The programmable nature of smart textiles makes them an indispensable part of an emerging new technology field. Smart textile-integrated microelectronic systems (STIMES), which combine microelectronics and technology such as artificial intelligence and augmented or virtual reality, have been intensively explored. A vast range of research activities have been reported. Many promising applications in healthcare, the internet of things (IoT), smart city management, robotics, etc., have been demonstrated around the world. A timely overview and comprehensive review of progress of this field in the last five years are provided. Several main aspects are covered: functional materials, major fabrication processes of smart textile components, functional devices, system architectures and heterogeneous integration, wearable applications in human and nonhuman-related areas, and the safety and security of STIMES. The major types of textile-integrated nonconventional functional devices are discussed in detail: sensors, actuators, displays, antennas, energy harvesters and their hybrids, batteries and supercapacitors, circuit boards, and memory devices.

Smart Textile-Integrated Microelectronic Systems for Wearable Applications

This work presents and assesses the power of various volumetric, sentiment, and social network approaches to predict crucial decisions from online social media platforms. The views of individuals play a vital role in the discovery of some critical decisions. Social media has become a well-known platform for voicing the feelings of the general population around the globe for almost decades. Sentiment analysis or opinion mining is a method that is used to mine the general population’s views or feelings. In this respect, the forecasting of election results is an application of sentiment analysis aimed at predicting the outcomes of an ongoing election by gauging the mood of the public through social media. This survey paper outlines the evaluation of sentiment analysis techniques and tries to edify the contribution of the researchers to predict election results through social media content. This paper also gives a review of studies that tried to infer the political stance of online users using social media platforms such as Facebook and Twitter. Besides, this paper highlights the research challenges associated with predicting election results and open issues related to sentiment analysis. Further, this paper also suggests some future directions in respective election prediction using social media content.

The emergence of social media data and sentiment analysis in election prediction

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.

/pdf/a-transformer-based-approach-to-irony-and-sarcasm-detection-18dgd3e89c.pdf

A transformer-based approach to irony and sarcasm detection

In this communication, a compact two-element ultra-wideband (UWB) wearable multiple-input multiple-output (MIMO) antenna with high port isolation is presented. The proposed structure is composed of jeans material in which an `8' shaped stub is placed on the middle position of the antenna backside and connected to the partially suppressed ground structure to improve the port isolation characteristics. The antenna covers the frequency range from 2.74 to 12.33 GHz (about 127.27%) with the port isolation of >26 dB over the entire UWB frequency range. The envelope correlation co-efficient is found to be 9.9) throughout the complete operating band. The channel capacity loss for the proposed MIMO antenna is <;0.13 bit/s/Hz. The imprinted optimised UWB MIMO antenna covers the area size of 55 × 35 mm
 2
. The performances of the proposed antenna by the simulation and experimentation equally designated it a blameless candidate for the UWB applications.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-map.2018.5599

Compact wearable MIMO antenna with improved port isolation for ultra-wideband applications

The range of applications of Wireless Sensor Networks (WSNs) is increasing continuously despite of their serious constraints of the sensor nodes’ resources such as storage, processing capacity, communication range and energy. The main issues in WSN are the energy consumption and the delay in relaying data to the Sink node. This becomes extremely important when deploying a big number of nodes, like the case of industry pollution monitoring. We propose an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC. In this technique, the network is trained on huge data set containing almost all scenarios to make the network more reliable and adaptive to the environment. Additionally, it uses group based methodology to increase the life-span of the overall network, where groups may have different sizes. An artificial neural network provides an efficient threshold values for the selection of a group's CN and a cluster head based on back propagation technique and allows intelligent, efficient, and robust group organization. Thus, our proposed technique is highly energy-efficient capable to increase sensor nodes’ lifetime. Simulation results show that it outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.

ELDC: An Artificial Neural Network Based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs

Numerical investigation of flow over a low aspect ratio elliptical cylinder near a moving wall

Funding information A novel meshing scheme, based on regular tetra-kaidecahedron, also referred to as truncated octahedron, cells is presented for use in spatial topology optimization. A tetrakai-decahedron mesh ensures face connectivity between elements thereby eliminating singular solutions from the solution space. Various other benefits of implementing the said mesh are also highlighted, and the corresponding finite element is introduced. Material mask overlay strategy orMMOS, a feature basedmethod for topology optimization is extended for use in 3-dimensions (MMOS-3D) via the aforementioned finite element and spheroidal negative masks. Formulation for density computation and sensitivity analysis for gradient based optimization is developed. Examples on traditional structural topology optimization problems are presented with detailed discussion on efficacy of the proposed approach.

Topology Optimization with Tetra-kai-decahedra and Spheroidal Masks

Abomasal impaction due to phytobezoariasis in buffalo.

The World Wide Web have a large amount of data, the user are find the some relevant information from WWW. If user want to search any information than numbers of URL or number of pages are opened on the web. The user want to be the show the important information are show on the top of the list than this page have a high ranking compare to other and this page is a most relevant page on the web site. The page rank are depends upon the user searching or clicking on the web pages, but some problem are created on given the page ranking about the page, in this paper we discussed the some problem about the page ranking on the world wide web. General Terms Page Ranking Algorithm

/pdf/survey-paper-on-random-surfer-model-in-page-ranking-12o97ao9qq.pdf

Survey Paper on Random surfer model in Page Ranking Algorithm

Wireless Sensor Network is web of sensors node, which has limiting energy .The main issue to design WSNs is, maximum Lifetime and optimal the energy used in network. Different approaches based upon clustering are proposed for optimum functionality. Quadrature-LEACH (Q-LEACH) for homogenous networks is examined with two gateways which is placed in the sensing area, which is used to enhances network life-time and optimal utilization of resources. Performance analysis and compared statistic results show that our proposed protocol perform well in terms of energy consumption and network lifetime.

/pdf/energy-efficient-q-leach-protocols-for-homogenous-wireless-1947j8uhko.pdf

Nikhil Kumar Singh

Papers

Numerical investigation of flow over a low aspect ratio elliptical cylinder near a moving wall

Topology Optimization with Tetra-kai-decahedra and Spheroidal Masks

Abomasal impaction due to phytobezoariasis in buffalo.

Survey Paper on Random surfer model in Page Ranking Algorithm

Energy Efficient Q-LEACH Protocols for Homogenous Wireless Sensor Networks