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This numerical research presents a simple hybrid structure comprised of TiO2-Cu-BaTiO3 for a modified Kretschmann configuration that exhibits high sensitivity and high resolution for biosensing applications through an angular interrogation method. Recently, copper (Cu) emerged as an exceptional choice as a plasmonic metal for developing surface plasmon sensors (SPR) with high resolution as it yields finer, thinner SPR curves than Ag and Au. As copper is prone to oxidation, especially in ambient conditions, the proposed structure involves the utilization of barium titanate (BaTiO3) film as a protection layer that not only preserves Cu film from oxidizing but enhances the performance of the sensor to a great extent. Numerical results also show that the utilization of a thin adhesive layer of titanium dioxide (TiO2) between the prism base and Cu film not only induces strong interaction between them but also enhances the performance of the sensor. Such a configuration, upon suitable optimization of the thickness of each layer, is found to enhance sensitivity as high as 552°/RIU with a figure of merit (FOM) of 136.97 RIU−1. This suggested biosensor design with enhanced sensitivity is expected to enable long-term detection with greater accuracy and sensitivity even when using Cu as a plasmonic metal.

A wireless sensor network (WSN) consists of several tiny sensor nodes to monitor, collect, and transmit the physical information from an environment through the wireless channel. The node failure is considered as one of themain issues in theWSN which creates higher packet drop, delay, and energy consumption during the communication. Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets. In this paper, Artificial Neural Network (ANN) based Node Failure Detection (NFD) is developed with cognitive radio for detecting the location of the node failure. The ad hoc on-demand distance vector (AODV) routing protocol is used for transmitting the data from the source node to the base station. Moreover, the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure. The performance of the proposed ANN-NFD method is analysed in terms of throughput, delivery rate, number of nodes alive, drop rate, end to end delay, energy consumption, and overhead ratio. Furthermore, the performance of the ANN-NFD method is evaluated with the header to base station and base station to header (H2B2H) protocol. The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol. Hence, the ANN-NFD method provides data consistency during data transmission under node and battery failure. [ABSTRACT FROM AUTHOR]

Artificial Intelligence-based Quorum systems are used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network’s actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively. [ABSTRACT FROM AUTHOR]

Based on the diffraction theory, the focusing properties of a radially polarized quadratic Bessel--Gaussian beam (QBG) with on-axis radial phase variance wavefront are investigated theoretically in the focal region of a high numerical aperture (NA) objective lens. The phase wavefront C and pupil beam parameter μ of QBG are the functions of the radial coordinate. The detailed numerical calculation of the focusing property of a QBG beam is presented. The numerical calculation shows that the beam parameter μ and phase parameter C have greater effect on the total electric field intensity distribution. It is observed that under the condition of different μ, evolution principle of focal pattern differs very remarkably on increasing C. Also, some different focal shapes may appear, including rhombic shape, quadrangular shape, two-spherical crust focus shape, two-peak shape, one dark hollow focus, two dark hollow focuses pattern, and triangle dark hollow focus, which find wide optical applications such as optical trapping and nanopatterning. [ABSTRACT FROM AUTHOR]

As the field of data science grows, document analytics has become a more challenging task for rough classification, response analysis, and text summarization. These tasks are used for the analysis of text data from various intelligent sensing systems. The conventional approach for data analytics and text processing is not useful for big data coming from intelligent systems. This work proposes a novel TF/IDF algorithm with the temporal Louvain approach to solve the above problem. Such an approach is supposed to help the categorization of documents into hierarchical structures showing the relationship between variables, which is a boon to analysts making essential decisions. This paper used public corpora, such as Reuters-21578 and 20 Newsgroups for massive-data analytic experimentation. The result shows the efficacy of the proposed algorithm in terms of accuracy and execution time across six datasets. The proposed approach is validated to bring value to big text data analysis. Big data handling with map-reduce has led to tremendous growth and support for tasks like categorization, sentiment analysis, and higher-quality accuracy from the input data. Outperforming the state-of-the-art approach in terms of accuracy and execution time for six datasets ensures proper validation. [ABSTRACT FROM AUTHOR]

This research aims to combine the effects of nanosilica and glass powder on the properties of self-compacting mortar at normal and at higher temperatures. The fine aggregate was replaced by waste glass powder at various percentage levels of 10%, 20%, 30%, 40% and 50%. The mechanical properties of self-compacting glass mortar (SGCM) were studied at elevated temperatures of 200, 400, 600 and 800 °C. Furthermore the effect of sudden and gradual cooling technique on the residual strength of glass mortar was also investigated In order to enhance the behavior of SCGM the nanosilica of 3% by weight of cement was added. From the results it was obtained that the glass powder replacement effectively contributed towards the thermal performance while the addition of nanosilica enhanced the mechanical performance. The enhanced physical properties were obtained mainly at the glass transition temperature thus showing the active participation of glass powders during high temperatures. Moreover the gradually cooled specimens exhibited improved strength characteristics than the suddenly cooled specimens. [ABSTRACT FROM AUTHOR]