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The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle).

This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.

This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification.

The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood.

In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles.

In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system.

This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.

The purpose of this paper is to answer the following two questions: Will Saudi Arabia get older? Will its pension system be sustainable?

The methodology/approach is to forecast KSA’s population with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson-Durbin recursion, then relies on an infinite impulse response prediction error filter.

Spectral analysis projections of Saudi age groups are more optimistic than the Bayesian probabilistic model sponsored by the United Nations Population Division: Saudi Arabia will not get older as fast as projected by the United Nations model. The KSA’s pension system will stay sustainable based on spectral analysis, whereas it will not based on the U.N. model.

Spectral analysis will provide better insight and understanding of population dynamics for Saudi government policymakers, as well as economic, health and pension planners.

To advance marketing research and practice, this study aims to examine the application of the innovative, mobile-applicable neuroimaging method – mobile functional near-infrared spectroscopy (mfNIRS) – in the field of marketing research, providing comprehensive guidelines and practical recommendations.

A general review and investigation of when and how to use mfNIRS in business-to-consumer and business-to-business marketing settings is used to illustrate the utility of mfNIRS.

The research findings help prospective marketing and consumer neuroscience researchers to structure mfNIRS experiments, perform the analysis and interpret the obtained mfNIRS data.

The application of mfNIRS offers opportunities for marketing research that allow the exploration of neural processes and associated behaviour of customers in naturalistic settings.

The application of mfNIRS as a neuroimaging method enables the investigation of unconscious neural processes that control customer behaviour and can act as process variables for companies.

This is one of the first studies to provide comprehensive guidelines and applied practical recommendations concerning when and how to apply mfNIRS in marketing research.

Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

Ride comfort is one of the important factors affecting passenger health. Therefore, the elevator industry usually uses the International Organization for Standardization (ISO) 18738-1 standard to evaluate elevator ride quality and optimize elevator design. However, this method has certain limitations in its evaluation of comfort due to the problem of boundary division. The ISO 2631-4 standard is used as a general method of comfort evaluation in the current rail transit system, but it has not been applied in the elevator industry. In order to explore the difference and connection between the two standards, the author aims to conduct a detailed analysis on this.

Based on the elevator internet, a large amount of measured data of normal and abnormal vibration of elevator car were collected and analyzed and preprocessed; based on ISO 18738-1:2012 standard and ISO 2631-4:2001 standard, the differences of ride comfort assessment methods in the two standards were analyzed, and the ride comfort assessment study of elevator under normal and abnormal vibration conditions was carried out.

The experimental results show that the comfort assessment results of ISO 2631-4:2001 standard and ISO18738-1:2012 standard are consistent under two vibration conditions. At the same time, ISO 2631-4:2001 can not only provide a more accurate quantitative description of comfort, but also roughly determine the comfort interval of each vibration, which can provide theoretical reference for elevator vibration classification and car comfort design.

The authors designed an Internet of Things (IOT)-based elevator vibration signal acquisition method to address the shortcomings of the previous elevator ride comfort assessment methods, which can realize the dynamic assessment of elevator ride comfort; by comparing the assessment results of elevator ride comfort under normal vibration and abnormal vibration, the feasibility of ISO 2631-4:2001 for elevator ride comfort assessment was fully verified. In addition, the experimental results also give the influence of abnormal vibration on elevator riding comfort under the stages of start-stop, uniform speed, acceleration and deceleration, which can provide theoretical support for elevator vibration suppression and comfort transformation.

The purpose of this paper is to show that major reversals of an index (specifically BIST-30 index) can be detected uniquely on the date of reversal by checking the extreme outliers in the rate of change series using daily closing prices.

The extreme outliers are determined by checking if either the rate of change series or the volatility of the rate of change series displays more than two standard deviations on the date of reversal. Furthermore; wavelet analysis is also utilized for this purpose by checking the extreme outlier characteristics of the A1 (approximation level 1) and D3 (detail level 3) wavelet components.

Paper investigates ten major reversals of BIST-30 index during a five year period. It conclusively shows that all these major reversals are characterized by extreme outliers mentioned above. The paper also checks if these major reversals are unique in the sense of being observed only on the date of reversal but not before. The empirical results confirm the uniqueness. The paper also demonstrates empirically the fact that extreme outliers are associated only with major reversals but not minor ones.

The results are important for fund managers for whom the timely identification of the initial phase of a major bullish or bearish trend is crucial. Such timely identification of the major reversals is also important for the hedging applications since a major issue in the practical implementation of the stock index futures as a hedging instrument is the correct timing of derivatives positions.

To the best of the author’ knowledge; this is the first study dealing with the issue of major reversal identification. This is evidently so for the BIST-30 index and the use of extreme outliers for this purpose is also a novelty in the sense that neither the use of rate of change extremity nor the use of wavelet decomposition for this purpose was addressed before in the international literature.

Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.

In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM.

The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τ_c method and P_d method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations.

At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

Since Islamic markets are growing substantially, there is an urgent need to gain a better understanding of how Muslim consumers perceive products from a religious perspective. The purpose of this paper is to investigate the brain responses of Muslim consumers to Halal and non-Halal products using a functional magnetic resonance imaging (fMRI) technology.

The research model is a simplified version of the theory of planned behavior. The initial experiment began by asking participants to divide a set of images into two groups: Halal and non-Halal products. The fMRI experiment uses a blocked design approach to capture brain activities resulting from presenting the two groups of images to participants, and to record the strength of their attitudes toward purchasing the products.

Across all participants, the level of brain activation in the ventromedial prefrontal cortex increased significantly when Halal images were presented to them. The same results emerged when the Halal images showed raw and cooked meat. The variations in the results may be due to the high emotional sensitivity of Muslim consumers to using religious products.

This study uses a unique approach to monitor brain activity to confirm that consumers from specific market segments respond differently to market products based on their internal beliefs. Findings from this study provide evidence that marketing managers targeting Muslim markets should consider the sensitivity of presenting products in ways that reflect religious principles, in order to gain higher acceptance in this market segment.

Although the literature reports considerable research on Muslim consumers’ behavior, most of the previous studies utilize conventional data collection approaches to target broad segments of consumers by using traditional products. This paper is the first to track the reactions of the Muslim consumer segment to specific types of market products.