In this paper the experimentally obtained conditions for the fusion splicing with photonic crystal fibers (PCF) having large mode areas were reported. The physical mechanism of the splice loss and the microhole collapse property of photonic crystal fiber (PCF) were studied. By controlling the arc-power and the arc-time of a conventional electric arc fusion splicer (FSM-60S), the minimum loss of splicing for fusion two conventional single mode fibers (SMF-28) was (0.00dB), which has similar mode field diameter. For splicing PCF (LMA-10) with a conventional single mode fiber (SMF-28), the loss was increased due to the mode field mismatch.

A standard theoretical neutron energy flux distribution is achieved for the triton-triton nuclear fusion reaction in the range of triton energy about ≤10 MeV. This distribution give raises an evidence to provide the global calculations including the characteristics fusion parameters governing the T-T fusion reaction.

Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various

Patients are very concerned about the lengthy nature of orthodontic treatment. It is necessary to find a non-invasive way to quicken physiologic tooth movement. This study's objective was to assess the effectiveness of low-intensity laser therapy in shortening the time and discomfort of orthodontic treatment. Experimental work: Using a split-mouth study to compare tooth movement with conventional treatment and laser-accelerated orthodontic tooth movement. A patient presenting with a class II division I malocclusion characterized by the misalignment of the upper and lower teeth as classified by Angle’s molar classification system was indicated to undergo fixed orthodontic appliance orthodontic treatment. The treatment plan involved bila

Background: Pain is one of the most reported side effects of orthodontic treatment despite the advanced technology in orthodontics. Many analgesics have been introduced to control orthodontic pain including acetaminophen and selective and nonselective nonsteroidal anti-inflammatory drugs. The great concern about these drugs is their adverse effect on rate of teeth movement. Aims: The purpose of this study was to evaluate and compare the effect of acetaminophen, ibuprofen and etoricoxib on pain perception and their influence on the rate of teeth movement during leveling and alignment stage. Methods: Forty patients were evenly and randomly distributed in a blinded way to one of four groups: placebo (starch capsules), acetaminophen 500mg th

Photoplethysmography (PPG) is a non-invasive optical technique that employs variations in light absorption produced by alteration in the blood volume in capillaries at the skin during the cardiac cycle. This study aims to understand factors related to PPG morphology; a hand-elevation, the study has modified blood flow to and from the finger was conducted in the laboratory. It is widely established that the position of the limb relative to the heart has an effect on blood flow in arteries and venous. Peripheral digital pulse wave (DPW) signals were obtained from 15 healthy volunteer participants during hand-elevation, and hand-lowering techniques wherein the right hand was lifted and lowered relative to heart level, while the left h

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.