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This study aims to propose a centralized optimal control model for automated left-turn platoon at contraflow left-turn lane (CLL) intersections.

The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness. The proposed model is cast into a mixed-integer linear programming problem and then solved by the branch-and-bound technique.

The proposed model has a promising control effect under different geometric controlled conditions. Moreover, the proposed model performs robustly under various safety time headways, lengths of the CLL and green times of the main signal.

This study proposed a centralized optimal control model for automated left-turn platoon at CLL intersections. The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness

The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

Halal certification is an acknowledgment of the halalness of a product or service issued by a halal regulator based on Islamic law. This study aims to investigate the intentions of consumers and regulators toward blockchain-based halal certification. Blockchain is useful for storing and verifying halal certificates, thereby increasing trust in products or services because the public cannot change or access data once it is stored.

This study uses a triangulation approach by distributing online questionnaires to consumers as a research instrument of a quantitative approach processed with smart partial least squares. Meanwhile, the qualitative approach is carried out through observation, in-depth interviews with the Ministry of Religion’s Halal Product Assurance Organizing Agency (BPJPH) and Halal Examination Agency (LPH), and forum group discussions (FGDs) with several related parties.

The observation results show that most consumers expect the government to provide an easy-to-use application to check halal food products and restaurants. Consumers’ intention to use this technology is influenced directly by attitudes and indirectly by their beliefs. Furthermore, the results of interviews and FGDs reported that LPH was not ready to apply blockchain technology, while BPJPH strongly supported adopting blockchain technology in the certification process.

This finding recommends that the Indonesian government apply blockchain technology to gain transparency and accountability regarding the halal product process.

This study fills the research gap by observing three perspectives from different stakeholders and using a triangulation approach to analyze the need for adoption of blockchain-based halal certification of halal food products.

The authors explore neuro-enhanced reality (NeR) as a novel approach for enhancing service communication between customers, frontline employees, and service organizations that extends beyond current state-of-the-art approaches based on augmented reality (AR) and virtual reality (VR) technologies.

The authors first take stock of research on reality-enhanced service communication with AR and VR, then complement these insights with emerging neuroscientific research to conceptualize how NeR enables innovative forms of service communication. On this basis, the authors develop a research agenda to guide the future study and managerial exploitation of NeR.

AR and VR already offer unique affordances for digital-to-physical communication, but these can be extended with NeR. Specifically, NeR supports neuro-to-digital and digital-to-neuro communication based on neuroimaging (e.g. controlling digital content through thought) and neurostimulation (e.g. eliciting brain responses based on digital content). This provides a basis for outlining possible applications of NeR across service settings.

The authors advance knowledge on reality-enhanced service communication with AR and VR, whilst also demonstrating how neuroscientific research can be extended from understanding brain activity to generating novel service interactions.