Design,modeling, and analysis of a high performance piezoelectric energy harvester for intelligent tires

Basic parameters affecting vehicle safety and performance such as pressure, temperature, friction coefficient, and contact‐patch dimensions are measured in intelligent tires via sensors that require electric power for operation and wireless communication to be synchronized to the vehicle monitoring and control system. Piezoelectric energy harvesters (PEHs) can extract a fraction of energy that is wasted as a result of deflection during rolling of tires, and this extracted energy can be used to power up sensors embedded in intelligent tires. A new design of PEH inspired from Cymbal PEHs is introduced, and its performance is evaluated in this paper. Cymbal PEHs are proven to be useful in vibration energy harvesting, and in this paper, for the first time, the modified shape of Cymbal energy harvester is used as strain‐based energy harvester for the tire application. The shape of the harvester is adjusted in a way that it can be safely embedded on the inner surface of tires. In addition to the high performance, ease of manufacturing is another advantage of this new design. A multiphysics model is developed and validated to determine the output voltage, power, and energy of the designed PEH. The modeling results indicated that the maximum output voltage, the maximum electric power, and the accumulated harvested energy are about 3.5 V, 2.8 mW, and 24 mJ/rev, respectively, which are sufficient to power two sensors. In addition, the possibility is shown to supply power to five sensors by increase in piezoelectric material thickness. The effect of rolling tire temperature on the performance of the proposed PEH is also studied.     

Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines

The development and implementation of better control strategies to improve the overall performance of a plant is often hampered by the lack of available measurements of key quality variables. One way to resolve this problem is to develop a soft sensor that is capable of providing process information as often as necessary for control. One potential area for implementation is in a hot steel rolling mill, where the final strip thickness is the most important variable to consider. Difficulties with this approach include the fact that the data may not be available when needed or that different conditions (operating points) will produce different process conditions. In this paper, a soft sensor is developed for the hot steel rolling mill process using least‐squares support vector machines and a properly designed bias update term. It is shown that the system can handle multiple different operating conditions (different strip thickness setpoints, and input conditions).     

Stealing PINs via mobile sensors: actual risk versus user perception

In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user’s PIN using an artificial neural network. Based on a test set of fifty 4-digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 74% which increases to 86 and 94% in the second and third attempts, respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. With the technical understanding of the information leakage caused by mobile phone sensors, we then study users’ perception of the risks associated with these sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our studies show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We confirm our results by interviewing our participants using two different approaches, within-subject and between-subject, and compare the results. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks.     

A review on the buyer–supplier dyad relationships in sustainable procurement context: past,present and future

Sustainable development is currently being applied in most fields of research. Procurement, focused on the buyer–supplier dyad, is one such discipline where sustainability is being widely applied. This paper provides a review of these research studies, conducting a systematic content analysis in order to present the state of the art in this domain. The paper carries out a detailed review of articles in international scientific journals and well-known international conferences related to green and sustainable supplier selection published between 2008 and 2014 inclusive. Seven designed research questions are proposed and answered based on this bibliography. Interesting results are reported in each section and gaps in the current body of literature are identified. The purpose of this review is to provide important future directions and limitations in this research topic.     

DOMtegrity: ensuring web page integrity against malicious browser extensions

In this paper, we address an unsolved problem in the real world: how to ensure the integrity of the web content in a browser in the presence of malicious browser extensions? The problem of exposing confidential user credentials to malicious extensions has been widely understood, which has prompted major banks to deploy two-factor authentication. However, the importance of the “integrity” of the web content has received little attention. We implement two attacks on real-world online banking websites and show that ignoring the “integrity” of the web content can fundamentally defeat two-factor solutions. To address this problem, we propose a cryptographic protocol called DOMtegrity to ensure the end-to-end integrity of the DOM structure of a web page from delivering at a web server to the rendering of the page in the user’s browser. DOMtegrity is the first solution that protects DOM integrity without modifying the browser architecture or requiring extra hardware. It works by exploiting subtle yet important differences between browser extensions and in-line JavaScript code. We show how DOMtegrity prevents the earlier attacks and a whole range of man-in-the-browser attacks. We conduct extensive experiments on more than 14,000 real-world extensions to evaluate the effectiveness of DOMtegrity.

     

Preparation and characterization of polyurethane/silica aerogel nanocomposite materials

In this investigation, silica aerogel (SA)/Rigid Polyurethane (PUR) foam composites and silica aerogel/Polyurethane (PU) composites were prepared by dry mixing of granular and grinded silica aerogels with polyol part. They were then combined with diisocyanate part. Three different types of PUR foams and an elastomeric coating grade of PU were studied as well. Results show that thermal conductivity of foams did not decrease by adding silica aerogel. It even increased for some grades which is assumed to be due to the change in cell configuration of these foams. It was also found that sound insulation performance of these cellular composites did not improve significantly. Unlike foam composites, addition of silica aerogel into elastomeric PU improved its thermal and acoustic insulation properties. Because of the more promising properties of elastomeric PU composites, further examinations including measurements of compression strength and water contact angle of silica aerogel/PU composites were also taken. Final results showed a significant improvement in general properties of PU coatings by adding little amounts of silica aerogel (1–4 wt %). © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44521.     

Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models

The integrated management of water supply and demand has been considered by many policymakers; due to its complexity the decision makers have faced many challenges so far. In this study, we proposed an efficient framework for managing water supply and demand in line with the economic and environmental objectives of the basin. To design this framework, a combination of ANFIS and multi-objective augmented ε-constraint programming models and TOPSIS were used. First, using hydrological data from 2001 to 2017, the rate of water release from the dam reservoir was estimated with the ANFIS model; afterwards, its allocation to agricultural areas was performed by combining multi-objective augmented ε-constraint models and TOPSIS. To prove the reliability of the proposed model, the southern Karkheh basin in Khuzestan province, Iran, was considered as a case study. The results have showed that this model is able to reduce irrigation water consumption and to improve its economic productivity in the basin.

     

Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

Since the fiber diameter determines the mechanical, electrical, and optical properties of electrospun nanofiber mats, the effect of material and process parameters on electrospun polymethyl methacrylate (PMMA) fiber diameter were studied. Accordingly, the prediction and optimization of input factors were performed using the response surface methodology (RSM) with the design of experiments technique and artificial neural networks (ANNs). A central composite design of RSM was employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the response factor, namely the PMMA fiber diameter (in nm). The parameters studied were polymer concentration (13–28 wt%), feed rate (1–5 mL/h), and tip-to-collector distance (10–23 cm). From the analysis of variance, the most significant factor that caused a remarkable impact on the experimental design response was identified. The predicted responses using the RSM and ANNs were compared in figures and tables. In general, the ANNs outperformed the RSM in terms of accuracy and prediction of obtained results.     

Model order reduction for hyperelastic materials

In this paper, we develop a novel algorithm for the dimensional reduction of the models of hyperelastic solids undergoing large strains. Unlike standard proper orthogonal decomposition methods, the proposed algorithm minimizes the use of the Newton algorithms in the search of non‐linear equilibrium paths of elastic bodies. The proposed technique is based upon two main ingredients. On one side, the use of classic proper orthogonal decomposition techniques, that extract the most valuable information from pre‐computed, complete models. This information is used to build global shape functions in a Ritz‐like framework. On the other hand, to reduce the use of Newton procedures, an asymptotic expansion is made for some variables of interest. This expansion shows the interesting feature of possessing one unique tangent operator for all the terms of the expansion, thus minimizing the updating of the tangent stiffness matrix of the problem. The paper is completed with some numerical examples in order to show the performance of the technique in the framework of hyperelastic (Kirchhoff–Saint Venant and neo‐Hookean) solids. Copyright © 2009 John Wiley & Sons, Ltd.