Pomegranate seed powder as a functional component of gluten‐free bread (Physical,sensorial and antioxidant evaluation)

The purpose of this study was to evaluate the effect of the addition of pomegranate seed powder (PSP) on physical, sensorial and antioxidant properties of gluten‐free bread. The PSP was incorporated at different levels (2.5%, 5.0%, 7.5% and 10%) into formula of gluten‐free bread. Control gluten‐free bread made without any addition of PSP was used for comparison. The results showed that the specific volume and springiness of gluten‐free breads increased, whereas hardness and chewiness decreased significantly with increasing PSP addition. The addition of PSP into gluten‐free bread decreased the lightness and yellowness of crumb and crust colour, while redness increased. Total phenolics content (TPC) increased from 46% to 181% with PSP addition (2.5%–10%). Moreover, antioxidant activity was significantly higher for bread with PSP. For bread with the highest percentage of PSP, the highest antioxidant activity was obtained. For sensorial and antioxidant activity, the optimum level of PSP addition was found to be 7.5%.     

Thermal-diffusion and diffusion-thermo effects on axisymmetric flow of a second grade fluid

This study investigates the thermal-diffusion and diffusion-thermo effects on the two-dimensional magnetohydrodynamics (MHD) axisymmertric flow of a second grade fluid. Mathematical analysis has been carried out in the presence of Joule heating and first order chemical reaction. Using momentum, energy and concentration laws, the governing partial differential equations have been reduced to the ordinary differential equations by suitable transformations. Series solutions are constructed by homotopy analysis method (HAM). Convergence of the derived series solutions is ensured. Plots are displayed in order to examine the influence of emerging parameters on the dimensionless components of velocity, temperature and concentration fields. Numerical computations for skin friction coefficient, Nusselt number and Sherwood number are tabulated.     

Grain size refinement through gas bubble stirring in AC4CH cast aluminium alloy

A comparison has been made of the mechanical properties of gravity and squeeze cast aluminium alloys that have been grain refined using gas bubbling and those that have not. To find the optimum gas bubbling conditions, the alloy melt temperature, the gas flow rate and the gas bubbling times were varied over wide ranges. The microstructure of the gas bubbled gravity and squeeze cast materials is fine, equiaxed and non-dendritic with an average primary α size of 52 μm and 163 μm respectively. However, gas bubbling has no effect on the morphology of the eutectic Si. There seems to be no noticeable difference between the measured mechanical properties of the gravity and squeeze cast materials with or without the gas bubbling. The lack of improvement in the mechanical properties of the gravity cast alloy is due to casting defects and porosity, which offset the effects of the grain refinement. The crystal separation and showering mechanisms are operative for the formation of equiaxed grains.     

A linear equalizer for high-speed APD-Based integrate-and-dump receivers

A linear minimum mean-square error (LMMSE) equalizer for avalanche-photodiode (APD) based integrate-and-dump receivers is designed to compensate for intersymbol-interference (ISI) introduced by the APD's random impulse response. Recent work on the characterization of the joint probability distribution of the APD's gain and buildup-time are adopted and utilized to determine the mean and correlation matrix of the APD-based receiver's random impulse response. This LMMSE equalizer is shown to improve the bit-error-rate dramatically at high transmission rates, resulting in an improvement in the receiver sensitivity by approximately 2.3 dB at 15 GHz.     

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-the-art violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.     

Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning

Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning. The artificial intelligence technique used is transfer learning on the deep neural network, Inception-v4. Two configuration variants of transfer learning are applied on Inception-v4: Fine-tune mode and fixed feature extractor mode. Both configuration modes have achieved decent accuracy values, but the fine-tuning method outperforms the fixed feature extractor configuration mode. Fine-tune configuration mode has gained 96.6% accuracy in early detection of DR and 97.7% accuracy in grading the disease and has outperformed the state of the art methods in the relevant literature.     

Coordinated opportunistic routing protocol for wireless mesh networks

Opportunistic routing is an emerging research area in Wireless Mesh Networks (WMNs), that exploits the broadcast nature of wireless networks to find the optimal routing solution that maximizes throughput and minimizes packet loss. Opportunistic routing protocols mainly suffer from computational overheads, as most of the protocols try to find the best next forwarding node. In this paper we address the key issue of computational overhead by designing new routing technique without using pre-selected list of potential forwarders. We propose a novel opportunistic routing technique named, Coordinated Opportunistic Routing Protocol for WMNs (CORP-M). We compare CORP-M with well-known protocols, such as AODV, OLSR, and ROMER based on throughput, delivery ratio, and average end-to-end delay. Simulation results show that CORP-M, gives average throughput increase upto 32%, and increase in delivery ratio (from 10% to 20%). We also analyze the performance of CORP-M and ROMER based on various parameters, such as duplicate transmissions and network collisions, by analysis depicts that CORP-M reduces duplicate transmissions upto 70% and network collisions upto 30%.     

Interactive Effects of Silicon and Soil pH on Growth,Yield and Nutrient Uptake of Maize

Silicon - Priming crop seeds with silicon (Si) to withstand various abiotic stresses is a novel approach. As soil pH affects dissolution of Si pools and Si uptake by plant, evaluation of this...     

Libra: a computational economy‐based job scheduling system for clusters

Clusters of computers have emerged as mainstream parallel and distributed platforms for high‐performance, high‐throughput and high‐availability computing. To enable effective resource management on clusters, numerous cluster management systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users' quality of service requirements. It is intended to work as an add‐on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the Portable Batch System. The scheduler offers market‐based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user‐perceived value (utility), determined by their budget and deadline rather than system performance considerations. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared with system‐centric scheduling strategies. Copyright © 2004 John Wiley & Sons, Ltd.     

Tea and Its Consumption: Benefits and Risks

The recent convention of introducing phytochemicals to support the immune system or combat diseases is a centuries’ old tradition. Nutritional support is an emerging advancement in the domain of diet-based therapies; tea and its constituents are one of the significant components of these strategies to maintain the health and reduce the risk of various malignancies. Tea is the most frequently consumed beverage worldwide, besides water. All the three most popular types of tea, green (unfermented), black (fully fermented), and oolong (semifermented), are manufactured from the leaves of the plant Camellia sinensis. Tea possesses significant antioxidative, anti-inflammatory, antimicrobial, anticarcinogenic, antihypertensive, neuroprotective, cholesterol-lowering, and thermogenic properties. Several research investigations, epidemiological studies, and meta-analyses suggest that tea and its bioactive polyphenolic constituents have numerous beneficial effects on health, including the prevention of many diseases, such as cancer, diabetes, arthritis, cardiovascular disease (CVD), stroke, genital warts, and obesity. Controversies regarding beneficialts and risks of tea consumption still exist but the limitless health-promoting benefits of tea outclass its few reported toxic effects. However, with significant rise in the scientific investigation of role of tea in human life, this review is intended to highlight the beneficial effects and risks associated with tea consumption.