Date palm by-products as a new ingredient for the meat industry: Application to pork liver pâté

Date palm is an interesting source of bioactive compounds that could be used as ingredient in meat products; thus a campagne type pork liver pâté was elaborated using fresh date by-products (0, 5, 10 and 15%). Physicochemical properties, pigment and lipid oxidation, residual nitrite level, texture and sensory analysis during 4 days after the elaboration process were evaluated. Date paste mostly incorporated moisture, fibre and phenolic compounds. Physicochemical parameters, myoglobin content and heme iron stability were not severely affected. Addition of 10% of date was enough to avoid lipid oxidation through all storage times. Colour was the most affected parameter; however, in terms of overall acceptability, panellists preferred samples with added date.     

Thermodynamic and economic analysis of the performance of a direct evaporative cooler working under extreme summer weather conditions

Journal of Mechanical Science and Technology - Riyadh is a desert region characterized by large daily and seasonal ambient temperature variations. Air cooling using mechanical vapor compression...     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

Micro-PIV measurements of flows induced by rotating microparticles near a boundary

We report on the hydrodynamics induced by single-digit micron-sized superparamagnetic particles rotating at low Reynolds number and analyze the resultant flow fields using microparticle image velocimetry (µPIV). Magnetic microparticles floating a few nanometers above a glass substrate, in an otherwise quiescent fluid, were actuated wirelessly using a rotating magnetic field controlled using two pairs of orthogonally positioned electromagnetic coils. A high-speed camera was used to sufficiently capture the motion of nanometer-sized seeding particles at 500 frames per second as well as track the rotation of microparticles. Data from µPIV are compared with the analytical solution for Stokes flow generated by a sphere in an infinite fluid and numerical simulations using finite element analysis. Two-dimensional velocity data obtained from stacks of planar flow fields at incremental depths for individual microparticles show non-symmetrical profiles that are an indication of increased viscous effects due to the boundary confining wall. Additionally, the flow fields generated by two particles, at various separation distances, are also analyzed. It is observed that as two synchronously rotating beads, of approximately equal diameter, are placed closed together, complex flows offset, superimpose, and merge into single, larger microvortices. We find that the flow fields generated by two physically bound microparticles, rotating as one unit, are well approximated by the flow generated by a single microparticle with twice the diameter.     

Influence of the sintering temperature on Young's modulus and the shear modulus of tricalcium phosphate – fluorapatite composites evaluated by ultrasound techniques

The mechanical properties of the tricalcium phosphate sintered between 1100 °C and 1450 °C for 1 h with different percentages of fluorapatite (13.26 wt%; 19.9 wt%; 26.52 wt%; 33.16 wt% and 40 wt%) have been characterized and evaluated using the ultrasound techniques. Young's modulus and the shear modulus were calculated from the point of the longitudinal and the transversal ultrasonic velocities. Young's modulus and the shear modulus of tricalcium phosphate increased with the sintering temperature and with the addition of the fluorapatite additive into the tricalcium phosphate matrix. At 1300 °C, the shear modulus and Young's modulus of the tricalcium phosphate – 40 wt% fluorapatite composites registered optimum values: 26 GPa and 66.2 GPa, respectively. Above 1300 °C, the mechanical properties of the tricalcium phosphate – fluorapatite composites were hindered by the tricalcium phosphate allotropic transformation and the formation of both the intragranular porosity and the cracks.     

Temperature compensated CMOS ring VCO for MEMS gas sensor

This paper presents a CMOS voltage controlled ring oscillator with temperature compensation circuit suitable for low-cost and low-power gas sensor. To operate at low frequency, a control voltage generated by a CMOS bandgap reference is described and the measurement results of the fabricated chips are presented. The output voltage of the reference is set by resistive subdivision. In order to achieve small area and low power consumption, n-well resistors are used. This design features a reference voltage of 1 V. The chip is fabricated in AMS 0.35 μm CMOS process with an area of 0.032 mm². Operating at 1.25 V, the output frequency is within 200 ± l0 kHz over the temperature range of ?25 to 80 °C with a power consumption of 810 μW.     

Novel integrations of molten salt cavity tubular solar central receiver with existing gas-fuelled conventional steam power plants

This paper introduces a new method to integrate the existing equipment of the AL-Hartha steam plant located in Basra, Iraq, using a molten salt cavity tubular solar central receiver (SCR). Cycle Tempo is used to simulate the existing natural gas-fuelled conventional steam power cycle with consideration of the heat and pressure losses. The heliostat field and the central receiver subsystems are coded using MATLAB. The model couples the heat balance with the temperature computation of the receiver walls for calculation and analysis of the thermal losses. The proposed modified codes are capable of calculating heat losses, evaluating the integrated power plant and satisfying a wide range of SCRs. The results are verified against plant data and previous works in the literature and good agreement is obtained. The results show the potential of using a molten salt cavity tubular for low-range temperature to integrate the economizer (EN) and air preheater, as well as the optimum scheme for the integration of the existing plant with an SCR. It is observed that the best improvement for the existing AL-Hartha steam plant and the integrated molten salt cavity tubular SCR can be achieved by integrating EN, and there is about 9.1% saving in gas fuel consumption.     

Phase distribution in dispersed liquid–liquid flow in vertical pipe: Mean and turbulent contributions of interfacial force

We applied an Eulerian–Eulerian two‐fluid model on an upward dispersed oil–water flow in vertical pipe with 80 mm diameter and 2.5 m length. The numerical profiles of the radial distribution of the oil drops at 1.5 m from the inflow are compared to the experimental data of Lucas and Panagiotopoulos (Flow Meas Instrum. 2009;20:127–135) This article analyzes the roles of turbulence and interfacial forces on the phase distribution phenomenon. In liquid–liquid flow the relative velocity is low and the distribution of the dispersed phase is mainly governed by the turbulence. This work highlights the important role of the turbulent contribution obtained by averaging the added mass force on the radial distribution profiles of the oil drops. The numerical results present improved profiles of the dispersed phase comparing to the experimental data when this turbulent contribution is taken into account in the momentum balance. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4214–4223, 2017     

Human Activity Recognition Based on Parallel Approximation Kernel K-Means Algorithm

Recently, owing to the capability of mobile and wearable devices to sense daily human activity, human activity recognition (HAR) datasets have become a large-scale data resource. Due to the heterogeneity and nonlinearly separable nature of the data recorded by these sensors, the datasets generated require special techniques to accurately predict human activity and mitigate the considerable heterogeneity. Consequently, classic clustering algorithms do not work well with these data. Hence, kernelization, which converts the data into a new feature vector representation, is performed on nonlinearly separable data. This study aims to present a robust method to perform HAR data clustering to mitigate heterogeneity in data with minimal resource consumption. Therefore, we propose a parallel approximated clustering approach to handle the computational cost of big data by addressing noise, heterogeneity, and nonlinearity in data using data reduction, filtering, and approximated clustering methods on parallel computing environments that have not been previously addressed. Our key contribution is to treat HAR as big data implemented by approximation kernel K-means approaches and fill the gap between the HAR clustering cost and parallel computing fields. We implemented our approach on Google cloud on a parallel spark cluster, which helped us to process large-scale HAR data across multiple machines of clusters. The normalized mutual information is used as validation metric to assess the quality of the clustering algorithm. Additionally, the precision, recall, f-score metrics values are obtained somehow to compare the results with a classification technique. The experimental results of our clustering approach prove its effectiveness compared with a classification technique and can efficiently detect physical activity and mitigate the heterogeneity of the datasets.