Effects of Microbial Polysaccharides on the Oil Absorption and Quality Characteristics of a Deep-Fried Snack Namkeen

This study investigates the potential of microbial polysaccharides (MP) gellan gum and pullulan to reduce oil uptake in a deep-fried snack (Namkeen). The concentration of MP in refined wheat flour varied between 1–10% (w/w) and the frying time and temperature are optimized. The results reveal that MP added namkeens showed a maximum relative oil reduction of 33.87% with gellan gum and 26.93% with pullulan at 10% (w/w). Gellan gum and pullulan result in oil reduction of 23.19% and 15.97% with 5% (w/w). The results reveal that gellan gum added namkeens exhibited 1.45 times lower oil uptake than pullulan added namkeen indicating potential oil reduction capabilities. The hardness of namkeen is observed to increase with an increase in MP concentration. Sensorial and textural characteristics of control and MP incorporated fried samples are comparable up to 5% (w/w) with MP concentration. The results for the control samples and MP incorporated samples are statistically significant. Furthermore, overall sensorial acceptability of pullulan added namkeens (7.74 ± 0.53) is slightly higher than that of gellan gum added namkeens (7.64 ± 0.36). Practical Application: Low-fat diet and functional foods are expected to help in managing the conditions of hyperlipidaemia. This study focuses on the potential of MP in reducing the oil content of deep-fried snacks. The efficacy of MP gellan gum and pullulan as functional ingredients for oil reduction in fried snacks demonstrates its aid in formulating low-fat foods thereby retaining sensorial characteristics.     

Improving Readability through Individualized Summary Extraction,Using Interactive Genetic Algorithm

The impact of individualized summarization is high, because a summary would be difficult to understand by all if summarized in a generic manner. When sentences are important as well as readable to the learner with reading difficulties, the summary might be useful for better comprehension of the text. In this article, we propose an Interactive Genetic Algorithm-based individualized summarization to maximize the readability of selected important sentences. The individualized summarization applies to the educational domain using readability-based features to extract readable important sentences. Inclusion of features representing reading difficulty should not dilute the informative score of the summary, moreover it aids the learner who has reading difficulties to comprehend the complete text better, using the summary as supplementary to the complete text and not as a substitute for it. The experimental results derived from intrinsic evaluation shows that individual summary extraction performs better than a GA-based approach and a baseline approach. Moreover, user -based direct evaluation also supports individualized summarization for improving the readability by the target audience.     

Hardware Acceleration for Neuromorphic Vision Algorithms

Neuromorphic vision algorithms are biologically inspired models that follow the processing that takes place in the primate visual cortex. Despite their efficiency and robustness, the complexity of these algorithms results in reduced performance when executed on general purpose processors. This paper proposes an application-specific system for accelerating a neuromorphic vision system for object recognition. The system is based on HMAX, a biologically-inspired model of the visual cortex. The neuromorphic accelerators are validated on a multi-FPGA system. Results show that the neuromorphic accelerators are 13.8× (2.6×) more power efficient when compared to CPU (GPU) implementation.     

Energy Efficient Cluster Based Routing Protocol Using Charged System Harmony Search Algorithm in WSN

In general, Wireless Sensor Networks (WSNs) is developed with a group of distributed and locative sensor nodes for sensing different environmental conditions. The primary challenges faced by WSN are: low network time and transmission data delay. In crucial applications like monitoring the ecosystem, military and disaster management, and data routing, the incorporation of WSN is very critical. Henceforth, a Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol was proposed but it was found to be uneconomical for energy management. Also, the optimization of Cluster Head (CH) is considered as NP hard problem. This research work deals the issues in optimal path selection in routing of wireless sensor networks to increase the network lifetime. Various techniques are available in metaheuristics, such as the Charged System Search (CSS), that effectively used to resolve the routing problem. Despite of this, most of the meta-heuristics suffer from local optima issues. A charged system search and harmony search algorithm based routing protocol is presented in this research work. Experimental results present the efficient performance of proposed HS model with increased cluster structures, improved network lifetime and reduced end-to-end delay and average packet loss rate.

     

Comparative Studies of Microwave and Sol–Gel-Assisted Combustion Methods of NiFe<Subscript>2</Subscript>O<Subscript>4</Subscript> Nanostructures: Synthesis,Structural, Morphological,Opto-magnetic,and Antimicrobial Activity

Nickel ferrite NiFe₂O₄ nanoparticles (NPs) were successfully synthesized by using nickel nitrate, ferric nitrate, citric acid, and ethyl cellulose as a surfactant by a simple sol–gel-assisted combustion method (SACM) and microwave-assisted combustion method (MACM). Structural, morphological, optical, and magnetic properties of the obtained powder were characterized by powder X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), photoluminescence (PL) spectroscopy, and vibrating sample magnetometry (VSM). XRD results show that the resultant powder was pure crystalline with cubic structure. The average crystalline size was found to be 18.8 and 10.2 nm synthesized by SACM and MACM, respectively. FT-IR spectra indicate the type of bonds between Ni–O and Fe–O (metal and oxygen). SEM images show that the morphology of the powder consists of well-defined structure. VSM results showed a ferromagnetic behavior of the sample. Antimicrobial activity of NiFe₂O₄ nanoparticles was performed. Both sample 1 (SACM) and sample 2 (MACM) show good inhibition in the zone 100 μg/ml. While comparing, sample 2 shows high inhibition than sample 1.     

Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network

Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency.     

The binder-free mesoporous CoNi2S4 electrode for high-performance symmetric and asymmetric supercapacitor devices

Journal of Materials Science - Mixed metal sulphides have attracted the attention of researchers working in the development of electroactive materials for high-energy supercapacitors. Mesoporous...     

Accumulation of metals by naturally growing herbaceous and tree species in iron ore tailings

The aim of the study was to identify pioneering species that naturally colonize Fe tailings and accumulation of heavy metals. Total, bioavailable, acid extractable and water‐soluble fractions were studied. After the second year onwards, along with nine herbaceous pioneering species, four tree species (Teactona grandis, Alstonia scholaris, Azadirachta indica and Peltaphorum) were found growing naturally. The study shows that some species could accumulate relatively high metal concentrations indicating internal detoxification of metals. The study revealed that T. grandis accumulated a higher concentration of metals than A. scholaris in the Fe tailings, but all concentrations were within the normal range. Native naturally colonizing plant species may be used for the bioremediation of iron tailings as initial cover species to stabilize and reduce erosion. The findings concluded that metal content in Fe tailings would not affect the natural establishment and growth of tree species; rather that higher Fe content promotes luxurious growth.     

Efficient compressed sensing based object detection system for video surveillance application in WMSN

Limited memory, energy and bandwidth are the major constraints in wireless visual sensor network (WVSN). Video surveillance applications in WVSN attracts a lot of attention in recent years which requires high detection accuracy and increased network lifetime that can be achieved by reducing the energy consumption in the sensor nodes. Compressed sensing (CS) based background subtraction plays a significant role in video surveillance application for detecting the presence of anomaly with reduced complexity and energy. This paper presents a system based on CS for single and multi object detection that can detect the presence of an anomaly with higher detection accuracy and minimum energy. A novel and efficient mean measurement differencing approach with adaptive threshold strategy is proposed for detection of the objects with less number of measurements, thereby reducing transmission energy. The performance of the system is evaluated in terms of detection accuracy, transmission energy and network lifetime. Furthermore, the proposed approach is compared with the conventional background subtraction approach. The simulation results show that the proposed approach yields better detection accuracy with 90% reduction in samples compared to conventional approach.