Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

This paper proposes two novel packet scheduling schemes, called as throughput enhanced scheduling (TES) and TES plus (TES+), for future ultra‐dense networks. These schemes introduce two novel parameters to the scheduling decision making and reformulate the parameters used by the state‐of‐the‐art schemes. The aim is to have a more balanced weight distribution between delay and throughput‐related parameters at scheduling decisions. Also include a new telecommunications related parameter into scheduling decision making that has not been studied by popular schedulers. The performance of novel schemes is compared with well‐known schemes—proportional fairness (PF), exponential/proportional fairness (EXP/PF), and M‐LWDF. For performance evaluation, five performance metrics—average spectral efficiency and delay, quality of service (QoS) violation ratio, jitter, and Jain's fairness index—are investigated. The simulation results show that proposed schemes can outperform all the compared scheduling schemes.     

BinEHO: a new binary variant based on elephant herding optimization algorithm

One of the new optimization techniques proposed in recent years is elephant herding optimization (EHO) algorithm. Despite its short history, EHO has been used to solve many engineering and real-world problems by attracting researcher attention with its advantages such as efficient global search ability, having fewer control parameters and ease of implementation. However, there is no remarkable binary variant of EHO algorithm in the literature. A new binary approach based on EHO algorithm is proposed in this study. The newer binary variant of EHO named as BinEHO is binarized with preserving the search ability of basic EHO. The main purpose of the study is to present a simple, efficient and robust binary variant which copes with different binary problems. Therefore, the proposed method is tested on three important binary optimization problems, 0–1 knapsack, uncapacitated facility location and wind turbine placement, in order to show its performance and accuracy. In addition, the BinEHO is compared with various binary variants on these problems. Experimental results and comparisons show that the BinEHO algorithm is a robust and efficient tool for binary optimization.

     

Developing Predictive Equations to Model the Visual Demand of In-Vehicle Touchscreen HMIs

Touchscreen human–machine interfaces (HMIs) are commonly employed as the primary control interface and touch-point of vehicles. However, there has been very little theoretical work to model the demand associated with such devices in the automotive domain. Instead, touchscreen HMIs intended for deployment within vehicles tend to undergo time-consuming and expensive empirical testing and user trials, typically requiring fully functioning prototypes, test rigs, and extensive experimental protocols. While such testing is invaluable and must remain within the normal design/development cycle, there are clear benefits, both fiscal and practical, to the theoretical modeling of human performance. We describe the development of a preliminary model of human performance that makes a priori predictions of the visual demand (total glance time, number of glances, and mean glance duration) elicited by in-vehicle touchscreen HMI designs, when used concurrently with driving. The model incorporates information theoretic components based on Hick–Hyman Law decision/search time and Fitts’ Law pointing time and considers anticipation afforded by structuring and repeated exposure to an interface. Encouraging validation results, obtained by applying the model to a real-world prototype touchscreen HMI, suggest that it may provide an effective design and evaluation tool, capable of making valuable predictions regarding the limits of visual demand/performance associated with in-vehicle HMIs, much earlier in the design cycle than traditional design evaluation techniques. Further validation work is required to explore the behavior associated with more complex tasks requiring multiple screen interactions, as well as other HMI design elements and interaction techniques. Results are discussed in the context of facilitating the design of in-vehicle touchscreen HMI to minimize visual demand.     

Modeling of ferrofluid magnetic actuation with dynamic magnetic fields in small channels

One of the most important and promising research areas in biomedical and micropumping applications is magnetic actuation of ferrofluids with dynamic magnetic fields. For ensuring the use of ferrofluids in various applications in engineering fields, their flows generated by magnetic fields should be extensively investigated and simulated. In this study, simulations of ferrofluid actuation with dynamic magnetic fields were performed by modeling it using the COMSOL Multiphysics software, and iron oxide nanoparticle-based ferrofluids at different angles of rotating magnets were considered to provide insight into ferrofluid flow in small channels. Ferrofluid flows were modeled at different magnetic flux densities provided by rotating magnets, and velocity profiles inside the channel were analyzed. It was shown that ferrofluid actuation can be considered as a futuristic micropumping alternative, simulation results matched well with the experimental results of previous work, and the established model could serve as a tool to analyze ferrofluid flows generated by dynamic magnetic fields. The results of the model show that flow rates up to 100 µl/s can be reached at a rotation angle of 30° by using dynamic magnetic fields. Various applications including biomedical applications might be envisaged.     

Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation

User adoption of mobile payment (m-payment) is low compared to the adoption of traditional forms of payments. Lack of user trust has been identified as the most significant long-term barrier for the success of mobile finances systems. Motivated by this fact, we proposed and tested an initial trust theoretical model for user adoption of m-payment systems. The model not only theorizes the role of initial trust in m-payment adoption, but also identifies the facilitators and inhibitors for a user’s initial trust formation in m-payment systems. The model is empirically validated via a sample of 851 potential m-payment adopters in Australia. Partial least squares structural equation modelling is used to assess the relationships of the research model. The results indicate that perceived information quality, perceived system quality, and perceived service quality as the initial trust facilitators are positively related to initial trust formation, while perceived uncertainty as the initial trust inhibitor exerts a significant negative effect on initial trust. Perceived asset specificity is found to have insignificant effect. In addition, the results show that initial trust positively affects perceived benefit and perceived convenience, and these three factors together predict usage intention. Perceived convenience of m-payment is also found to have a positive effect on perceived benefit. The findings of this study provide several important implications for m-payment adoption research and practice.     

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern,dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

     

Tests of olivinite-based ladle refractory brick

A lining of burned forsterite—chromite brick, with the olivinite mined at the Khabozerovskoe deposit as the base, was tested in the 385-ton steel-teerning ladle of the converter plant at the Severstal' Works. The brick was made by the method developed at the St. Petersburg Institute of Refractories. The characteristics of the brick are given. The ladle lining was tested on a wide range of converter steel grades. At the end of the service period, the lining was examined and the nature of its wear was identified. The experimental lining withstood 23 heats. It is thought promising to use refractory brick of the forsterite composition in large-tonnage steel-teeming ladles.     

A comparison of clustering-based privacy-preserving collaborative filtering schemes

Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.In this study, we first propose a content-based profiling (CBP) of users to overcome sparsity issues while performing clustering because the very sparse nature of rating profiles sometimes do not allow strong discrimination. To cope with scalability and accuracy problems of PPCF schemes, we then show how to apply k-means clustering (KMC), fuzzy c-means method (FCM), and self-organizing map (SOM) clustering to CF schemes while preserving users’ confidentiality. After presenting an evaluation of clustering-based methods in terms of privacy and supplementary costs, we carry out real data-based experiments to compare the clustering algorithms within and against traditional CF and PPCF approaches in terms of accuracy. Our empirical outcomes demonstrate that FCM achieves the best low cost performance compared to other methods due to its approximation-based model. The results also show that our privacy-preserving methods are able to offer precise predictions.     

作用在防抱制动系统上的外转矩

本文研究了采用一个附加的驱动力矩作用于一带ABS系统的控制机构来缩短制动距离的可能性。在该ABS的控制周期内，只有减小制动压力才能使锁止轮再加速，当在制动压力比较低的阶段可采用一外加牵引力矩来缩短加速时间。可用车辆发动机或一个附加电机作为一个外加牵引力矩源。可以用一个数字模型来研究该系统缩短制动距离的可能性。采用一个仿真模型来分析该附加加速力的影响，并获得可缩短制动距离达到10％的结论。