A flexible hair-like laser induced graphitic sensor for low flow rate sensing applications

Direct low flow sensing is of interest to many applications in medical and biochemical industries. Low flow rate measurement is still challenging, and conventional flow sensors such as hot films, hot wires and Pitot probes are not capable of measuring very low flow rates accurately. In some applications that require flow measurement in a small diameter tubing (e.g. intravenous (IV) infusion), using such sensors also becomes mechanically impractical. Herein, a flexible laser-induced graphitic (LIG) piezoresistive flow sensor has been fabricated in a cost-effective single processing step. The capability of the LIG sensor in very low flow rate measurement has been investigated by embedding the sensor within an intravenous (IV) line. The embedded LIG hair-like sensor was tested at ambient temperature within the IV line at flow rates ranging from 0 m/s to 0.3 m/s (IV infusion free-flow rate). The LIG hair-like sensor presented in this study detects live flow rates of IV infusions with a threshold detection limit as low as 0.02 m/s. Moreover, the deformation of the LIG hair-like sensor that lead to resistance change in response to various flow rates is simulated using COMSOL Multiphysics

MEMS piezoresistive flow sensors for sleep apnea therapy

© 2018 Elsevier B.V. A MEMS liquid crystal polymer (LCP), used in the membrane-based pressure sensor, has been found highly useful as a flow sensor. Here we conducted a set of elaborate experiments using an air flow generator to investigate the potential of our LCP flow sensor for sleep apnea therapy. Critical properties of the LCP flow sensor, including flow range, resolution (sensitivity), accuracy, and response time, have been systematically characterized. As a result, LCP flow sensor achieves a limit of detection of 8 LPM to measure flow rate, better than the commercial flow sensor (>10 LPM). Our LCP flow sensor shows a favourable response in a large flow range (8–160 LPM) with a sensitivity of detecting a linear voltage response of 0.004 V per 1 LPM flow rate. With minimum detectable flow, high sensitivity and resolution, we further demonstrated our LCP flow sensor for detecting human respiration. Moreover, using a two- dimensional simulation in COMSOL Multiphysics, we demonstrated the deformation of LCP membrane in response to different flow velocities which leads to resistance change in sensor's strain gauge

Post traumatic fibrous dysplasia of ribs at the chest tube site (a case report)

Fibrous Dysplasia (FD) is a benign non-inherited and rare skeletal disorder that can affect any bone of human body. FD is caused by a mutation in GNAS1 gene, but the mutation is not inherited from parents. While there are some reports of post-traumatic fibrous dysplasia, but the relationship between fibrous dysplasia and trauma or previous injury is unclear. Most of the time, fibrous dysplasia is diagnosed by accident in x-ray imaging. While there is much information on fibrous dysplasia, the main etiology is still unknown, however it seems to be linked to a gene mutation.A mutation in this gene affects differentiation and proliferation of cells. This benign fibro-osseous lesion is a result of disturbances of normal bone metabolism by a mutation of the alpha subunit of guanine nucleotide binding protein alpha stimulating. Here we report a case with two adjacent ribs involvement in a 25-year old man who admitted to our hospital with an acute right chest pain and swelling with history of major trauma in a motor vehicle accident 4 years ago with associated hemo-pneumothorax resulted in thoracostomy tube insertion and with subcutaneous mass was detected in the right chest wall which was located exactly at the thoracostomy tube insertion site. © 2019, Indian Journal of Forensic Medicine and Toxicology. All rights reserved

Post traumatic fibrous dysplasia of ribs at the chest tube site (a case report)

Fibrous Dysplasia (FD) is a benign non-inherited and rare skeletal disorder that can affect any bone of human body. FD is caused by a mutation in GNAS1 gene, but the mutation is not inherited from parents. While there are some reports of post-traumatic fibrous dysplasia, but the relationship between fibrous dysplasia and trauma or previous injury is unclear. Most of the time, fibrous dysplasia is diagnosed by accident in x-ray imaging. While there is much information on fibrous dysplasia, the main etiology is still unknown, however it seems to be linked to a gene mutation.A mutation in this gene affects differentiation and proliferation of cells. This benign fibro-osseous lesion is a result of disturbances of normal bone metabolism by a mutation of the alpha subunit of guanine nucleotide binding protein alpha stimulating. Here we report a case with two adjacent ribs involvement in a 25-year old man who admitted to our hospital with an acute right chest pain and swelling with history of major trauma in a motor vehicle accident 4 years ago with associated hemo-pneumothorax resulted in thoracostomy tube insertion and with subcutaneous mass was detected in the right chest wall which was located exactly at the thoracostomy tube insertion site. © 2019, Indian Journal of Forensic Medicine and Toxicology. All rights reserved

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria

Wind turbine power output prediction using a new hybrid neuro-evolutionary method

Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we deployed a novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, which is developed in three stages. At the beginning stage (pre-processing), the k-means clustering method and an autoencoder are employed to detect and filter noise in the SCADA measurements. In the Next step (decomposition), in order to decompose the SCADA time-series data, we proposed a new hybrid variational mode decomposition (HVMD) method, that consists of VMD and two heuristics: greedy Nelder-Mead search algorithm (GNM) and adaptive randomised local search (ARLS). Both heuristics are applied to tune the hyper-parameters of VMD that results in improving the performance of the forecasting model. In the third phase, based on prior knowledge that the underlying wind patterns are highly non-linear and diverse, we proposed a novel alternating optimisation algorithm that consists of self-adaptive differential evolution (SaDE) algorithm and sine cosine optimisation method as a hyper-parameter optimizer and then combine with a recurrent neural network (RNN) called Long Short-term memory (LSTM). This framework allows us to model the power curve of a wind turbine on a farm. A historical dataset from supervisory control and data acquisition (SCADA) systems were applied as input to estimate the power output from an onshore wind farm in Sweden. Two short time forecasting horizons, including 10 min ahead and 1 h ahead, are considered in our experiments. The achieved prediction results supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models applied in this paper