A semantic image classifier based on hierarchical fuzzy association rule mining

One of the major challenges in the content-based information retrieval and machine learning techniques is to-build-the-so-called “semantic classifier” which is able to effectively and efficiently classify semantic concepts in a large database. This paper dealt with semantic image classification based on hierarchical Fuzzy Association Rules (FARs) mining in the image database. Intuitively, an association rule is a unique and significant combination of image features and a semantic concept, which determines the degree of correlation between features and concept. The main idea behind this approach is that any image visual concept has some associated features, so that, there are strong correlations between the concepts and their corresponding features. Regardless of the semantic gap, an image concept appears when the corresponding features emerge in an image and vice versa. Specially, this paper’s contribution was to propose a novel Fuzzy Association Rule for improving traditional association rules. Moreover, it was concerned with establishing a hierarchical fuzzy rule base in the training phase and setup corresponding fuzzy inference engine in order to classify images in the testing phase. The presented approach was independent from image segmentation and can be applied on multi-label images. Experimental results on a database of 6000 general-purpose images demonstrated the superiority of the proposed algorithm.     

Noble Metal Ion-Directed Assembly of 2D Materials for Heterostructured Catalysts and Metallic Micro-Texturing

Assembling 2D-material (2DM) nanosheets into micro- and macro-architectures with augmented functionalities requires effective strategies to overcome nanosheet restacking. Conventional assembly approaches involve external binders and/or functionalization, which inevitably sacrifice 2DM's nanoscale properties. Noble metal ions (NMI) are promising ionic crosslinkers, which can simultaneously assemble 2DM nanosheets and induce synergistic properties. Herein, a collection of NMI–2DM complexes are screened and categorized into two sub-groups. Based on the zeta potentials, two assembly approaches are developed to obtain 1) NMI-crosslinked 2DM hydrogels/aerogels for heterostructured catalysts and 2) NMI–2DM inks for templated synthesis. First, tetraammineplatinum(II) nitrate (TPtN) serves as an efficient ionic crosslinker to agglomerate various 2DM dispersions. By utilizing micro-textured assembly platforms, various TPtN–2DM hydrogels are fabricated in a scalable fashion. Afterward, these hydrogels are lyophilized and thermally reduced to synthesize Pt-decorated 2DM aerogels (Pt@2DM). The Pt@2DM heterostructures demonstrate high, substrate-dependent catalytic activities and promote different reaction pathways in the hydrogenation of 3-nitrostyrene. Second, PtCl₄ can be incorporated into 2DM dispersions at high NMI molarities to prepare a series of PtCl₄–2DM inks with high colloidal stability. By adopting the PtCl₄–graphene oxide ink, various Pt micro-structures with replicated topographies are synthesized with accurate control of grain sizes and porosities.     

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

     

Fuzzy logistic regression based on the least squares approach with application in clinical studies

To model fuzzy binary observations, a new model named “Fuzzy Logistic Regression” is proposed and discussed in this study. In fact, due to the vague nature of binary observations, no probability distribution can be considered for these data. Therefore, the ordinary logistic regression may not be appropriate. This study attempts to construct a fuzzy model based on possibility of success. These possibilities are defined by some linguistic terms such as …, low, medium, high…. Then, by use of the Extension principle, the logarithm transformation of “possibilistic odds” is modeled based on a set of crisp explanatory variables observations. Also, to estimate parameters in the proposed model, the least squares method in fuzzy linear regression is used. For evaluating the model, a criterion named the “capability index” is calculated. At the end, because of widespread applications of logistic regression in clinical studies and also, the abundance of vague observations in clinical diagnosis, the suspected cases to Systematic Lupus Erythematosus (SLE) disease is modeled based on some significant risk factors to detect the application of the model. The results showed that the proposed model could be a rational substituted model of an ordinary one in modeling the clinical vague status.     

Ranking alternatives in a preferential voting system using fuzzy concepts and data envelopment analysis

In this paper, a new method for aggregating the opinions of experts in a preferential voting system is proposed. The method, which uses fuzzy concept in handling crisp data, is computationally efficient and is able to completely rank the alternatives. Through this method, the number of votes for certain rank position that each alternative receives are first grouped together to form fuzzy numbers. The nearest point to a fuzzy number concept is then used to introduce an artificial ideal alternative. Data envelopment analysis is next used to find the efficiency scores of the alternatives in a pair-wise comparison with the artificial ideal alternative. Alternatives are rank based on these efficiency scores. If the alternatives are not completely ranked, a weight restriction method also based on fuzzy concept is used on the un-discriminated alternatives until they are completely ranked. Two examples are given for illustration of the method.     

Colour and ergonomics: On the selection of a “colour of the year”

The key role of colour in ergonomics has been emphasized by a number of researchers and design professionals. Although several research studies have been published regarding the use of colour in ergonomics, there are still some areas that need to be considered. The issue of deciding a “colour of the year” is an example, which represents a new challenge for researchers in the field of ergonomics. This is of particular interest considering the fact that the nature of research on the selection of a “colour of the year” is generally based on user experience. This paper argues that ergonomics should play a more prominent role in this field to ensure better user experience and performance. This paper highlights specific areas that need further study and development.     

A Puzzle of Quantum Phase Transition on Gd-IMC by Tuning of Conduction Electron Concentration

Based on the critical unstable of both crystal and magnetic structure of Gd-intermetallic compound near the competition of two strongly independent subsystem （＂local 4f7＂ and ＂conduction electron concentration＂）, a new QPT （quantum point transition） is predicted by calculation of： （1） The band structure and density of state by density functional theory where a strong narrowing fluidity of fermions around EF with shifted to negative value ＂-0.8 eV ＂is observable in the Gd-intermetalliccompound system while in the Y-case, it is not dominated. An antiferromagnetic state on the fluidity of conduction band can be investigated; （2） The internal magnetic field due to short range exchange interaction Jij between the nearest neighbor of local magnetic moment of stable s-state of Gd （L = 0） through the mean field approximation and of Eigenvalue-Eigenfunction ~.（k） are calculated. While a strong negative value of Jy is predicted, the eigenvalue L（k） of the system shows a strong antiferromagnetic phase in the reciprocal lattice direction 〈010〉, 〈001〉 in the correlation length 3.38 ~A. Although the antiferromagnetic state at Rc 〈_ 3.38 °A is a puzzle but it is completely dominated at Rc = 9 °A after passing through the competition between ）.λmin（O） and λmin（π） in the ranger of 3.2 °A 〈 Rc 〈 9 °A. Since both of the antiferromagnetic subsystems are sensitive to the predicted KF, the effect of decreasing of conduction electron is proposed to investigate, the change of the antiferromagnetic ordering state to the competition of ferromagnetic state （in direction 〈010〉） and antiferromagnetic state （in direction 〈001 〉 and 〈 100〉） resulted to paramagnetic state in the long range Rc = 9 °A.     

Synthesis and characterization of poly(ethylene tetrasulfide)/graphene oxide nanocomposites by in situ polymerization method

Poly(ethylene tetrasulfide) (PSP) is synthesized via interfacial polycondensation of 1,2 dichloroethane and sodium tetrasulfide, in the presence of graphene oxide (GO). This process resulted in homogeneously dispersed PSP/GO nanocomposites. Nanocomposites of 0.3 and 0.5?wt% of GO are synthesized and their morphology, chemical characteristics behavior are studied employing field emission scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction techniques. Thermal characterization of composites is performed by differential scanning calorimetry and thermogravimetry analysis. Results indicate that the addition of only small amounts (0.5?wt%) of well-dispersed GO can increase the melting point more than 16°C resulting in better thermal properties for the composite. The solubility of nanocomposite is also studied and results show that the solubility depends on solvent concentration in addition to reinforcement (GO) deals.