A SWOT-AHP analysis on biodiesel as an alternative future marine fuel

Clean Technologies and Environmental Policy - Alternative fuels especially those produced in a green way are essential for meeting supplying the world's growing energy needs. Biodiesel is...     

Hertzian crack propagation in graded glass/ceramic composites

In literature, the concept of material gradation is shown to inhibit surface crack initiation in glass/ceramic composites subjected to Hertzian indentation. However, surface cracks could yet initiate due to relatively higher loadings or in the presence of surface flaws/defects. Hence, characterization of graded composites concerning the resistance against Hertzian crack initiation and propagation manifests itself as a prominent matter. In this study, axisymmetric Hertzian cracks evolving in graded glass/ceramic composites propelled by a rigid cylindrical punch are investigated employing a novel recursive method, called the stacked-node propagation procedure. Crack trajectories and their propagation susceptibilities are predicted via the minimum strain energy density (MSED) criterion regarding the crack growth resistance (R-curve) of ceramics. The stress trajectory approach is also considered for a homogeneous glass to reveal the reliance and effectiveness of the MSED criterion in the present crack problems. The Mori–Tanaka relations are adopted to model the elastic modulus and Poisson's ratio variations through the composites, which are implemented on the simulations via the homogeneous finite element approach. Hertzian crack problem of a practically producible graded composite comprised of oxynitride glass and a fine-grained silicon nitride ceramics (Si₃N₄) is treated as a case study. The degree of material gradation is assessed for the mitigation of surface crack initiation and propagation risks.     

A novel feature ranking algorithm for text classification: Brilliant probabilistic feature selector (BPFS)

Text classification (TC) is a very crucial task in this century of high-volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter-based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based on its algorithm to order the features. The classification process is then carried out by choosing the top-N features. However, each method's feature order is distinct from the others. Each method selects by giving the qualities that are critical to its algorithm a high score, but it does not select by giving the features that are unimportant a low value. In this paper, we proposed a novel filter-based FS method namely, brilliant probabilistic feature selector (BPFS), to assign a fair score and select informative features. While the BPFS method selects unique features, it also aims to select sparse features by assigning higher scores than common features. Extensive experimental studies using three effective classifiers decision tree (DT), support vector machines (SVM), and multinomial naive bayes (MNB) on four widely used datasets named Reuters-21,578, 20Newsgroup, Enron1, and Polarity with different characteristics demonstrate the success of the BPFS method. For feature dimensions, 20, 50, 100, 200, 500, and 1000 dimensions were used. The experimental results on different benchmark datasets show that the BPFS method is more successful than the well-known and recent FS methods according to Micro-F1 and Macro-F1 scores.     

The Use of Digital Image Processing Method to Estimate the Foam Characteristics of Polyurethane

Four air-bubbled polyurethane (PU) foams with different polyol:PMDI wt.% are produced, respectively. The chemical reaction mechanisms of polyurethane and bubble formation are proposed by performing standard Gibbs free energy calculations using the DFT M06-2X/6-31+G(d,p) method. The local minima, transition states, and intermediates in reaction mechanisms are detected. It is concluded that both reactions are exothermic. Then, raw images of the produced PU foams are taken with a 13 MP mobile phone camera, which can be considered inexpensive, and the mean radii of the pores are calculated by an image processing based method (IPBM) on a standard desktop computer with an i5 processor. It is determined that there is a close relationship between the calculated mean radius and instrumentally measured thermal conductivity coefficient of the foams. However, the thermal conductivity coefficients are independent of the calculated number and percentage of the pores. The mean radii of the samples calculated by the proposed IPBM are close to that of the SEM, with acceptable relative errors of less than 10%. Finally, it is concluded that IPBM, which is a more cost-effective, cleaner, and faster method than SEM, might replace SEM in the air bubble analysis of PU foams.     

Stability analysis and controller design for consensus in discrete-time networks with heterogeneous agent dynamics

In this paper, we study the heterogeneous consensus problem in directed networks consisting of first- and second-order agents that can only receive the position states of their neighbors. Necessary and sufficient conditions on the controller parameters are obtained in order to achieve consensus in the network. The mathematical expressions of the consensus equilibria are given for two different scenarios. Furthermore, we propose a systematic method for choosing controller parameters to ensure stability in a network of agents with heterogeneous dynamics. Several numerical examples are also provided to illustrate the theoretical results.