Comparison of wetting models and geometrical analysis in describing the effect of cavity number,size, and position on apparent contact angle

A critical subject of surface science is whether contact area or the triple-phase contact line (TPCL) directly affects the apparent contact angle (APCA). On this premise, effect of cavity size and position on APCA is studied. Cavities were created using a laser machining process. Optimal conditions for laser patterning were obtained via trial and error. It was impossible to deposit a droplet that could rest on all of the cavities for about 0.16 mm distance between the edge of the patterns and the drop’s perimeter. Neither Wenzel nor Cassie–Baxter models can fully explain the acquired data from static contact angle measurements. Revised Cassie–Baxter equation also fails, while a simple geometrical analysis based on Spherical Cap assumption shows a better agreement with the data as long as the cavities are sufficiently far from TPCL. Based on our results, we believe that theoretical analyses regarding wetting models are not in line with engineering aspects of wetting which should be focused on TPCL.     

Two-phase absorption complicated by a second-order chemical reaction in the liquid phase in irrigated channels (a solution for the first quadrant of the hydrodynamic plane. Theory. Calculation)

Two-phase film absorption complicated by an irreversible chemical reaction in the liquid is studied on the basis of a solution of diffusion transfer equations in the gas and liquid phases. The concentration distribution of the soluble component and the reaction product on the interface is studied theoretically. The dimensionless diffusion flow is calculated numerically as a function of the length of the channel with arbitrary controlling parameters.     

The HIPERLAN equalizer ASIC complexity and its relationship with the training header

In this document, an attempt is made to estimate the size of the HIPERLAN equalizer ASIC by extrapolating from existing adaptive equalizer ASICs and making certain assumptions regarding the process and methodology used to design the HIPERLAN equalizer. Two scenarios are considered, first, an equalizer using the LMS algorithm every baud to update the tap coefficients and second, an equalizer using an RLS algorithm performing a set of updates every ten baud intervals. It was discovered that the computational complexity of both approaches are within the same order of magnitude, however, the LMS ASIC will occupy at most half the size of its RLS counterpart. The smaller IC will reduce the overall system cost at the expense of the longer convergence time required by the LMS algorithm. The paper also demonstrates that when the overall packet processing delay is taken into account, the slower-converging and cheaper LMS type equalizer will actually produce faster turn-around times for short packets than its RLS counter part. Thus, a new training header length is suggested that would allow vendors some flexibility in choosing the structure that best suites their product.     

Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling

The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.

     

A swift technique for damage detection of determinate truss structures

In this study, a novel two-stage approach for damage detection of determinate truss structures is proposed. The method lies in the group of vibration-based methods but it just needs the first natural frequency and mode shape vector of these structures for identifying the location and severity of damage. In the first stage, the modal residual force vector for different modes of a structure is introduced and the one associated with the first mode is applied to the structure as an external nodal force vector. Then, the residual local nodal force vector can be computed for all elements of the structure. Next, the elements with non-zero residual internal force are considered as damaged elements. In the second stage, the damage severity of each damaged element is determined using a new relation which can be categorized as a force–displacement relation. To show the efficiency and simplicity of the proposed method, three truss structures including a 13-bar planar truss, a 29-bar planar truss, and a 77-bar planar truss under different damage scenarios are studied; the results of which indicate that the method is innovatively capable of suitably detecting, for determinate truss structures, not only damaged members but also their individual damage severity by carrying solely one analysis.

     

Nonlinear secondary resonance of FG porous silicon nanobeams under periodic hard excitations based on surface elasticity theory

To impart desirable material properties, functionally graded (FG) porous silicon has been produced in which the porosity changes gradually across the material volume. The prime objective of this work is to predict the influence of the surface free energy on the nonlinear secondary resonance of FG porous silicon nanobeams under external hard excitations. On the basis of the closed-cell Gaussian-random field scheme, the mechanical properties of the FG porous material are achieved corresponding to the uniform and three different FG patterns of porosity dispersion. The Gurtin–Murdoch theory of elasticity is implemented into the classical beam theory to construct a surface elastic beam model. Thereafter, with the aid of the method of multiple time-scales together with the Galerkin technique, the size-dependent nonlinear differential equations of motion are solved corresponding to various immovable boundary conditions and porosity dispersion patterns. The frequency response and amplitude response associated with the both subharmonic and superharmonic hard excitations are obtained including multiple vibration modes and interactions between them. It is found that for the subharmonic excitation, the nanobeam is excited within a specific range of the excitation amplitude, and this range shifts to higher excitation amplitude by incorporating the surface free energy effects. For the superharmonic excitation, by taking surface stress effect into account, the excitation amplitude associated with the peak of the vibration amplitude enhances. Moreover, in the subharmonic case, it is demonstrated that by increasing the porosity coefficient, the value of the excitation frequency at the joint point of the two branches of the frequency-response curve reduces. In the superharmonic case, it is revealed that an increment in the value of porosity coefficient leads to decrease the peak of the oscillation amplitude and the associated excitation frequency.

     

A swift technique for damage detection of determinate truss structures (2)

This paper introduces a novel and robust probable statistical approach for the applied damage detection of determinate truss structures. This technique involves two steps; the first is called most probable damaged element identification step and the second is called probable damage severity prediction step. In the first step, a new index based on modal residual forces plays a major role to independently identify damage-suspected elements for each considered mode. Then among them, the elements, the most probable to damage, are extracted. In the second step, the probable damage severity for each most probable damaged element is individually predicted using a novel statistical approach. Finally, to justify the validity and robustness of the technique, three commonly used bridge trusses including a 29-bar Pratt truss, a 29-bar Warren truss, and finally, a 37-bar K truss under different damage scenarios are thoroughly studied while their modal parameters are corrupted by noise. The obtained results indicate that the method is innovatively capable of swiftly predicting, for determinate truss structures, not only damaged elements but also their damage severities by carrying out solely few structural analyses.

     

Finding the differential characteristics of block ciphers with neural networks

We have developed a model to represent the differential operation of block ciphers in order to help finding differential characteristics. Through this model, the whole space of differential characteristics for a block cipher is represented by a multi-level weighted directed graph. In this way, the problem of finding the best differential characteristic for a block cipher reduces to the problem of finding the minimum-weight multi-branch path between two known nodes in the proposed graph. In this paper, we use recurrent neural networks to find such a path in the differential operation graph of a block cipher. The path is found through minimization of the network cost function. We use the Hopfield network and the Boltzmann machine with and without chaos to minimize the cost function. Chaos is introduced to assist the network to escape from the local minima of the cost function. Experimental results indicate the usefulness of the approach and comparison of the performance of the used techniques shows that the Boltzmann machine algorithm incorporating simulated annealing produces the best result.     

Evolutionary coincidence-based ontology mapping extraction

Abstract: Ontology matching is a process for selection of a good alignment across entities of two (or more) ontologies. This can be viewed as a two-phase process of (1) applying a similarity measure to find the correspondence of each pair of entities from two ontologies, and (2) extraction of an optimal or near optimal mapping. This paper is focused on the second phase and introduces our evolutionary approach for that. To be able to do so, we need a mechanism to score different possible mappings. Our solution is a weighting mechanism named coincidence-based weighting . A genetic algorithm is then introduced to create better mappings in successive iterations. We will explain how we code a mapping as well as our crossover and mutation functions. Evaluation of the algorithm is shown and discussed.