A finite element study of the effect of the angle between two intersecting cylindrical shells subjected to internal pressure

The results of a study of the critical region of two intersecting cylindrical shells due to internal pressure loading are presented as a function of the angle between the two axes. The investigation was performed using the thin shell element of a three-dimensional finite element program. Three models with the angle between the axes of the two cylindrical shells equal to 30, 60, and 90°, were analyzed. In all of the three models, the diameter ratio of the two shells was 0.5283; the diameter to thickness ratio of the larger or main shell was 44.76, while the same ratio for the smaller or attached shell was 15.487. The results of these analyses show that the stresses in the critical intersection region are least when the two axes are perpendicular to each other; for other angular configurations, the stresses increase as the acute angle between the two axes decrease. This effect of inclination for pressure loading is just opposite of the effect found by authors [1] for external moments. In that case, for in-plane as well as out-ofplane moments, the stresses are larger for normally intersecting shells as compared to other angular configurations.     

Efficient conversion of light paraffinic naphtha to aromatics over metal-modified Mo/MFI catalysts

Journal of Porous Materials - The transformation of light naphtha to value-added aromatic compounds is gaining momentum in the petrochemical industry. In this work, a series of metal modified...     

Boosted hydrogen evolution activity from Sr doped ZnO/CNTs nanocomposite as visible light driven photocatalyst

CNTs were decorated onto Sr doped ZnO nanoparticles to construct an efficient photocatalyst via a facile sol-gel method. The as-fabricated Sr doped ZnO/CNTs with recyclability exhibits Sr and CNTs content dependent hydrogen evolution activit under visible light illumination. The Sr doped ZnO/CNTs photocatalyst shows the highest hydrogen evolution rate of 2732.2 μmolh^?1g^?1, which is 33.7 and 2.83 times higher than pure ZnO and Sr doped ZnO photocatalysts, respectively. The improved hydrogen evolution activity of Sr doped ZnO/CNTs is primarily assigned to high surface area, Sr doping and construction of heterojunction, which can extend the light absorption, decrease the optical band gap and improve the charge separation. Moreover, the underlying photocatalytic mechanism is proposed on the basis of Mott-Schottky study and explains the interfacial charge transfer process from ZnO to CNTs and Sr. This work open new strategies to synthesize CNTs based nanocomposite for hydrogen evolution.     

A critical review on self-lubricating ceramic-composite cutting tools

Self-lubricating ceramic cutting tools have recently gained considerable attention as the tool wear in cutting hard-to-cut materials greatly affects the production cost, integrity of the machined surface, and productivity. In an attempt to compile the progress made in this important research area, a critical review has been performed covering a range of aspects. These include the current research trends and the need for self-lubricating ceramic tools, identification of prospective high-temperature solid lubricants and their limitations followed by a presentation of recent experimental and numerical work conducted related to self-lubricant ceramic cutting tools. Various lubrication mechanisms involved in the cutting process are also examined to identify general tribological response under various tribo-systems, which is expected to provide useful directions for the researchers and cutting industry. The current and emerging synthesis techniques are discussed in detail and compared with respect to ceramic cutting tools. Finally, some research gaps and future directions are suggested that could lead to optimum design and development of innovative self-lubricating ceramic tools.     

Object detection using hybridization of static and dynamic feature spaces and its exploitation by ensemble classification

This paper presents a learning mechanism based on hybridization of static and dynamic learning. Realizing the detection performances offered by the state-of-the-art deep learning techniques and the competitive performances offered by the conventional static learning techniques, we propose the idea of exploitation of the concatenated (parallel) hybridization of the static and dynamic learning-based feature spaces. This is contrary to the cascaded (series) hybridization topology in which the initial feature space (provided by the conventional, static, and handcrafted feature extraction technique) is explored using deep, dynamic, and automated learning technique. Consequently, the characteristics already suppressed by the conventional representation cannot be explored by the dynamic learning technique. Instead, the proposed technique combines the conventional static and deep dynamic representation in concatenated (parallel) topology to generate an information-rich hybrid feature space. Thus, this hybrid feature space may aggregate the good characteristics of both conventional and deep representations, which are then explored using an appropriate classification technique. We also hypothesize that ensemble classification may better exploit this parallel hybrid perspective of the feature spaces. For this purpose, pyramid histogram of oriented gradients-based static learning has been incorporated in conjunction with convolution neural network-based deep learning to produce concatenated hybrid feature space. This hybrid space is then explored with various state-of-the-art ensemble classification techniques. We have considered the publicly available INRIA person and Caltech pedestrian standard image datasets to assess the performance of the proposed hybrid learning system. Furthermore, McNemar’s test has been used to statistically validate the outperformance of the proposed technique over various contemporary techniques. The validated experimental results show that the employment of the proposed hybrid representation results in effective detection performance (an AUC of 0.9996 for INRIA person and 0.9985 for Caltech pedestrian datasets) as compared to the individual static and dynamic representations.

     

An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

A large amount of data is present on the web which can be used for useful purposes like a product recommendation, price comparison and demand forecasting for a particular product. Websites are designed for human understanding and not for machines. Therefore, to make data machine-readable, it requires techniques to grab data from web pages. Researchers have addressed the problem using two approaches, i.e., knowledge engineering and machine learning. State of the art knowledge engineering approaches use the structure of documents, visual cues, clustering of attributes of data records and text processing techniques to identify data records on a web page. Machine learning approaches use annotated pages to learn rules. These rules are used to extract data from unseen web pages. The structure of web documents is continuously evolving. Therefore, new techniques are needed to handle the emerging requirements of web data extraction. In this paper, we have presented a novel, simple and efficient technique to extract data from web pages using visual styles and structure of documents. The proposed technique detects Rich Data Region (RDR) using query and correlative words of the query. RDR is then divided into data records using style similarity. Noisy elements are removed using a Common Tag Sequence (CTS) and formatting entropy. The system is implemented using JAVA and runs on the dataset of real-world working websites. The effectiveness of results is evaluated using precision, recall, and F-measure and compared with five existing systems. A comparison of the proposed technique to existing systems has shown encouraging results.     

A new rectangular window based image cropping method for generalization of brain neoplasm classification systems

Classification of brain neoplasm images is one of the most challenging research areas in the field of medical image processing. The main objective of this study is to design a brain neoplasm classification system that can be trained using multiple various sized MR images of different institutions. The proposed method is a generalized classification system; it can be used in a single institute or in a number of institutions at the same time, without any restriction on image size. The generalization and unbiased capability of the proposed method can bring researchers on a single platform to work on some standard forms of computer aided diagnosis system with more efficient diagnostic capabilities. In this study, a suitable size of moveable rectangular window is used between segmentation and feature extraction stages. A semiautomatic, localized region based active contour method is used for segmentation of brain neoplasm region. Discrete wavelet transform for feature extraction, principal component analysis for feature selection and support vector machine is used as classifier. For the first time MR images of 2 sizes and from different institutions are used in training and testing of brain neoplasm classifier. Three glioma grades were classified using 92 MR images. The proposed method achieved the highest accuracy of 88.26%, the highest sensitivity of 92.23% and the maximum specificity of 93.93%. In addition, the proposed method is computationally less complex, requires shorter processing time and is more efficient in terms of storage capacity.