Stability of electronic nose (e-nose) as determined by considering date-pits heated at different temperatures

Variability and sensitivity of a portable electronic nose (32 sensors) was assessed by considering different variables for measurement (i.e., reference, standard, first and second purges, sample draw time, waiting time for the volatiles released in the headspace, and mass of sample or headspace volume of the jar containing sample). In this study, dried date-pits were used a model sample. The highest stability was achieved when both reference and standard (i.e., dried date-pits, no heating treatment) were used before test sample measurement. Higher sample draw time more than 10 s significantly decreased the stability, whereas optimum second purge was observed at 50 s. Optimum time to generate volatile was observed as 24 h. A sample of 100 g increased the signal intensity compared to the 50 g sample for the 60°C and 100°C treated samples, while an opposite trend was observed for the 150°C treated sample. Finally, the responses of volatile components in date-pits heated at different temperatures (60°C, 100°C, and 150°C) were measured using the optimum operating conditions. Principal component analysis explored the relationships between the volatile features and classified date-pits heated at different temperatures. The results showed that an electronic nose was able to classify date-pits based on their volatile components generated by different degrees of heating (93.3% accuracy).     

Optimize adaptive media playout using dynamic fuzzy logic control for video streaming

Multimedia Tools and Applications - Adaptive Media Playout (AMP) controls adapt playout rate to prevent buffer outage and to reduce delay in playout. Most AMP techniques use buffer fullness or its...     

A novel compound fast fractional integral sliding mode control and adaptive PI control of a MEMS gyroscope

Microsystem Technologies - This study considers a novel compound fast fractional integral sliding mode control and adaptive PI control (APIFFOISMC) of a MEMS gyroscope. MEMS gyroscope has been...     

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Machine Learning - Covariance estimation for high-dimensional datasets is a fundamental problem in machine learning, and has numerous applications. In these high-dimensional settings the number of...     

Efficient 3D point clouds classification for face detection using linear programming and data mining

Most of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.     

Ergonomic mismatch between students anthropometry and university classroom furniture

University students spend most of their time in classrooms, mostly in a sitting position. Prolonged sitting on ill-fitted furniture and the resulting bad posture is making students suffer from different musculoskeletal disorders (MSDs). This study evaluates the potential mismatch between furniture dimensions and anthropometric measurements. To determine potential mismatch, 13 anthropometric measurements of 550 students and 11 dimensions of two types of classroom furniture (mounted-desktop and chair-with-table) were measured and then compared. Additionally, chi-square test was performed to compare the relation between anthropometry and relevant furniture dimensions. Results showed that a significant number of mismatches emerged between anthropometric measurements and furniture dimensions. For both types of furniture, seat height was too high and seat depth was too deep. Moreover, for mounted desktop, desk height is almost appropriate for all students whereas for chair and table, it was too high for the males. These circumstances may lead to increase discomfort and increase MSDs problems among all students. It can be concluded that the dimensions of both types of classroom furniture were not appropriate for users according to anthropometric measurements. This analysis recommends the measurements of furniture dimensions based on participants’ anthropometric measurements to avoid or minimise discomfort and MSDs problems.     

Increasing fermentable sugar yields by high‐pressure treatment during beer mashing

High‐pressure treatment, which is an effective means of enhancing enzymatic reactions, was implemented during beer mashing to increase the production yield of fermentable sugar (FS). The malt solution was heated (62, 67, and 72°C) under pressure [0.1 (1 atm), 2, 50 and 100 MPa], and the FS was measured. The amount of FS reached an equilibrium level, which was the highest at 67°C and at 2 MPa. The pressures were 2, 50, 100, and 0.1 MPa in decreasing order of FS amount at 67°C. The temperatures were 67, 72 and 62°C in decreasing order of FS amount at 2 MPa. With a mechanistic approach, only the effect of pressure on gelatinization was analysed. The gelatinization degrees were also higher at pressures higher than 0.1 MPa. This observation highlights the positive effect (increasing the FS yield) of high‐pressure treatment on beer mashing. Copyright © 2015 The Institute of Brewing & Distilling