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This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

This paper aims to analyze unexplored connections between economic, environmental and social dimensions of corporate social responsibility (CSR) and knowledge management (KM) strategies (exploration, exploitation), also considering environmental dynamism as an influencing variable on these connections. The predicted CSR-KM interplay suggests, from stakeholder and knowledge-based views of the firm, the existence of ideal configurations between CSR and KM strategies that generate differentiated impacts on companies’ innovation capabilities, especially in dynamic environments.

Structural equation modeling by means of the partial least squares technique was used to test the study’s hypotheses after collecting survey data from Spanish companies of the renewable energy sector.

The study findings show that in highly dynamic environments, companies will tend to commit prominently in CSR, although their orientation (economic, environmental, social) and effects on innovation capabilities will depend mainly on the selected KM strategies. Social and environmental CSR are found to be highly related to KM exploration, whereas economic CSR is highly related to KM exploitation. Nevertheless, while a significant indirect effect of economic CSR by means of the KM exploitation strategy on innovation capabilities is found, the proposed indirect effect of both environmental and social CSR through the KM exploration strategy on innovation capabilities is not significant.

The results suggest that company managers should be aware of the advantages of following specific paths of investment in KM and CSR initiatives in highly dynamic environments, as there is a potential payoff in terms of innovation capability improvement. The results also suggest that “good” relationships with stakeholders, built from specific CSR investments, make firms able to get valuable knowledge that it is useful to develop KM strategies for innovation capability development.

Previous studies do not consider the interplay between KM strategies and CSR as a catalyzer for developing a firm’s innovation capabilities. This paper contributes to the KM and innovation literatures by introducing CSR into the conversation about how to improve innovation capabilities in dynamic and sustainable industries by using configurations of KM strategies and specific CSR investments in economic, social and environmental areas.

In this paper, we evaluated the impact of the US “Chip Act” on the participation of the Chinese electronics industry in the global value chain based on the dynamic CGE model. This is a meaningful attempt to use the GTAP-VA model to analyze the electronics industry in China.

We employ a Dynamic GTAP-VA Model to quantitatively evaluate the economic repercussions of the “Chip Act” on the Chinese electronic industries' GVC participation from 2023 to 2040.

The findings depict a discernible contraction in China’s electronic sector by 2040, marked by a −2.95% change in output, a −3.50% alteration in exports and a 0.45% increment in imports. Concurrently, the U.S., EU and certain Asian economies exhibit expansions within the electronic sector, indicating a GVC realignment. The “Chip Act” implementation precipitates a significant divergence in GVC participation across different countries and industries, notably impacting the electronics sector.

Through a meticulous temporal analysis, this manuscript unveils the nuanced economic shifts within the GVC, substantially bridging the empirical void in existing literature. This narrative accentuates the profound implications of policy regulations on global trade dynamics, contributing to the discourse on international economic policy and industry evolution.

We evaluated the impact of the US “Chip Act” on the participation of the Chinese electronics industry in the global value chain based on the dynamic CGE model. This is a meaningful attempt to use the GTAP-VA model to analyze the electronics industry in China.

The interaction between policy regulations and global value chain (GVC) dynamics is pivotal in understanding the contemporary global trade framework, especially within technology-driven sectors. The US “Chips Act” represents a significant regulatory milestone with potential ramifications on the Chinese electronic industries' engagement in the GVC.

The significance of this paper is that it quantifies for the first time the impact of the US Chip Act on the GVC participation index of East Asian countries in the context of US-China decoupling. With careful consideration of strategic aspects, this paper substantially fills the empirical gap in the existing literature by presenting subtle economic changes within GVCs, highlighting the profound implications of policy regulation on global trade dynamics.

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.