Hydrogen Production from Co‐Gasification of Coal and Biomass in the Presence of CaO as a Sorbent

Among the options for clean energy production, the gasification process is receiving increasing attention as it offers the best combination of investment and value of produced electricity compared to other methods. An Aspen Plus model of co‐gasification of biomass and coal with in situ CO₂ capture was developed to evaluate its potential for hydrogen production and cracking of organic impurities, i.e., tars. The effects of some critical operational variables on gas composition and yields of hydrogen gas and tar were investigated. The obtained results indicate that the fuel particle size plays a minor role in the process; smaller particles favor the conversion of tar and production of more hydrogen gas.     

The Influence of the Coarse Wool Treatment on Fiber Structure and Physico-Mechanical Parameters

In this research, the influence of slendering on coarse wool fiber which then improved by m-TGase treatment was studied. The coarse wool fiber was first treated by reducing agent then it was stretched at different conditions over various periods of times and temperatures and finally steam set. The wool fibers were then tested for some mechanical and physical properties. To overcome tenacity loss of the fibers as a result of slendering treatment, after-treatment with microbial trans-glutaminases was examined. The results show that the fineness of the fiber was improved about 17% but the tenacity decreased about 15%. XRD analysis show that the degree of crystallinity related to stretching ratio and this increasing of stretching alters the degree of crystallinity. SEM shows that the cuticle of the treated samples causes excessive damage and the reducing agent can affect on wool surface. Furthermore, alkaline solubility was increased by reduction treatment, but m-TGase can compensate a little. The fiber yellowness was significantly decreased after slendering.     

Development of a lumbar EMG-based coactivation index for the assessment of complex dynamic tasks

The objective of this study was to develop and test an EMG-based coactivation index and compare it to a coactivation index defined by a biologically assisted lumbar spine model to differentiate between tasks. The purpose was to provide a universal approach to assess coactivation of a multi-muscle system when a computational model is not accessible. The EMG-based index developed utilised anthropometric-defined muscle characteristics driven by torso kinematics and EMG. Muscles were classified as agonists/antagonists based upon ‘simulated’ moments of the muscles relative to the total ‘simulated’ moment. Different tasks were used to test the range of the index including lifting, pushing and Valsalva. Results showed that the EMG-based index was comparable to the index defined by a biologically assisted model (r² = 0.78). Overall, the EMG-based index provides a universal, usable method to assess the neuromuscular effort associated with coactivation for complex dynamic tasks when the benefit of a biomechanical model is not available.

Practitioner Summary: A universal coactivation index for the lumbar spine was developed to assess complex dynamic tasks. This method was validated relative to a model-based index for use when a high-end computational model is not available. Its simplicity allows for fewer inputs and usability for assessment of task ergonomics and rehabilitation.     

Reconfigurable Compact Wideband Circularly Polarised Dielectric Resonator Antenna for Wireless Applications

In this work, a novel compact wideband reconfigurable circularly polarised (CP) dielectric resonator antenna (DRA) is presented. The L-shaped Dielectric resonator antenna is excited by an inverted question mark shaped feed. This arrangement of feed-line helps to generate two orthogonal modes inside the DR, which makes the design circularly polarised. A thin micro-strip line placed on the defected ground plane not only helps to generate a wideband response but also assist in the positioning of the two diode switches. These switches located at the left and right of the micro-strip line helps in performing two switching operations. The novel compact design offers the reconfigurability between 2.9–3.8 GHz which can be used for different important wireless applications. For the switching operation I, the achieved impedance bandwidth is 24% while axial ratio bandwidth (ARBW) is 42%. For this switching state, the design has 100% CP performance. Similarly, the switching operation II achieves 60% impedance bandwidth and 58.88% ARBW with 76.36% CP performance. The proposed design has a maximum measured gain of 3.4 dBi and 93% radiation efficiency. The proposed design is novel in terms of compactness and performance parameters. The prototype is fabricated for the performance analysis which shows that the simulated and measured results are in close agreement.     

CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks. Since energy is a scarce resource in sensor networks, it becomes the focus of research, and researchers strive to propose new solutions that are energy efficient. The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization. This paper introduces a Cluster-based Node Recovery (CNR) connectivity restoration mechanism based on the concept of clustering. Clustering is a well-known mechanism in sensor networks, and it is known for its energy-efficient operation and scalability. The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes, while Cluster Heads (CHs) play a significant role in the restoration of connectivity. Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques. The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms. The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.     

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept. Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells. Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data. The proposed technique is validated on acquired 203 fluorescent microscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate (CFO@BTO) magnetoelectric nanoparticles in vitro. The developed approach achieved high prediction with accuracy of 97.5% and sensitivity of 100% and outperformed other approaches. The high performance reveals the effectiveness of the approach. It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung, brain tumor and breast cancer.     

Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images

Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease.     

Performance comparison of JPEG,JPEG 2000, and newly developed CSI‐JPEG by adopting different color models

The major issues of using less storage space and wanting higher transmission rates for information in the form of high quality color images was taken into consideration. Two experiments were conducted in order to investigate and compare performance of compression standard including JPEG 1992 and JPEG 2000, and a newly developed CSI‐JPEG. The CSI‐JPEG is an amalgamation of Cubic Spline Interpolation (CSI) with baseline JPEG 1992 algorithm. The performance of different image compression algorithms was evaluated using different color models/spaces in terms of compression rate, color accuracy, and visual quality. The results from three assessment methods consistently showed that JPEG 2000 and CSI‐JPEG performed significantly better compared with JPEG 1992 for small color differences (in the range of acceptability). Moreover, the CAM02‐UCS performed best among other selected models in terms of compression rate and image performance for all three image compression algorithms. The results from the visual assessment also confirmed this. It was also found that CIEDE2000 can be reliably used for assessing quality of compressed images with low levels of distortion. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 460–473, 2017     

Soret and Dufour effects for three-dimensional flow in a viscoelastic fluid over a stretching surface

This article deals with the Soret and Dufour effects on three-dimensional boundary layer flow of viscoelastic fluid over a stretching surface. The governing partial differential equations are transformed into a dimensionless coupled system of non-linear ordinary differential equations and then solved analytically by the homotopy analysis method (HAM). Graphs are plotted to analyze the variation of different parameters of interest on the velocity, concentration and temperature fields.