Comparison between the artificial neural network system and SAFT equation in obtaining vapor pressure and liquid density of pure alcohols

Vapor pressure and liquid density of 20 pure alcohols were correlated using an artificial neural network (ANN) system and statistical associating fluid theory (SAFT) equation of state. The SAFT equation has five adjustable parameters as temperature-independent segment diameter, square-well energy, number of segment per chain, association energy and association volume. These parameters can be obtained by a non-linear regression method using the experimental vapor pressure and liquid density data. In continue, the vapor pressure and liquid densities of pure alcohols were estimated by using an artificial neural network (ANN) system. In the neural network system, it is assumed that thermodynamic properties of pure alcohols depend on temperature, critical properties and acentric factor. The best network topology was obtained as (4-10-2). The weights connection and biases were obtained using batch back propagation (BBP) method for 611 experimental data points. The average absolute deviation percent (ADD%) for vapor pressure of pure alcohols for ANN system and SAFT equation of state are 3.593% and 3.378%, respectively. Also, the average absolute deviation percent (ADD%) for liquid density of pure alcohols for ANN system and SAFT equation of state are 0.792% and 1.367%, respectively. The results emphasized that the artificial neural network can more accurately predict thermophysical properties of pure alcohols than the SAFT equation of state.     

Leveraging business-IT alignment through enterprise architecture—an empirical study to estimate the extents

Achieving business-IT alignment (BITA) as a long-term and appraising management issue can be accomplished in a few ways, enterprise architecture (EA) being one of them. This paper attempts to give a critical understanding of the effects of performing EA on different aspects of BITA maturity through a global survey. A total of 236 respondents from 60 countries, a relatively large response for a survey, were selected. The main purpose of the research is to examine these impacts and to identify directions for innovative practices in the future, the unique contributions of this work. A questionnaire designed on the Luftman’s maturity model as well as various other statistical methods, including PLS path modeling, Wilcoxon matched-pairs signed-ranks test and Mann–Whitney U test, are applied to understand how the EA can deliver benefits. The implications of our findings in this study as well as its limitations are discussed from different viewpoints to enable both academics and practitioners to detect the flaws in the existing EA frameworks and propose improvements.     

Enabling the new economic actor: data protection,the digital economy,and the Databox

This paper offers a sociological perspective on data protection regulation and its relevance to design. From this perspective, proposed regulation in Europe and the USA seeks to create a new economic actor—the consumer as personal data trader—through new legal frameworks that shift the locus of agency and control in data processing towards the individual consumer or “data subject”. The sociological perspective on proposed data regulation recognises the reflexive relationship between law and the social order, and the commensurate needs to balance the demand for compliance with the design of computational tools that enable this new economic actor. We present the Databox model as a means of providing data protection and allowing the individual to exploit personal data to become an active player in the emerging data economy.     

A subjective framework for seat comfort based on a heuristic multi criteria decision making technique and anthropometry

Consumer expectations for automobile seat comfort continue to rise. With this said, it is evident that the current automobile seat comfort development process, which is only sporadically successful, needs to change. In this context, there has been growing recognition of the need for establishing theoretical and methodological automobile seat comfort. On the other hand, seat producer need to know the costumer’s required comfort to produce based on their interests. The current research methodologies apply qualitative approaches due to anthropometric specifications. The most significant weakness of these approaches is the inexact extracted inferences. Despite the qualitative nature of the consumer’s preferences there are some methods to transform the qualitative parameters into numerical value which could help seat producer to improve or enhance their products. Nonetheless this approach would help the automobile manufacturer to provide their seats from the best producer regarding to the consumers idea. In this paper, a heuristic multi criteria decision making technique is applied to make consumers preferences in the numeric value. This Technique is combination of Analytical Hierarchy Procedure (AHP), Entropy method, and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A case study is conducted to illustrate the applicability and the effectiveness of the proposed heuristic approach.     

Deep feature learning versus shallow feature learning systems for joint use of airborne thermal hyperspectral and visible remote sensing data

Recently, the development of various remote sensing sensors has provided more reliable information and data for identification of different ground classes. Accordingly, multisensory fusion techniques are applied to enhance the process of information extraction from complementary airborne and spaceborne remote sensing data. Most of previous research in the literature has focused on the extraction of shallow features from a specific sensor and on classification of the resulted feature space using decision fusion systems. In recent years, Deep Learning (DL) algorithms have drawn a lot of attention in the machine learning area and have had different remote sensing applications, especially on data fusion. This study presents two different feature-learning strategies for the fusion of hyperspectral thermal infrared (HTIR) and visible remote sensing data. First, a Deep Convolutional Neural Network (DCNN)-Support Vector Machine (SVM) was utilized on the features of two datasets to provide the class labels. To validate the results with other learning strategies, a shallow feature model was used, as well. This model was based on feature fusion and decision fusion that classified and fused the two datasets. A co-registered thermal infrared hyperspectral (HTIR) and Fine Resolution Visible (Vis) RGB imagery was available from Quebec of Canada to examine the effectiveness of the proposed method. Experimental results showed that, except for the computational time, the proposed deep learning model outperformed shallow feature-based strategies in the classification performance that was based on its accuracy.     

A novel hybrid algorithm for scheduling steel-making continuous casting production

In this paper, steel-making continuous casting (SCC) scheduling problem (SCCSP) is investigated. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making. Since classic optimization methods fail to obtain an optimal solution for this problem over a suitable time, a novel iterative algorithm is developed. The proposed algorithm, named HANO, is based on a combination of ant colony optimization (ACO) and non-linear optimization methods.     

Worst case dimensioning and modeling of reliable real-time multihop wireless sensor network

Wireless Sensor Network (WSN) should be capable of fulfilling its mission, in a timely manner and without loss of important information. In this paper, we propose a new analytical model for calculating RRT (Reliable Real-Time) degree in multihop WSNs, where RRT degree describes the percentage of real-time data that the network can reliably deliver on time from any source to its destination. Also, packet loss probability is modeled as a function of the probability of link failure when the buffer is full and the probability of node failure when node’s energy is depleted. Most of the network properties are considered as random variables and a queuing theory based model is derived. In this model, the effect of network load on the packets’ delay, RRT degree, and node’s energy depletion rate are considered. Also network calculus is tailored and extended so that a worst case analysis of the delay and queue quantities in sensor networks is possible. Simulation results are used to validate the proposed model. The simulation results agree very well with the model.     

A robust design of an innovative shaped rebar system using a novel uncertainty model

The current paper has investigated a newly developed re-bar system by implementing uncertainty models to optimise its geometry. The study of the design parameters of this re-bar system has been carried out utilising a novel uncertainty model that has been developed at Swansea University. The importance of this invention comes from the fact that the whole process of optimisation has been automated by linking ANSYS Workbench to MATLAB via the in-house written code, Despite the fact that in the past, ANSYS APDL was linked to MATLAB, however, the APDL was very limited to only simple geometries and boundary conditions unlike the Workbench which can simulate complex features. These shortfalls have been overcome by automating the process of optimisation, identifying the key influential parameters and the possibility to carry out a huge number of trials. Moreover, the tools that have been developed can pave the way for robust optimisation of this proposed structure. The uncertainty in the design parameters of this re-bar system is of a paramount importance in order to optimise the bond strength between the newly developed rebar and the concrete matrix as well as to fully understand the behaviour of the proposed system under pull-out conditions. The interface between the rebar and the concrete matrix was considered as a ‘cohesive zone’ whereby the interfacial area is studied as a function of the bonding strength.     

Thermal quantum correlation and entanglement in the Bose–Hubbard Hamiltonian

We study a two-qutrit system which is described by the Bose–Hubbard Hamiltonian with two external magnetic fields. The entanglement (through the negativity) and quantum correlation (through the geometric discord) between the qutrits are calculated as functions of the magnetic field (B), the temperature (T), the linear and nonlinear coupling constants among two spins (J and K). Then, we compare the effect of these parameters on entanglement and quantum correlation of this system. For some values of system parameters, we show that the negativity is zero while, the geometric discord is nonzero. Moreover, we investigate the effect of finite external magnetic fields direction on these measures. This study leads to some new and interesting results as well.     

Experimental and computational study of gas bubble removal in a microfluidic system using nanofibrous membranes

This paper presents a simple and efficient method for removing gas bubbles from a microfluidic system. This bubble removal system uses a T-junction configuration to generate gas bubbles within a water-filled microchannel. The generated bubbles are then transported to a bubble removal region and vented through a hydrophobic nanofibrous membrane. Four different hydrophobic Polytetrafluorethylene membranes with different pore sizes ranging from 0.45 to 3 μm are tested to study the effect of membrane structure on the system performance. The fluidic channel width is 500 μm and channel height ranges from 100 to 300 μm. Additionally, a 3D computational fluid dynamics model is developed to simulate the bubble generation and its removal from a microfluidic system. Computational results are found to be in a good agreement with the experimental data. The effects of various geometrical and flow parameters on bubble removal capability of the system are studied. Furthermore, gas–liquid two-phase flow behaviors for both the complete and partial bubble removal cases are thoroughly investigated. The results indicate that the gas bubble removal rate increases with increasing the pore size and channel height but decreases with increasing the liquid flow rate.