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The researchers wanted to see whether ozonated water with ultrasonication and sodium hypochlorite can destroyA total of 40 single-rooted human teeth were used. A total of 100 roots were harvested and mechanically prepared. The root canals were randomly divided into four classes (NaOCl and aqueous ozone provide antibacterial effects in

The major demand need of wireless communication are less weight, optimization in cost and antennas with Small height that are adequate of obtaining high efficient performance over a frequencies of wide range. Micro-strip patch antennas are widely used for the range of 2 to 4 GHz (S-band) frequencies. Antenna design and simulation was done using HFSS (High Frequency Structural Simulator). Generally these antennas uses different feeding techniques like Micro Strip Line, co-axial and proximity, aperture coupled feeds. The proposed reconfigurable rectangular microstrip patch antenna has been tested using Co-axial, Aperture coupled Feed Techniques to improve the antenna parameters like Frequency and VSWR, Return Loss.

In ultrasound images, speckle noise emerges as the major cause, which has the tendency to limit the contrast resolution. This affects the detection of low contrast and small lesions. To improve clinical diagnosis, speckle reduction filters are designed to enhance the visual quality as well as preserve important features in ultrasound images. Ultrasound imaging is mainly utilized for the detection of thyroid diseases. Several despeckling filters have been proposed for the reduction of speckle noise in ultrasound images. Despecking filters are based on statistics filtering, transform domain filtering, fuzzy logic filtering and partial differential equation based filtering. This paper presents an exhaustive search in literature on the existing methods for speckle reduction in ultrasound images.

The main aim is to secure the medical records by ensuring safe communication between the sender and the receiver by adding multiple layers of security. This project is a combined approach of both encryption and steganography to give security the utmost importance. In the sender, only the authorized person can send the secret image to the receiver. In the first stage, an image steganography system is processed using confidential medical X-ray images which are hidden under the cover images. The encryption method used is based on Random Number Generator and the pixel indicator, in other words, by moving the position of the pixel in the image to encrypt the image on the transmitting side. Whereas on the receiver side, this project proposes a method to authenticate the receiver by incorporating Email Authentication and OTP Verification to decrypt the encrypted image.

The appearance, deployment, as well as the evaluation of a computing device for the Fleet Management Solution including trucks that transport fuel are presented in this article. The key aim is to build a framework that can track the backlog instantaneously. The approach are imagined any road tankers that transport petrol transport End customers, such as service stations, get petrol in oil warehouses. However, as code, this technique is reliant on hardware. Shipment electronics, Diy on-board, Wireless communications, and Gp modules are all included. make up the hardware version 0.5. whereas LabView is used for data functions in PC code 0.5. This approach proposes the use of an Automatic Electronic System (AES) to replace manual fuel monitoring. This system tests fuel volume and sends the details to the owner’s cell phone as well as the owner’s device through the GSM network. It also offers a tool for identifying stealing or fraud incidents in the event of gasoline robbery or discharge. Furthermore, the device will monitor the activities every carrier transporting energy in order to determine the location of any incidents Which should have occurred.. Thanks to inadequate and easy-to-acquire electronic elements, this approach allows for automated backlog tracking and tracking at a lower cost. GSM, GPS, AES, and ARDUNIO are some of the terminology used in this article.

In today’s world more death occurs due to not immediate treatment for bed ridden patients. It also requires more man power. This document presents the model, installation and looks of a hardware and software platform for health monitoring system for patients. The main purpose of this system is to set up a system that can watch over bedridden patient’s health. This project depends on both hardware and software. It is developed using sp02, temperature, heartbeat connected to a raspberry pi micro controller, which get the sensor values and process it in machine learning process to produce a dosage prediction. It also intimates the doctor by triggering an automatic email. In addition, one can know the patients records and prediction of dosage from anywhere using the mobile application linked with it.

Study objective Online educational resources such as blogs are increasingly used for education by emergency medicine clinicians. The Social Media Index was developed to quantify their relative impact. The Medical Education Translational Resources: Indicators of Quality (METRIQ) study was conducted in part to determine the association between the Social Media Index score and quality as measured by gestalt and previously derived quality instruments. Methods Ten blogs were randomly selected from a list of emergency medicine and critical care Web sites. The 2 most recent clinically oriented blog posts published on these blogs were evaluated with gestalt, the Academic Life in Emergency Medicine Approved Instructional Resources (ALiEM AIR) score, and the METRIQ-8 score. Volunteer raters (including medical students, emergency medicine residents, and emergency medicine attending physicians) were identified with a multimodal recruitment methodology. The Social Media Index was calculated in February 2016, November 2016, April 2017, and December 2017. Pearson's correlations were calculated between the Social Media Index and the average rater gestalt, ALiEM AIR score, and METRIQ-8 score. Results A total of 309 of 330 raters completed all ratings (93.6%). The Social Media Index correlated moderately to strongly with the mean rater gestalt ratings (range 0.69 to 0.76) and moderately with the mean rater ALiEM AIR score (range 0.55 to 0.61) and METRIQ-8 score (range 0.53 to 0.57) during the month of the blog post's selection and for 2 years after. Conclusion The Social Media Index's correlation with multiple quality evaluation instruments over time supports the hypothesis that it is associated with overall Web site quality. It can play a role in guiding individuals to high-quality resources that can be reviewed with critical appraisal techniques.

Despite the ability of Artificial Neural Network (ANN) to handle nonlinear relationships in data, there are instances where ANNs have not been able to predict accurately in the presence of extremes values or other inherent groupings in the data. Although the ANN modeling expects the data to be evenly distributed over an entire data space, in practical situations data often consist of clusters or extreme values. Thus, instead of modeling the data as it is, appropriate mechanisms should be followed to handle those inconsistancies. This paper presents one such mechanism based on two clustering algorithms, k-means and fuzzy c-means. The base model is Nonlinear Autoregressive Artificial Neural Network (NAR-ANN). Altogether 14 model formulations of NAR-ANN were compared here with varying number of clusters and a trimming mechanism. Results suggest the superiority of cluster based NAR-ANN over the single NAR-ANN. Modular ANN approach with an optimum combination of the 14 models can be used for better results. The proposed cluster based NAR-ANN used in this paper is a novel generalization to NAR-ANN where the cluster effect is incorporated as a binary exogenous variable (NARX).