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The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.

An optimization algorithm, model predictive control (MPC) and Karush–Kuhn–Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.

The effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption.

This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

The purpose of this paper is to select a theoretical framework for effective school leadership that is connected with research, standards and current practices in the USA, and examine its validity and generalizability cross-culturally.

The paper uses both qualitative and quantitative methods through expert panel evaluation, cognitive interviews, and field testing of the instrument. The author asks: How well does the Learning-Centered Leadership (LCL) framework align with the professional standards and current practices of principals in urban Chinese schools in the opinion of the experts? Is there evidence that its leadership assessment instrument has construct validity in Chinese urban schools based on the re-examination of its content and measurement criteria? And is there evidence that the instrument is yielding consistent results when taken by the intended participants? How effective are the analytic strategies employed by this study in detecting the equivalences and discrepancies in how educational leadership is defined and evaluated, between two vastly different educational systems?

The paper finds evidences that give support to the claim that there is strong cross-cultural alignment on the overarching goal of improving student learning. However, the US framework and assessment will need to be modified to reflect the Chinese reform priorities that emphasize the balance between academic and social learning.

The belief that there are common elements in contemporary international educational policy has brought growing interest in sharing leadership theories and successful models cross-culturally. This paper addresses the challenges in understanding the complexity of cross-cultural translation of theories and applications, and explores viable solutions to meaningful adaptation.

The purpose of this research is to design and develop a technique for polyphase code design for the radar system.

The proposed fractional harmony search algorithm (FHSA) performs the polyphase code design. The FHSA binds the properties of the harmony search algorithm and the fractional theory. An optimal fitness function based on the coherence and the autocorrelation is derived through the proposed FHSA. The performance metrics such as power, autocorrelation and cross-correlation measure the efficiency of the algorithm.

The performance metrics such as power, autocorrelation and cross-correlation is used to measure the efficiency of the algorithm. The simulation results show that the proposed optimal phase code design with FHSA outperforms the existing models with 1.420859, 4.09E−07, 3.69E−18 and 0.000581 W for the fitness, autocorrelation, cross-correlation and power, respectively.

The proposed FHSA for the design and development of the polyphase code design is developed for the RADAR is done to reduce the effect of the Doppler shift.

The purpose of this paper is to design petroleum products’ supply chain management, which includes efficient integration of suppliers, manufacturers, storehouses and retailers.

This paper proposes that a three-level supply chain will be turned into a bi-level supply chain of petroleum products by simultaneous integration of the middle level with the upstream and downstream levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels.

The concepts of the design, structure and outputs are led by the model's solution. The model also responds to the variations in the market via coordination in the related decisions to the distribution, production and inventory issues, and also coordinating between the demands and production.

This paper has limited its analysis to definite values due to the over-expansion of calculations and analysis. Future works can study other aspects of the proposed model for a multi-level petroleum product supply chain in different states of certain parameters and time zones.

The designed model can directly and transparently help the oil managers and decision-makers lower the costs of manufacturing, distribution and sales with respect to the determined criteria.

This paper establishes that effectiveness of the dynamic petroleum materials supply chain design will increase by considering maintained and increased production costs and coordinate management flows at all levels by supply chain creation’s integration.