A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection

Neural Computing and Applications - To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related...     

Bioaccessible Iron and Zinc in Native and Fortified Enzyme Hydrolyzed Casein and Soya Protein Matrices

Casein and soybean are superior quality proteins; however, these are known to inhibit mineral absorption. These investigations were aimed at enzymatic modification of these proteins and to study their effect on mineral bioaccessibility. Casein and soybean proteins were hydrolyzed with alcalase and trypsin individually under optimum conditions. The protein hydrolysates, prepared with different degrees of hydrolysis, were freeze dried, fortified with either iron or zinc and analyzed for bioaccessible minerals in vitro. Proteolytic hydrolysis enhanced the bioaccessibility of iron and zinc in proportion to the degree of hydrolysis. Tryptic hydrolysis enhanced the bioaccessible iron from 1–4% in casein and from 1.3–3.3% in soybean. Alcalase hydrolysis showed a comparatively higher enhancement with both the proteins. Tryptic hydrolysis enhanced zinc bioaccessibity by 3-fold in casein and alcalase hydrolysis enhanced by 2- to 2.5-fold. In soybean, dephytinization showed a synergistic effect. In conclusion, enzymatic hydrolysis of proteins looks promising for enhancing bioaccessibility of minerals in protein matrices.     

Dispersal and sink pathways of suspended particulate matter from the orographically enhanced SWM regime of the SE Arabian Sea

The magnitude of precipitation plays an important role in the yield and supply of terrigenous matter into the sea through fluvial supply. The influence of climate on the influx of total suspended matter (TSM) into the continental margin of the SE Arabian Sea has been evaluated from subweekly synoptic variations in TSM and its advection rates, currents and winds during the southwest monsoon (SWM) and postmonsoon season. Our study endorses the high influx of TSM during the SWM (>82 mg l^?1; advection 26–110 mg m^?2 s^?1), albeit that most of it is sequestered into the shallow coastal region. Over the mid-outer shelf, there is uniformly low TSM (12–24 mg l^?1) and a weak TSM advection (9–4 mg m^?2 s^?1) throughout the year. This trend is persistent also in areas having upwelling-induced high marine productivity. We therefore surmise that higher fluvial influx and primary production during the SWM do not necessarily enhance the supply of particulate matter into the deeper offshore regions of the SE Arabian Sea. We ascribe a vital role to the prevailing morphodynamic processes.     

Effect of iron content on permeability and power loss characteristics of Li0.35Cd0.3Fe2.35O4 and Li0.35Zn0.3Fe2.35O4

Substituted lithium ferrites having the chemical formula, Li_0.35Cd_0.3Fe_2.35O₄ and Li_0.35Zn_0.3Fe_2.35O₄, with different iron (metal) contents (2, 4, 6, 8 and 10) in wt% have been prepared by solid-state technique. Complex permeability and power loss of all samples have been measured by network analyser in the frequency range of 50–5000 kHz. Magnetic properties like saturation magnetization, coercivity, retentivity have been measured by vibrating sample magnetometer (VSM). The permeability of cadmium doped lithium ferrites exhibited higher values than zinc doped lithium ferrites. The power loss of cadmium doped lithium ferrites is lesser as compared to zinc doped lithium ferrites in the frequency range of 50–5000 kHz and at flux density of 10 mT. The behaviour of power loss with flux density has been found near about same for both series. Magnetic and power loss behaviour of the samples suggest that a small amount of Fe content can improve the properties of ferrite samples for microwave devices.     

Metal ion sensing and light activated antimicrobial activity of aloe-vera derived carbon dots

Carbon dots (CDs) have emerged out as a potential material amongst the carbon family for a wide range of applications including chemical/biological sensing, photocatalysis, bioimaging, etc. The green synthesis of these CDs from natural sources is gaining the significant interest of peer community for their wide utility. Herein, we present a facile one-step pyrolysis method for CDs synthesis from Aloe-Vera extract, which show bright blue luminescence under UV light with a quantum yield of 12.3%. Further, ex-situ morphological, structural and optical characterizations reveal their high quality and excitation independent emission behavior with the presence of carboxyl, hydroxyl functional groups. Furthermore, these CDs were studied for Fe(III) sensing in water without any surface modifications and assessed for their light activated antibacterial activity against E.Coli and Staphylococcus aureus.     

Combination of Fitness-Mated Lion Algorithm with Neural Network for Optimal Query Ordering Data Aggregation Model in WSN

Query processing can be briefly defined as a database that comprises of an organized collection of data for one or more users either in digital form or in analog form such that it can portray exactly. A Wireless Sensor Network (WSN) is a specialized network of minimum cost, and power sensor nodes that can be described as the ability of performing some processing, gathering sensory information and communicating with each other. Query ordering with data aggregation is the process of scheduling of the nodes to receive the useful data from sensors. Data aggregation is considered as one of the fundamental processing procedures for saving the energy. In WSN data aggregation is an effective way to save the limited resources. This paper proposes a novel query-based data aggregation model with the aid of intelligent techniques. The framing of the query order takes place and the frames are ranked on the basis of a multi-objective function. The newly developed multi-objective function includes Latency, Throughput, and Data freshness. Initially, the solution corresponding to query order is trained in NN using the proposed Fitness-Mated Lion Algorithm (FM-LA). The optimally generated query order from NN is further given for second-level solution generation, which is again applied to FM-LA for subsequent query order optimization. Hence the two-stage optimization process with NN for query ordering is compared over the conventional methods in terms of performance measures like Latency, throughput, and data freshness. Hence, substantiated performance and comparative analysis validate the improved performance of the proposed model.

     

Assessment of Cartosat-1 DEM for Modeling Floods in Data Scarce Regions

Water Resources Management - Digital Elevation Model (DEM) plays an important role in modeling floods. In data scarce developing countries, unavailability of high resolution topography and river...     

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.