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Marketing and corporate social responsibility (CSR) are crucial factors that affect closed-loop supply chain network (CLSCN) equilibrium. And the environment of enterprises’ behaviors often occurs during durations and dynamics. To explore the optimal marketing and CSR strategies in a dynamic multi-period CLSCN system, this paper investigates the dynamic multi-period CLSCN equilibrium problem considering marketing and CSR. A multi-period CLSCN system includes manufacturers, retailers, recyclers, and demand markets. Based on Nash non-cooperative game theory and variational inequality, we design the optimal behavior and equilibrium conditions of members. Then, a new equilibrium model of a multi-period CLSCN is constructed. In this model, marketing is the responsibility of manufacturers and retailers, and CSR is the responsibility of manufacturers. Numerical examples are provided to verify the effectiveness of the model and analyzed to describe how marketing and CSR affect equilibrium results. We find that the retailers are responsible for marketing, and the level of CSR activities of the manufacturers is higher during the early period, which is most beneficial to the multi-period CLSCN system and social welfare. Based on the conclusions of this paper, the management enlightenments from the perspectives of enterprises and government are also proposed.

Machine learning (ML) lifecycle is a cyclic process to build an efficient ML system. Though a lot of commercial and community (non-commercial) frameworks have been proposed to streamline the major stages in the ML lifecycle, they are normally overqualified and insufficient for an ML system in its nascent phase. Driven by real-world experience in building and maintaining ML systems, we find that it is more efficient to initialize the major stages of ML lifecycle first for trial and error, followed by the extension of specific stages to acclimatize towards more complex scenarios. For this, we introduce a simple yet flexible framework, MLife, for fast ML lifecycle initialization. This is built on the fact that data flow in MLife is in a closed loop driven by bad cases, especially those which impact ML model performance the most but also provide the most value for further ML model development—a key factor towards enabling enterprises to fast track their ML capabilities. Better yet, MLife is also flexible enough to be easily extensible to more complex scenarios for future maintenance. For this, we introduce two real-world use cases to demonstrate that MLife is particularly suitable for ML systems in their early phases.

When an aircraft flies in a cloud layer containing supercooled water droplets, under the combined action of meteorological conditions and the aircraft's own flight status, it is easy to form an ice layer on the surface of the windward parts of the aircraft. The icing of different parts makes the flight safety of the aircraft threatened. Aircraft icing is an important research topic in the aviation field. It is often necessary to predict the icing type of aircraft and analyze the impact of icing on aircraft flight. This article summarizes the ice types of aircraft icing and their formation conditions, and analyzes several methods commonly used in aircraft icing research, hoping to be helpful for the follow-up more in-depth research.

Recent research results and on-site operation experience have shown that there may be small and sporadic partial discharge (PD) before some insulation flashover, but the existing online monitoring methods cannot effectively monitor them, which may cause non-early-warning failures. Pulse excitation measurement method can be used to effectively improve the data storage efficiency, and can realize the high-precision and long-term measurement of sporadic pulses. However, further analysis of discharge characteristics and fault location need to screen and identify discharge pulses from a large number of signals, including interference signals. In this process, small and sporadic pulses are very likely to be ignored, which will affect subsequent analysis and judgment. Therefore, in this paper, a feature extraction method based on the signal spectrum was proposed, and the DBSCAN density clustering algorithm was used to process the discharge signals, which can classify the discharge signal automatically and effectively so that a large number of pulse signals can be screened and identified efficiently and quickly, to solve the identification problem of small and sporadic pulses, and to avoid the non-early-warning faults of the equipment to a large extent. At the same time, it also lays a good foundation for the subsequent pattern recognition and fault location, which makes greatly improve the efficiency of the overall processing of the PD signal.

Locomotor training using treadmill has been shown to elicit significant improvements in locomotor ability for some children with cerebral palsy (CP), the functional gains are relatively small and it requires greater involvement from a physical therapist. Current robotic gait training systems are effective in reducing the strenuous work of a physical therapist during locomotor training, but are less effective in improving locomotor function in some children with CP due to the limitations of the systems. Thus, a 3D cable-driven robotic gait training system was developed and tested in five children with CP through a 6 week of long-term gait training. Results indicated that both overground walking speed and 6 minute walking distance improved after robot assisted treadmill training through the cable-driven robotic system, and partially retained at 8 weeks after the end of training. Results from this pilot study indicated that it seems feasible to conduct locomotor training in children with CP through the 3D cable-driven robotic system.

Gaussian bare-bones differential evolution (GBDE) is a new differential evolution (DE) variant, which has shown good search abilities on low-dimensional optimization problems. In this paper, we present a scalability test of GBDE on high-dimensional optimization problems with dimensions 100, 200, 500, and 1000. Experimental results show that GBDE achieves promising results on the majority of test functions.

A model of Polygon Circle is proposed to simulate the dynamic burning trajectories in a polygonal pasture. Several technologies are used to achieve this algorithm in this paper. They are: (1) The computing for the minimum and maximum distance of a viewpoint to a vector line. (2) Total 17 kinds of locations for the position relationships of a circle to vector lines. (3) The definition for IntoPoint and OutPoint, and the count of both points are also used to judge whether drawing an arc. (4) The visible division and the deep visible division for a polygon. (5) The shortest path and its expansion. This algorithm can achieve such things that drawing the burning trajectories at different times, computing the burning times in different positions, estimating the longest distance and the longest time of burning. This algorithm is stabile by VC++ programming testing.

In the framework of optimal law enforcement theory, the existed literatures have analyzed the acts of selective law enforcement under the static conditions. In this paper, we use incomplete information dynamic game model to analyze the process of dynamic game between the enforcer and the potential offenders. The result shows that the potential offenders can get relevant enforcement information by observing, studying, and predicting the situations and modify their behaviors based on this information. These opportunistic behaviors not only bring the inefficient allocation of resources, but also damage the credibility and justice of the law.