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.     

Disturbance rejection in formation keeping control of nonholonomic wheeled robots

This paper presents the results of formation keeping control of a group of nonholonomic wheeled robots within the port‐Hamiltonian framework and in the presence of matched input disturbances. Two scenarios on the internal damping of the dynamics of the robots are considered: strictly output passive and loss less robots. For strictly output passive robots, the distributed formation keeping controllers drive the robots towards a desired formation, while internal‐model‐based controllers locally compensate the harmonic input disturbance for each of the robots. Moreover, the effect of constant input disturbances is studied considering internal‐model‐based controllers. For lossless robots, results on formation keeping control are presented. Simulation results illustrate the effectiveness of the approach. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Solving fully fuzzy polynomials using feed-back neural networks

Recently, there has been a considerable amount of interest and practice in solving many problems of several applied fields by fuzzy polynomials. In this paper, we have designed an artificial fuzzified feed-back neural network. With this design, we are able to find a solution of fully fuzzy polynomial with degree n. This neural network can get a fuzzy vector as an input, and calculates its corresponding fuzzy output. It is clear that the input–output relation for each unit of fuzzy neural network is defined by the extension principle of Zadeh. In this work, a cost function is also defined for the level sets of fuzzy output and fuzzy target. Next a learning algorithm based on the gradient descent method will be defined that can adjust the fuzzy connection weights. Finally, our approach is illustrated by computer simulations on numerical examples. It is worthwhile to mention that application of this method in fluid mechanics has been shown by an example.     

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.     

Wave propagation simulation in an electrically open shell reinforced with multi-phase nanocomposites

Wave propagation simulation in a multi-hybrid nanocomposite (MHC)-reinforced doubly curved open shell covered with piezoelectric actuator is examined for the first time. The third-order shear deformation theory (third-order SDT) is applied to formulate the stress–strain relations. Rule of the mixture and modified Halpin–Tsai model are engaged to provide the effective material constants of the MHC-reinforced open shell. By employing Hamilton’s principle, the governing equations of the structure are derived. Via the compatibility rule, the bonding between the smart layer and sandwich open shell is modeled. Also, with the aid of Maxwell's equation, the mechanics of the piezoelectric layer are formulated. Afterward, a parametric study is carried out to investigate the effects of the CNTs’ weight fraction, various FG face sheet patterns, small radius to total thickness ratio, the thickness of the smart layer, externally applied voltage, and carbon fiber angle on the phase velocity of the MHC-reinforced open shell. Another necessary consequence is that as the externally applied voltage to the piezoelectric layer of the smart open shell increases, there will be seen an enhancement on the phase velocity or wave response of the system and without a doubt this issue is much more substantial at the lower wave number. It is also observed that when the applied voltage is more than zero, we can find a range for the fiber angle that these values are the critical fiber angle and this critical range will expand by increasing the external electrical load. The useful suggestion of this study is that for designing the structure, we should attention to the FG pattern and higher value of the wavenumber, simultaneously. The presented study outputs can be used in ultrasonic inspection techniques and structural health monitoring.

     

A survey on security challenges in cloud computing: issues,threats, and solutions

Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.

     

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.