No abstract

With the emergence of autonomous ground vehicles and the recent advancements in Intelligent Transportation Systems, Autonomous Traffic Management has garnered more and more attention. Autonomous Intersection Management (AIM), also known as Cooperative Intersection Management (CIM) is among the more challenging traffic problems that poses important questions related to safety and optimization in terms of delays, fuel consumption, emissions and reliability. Previously we introduced two stop-sign based policies for autonomous intersection management that were compatible with platoons of autonomous vehicles. These policies outperformed regular stopsign policy both in terms of average delay per vehicle and variance in delay. This paper introduces a reservation-based policy that utilizes the cost functions from our previous work to derive optimal schedules for platoons of vehicles. The proposed policy guarantees safety by not allowing vehicles with conflicting turning movement to be in the conflict zone at the same time. Moreover, a greedy algorithm is designed to search through all possible schedules to pick the best that minimizes a cost function based on a trade-off between total delay and variance in delay. A simulator software is designed to compare the results of the proposed policy in terms of average delay per vehicle and variance in delay with that of a 4-phase traffic light.

Police officers may be at a greater risk for cardiovascular disease (CVD) than the general population due to their highstress occupation. This study evaluated how an educational program based on the health belief model (HBM) may protect police officers from developing CVD. Methods: In this single-group experimental study, 58 police officers in Iran participated in a 5-week intervention based on HBM principles. Outcomes included changes in scores on an HBM scale, time spent on moderate to vigorous physical activity (International Physical Activity Questionnaire), body mass index (BMI), blood lipid profile, blood glucose, and blood pressure. The intervention consisted of 5 HBM-based educational sessions. Follow-up was conducted at 3 months post-intervention. The paired t-test was used to examine differences between baseline and follow-up scores. Results: All aspects of the HBM scale improved between baseline and follow-up (p<0.05), except the cues to action subscale. Self-efficacy and preventive behaviors improved the most. BMI decreased from 26.7±2.9 kg/m 2 at baseline to 25.8±2.4 kg/m 2 at follow-up. All components of the lipid profile, including triglycerides, cholesterol, high-density lipoprotein, and low-density lipoprotein, showed significant improvements post-intervention. Blood glucose and blood pressure also decreased, but not significantly. Nearly 25% of participants who were not physically active at baseline increased their physical activity above or beyond the healthy threshold. Conclusions: A relatively brief educational intervention based on HBM principles led to a significant improvement in CVD risk factors among police officers. Further research is needed to corroborate the effectiveness of this intervention.

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

A very challenging issue in robot navigation or path planning in an unknown environment is to find a globally optimal path from the start to the target point at the same time avoid collisions. This paper presents a new sensor-based online method for generating collision-free optimal path for mobile robots to take a target amidst static obstacles. It is assumed that, target is static and the location of obstacles is completely unknown for robot and all of these materials will be calculated online. Although the area that is under vision of robot’s sensor is confined, we have to consider an inevitable assumption that target is detectable by robot in everywhere. Proposed algorithm to avoid colliding the obstacles in its way toward target, detects a short and feasible paths by utilizing an innovative and effective method. Following that, shortest of them to will be chosen for navigation. There are different important factors which are considered in robot motion planning problems. First, required time for robot’s action in unpredicted circumstances that the running time of the used algorithm plays a major role in this term. Second, the length of traveled path from start point to target point that represents the efficiency of exploited algorithm for leading the robot. The presented algorithm of this paper has proved its efficiency in both of mentioned issues. Simulation results show that traveled path has the least length and the running time is remarkably less than other presented algorithms until now. In addition, the effectiveness of presented algorithm in complex situation is discernible. These results prove that the presented novel and effective robot navigation algorithm is very suitable for real-time navigation in complex environments