATMENT: SYNTHESIS AND CHARACTERISATION

Objective: To study the photocatalytic degradation of pharmaceutical drug zidovudine (ZDV) by synthesized undoped zinc oxide nanoparticles (ZONPs) and 5% (mole ratio) barium doped zinc oxide nanoparticles (BZONPs) during water treatment.Methods: Kinetics studies were carried out with the help of UV-Visible Spectrophotometer. High-Resolution Mass Spectrophotometry (HR-MS) was used to identify products. A photo-reactor with mercury lamp was used as an external source of light energy. Optical power meter was used for the measurement of light intensity. The particle size of the synthesized photocatalysts was identified with the help of siemens x-ray diffractometer (XRD). The surface topography of photocatalysts was done by scanning electron microscope (SEM). Transmission electron microscopy (TEM) was used for the studies of particle size and morphology.Results: Five degraded products of ZDV are identified by HR-MS. A suitable electron-hole pair mechanism is projected. XRD patterns show that the intensity of peak is slightly stronger in ZONPs. There is an increase in the rate of photocatalytic degradation of ZDV by adding different quantities of photocatalyst from 0.05 g l-1 to 0.1 g l-1. The kinetic data reveals that there is an initial increase in the values of rate constants with the increase in the concentration of ZDV. The kinetic data indicate that the values of rate constants are higher at pH = 9. There is an increase in the rate constant values with an increase in the light intensities of UV lamp.Conclusion: The rates of photocatalytic degradation of ZDV were found to be higher using 5 % (mole ratio) BZONPs as a photocatalyst.Â

CERTAIN SUBCLASSES OF P-VALENT MEROMORPHIC CONVEX FUNCTIONS ASSOCIATED WITH MOSTAFA OPERATOR

In this paper we study certain subclasses of analytic p-valent meromorphic convex functions with positive coefficients in the puncture unit disk. The result presented coefficient estimate, growth and distortion properties for functions belonging to this subclasses. Further results of modified hadamard product, inclusion properties, radii of close-to-convexity; starlikeness and convexity for functions belonging to the subclasses are discussed. Keywords: p-Valent meromorphic functions, convex function, Modified Hadamard product, inclusion properties and radii of starlikeness. AMS Subject Classification: 30C4

Crime Monitoring and Controlling System by Mobile Device

The Closed Circuit Television (CCTV) have been used at very large scale for monitoring, recording and getting popular in whole world. The major goal of Closed Circuit Television system is monitoring or observing crime and tracking the objects. The smart phone Mobile world is also expanding at a rapid scale since the technology was invented. Most of smart phones users live in those countries where usage of CCTV system is very common in life. This project studies a monitoring system for smart phone mobile users based on CCTV system, where information will be sent from mobile phones to server so that CCTV system can work more specifically and accurately by monitoring and tracking objects. A safety assurance approach is proposed, in which a user can inform his location for close observation. If he/she feels like a potential threat. In that case of emergency situation, location, problem and all possible difficulties can be determined in comparatively less time by concern authorities like police as they have already monitoring the situation. DOI: 10.17762/ijritcc2321-8169.15012

The P-ART framework for placement of virtual network services in a multi-cloud environment

Carriers network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If these virtual services are hosted dynamically over multiple clouds, greater flexibility in optimizing performance and cost can be achieved. On the flip side, when orchestrated over multiple clouds, the stringent performance norms for carrier services become difficult to meet, necessitating novel and innovative placement strategies. In selecting the appropriate combination of clouds for placement, it is important to look ahead and visualize the environment that will exist at the time a virtual network service is actually activated. This serves multiple purposes clouds can be selected to optimize the cost, the chosen performance parameters can be kept within the defined limits, and the speed of placement can be increased. In this paper, we propose the P-ART (Predictive-Adaptive Real Time) framework that relies on predictive-deductive features to achieve these objectives. With so much riding on predictions, we include in our framework a novel concept-drift compensation technique to make the predictions closer to reality by taking care of long-term traffic variations. At the same time, near real-time update of the prediction models takes care of sudden short-term variations. These predictions are then used by a new randomized placement heuristic that carries out a fast cloud selection using a least-cost latency-constrained policy. An empirical analysis carried out using datasets from a queuing-theoretic model and also through implementation on CloudLab, proves the effectiveness of the P-ART framework. The placement system works fast, placing thousands of functions in a sub-minute time frame with a high acceptance ratio, making it suitable for dynamic placement. We expect the framework to be an important step in making the deployment of carrier-grade VNS on multi-cloud systems, using network function virtualization (NFV), a reality.This publication was made possible by NPRP grant # 8-634-1-131 from the Qatar National Research Fund (a member of Qatar Foundation), National Science Foundation, USA � CNS-1718929 and National Science Foundation, USA � CNS-1547380 .Scopu

The P-ART framework for placement of virtual network services in a multi-cloud environment

Carriers’ network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If these virtual services are hosted dynamically over multiple clouds, greater flexibility in optimizing performance and cost can be achieved. On the flip side, when orchestrated over multiple clouds, the stringent performance norms for carrier services become difficult to meet, necessitating novel and innovative placement strategies. In selecting the appropriate combination of clouds for placement, it is important to look ahead and visualize the environment that will exist at the time a virtual network service is actually activated. This serves multiple purposes — clouds can be selected to optimize the cost, the chosen performance parameters can be kept within the defined limits, and the speed of placement can be increased. In this paper, we propose the P-ART (Predictive-Adaptive Real Time) framework that relies on predictive-deductive features to achieve these objectives. With so much riding on predictions, we include in our framework a novel concept-drift compensation technique to make the predictions closer to reality by taking care of long-term traffic variations. At the same time, near real-time update of the prediction models takes care of sudden short-term variations. These predictions are then used by a new randomized placement heuristic that carries out a fast cloud selection using a least-cost latency-constrained policy. An empirical analysis carried out using datasets from a queuing-theoretic model and also through implementation on CloudLab, proves the effectiveness of the P-ART framework. The placement system works fast, placing thousands of functions in a sub-minute time frame with a high acceptance ratio, making it suitable for dynamic placement. We expect the framework to be an important step in making the deployment of carrier-grade VNS on multi-cloud systems, using network function virtualization (NFV), a reality

Stock Value Prediction System

The use of artificial neural network is gaining popularity in the research field. Neural network consist of interconnected neurons which deciphers value by using input data by feeding network values. The main aim of our project is to use backpropagation process to predict the future value.Stock market prediction models are the most challenging fields in computer science. The aim of this project is implementation of neural networks with back propagation algorithm for stock value prediction .A neural network is a powerful data-modeling tool that is able to capture and represent complex input/output relationships. We apply Data mining technology to the stock in order to research the trend of the market. Our proposed system provides methods to develop machine learning stock market predictor based on Neural Networks using Back propagationalgorithm, with intent of improving the accuracy. In this paper we have used data mining process along with artificial neural network networking to predict the future value of the stock. This paper overcomes the all traditional statistical methods of the stock market value prediction. DOI: 10.17762/ijritcc2321-8169.16049