Vapour treatments with methyl salicylate or methyl jasmonate alleviated chilling injury and enhanced antioxidant potential during postharvest storage of pomegranates

Pomegranates were treated after harvest with methyl jasmonate (MeJa) or methyl salicylate (MeSa) at two concentrations (0.01 and 0.1 mM), and then stored under chilling temperature for 84 days. Control fruits exhibited chilling injury (CI) symptoms manifested by pitting and browning, the severity being enhanced as storage time advanced, and accompanied by softening and electrolyte leakage (EL). The CI symptoms were significantly reduced by MeJa or MeSa treatments, without significant differences among treatments or applied dose. In addition, both treatments significantly increased total phenolics and anthocyanins with respect to controls. Hydrophilic (H-TAA) and lipophilic (L-TAA) total antioxidant activity decreased in control arils, but in both MeJa and MeSa treated fruits H-TAA increased while no significant changes occurred for L-TAA. Results would suggest that both MeJa and MeSa have potential postharvest applications in reducing CI, maintaining quality and improving the health benefits of pomegranate fruit consumption by increasing the antioxidant capacity.     

Micro‐modeling of porous composite anodes for solid oxide fuel cells

A new anode micromodel for solid oxide fuel cells to predict the electrochemical performance of hydrocarbon‐fuelled porous composite anodes with various microstructures is developed. In this model, the random packing sphere method is used to estimate the anode microstructural properties, and the complex interdependency among the multicomponent mass transport, electron and ion transports, and electrochemical and chemical reactions is taken into account. As a case study, a porous Ni–YSZ composite anode operated with biogas fuel is simulated numerically and distributions of the current density, polarization, and mole fraction and rate of flux of the fuel components along the thickness of the anode are determined. The effect of the anode microstructural variables including the porosity, thickness, particle‐size ratio, and particle size and volume fraction of Ni particles on the anode electrochemical performance is also studied. © 2011 American Institute of Chemical Engineers AIChE J, 58: 1893–1906, 2012     

Automated Quality Characterization for Composites Using Hybrid Ultrasonic Imaging Techniques

An enhanced technique using image processing has been developed for automated ultrasonic inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan images. This work presents results performed on two composite samples with simulated delamination defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective and healthy regions in the produced C-Scan image. In this test campaign, different filtering and thresholding algorithms are evaluated and compared in terms of their effectiveness on defect identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and depth, respectively. The results demonstrates the applicability of the proposed technique for accurate defect localization and characterization of composite materials.     

Size-dependent nonlinear vibration of an electrostatic nanobeam actuator considering surface effects and inter-molecular interactions

In this paper, the size-dependent nonlinear vibration of an electrostatic nanobeam actuator is investigated based on the nonlocal strain gradient theory, incorporating surface effects. A comprehensive model regarding the von Karman geometrical nonlinearity, inter-molecular forces and both components of the electrostatic excitation (AC and DC) is proposed to explore the system behavior near the primary resonance. Utilizing Hamilton’s principle, the nonlinear equation of motion of the system is derived. The natural frequency and dynamic response of the system, comprising frequency and force response diagrams, are obtained analytically via multiple scales technique in conjunction with the differential quadrature method and validated through a numerical approach. The roles of the nonlocal and strain gradient parameters, surface elasticity, inter-molecular forces and quality factor on the system oscillations are examined. The acquired results unveiled that the size-dependent parameters can significantly displace the multi-valued portions and instability thresholds of the dynamical response. Furthermore, it is deduced that the surface effects induce the stiffness hardening of the nanobeam, whereas the inter-molecular forces impose the stiffness softening effect.

     

Symmetric deformable image registration via optimization of information theoretic measures

The use of information theoretic measures (ITMs) has been steadily growing in image processing, bioinformatics, and pattern classification. Although the ITMs have been extensively used in rigid and affine registration of multi-modal images, their computation and accuracy are critical issues in deformable image registration. Three important aspects of using ITMs in multi-modal deformable image registration are considered in this paper: computation, inverse consistency, and accuracy; a symmetric formulation of the deformable image registration problem through the computation of derivatives and resampling on both source and target images, and sufficient criteria for inverse consistency are presented for the purpose of achieving more accurate registration. The techniques of estimating ITMs are examined and analytical derivatives are derived for carrying out the optimization in a computationally efficient manner. ITMs based on Shannon’s and Renyi’s definitions are considered and compared. The obtained evaluation results via registration functions, and controlled deformable registration of multi-modal digital brain phantom and in vivo magnetic resonance brain images show the improved accuracy and efficiency of the developed formulation. The results also indicate that despite the recent favorable studies towards the use of ITMs based on Renyi’s definitions, these measures are seen not to provide improvements in this type of deformable registration as compared to ITMs based on Shannon’s definitions.     

A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method

In this article, a new fuzzy rough set (FRS) method was proposed for extracting rules from an adaptive neuro-fuzzy inference system (ANFIS)-based classification procedure in order to select the optimum features. The proposed methodology was used to classify lidar data and digital aerial images acquired for an urban environment to detect four classes, including trees, buildings, roads, and natural grounds. In this regard, 16 potentially primary features were produced for classification using the lidar data and the digital aerial images. The training and checking inputs of the proposed ANFIS were collected from the generated features for further training and evaluation processes. Also, the fuzzy c-mean clustering algorithm was used to initialize the fuzzy inference system of the proposed ANFIS-based classification method. By considering all states of fuzzy rules for each training input, the fuzzy rule with the maximum firing value was selected. Accordingly, these fuzzy rules were used as the inputs of the Rough Set Theory. Accordingly, the optimum features were acquired by the basic minimal covering algorithm as the rule induction method. To validate our proposed methodology, the procedure of classification was repeated by the achieved optimum features. The results showed that the classification using the optimum features has reached better overall accuracy than those achieved by using the 16 potentially primary features. Also, comparing the results of our proposed methodology with the other well-known genetic-algorithm-based feature selection methods indicated the significance of the proposed FRS method to select optimum features with high accuracy in a short running time.     

Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail.     

Computing with words for hierarchical competency based selection of personnel in construction companies

As part of human resource management policies and practices, construction firms need to define competency requirements for project staff, and recruit the necessary team for completion of project assignments. Traditionally, potential candidates are interviewed and the most qualified are selected. Applicable methodologies that could take various candidate competencies and inherent uncertainties of human evaluation into consideration and then pinpoint the most qualified person with a high degree of reliability would be beneficial. In the last decade, computing with words (CWW) has been the center of attention of many researchers for its intrinsic capability of dealing with linguistic, vague, interdependent, and imprecise information under uncertain environments. This paper presents a CWW approach, based on the specific architecture of Perceptual Computer (Per-C) and the Linguistic Weighted Average (LWA), for competency based selection of human resources in construction firms. First, human resources are classified into two types of main personnel: project manager and engineer. Then, a hierarchical criteria structure for competency based evaluation of each main personnel category is established upon the available literature and survey. Finally, the perceptual computer approach is utilized to develop a practical model for competency based selection of personnel in construction companies. We believe that the proposed approach provides a useful tool to handle personnel selection problem in a more reliable and intelligent manner.     

Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms

We present an evaluation and comparison of the performance of four different texture and shape feature extraction methods for classification of benign and malignant microcalcifications in mammograms. For 103 regions containing microcalcification clusters, texture and shape features were extracted using four approaches: conventional shape quantifiers; co-occurrence-based method of Haralick; wavelet transformations; and multi-wavelet transformations. For each set of features, most discriminating features and their optimal weights were found using real-valued and binary genetic algorithms (GA) utilizing a k-nearest-neighbor classifier and a malignancy criterion for generating ROC curves for measuring the performance. The best set of features generated areas under the ROC curve ranging from 0.84 to 0.89 when using real-valued GA and from 0.83 to 0.88 when using binary GA. The multi-wavelet method outperformed the other three methods, and the conventional shape features were superior to the wavelet and Haralick features.