Rate-dependent mechanical behavior of VHB 4910 elastomer

This article experimentally studied the large nonlinear deformation of VHB 4910 elastomer by uniaxial tests. The study reveals that the monotonic tensile stress-strain, hysteresis, cyclic stress softening, and multistep stress relaxation of this elastomer exhibit rate-sensitivity. Toughness, failure stress, and failure strain are shown to vary with strain rate. Maximum cyclic stress, hysteresis loss, residual strains in cyclic loading-unloading, and stress relaxation in multistep relaxation tests are also shown to be rate-sensitive. The analytical models are also proposed to predict certain important parameters, such as dissipative work, cyclic stress softening, cyclic residual strain, and relaxation stress in different states of deformation.     

Effect of Disorder on Electronic,Magnetic, and Optical Properties of Co2CrZ Heusler Alloys (Z = Al,Ga, Si,Ge)

This paper presents the effect of disorder on electronic, magnetic, and optical properties of Co₂CrZ (Z = Al, Si, Ga, Ge) Heusler alloy using density functional theory. Binary mixing is the most common form of atomic disorder in these compounds. We have considered three types of disorders: DO ₃, A2, and B2 disorder which corresponds to X-Y, X-Z, and Y-Z mixing, respectively. After structural optimization, we found that A2 disorder has high formation energy and is most unlikely to occur. The half-metallic nature of the alloy is destroyed in the presence of DO ₃ and A2 disorder. The destruction of half-metallicity is due to reconstruction of energy states. B2 disorder retains the half-metallic nature of the alloy but spin-polarization value is reduced slightly as compared to the ordered alloy. In addition, the optical properties such as dielectric function, refractive index, absorption spectra, optical conductivity, reflectivity, and energy loss function of these alloys have also been investigated.     

Hydrothermally Oxidized Single‐Walled Carbon Nanotube Networks for High Volumetric Electrochemical Energy Storage

Improving volumetric energy density is one of the major challenges in nanostructured carbon electrodes for electrochemical energy storage device applications. Herein, a simple hydrothermal oxidation process of single‐walled carbon nanotube (SWNT) networks in dilute nitric acid is reported, enabling simultaneous physical densification and chemical functionalization of the as‐assembled randomly‐packed SWNT films. After the hydrothermal oxidation process, the density of the SWNT films increases from 0.63 to 1.02 g cm^?3 and a considerable amount of redox‐active oxygen functional groups are introduced on the surface of the SWNTs. The functionalized SWNT films are used as positive electrodes against Li metal negative electrodes for potential Li‐ion capacitors or Li‐ion battery applications. The functionalized SWNT electrodes deliver high volumetric as well as gravimetric capacities, 154 Ah L^?1 and 152 mAh g^?1, respectively, owing to the surface redox reactions between the introduced oxygen functional groups and Li ions. In addition, these electrodes exhibit a remarkable rate‐capability by retaining its high capacity of 94 Ah L^?1 (92 mAh g^?1) at a high discharge rate of 10 A g^?1. These results demonstrate the simple hydrothermal oxidation process as an attractive strategy for improving the volumetric performance of nanostructured carbon electrodes.     

Optimization study of adsorption parameters for removal of phenol on gastropod shell dust using response surface methodology

Phenolics have recently been of great concern because of the extreme toxicity and persistence in the environment. This study explores the possibility of using gastropod shell dust (GPSD) to remove phenol from aqueous solutions. The removal of phenol was investigated in batch mode. The influence of different experimental parameters—initial pH, adsorbent dose, initial concentration, contact time, stirring rate, temperature, and their interaction during phenol adsorption—were determined by response surface methodology based on three-level four-factorial Box–Behnken design. Optimized values of initial phenol concentration, pH, adsorbent dose, and contact time were found as 10.16 mg/L, 4.22, 0.50 g/L, and 33.47 min, respectively. The experimental equilibrium data were tested by four widely used isotherm models namely, Langmuir and Freundlich, D–R, and Temkin. It was found that adsorption of phenol on gastropod shell dust correlated with the Langmuir isotherm model, implying monolayer coverage of phenol onto the surface of the adsorbent. The maximum adsorption capacity was found to be 56.89 mg g^?1 at 333 K. Regeneration study revealed that about 92 % phenol can be regenerate within 90 min from the spent GPSD. Kinetics of the adsorption process was tested by pseudo-first-order, pseudo-second-order kinetics, and intra-particle diffusion mechanism. Pseudo-second-order kinetic model provided a better correlation for the experimental data studied in comparison to the pseudo-first-order model. Intra-particle diffusion was not the sole rate-controlling factor. The activation energy of the adsorption process (E _a) was found to be 2.68 kJ mol^?1, indicating physisorption nature of phenol adsorption onto gastropod shell dust. A thermodynamic study showed spontaneous nature and feasibility of the adsorption process. A negative enthalpy (ΔH°) value indicated that the adsorption process was exothermic. The results revealed that gastropod shell dust can be used as an effective and low-cost adsorbent to remove phenol from aqueous solutions.     

Comparison of predicted vehicular pollution concentration with air quality standards for different time periods

Air pollution is caused by variety of sources such as industries, vehicles, cremation, bakeries, and open burning. These sources have variation in emission with different time scales. Industry and bakeries have variation in emission with day or week, rest of the sources like vehicles and domestic sector have variation with time in a day. In fact, vehicles have a large variation in emission with time period of the day. The average concentration of 24 h is much less than hourly concentration of peak time when there is heavy vehicular emissions. The hourly concentration of off-peak time or lean time is very low due to low emission for that period. The air quality standards of India are prescribed for 24-h average concentration with which the predicted average concentration from models is compared. However, the peak time concentration may be much higher than the standard. In the peak time, outdoor concentration is more and since a large proportion of the population is out the exposure is also very high and can cause severe health effect. In this paper, vehicular pollution modeling has been carried out using AERMOD with simulated meteorology by Weather Research and Forecasting model. NO_x and PM concentrations were 3.6 and 1.45 times higher in peak time than off-peak and evening peak, respectively. Lean time has higher concentration for both NO_x and PM than off-peak and evening peak. It shows the misleading concept of comparing average predicted concentration of 24 h with standards for vehicles.

     

A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system

Reconfigurable manufacturing systems are designed to deliver exact functionality and capacity that is needed, when it is needed. The reconfigurable machine tool (RMT) plays a pivotal role in the accomplishment of this objective through their built in modular structure consisting of basic and auxiliary modules along with the open architecture software.     

Data quality assessment: The Hybrid Approach

Various techniques have been proposed to enable organisations to assess the current quality level of their data. Unfortunately, organisations have many different requirements related to data quality (DQ) assessment. For example, some organisations may need to focus on ensuring regulations are met rather than reducing costs. Due to this, organisations may be forced to follow an assessment technique, which may not wholly fit their needs and current situation. Therefore, we propose and evaluate the Hybrid Approach to assessing DQ, which demonstrates how to dynamically configure an assessment technique as needed while leveraging the best practices from existing assessment techniques.     

Double auction-inspired meta-scheduling of parallel applications on global grids

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users’ individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users’ service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.     

Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds

A system for the detection, segmentation and recognition of multi-class hand postures against complex natural backgrounds is presented. Visual attention, which is the cognitive process of selectively concentrating on a region of interest in the visual field, helps human to recognize objects in cluttered natural scenes. The proposed system utilizes a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region. Feature based visual attention is implemented using a combination of high level (shape, texture) and low level (color) image features. The shape and texture features are extracted from a skin similarity map, using a computational model of the ventral stream of visual cortex. The skin similarity map, which represents the similarity of each pixel to the human skin color in HSI color space, enhanced the edges and shapes within the skin colored regions. The color features used are the discretized chrominance components in HSI, YCbCr color spaces, and the similarity to skin map. The hand postures are classified using the shape and texture features, with a support vector machines classifier. A new 10 class complex background hand posture dataset namely NUS hand posture dataset-II is developed for testing the proposed algorithm (40 subjects, different ethnicities, various hand sizes, 2750 hand postures and 2000 background images). The algorithm is tested for hand detection and hand posture recognition using 10 fold cross-validation. The experimental results show that the algorithm has a person independent performance, and is reliable against variations in hand sizes and complex backgrounds. The algorithm provided a recognition rate of 94.36 %. A comparison of the proposed algorithm with other existing methods evidences its better performance.     

High-dimensional MRI data analysis using a large-scale manifold learning approach

A novel manifold learning approach is presented to efficiently identify low-dimensional structures embedded in high-dimensional MRI data sets. These low-dimensional structures, known as manifolds, are used in this study for predicting brain tumor progression. The data sets consist of a series of high-dimensional MRI scans for four patients with tumor and progressed regions identified. We attempt to classify tumor, progressed and normal tissues in low-dimensional space. We also attempt to verify if a progression manifold exists—the bridge between tumor and normal manifolds. By identifying and mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression. This could be greatly beneficial for patient management. Preliminary results have supported our hypothesis: normal and tumor manifolds are well separated in a low-dimensional space. Also, the progressed manifold is found to lie roughly between the normal and tumor manifolds.