Corrosion Behavior of Sn-3.0Ag-0.5Cu Lead-Free Solder in Potassium Hydroxide Electrolyte

The corrosion behavior of Sn-3.0Ag-0.5Cu (SAC305) solder alloy in 6?M potassium hydroxide electrolyte was investigated using polarization analysis. The results revealed that SAC305 is susceptible to corrosion because of the dissolution of the Sn phase. The corrosion potential (E _corr) and corrosion current density (i _corr) obtained from the sample was ?C1.108?V vs Hg/HgO and 1.795?×?10^?4?A cm^?2, respectively. In addition, microstructural and elemental characterization revealed the presence of tin oxide, Cu, and/or Ag-containing corrosion product on the surface of the corroded sample. The morphology of the samples was also observed to contain several pits, cracks, and pore-like structures.     

Time-Delayed Positive Velocity Feedback Control Design for Active Control of Structures

In this paper we present and propose a design methodology that uses intentional time delays for the active control of structures. We use here positive velocity-feedback, time-delayed control and show that its performance is, in general, superior to the previously developed methodology of using time delayed, negative velocity-feedback control. A detailed study carried out in this paper of the nonsystem poles and their interaction with the system poles reveals the reasons for this. Analytical results related to performance and stability of the new method are presented. We apply the time delayed positive velocity feedback active control methodology to a multidegree-of-freedom system subjected to the S00E component of ground acceleration recorded during the El Centro 1940 earthquake. The excellent behavior in terms of stability, performance, and control efficiency that is demonstrated by our time-delayed control design as well as its facile implementation makes it attractive for earthquake hazard mitigation in a practical sense.     

A Note on Nonproportional Damping

This note deals with three aspects of nonproportional damping in linear damped vibrating systems in which the stiffness and damping matrices are not restricted to being symmetric and positive definite. First, we give results on approximating a general damping matrix by one that commutes with the stiffness matrix when the stiffness matrix is a general diagonalizable matrix, and the damping and stiffness matrices do not commute. The criterion we use for carrying out this approximation is closeness in Euclidean norm between the actual damping matrix and its approximant. When the eigenvalues of the stiffness matrix are all distinct, the best approximant provides justification for the usual practice in structural analysis of disregarding the off-diagonal terms in the transformed damping matrix. However, when the eigenvalues of the stiffness matrix are not distinct, the best approximant to a general damping matrix turns out to be related to a block diagonal matrix, and the aforementioned approximation cannot be justified on the basis of the criterion used here. In this case, even when the damping and stiffness matrices commute, decoupling of the modes is not guaranteed. We show that for general matrices, even for symmetric ones, the response of the approximate system and the actual system can be widely different, in fact qualitatively so. Examples illustrating our results are provided. Second, we present some results related to the difficulty in handling general, nonproportionally damped systems, in which the damping matrix may be indefinite, by considering a simple example of a two degrees-of-freedom system. Last, we use this example to point out the nonintuitive response behavior of general nonproportionally damped systems when the damping matrix is indefinite. Our results point to the need for great caution in approximating nonproportionally damped systems by damping matrices that commute with the stiffness matrix, especially when considering general damping matrices. Such approximations could lead to qualitatively differing responses between the actual system and its proportionally damped approximation.     

Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.     

Discovering optimal features using static analysis and a genetic search based method for Android malware detection

Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communi-cations. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features.     

Evaluation of the performance of a micro gas turbine cogeneration system in a sewage treatment facility: Optimized configuration of a cogeneration system

In this study, efficient configuration of a biogas‐fuelled cogeneration system (CGS) in a sewage treatment facility was investigated. The efficient configuration of the CGS was clarified on the basis of the relationship between exhaust heat recovery efficiency (η_ehr) of the CGS and the ratio of yearly average heat demand to yearly average biogas production of the facility (Q_h.d/Q_b.p). The CGS was assumed to be used under Q_h.d/Q_b.p<η_ehr,Q_h.d/Q_b.p≈η_ehr, and Q_h.d/Q_b.p>η_ehr conditions. It was found that although the CGS was able to cover total heat demand of the facility by only consuming biogas produced, from the point of view of energy utilization, reduction of unutilized biogas and reduction of electricity demand efficiencies, the most efficient CGS was obtained under the Q_h.d/Q_b.p≈η_ehr condition. Under the Q_h.d/Q_b.p≈η_ehr condition, energy utilization, reduction of unutilized biogas, and reduction of electrical demand efficiencies were 0.64, 0.99, and 0.32, respectively, whereas under the Q_h.d/Q_b.p<η_ehr and Q_h.d/Q_b.p>η_ehr conditions, energy utilization, reduction of unutilized biogas, and reduction of electrical demand efficiencies were in ranges of 0.56–0.64, 0.43–0.99, and 0.16–0.20, respectively. A more efficient system can be obtained if a CGS with lower η_ehr such as a fuel cell is used under the Q_h.d/Q_b.p<η_ehr condition and if a CGS with higher η_ehr such as a steam turbine is used under the Q_h.d/Q_b.p>η_ehr condition. © 2011 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.20389