Synthesis of Cellulose-Based Macromolecular Coupling Agent and its Utilization in Poly (butylene succinate)/Wood Flour Composites

A long fatty side chain was introduced into the macromolecule of hydroxyethyl cellulose (HEC) via esterification reaction. The hydrophobicity of hydroxyethyl cellulose lauric acid ester (HECLAE) was enhanced in comparison with HEC. The obtained HECLAE was used as macromolecular coupling agent in poly (butylene succinate)/wood flour composites and exhibited a positive influence on improving the mechanical performance of composites. Besides, HECLAE plays a role as a hydrophobic agent in composites. A significant increase in storage modulus (E’) was observed upon the incorporation of treated wood flour. SEM images showed that the dispersion of treated wood flour in PBS matrix was improved.     

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy.     

Traumatic brain injury,driver aggression and motor vehicle collisions in Canadian adults

ObjectiveThis study examines the associations between lifetime traumatic brain injury (TBI), driver aggression, and motor vehicle collisions among a population sample of adults who reside in the province of Ontario, Canada.MethodA cross-sectional sample of 3993 Ontario adults, aged 18–97 were surveyed by telephone in 2011 and 2012 as part of Center for Addiction and Mental Health’s ongoing representative survey of adult mental health and substance use in Canada. TBI was defined as trauma to the head that resulted in loss of consciousness for at least five minutes or overnight hospitalization.ResultsAn estimated 91% (95% CI: 90.0, 91.9) of individuals in this sample held a valid Ontario driver’s license at the time of testing. Among those, 16.7% reported a history of lifetime TBI and 83.3% reported no TBI. The prevalence of TBI was higher among men than women. Relative to licensed adults without TBI, adults with a history of TBI had significantly higher odds of engaging in serious driver aggression in the past 12 months, such as making threats to hurt another driver, passenger or their vehicle (AOR = 4.39). These individuals also reported significantly higher odds (AOR = 1.74) of being involved in a motor vehicle collision that resulted in hurting themselves, their passenger(s) or their vehicle.ConclusionThis is the first population-based study to demonstrate a relationship between a history of TBI and higher rates of serious driver aggression and collision involvement. Given the large proportion of adult drivers with a history of TBI, these individuals may account for a disproportion burden of all traffic safety problems. Whether the increased road safety risk of adults with a history of TBI is reflective of neurocognitive deficits or is merely evidence of a cluster of unsafe activities produced by a higher risk lifestyles requires further research attention.     

Room temperature ferromagnetic ordering of NiO films through exchange coupling

NiO films were deposited by sputtering with a relatively high oxygen partial pressure, followed by subsequent annealing in air. The as-deposited film is amorphous. The amorphous film gradually turns to crystalline structure with the increase in annealing temperature. The film annealed at 673 K shows strong ferromagnetism at room temperature, whereas, there is no room-temperature ferromagnetic ordering for the as-deposited amorphous NiO film or the film after annealed at 873 K. The ferromagnetism is due to the finite size effect and the high Curie temperature above room temperature is associated with the interaction between ferromagnetic NiO crystalline nanoclusters and antiferromagnetic amorphous matrix.     

Prism coupling method to evaluate the light extraction efficiency of outcoupling substrates

One of the key challenges in organic light-emitting diodes (OLEDs) for lighting applications is efficient light extraction from the planar, multi-layered OLED stack. Several different light extraction approaches are being explored currently by researchers, however characterizing light extraction films after fabricating OLEDs is not a viable approach when the outcoupling films have large surface roughness and is time consuming as well. Here we apply prism coupling method (PCM), a simple and elegant tool, to characterize outcoupling films. We show the effectiveness of PCM in estimating light extraction efficiency of outcoupling films. PCM can expedite selection and optimization of various light extraction approaches without the need to build OLEDs. The experimental results are corroborated by the optical simulations done using ray tracing method taking into account Mie scattering from wavelength sized spherical inclusions in an outcoupling film.     

低碳时变城配送车辆路径-发车调度集成优化

针对交通拥挤环境下日益增长的城市配送需求，通过分析时序依赖对成本和碳排放的影响，引入车辆在节点等待和离散调度策略，研究基于时序依赖的低碳城市配送车辆路径与离散调度问题。为求解该问题，设计基于遗传算法与局部搜索相结合的混合进化搜索算法对模型求解，用积极的局部搜索机制替代随机的变异操作，并通过可行解构造算法、变概率交叉和多种局部搜索策略来提高算法求解质量和求解效率。通过对比仿真实验对算法和模型的有效性进行了验证。     

A multiagent‐based microgrid operation method considering charging and discharging strategies of electric vehicles

In this paper, we propose a multiagent‐based microgrid (MG) operation method considering charging and discharging electric vehicles (EVs). The proposed system consists of five types of agents: single microgrid controller agent, several load agents, several gas turbines/engine agents, several photovoltaic generation agents, and several electric vehicle agents. In the proposed method, the load balancing can be realized by suppressing sudden fluctuations in supply and demand balance due to the synchronization of charging and discharging of EVs. From the simulation results, it can be seen that the proposed multiagent system could realize the load equalization in MG.     

Towards the Unified Principles for Level 5 Autonomous Vehicles

The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.