The Roots of the Uncertainty in the Enterprise Tech-innovation Process under the Net-Environment

This paper applies evolutionary view to investigate the enterprise tech-innovation process and study the uncertainty of the routine, net environment and the innovational technique. In the end the measurement of risk is described as the uncertainty characteristic then induced the coherent characteristic between the risk and the uncertainty, which can be applied in the theory on the innovation management

Pricing Strategy and Quick Response Adoption System with Strategic Customers

This study determined the competitive advantage of a quick response (QR) system when a firm faces forward-looking customers with heterogeneous and uncertain valuations for a product, uncertain demand, and two selling periods. We identify two classes of pricing strategies, namely, no-price commitment strategy and price commitment strategy. Interestingly, the unique equilibrium is proven to exist if and only if most customers have high tastes on a product’s value. We also prove that when customers possess beliefs about the markdown in the second period being smaller enough, a firm obtains a high profit with price commitment; otherwise he obtains a high profit without price commitment. Moreover, we distinguish the competitive advantage of a QR system from two strategies. When a firm uses no-price commitment strategy, the value of QR system in the first period decreases and in the second period increases with customer’s strategic behavior. When a firm provides price commitment, the value of QR system in the first period may increase, decrease, or decrease first and then increase with customer’s strategic behavior. And the value of QR in the second period under price commitment strategy decreases or rises first and then decreases with customer’s strategic behavior

Dynamic Energy-Efficient Path Planning for Electric Vehicles Using an Enhanced Ant Colony Algorithm

Electric vehicles (EVs) energy efficient path planning is crucial for maximizing the range of EVs. However, existing path planning algorithms often prioritize least time or shortest path without considering energy efficiency, leading to issues such as long computation time, slow convergence, and suboptimal solutions in complex environments. To address these challenges, this study proposes an improved ant colony optimization (E-ACO) algorithm for dynamic energy efficient path planning of EVs. The E-ACO algorithm incorporates a traffic flow prediction model and an energy consumption model specific to EVs. By redesigning heuristic factors and state transition rules, the algorithm enhances the efficiency and accuracy of path planning. Moreover, to address the challenge of selecting optimal charging station locations based on existing battery levels, a charging path planning method is introduced. This method utilizes the E-ACO algorithm and employs charging station pre-screening strategies to identify the most suitable charging station for completing the charging process. Experimental results show that the E-ACO algorithm reduces energy consumption by approximately 7% compared to the traditional ant colony optimization (ACO) algorithm. Additionally, through data analysis, a pre-screening threshold of 10 charging stations is determined based on the relationship between distance and energy consumption. To provide a visual representation of the path planning results, software is used to display the optimized paths. This allows users to easily interpret and analyze the recommended routes. Overall, the proposed E-ACO algorithm offers an effective and efficient solution for energy-efficient path planning in EVs. The incorporation of charging station pre-screening strategies further enhances the charging process. The study\u27s findings contribute to the development of more sustainable and efficient EV routing strategies, benefiting both EV users and the environment

A robust and fast sliding mode controller for position tracking control of permanent magnetic synchronous motor

The permanent magnet synchronous motor (PMSM) is important in position tracking applications. A performance degradation is caused by the internal uncertainties and external load disturbance. To achieve a high control performance, a fast and precise control scheme with great robustness has to be applied. In this paper, we propose a composite control method for PMSM systems by combing the extended state observer (ESO) technique with fast terminal sliding mode (FTSM) control. The FTSM guarantees the fast convergence rate and the ESO can estimate the disturbance accurately. The proposed method has a fast response and a good disturbance rejection property compared with other sliding mode methods. Simulations are carried out to show the effectiveness of this method

The Role of Paragus quadri-fasciatus Meigen on Aphid Control and the Observations of its Biological Characteristics

Four-strip small syrphid fly, Paragus quadri-fasciatus Meigen is the important natural enemy of aphids in our region. A fly can eat about 800 aphids during its whole life. There are more than 10 kinds of aphids can be food of this fly, such as soybean aphid, Chinese sorghum aphid and radish aphid etc. The fly has 3~4 generations each year in Tonghua county, Jilin province. The adult of the first generation appears after the last ten-day period of April each year. It takes 30~35 days to complete one generation. The fly can oviposit 84~124 eggs in its whole life. Major natural enemies of the fly are ichneumon wasps, spiders, lacewings and etc.Originating text in Chinese.Citation: Gao, Junfeng, Jiang, Lianfeng, Zhang, Guangxin, Li, Chunshan, Zhao, Guangquan. (1996). The Role of Paragus quadri-fasciatus Meigen on Aphid Control and the Observations of its Biological Characteristics. Journal of Jilin Agricultural Sciences, 5(2), 60-61