Metal organic frameworks as electrocatalysts: Hydrogen evolution reactions and overall water splitting

The development of clean energy technologies to protect the environment is an important demand of the times. Electrocatalysis is emerging as a promising method for evolution of hydrogen and overall water splitting. Nowadays, metal organic frameworks (MOFs) have emerged as electrocatalysts having uniformly distributed active sites and high electrical conductivity. This review summarizes the latest advances in heterogeneous catalysis by MOFs and their composite/derivatives for efficient hydrogen evolution reaction (HER) and water splitting. Pristine MOFs with their recent development are summarized first followed by composites of MOFs with their enhanced electrocatalytic performances. Overall water splitting by using bifunctional electrocatalysts derived from MOFs with different synthetic approaches is provided and this review gives the metal-based categorisation of precursor MOFs. Different strategies to improve chemical stability, conductivity, and overall electrocatalytic properties have been discussed. In the last, perspectives on the synthesis of efficient MOF-based electrocatalyst materials are provided.     

Phase separation and crystallization in double crystalline symmetric binary polymer blends

Blending of two or more pure polymers is an effective way to produce composites with tunable properties. In this paper, we report dynamic Monte Carlo simulation results on the crystallization of crystalline/crystalline (A/B) symmetric binary polymer blend, wherein the melting temperature of A-polymer is higher than B-polymer. We study the effect of segregation strength (arises from the immiscibility between A- and B-polymers) on crystallization and morphological development. Crystallization of A-polymer precedes the crystallization of B-polymer upon cooling from a homogeneous melt. Simulation results reveal that the morphological development is controlled by the interplay between crystallization driving force (viz., attractive interaction) and de-mixing energy (viz., repulsive interaction between two polymers). With increasing segregation strength, the interface becomes more rigid and restricts the development of crystalline structures. Mean square radius of gyration shows a decreasing trend with increasing segregation strength, reflecting the increased repulsive interaction between A- and B-polymers. As a consequence, a large number of smaller size crystals form with lesser crystallinity. Isothermal crystallization reveals that the transition pathways strongly depend on segregation strength. We also observe a path-dependent crystallization behavior in isothermal crystallization: two-step (sequential) isothermal crystallization yields superior crystalline structure in both A- and B-polymers than one-step (coincident) crystallization.     

Modeling risk based maintenance using fuzzy analytic network process

The study deals with the problem of maintenance policy selection for an industrial unit. Maintenance policy selection is a multiple criteria decision making problem. Criteria considered here are ‘risk of equipment failure’ and the ‘cost of maintenance’. The maintenance policies considered are Corrective Maintenance (CM), Time Based Maintenance (TBM), Condition Based Maintenance (CBM) and Shutdown Maintenance (SM). For modeling, fuzzy analytic network process (FANP) has been employed. Chang’s extended analysis has been applied to deal with the fuzzy variables and the preferred maintenance policy alternative is found out using FANP analysis. The methodology was applied to a unit of a chemical plant and the suitable maintenance policy was found out for each of the 13 equipment of the unit. The results were compared to the earlier study using Analytic Hierarchal Process and Goal Programming (Arunraj and Maiti, 2010) vis-a-vis the existing practices. The results show that CBM is preferred when the risk possessed by an equipment is very high while CM is preferred in those cases where risk is low and cost is the main consideration. But in cases where both cost and risk are somewhat equally important, TBM is the preferred option.     

A reference based analysis framework for understanding anomaly detection techniques for symbolic sequences

Anomaly detection for symbolic sequence data is a highly important area of research and is relevant in many application domains. While several techniques have been proposed within different domains, understanding of their relative strengths and weaknesses is limited. The key factor for this is that the nature of sequence data varies significantly across domains, and hence while a technique might perform well in its original domain, its performance is not guaranteed in a different domain. In this paper, we aim at establishing this understanding for a wide variety of anomaly detection techniques for symbolic sequences. We present a comparative evaluation of a large number of anomaly detection techniques on a variety of publicly available as well as artificially generated data sets. Many of these are existing techniques while some are slight variants and/or adaptations of traditional anomaly detection techniques to sequence data. The analysis presented in this paper allows relative comparison of the different anomaly detection techniques and highlights their strengths and weaknesses. We extend the reference based analysis (RBA) framework, which was originally proposed to analyze multivariate categorical data, to analyze symbolic sequence data sets. We visualize the symbolic sequences using the characteristics provided by the RBA framework and use the visualization to understand various aspects of the sequence data. We then use the characterization done by RBA to understand the performance of the different techniques. Using the RBA framework, we propose two anomaly detection techniques for symbolic sequences, which show consistently superior performance over the existing techniques across the different data sets.     

A compact uniplanar ACS fed multi band low cost printed antenna for modern 2.4/3.5/5 GHz applications

Microsystem Technologies - A uniplanar compact triple operating band asymmetric coplanar strip (ACS) fed printed antenna loaded with multiple L-shaped branches and the asymmetric rectangular ground...     

Analysis of Frequency,Intensity, and Interference of Discomfort in Computerized Numeric Control Machine Operations

The introduction of Computerized Numeric Control (CNC) technology in manufacturing industries revolutionized the production process a few decades back. There are some health and safety problems associated with these machine operations, however. The main objectives of the present work is to study the health and safety issues associated with the CNC machines with respect to control and display, specifically to determine the frequency, intensity of discomfort, and its interference in operating different CNC machines as reported by the workers operating them. The postural discomfort associated with CNC machines was studied, and the frequency, intensity of discomfort, and interference of the level of discomfort with the participants' ability to work were recorded and analyzed. The study revealed that 20.5% of the operators reported discomfort 1 or 2 times, 25.4% experienced discomfort 3 to 4 times in a week, 37.7% had discomfort daily, and 16.4% reported discomfort several times a day. Discomfort was reported in all the body parts involved (lower back, neck, upper back, shoulder, and leg), but the highest discomfort scores were associated with the shoulder and arm region. Workers reporting discomfort several times a day also reported high mean discomfort scores. The study established that the frequency and intensity of the discomfort in all body parts is related to the position of the control panel and display. © 2012 Wiley Periodicals, Inc.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.     

Metallurgical Studies of Austenitic Stainless Steel 304 under Warm Deep Drawing

Austenitic stainless steel 304 was deep drawn with different blank diameters under warm conditions using 20 t hydraulic press. A number of deep drawing experiments both at room temperature and at 150 ℃ were conducted to study the metallography. Also, tensile test experiments were conducted on a universal testing machine up to 700 ℃ and the broken specimens were used to study the fractography of the material using scanning electron microscopy in various regions. The microstructure changes were observed at limiting draw ratio （LDR） when the cup is drawn at different temperatures. In austenitic stainless steel, martensite formation takes place that is not only affected by temperature, hut also influenced by the rate at which the material is deformed. In austenitic stainless steel 304, dynamic strain regime appears above 300 ℃ and it decreases the formability of material due to brittle fracture as studied in its fractography. From the metallographic studies, the maximum LDR of the material is observed at 150 ℃ before dynamic strain regime. It is also observed that at 150 ℃, grains are coarse in the drawn cups at LDR.