Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

Fracture toughness of foams with tetrakaidecahedral unit cells using finite element based micromechanics

Fracture toughness of open-cell foams consisting of tetrakaidecahedral unit cells is predicted by simulating crack propagation using a finite element (FE) based micromechanical model. The inputs to the model are the geometric parameters required to model the repeating unit cell and tensile strength of the foam ligament or strut. Cracks are created by removing certain number of cells pertaining to a crack length. The FE model consists of a local micro-scale region surrounding the crack tip. For an assumed stress intensity factor, the displacements along the boundary of the local model are calculated based on linear elastic fracture mechanics for orthotropic materials. The stresses in the ligaments ahead of the crack tip calculated from this micro-model in conjunction with the tensile strength of the strut material are used to predict fracture toughness. A parametric study with different micro-model sizes and different crack lengths is performed to check for convergence of predicted Mode-I, Mode-II and mixed mode fracture toughness values. The effect of applying rotations as additional boundary conditions along with translational displacement boundary conditions on the predicted fracture toughness values is also studied.     

Automatic classification of computed tomography brain images using ANN,k-NN and SVM

Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective.     

Review: Heart Diseases Detection by Machine Learning Classification Algorithms

Most human deaths are caused by heart diseases. Such diseases cannot be efficiently detected for the lack of specialized knowledge and experience. Data science is important in healthcare sector for the role it plays in bulk data processing. Machine learning (ML) also plays a significant part in disease prediction and decision-making in medical care industry. This study reviews and evaluates the ML approaches applied in heart disease detection. The primary goal is to find mathematically effective ML algorithm to predict heart diseases more accurately. Various ML approaches including Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), t-Distributed Stochastic Neighbor Embedding (t-SNE), Nave Bayes, and Random Forest were utilized to process heart disease dataset and extract the unknown patterns of heart disease detection. An analysis was conducted on their performance to examine the effecacy and efficiency. The results show that Random Forest out-performed other ML algorithms with an accuracy of 97%.     

Group B Streptococcal septicaemia/meningitis in neonates in a Singapore teaching hospital

Several studies have indicated that frequent allelic losses in some specific chromosomal regions occur during colorectal cancer (CRC) progression. To clarify the correlation between such allelic losses and metastatic potential, the allelotype of lymph node-positive early CRCs, which are small but extremely malignant cancers consisting of metastatically competent cells, were investigated. Nineteen paraffin-embedded specimens of early CRC (pT1 tumors according to TNM classification) with positive lymph nodes were collected. The tumor tissues were examined for loss of heterozygosity (LOH), using microsatellite markers on chromosomes 1p34-36, 8p21-22, 14q32, 18q21 and 22q12-13. The relationship between p53 protein expression and the metastatic status was also investigated by immunohistochemical staining. A group of 20 early CRCs with negative lymph nodes having a similar distribution of macroscopic appearance were used as controls. Among the 19 node-positive tumors, LOH at 8p21-22 and 18q21 was detected in 11 cases (57.9%) and 17 cases (89.4%), respectively. Allelic losses within these 2 regions in node-positive tumors were significantly more frequent than that in node-negative ones (p < 0.01). No significant correlation was found between LOH at 1p34-36, 14q32 or 22q12-13 and lymph node metastasis. p53 protein expression was not significantly associated with lymph node metastasis. Our results suggest that putative tumor suppressor genes, which may be involved in the metastatic process of CRC, are located on chromosomes 8p21-22 and 18q21. Allelic losses in these regions are possible risk factors for lymph node metastasis of early CRC.     

Access control for web data: models and policy languages

The web has made easily available an enormous amount of information in digital form and has facilitated the exchange of such information. In this environment, access control is a primary concern. The key issue is how to trade-off between maximizing the sharing of information and enforcing a controlled access to web data. In this paper we start by outlining which are the main access control requirements of web data. Then, we review researches carried on in the field, by mainly focusing on xml. Finally, we discuss policy languages for the semantic web, and outline which are the main research directions in this field.     

A NEW VIEW OF INFORMATION MODELING

The information model was conceived to address the complexities of managing large volumes of data, processes, designs, and tools that are shared by many business users with differing requirements. Because an information model derives much of its features from data models, the distinction between information modeling and data modeling is sometimes unclear. One perspective is that information modeling is context dependent: when a model is viewed as a representation scheme for users to comprehend, it is an information model. When used as a representation scheme to be processed by a computer, it is a data model.     

Generation and selection of likely interpretations during plan recognition in task-oriented consultation systems

This paper presents a mechanism which infers a user's plans from his/her utterances by directing the inference process towards the more likely interpretations of a speaker's statements among many possible interpretations. Our mechanism uses Bayesian theory of probability to assess the likelihood of an interpretation, and it complements this assessment by taking into consideration two aspects of an interpretation: its coherence and its information content. The coherence of an interpretation is determined by the relationships between the different statements in the discourse. The information content of an interpretation is a measure of how well defined the interpretation is in terms of the actions to be performed on the basis of this interpretation. This measure is used to guide the inference process towards interpretations with higher information content. The information content of an interpretation depends on the specificity and the certainty of the inferences in it, where the certainty of an inference depends on the knowledge on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of travel booking.     

Experimental study of composite cold-formed steel C-section floor joists

Twelve large-scale slab specimens and twenty-two companion push-out specimens were tested to study the behavior and capacity of composite slab joists consisting of cold-formed steel C-sections and concrete. Four shear transfer mechanisms, including surface bond, pre-fabricated bent-up tabs, pre-drilled holes, and self-drilling screws, were employed on the surface of the flange embedded in the concrete to provide shear transfer capacity. Results indicated that specimens employed with shear transfer enhancements showed a marked increase in strength and reduced deflection compared with those relying only on a natural bond between steel and concrete to resist shear. Of the three shear transfer enhancements investigated, bent-up tabs provided the best performance at both the strength and serviceability limit states, followed by drilled holes in the embedded flanges. The use of self-drilling screws resulted in the lowest strength increase. The correlation of shear transfer capacity of push-out specimens with the ultimate capacity of large-scale specimens indicated that the average experimental flexural capacity of the slab specimens was approximately 1.16 times the average predicted value based on push-out test results.