Design and Analysis of Fiber Bragg Grating Sensor to Monitor Strain and Temperature for Structural Health Monitoring

In this paper, we have proposed a bragg grating based sensor to monitor health of civil structures at distinct temperatures. We have considered increased number of gratings with suitable refractive index to enhance sensitivity of fiber bragg grating sensor. Analysis of Bragg wavelength with respect to load and temperature is successfully studied. The simulation results reveal that when independently strain (50 units per simulation) and temperature (25 ℃) are increased uniformly, a linear shift in Bragg wavelength 0.064 nm and 0.347 nm is observed, respectively. Similarly, when both strain and temperature are increased (ε = 50 & T= 25 ℃) concurrently, a directly proportional relation is found in bragg wavelength (0.403 nm). The results verify the enhanced performance of as-proposed sensor, employing it could be potentially used in civil, bio-medical and military domains.

Is Sitting Always Inactive and Standing Always Active? A Simultaneous Free-Living activPal and ActiGraph Analysis

Sedentary Behavior (SB), defined as sitting with minimal physical activity, is an emergent public health topic. However, the measurement of SB considers either posture (e.g., activPal) or physical activity (e.g., ActiGraph), and thus neglects either active sitting or inactive standing. The aim of this study was to determine the true amount of active sitting and inactive standing in daily life, and to analyze by how much these behaviors falsify the single sensors’ sedentary estimates. Sedentary time of 100 office workers estimated with activPal and ActiGraph was therefore compared with Bland-Altman statistics to a combined sensor analysis, the posture and physical activity index (POPAI). POPAI classified each activPal sitting and standing event into inactive or active using the ActiGraph counts. Participants spent 45.0% [32.2%–59.1%] of the waking hours inactive sitting (equal to SB), 13.7% [7.8%–21.6%] active sitting, and 12.0% [5.7%–24.1%] inactive standing (mean [5th–95th percentile]). The activPal overestimated sedentary time by 30.3% [12.3%–48.4%] and the ActiGraph by 22.5% [3.2%–41.8%] (bias [95% limit-of-agreement]). The results showed that sitting is not always inactive, and standing is not always active. Caution should therefore be paid when interpreting the activPal (ignoring active sitting) and ActiGraph (ignoring inactive standing) measured time as SB.

Measurement of Kinetic Friction Coefficient through Magnetic Induction Using Audacity Application in Physics Learning

This study is designed to produce an experimental device for the measurement of the kinetic friction coefficient of material through magnetic induction using Audacity application. The objects of material the kinetic friction coefficient of which is going to be measured is rough acrylic with rough acrylic and wood with rough acrylic. The experimental setup was performed on the motion system of the mass of objects <em>m</em>₁ and <em>m</em>₂ that were connected through a pulley on a table and three solenoids are put under a table as the Electromagnetic Field (EMF) sensor. Analysis of the calculation of kinetic friction coefficient used two concepts, i.e. the kinematics concept to identify the load acceleration and the concept of the dynamic to find the kinetic friction coefficient. The results of the measurement of kinetic friction coefficient in this study have a match with the results obtained in the other studies. Hence, the tools and experimental approaches generated in this study are excellent for use in the learning activities so that teachers can teach physics applicatively.