Geely (China)

In mixed traffic environments, the spatial distribution of Connected and Automated Vehicles (CAVs) plays a decisive yet previously unquantified role in platoon stability and safety. This study establishes a generalized stability modeling framework for heterogeneous platoons composed of Human-Driven Vehicles (HDVs), Autonomous Vehicles (AVs), and CAVs. By introducing a spatial weighting coefficient (γ), the proposed model captures the influence of longitudinal CAV positioning and allows for flexible representation of any vehicle composition pattern. Linear stability analysis and time-domain simulations are conducted to investigate the interaction between spatial distribution, communication delay, and dynamic response. The results demonstrate that front- and center-loaded CAV configurations effectively suppress velocity perturbations and maintain string stability even under moderate delay, while rear-loaded configurations exhibit early instability. Furthermore, an optimal γ range of 0.3-0.5 is identified to minimize oscillation amplitude, providing a practical guideline for cooperative control strategies in mixed platoons. The findings offer theoretical insights and quantitative evidence for optimizing CAV deployment to enhance stability and robustness in future intelligent transportation systems.

In the present study, potato starch (PS) was functionalized with theaflavin (TF). Potato starch aldehyde (DPS)–theaflavin (DPS-TF) conjugates were prepared by conjugating TF with DPS. The synthesized DPS-TF conjugates were characterized via UV–visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), proton nuclear magnetic resonance (1H-NMR) and scanning electron microscopy (SEM) analysis and tested for antioxidant and antimicrobial activities. The UV–vis spectrum results demonstrated that DPS-TF conjugates exhibited the characteristic absorption peaks of theaflavin at 280 nm, which can be attributed to the benzotropolone structure present in theaflavin. The absorbance values of the peaks progressively intensified as the concentration of grafted theaflavins increased. FTIR confirmed the depletion of the aldehyde groups and the presence of TF-specific vibrations in the conjugates in DPS-TF. 1H-NMR demonstrated that the conjugation occurred between the H-6, H-8, H-6′, and H-8′ positions of theaflavin and the aldehyde groups of starch aldehyde. XRD demonstrated that the DPS-TF conjugates were in the amorphous state. SEM observation demonstrated that DPS-TF exhibited a mixed morphology of flakes and lumps, which differed from that of native starch and starch aldehyde. The scavenging activity of DPS-TF against DPPH and ABTS radicals was significantly higher than that of DPS (p < 0.05), with the antioxidant activity increasing in line with the concentration of theaflavins. In comparison with PS and DPS, DPS-TF conjugates demonstrated superior antimicrobial activity against Escherichia coli and Staphylococcus aureus. Furthermore, an elevated grafting ratio corresponds to a heightened level of these functional properties. This study highlights the promise of the starch aldehyde–theaflavin conjugates for use as a viable antioxidant and antimicrobial agent for food applications.

Against the backdrop of the deep integration of artificial intelligence (AI) technology and the field of education, as well as the gradual popularization of cross-campus teaching models, personalized English learning has become a key approach to enhance students’ learning efficiency. Based on core learning motivation theories such as Self-determination theory and Achievement Motivation theory, this study, which takes 192 students from Geely University as the research sample, systematically analyzes the current status, motivation characteristics, and path dilemmas of college students using AI tools for English learning in the cross-campus environment. These findings show that the study in this paper can, to some extent, recommend the most effective AI-empowered personalized English learning plan that adapts to cross-campus teaching scenarios, and provide practical reference and decision-making base for English teaching reform in colleges and universities in China.

Ultra-reliable and low-latency communication (URLLC) is one of the key requirements in future wireless communications. In practical URLLC scenarios, an enhanced mobile broadband (eMBB) message together with an URLLC message are simultaneously encoded as codewords and transmitted over the same channels. Traditionally, the coding design of URLLC message often treats the eMBB codeword as interference corrupting the coding performance. In this paper, for the additive white Gaussian noise (AWGN) channel, we show that if noise-free channel feedback is available, there exists a feedback control based coding scheme for the URLLC message, which not only perfectly eliminates the interference caused by the eMBB codeword, but also approaches the maximum rate of the URLLC message when the codeword length tends to infinity. Furthermore, we show that this scheme satisfies the physical layer security requirement by itself, which indicates that our proposed scheme is a self-secure scheme. The results of this paper are explicitly explained by numerical examples, and this work provides a possible way to design efficient coding schemes for URLLC message transmission.

Ne Zha 2, another major Chinese traditional culture animated film, premiered during the 2025 Spring Festival season. Building upon the success of its predecessor Ne Zha: The Birth of the Demon Boy, it further integrates traditional Chinese cultural elements while implementing innovative approaches to rejuvenate its brand image. This strategy has resulted in a more widely appealing and profound cinematic work. This paper examines how Ne Zha 2: The Demon Boy Rages at Sea transformed its brand IP image to appeal to younger audiences. Using case study methodology and analyzing data from social media platforms alongside audience feedback, it explores how traditional Chinese cultural animation films attract Gen Z viewers through brand image innovation. Following the case analysis, the study reveals that Ne Zha 2 implemented numerous innovative adjustments in character development, narrative techniques, and visual style. This leads to the conclusion that modernizing cultural IPs through innovation and diversifying promotional strategies are key to attracting Gen Z audiences. Simultaneously, this research provides theoretical foundations for the innovative development of the cultural and creative industries and offers directional guidance for the youth-oriented transformation of traditional Chinese cultural IPs.

To address the challenge of coordinated optimization between energy consumption and thermal comfort in electric vehicle thermal management systems under low-temperature conditions, this paper proposes an intelligent control framework integrating a channel-temporal attention mechanism with knowledge distillation. A channel-temporal attention-enhanced dual-delay deep deterministic policy gradient algorithm (CT-TD3) is developed to enable dynamic feature selection and long-term temporal dependency modeling of thermal states across coupled subsystems—including the battery, motor, and cabin—thereby significantly improving algorithmic convergence speed and enhancing the foresight of control decisions. Furthermore, response-layer knowledge distillation is employed to construct a lightweight thermal comfort prediction network, reducing computational latency to 0.514ms while preserving high prediction accuracy, thus enabling real-time optimization under comfort constraints. Simulation results demonstrate that, at a low ambient temperature of -10°C, the proposed method reduces total system energy consumption by 5.27% compared to the baseline strategy, while increasing the proportion of occupant thermal comfort ratio to 75.03%.

Abstract Green bonds have emerged as a crucial instrument in achieving dual carbon development goals. This study explores the carbon emission reduction effects and mechanisms of green bonds from an innovative local-neighbouring regional perspective, offering vital insights into realising comprehensive low-carbon transformation at the regional level. Utilising panel data from 30 Chinese provinces (including autonomous regions and municipalities directly under the central government) from 2007 to 2022, we investigate the mechanisms through which green bonds influence carbon emission intensity. Our findings demonstrate that green bonds significantly reduce carbon emission intensity, a conclusion that remains robust under Spatial Durbin Model testing. Upon decomposing the total effect, we reveal a nuanced impact: while green bonds effectively reduce local carbon dioxide intensity, disparities in environmental regulations and incentive policies across regions lead to the relocation of polluting enterprises to neighbouring areas. This study argues that while promoting the development of green bonds, it is imperative to strengthen inter-provincial communication and collaboration. Breaking down regional barriers and achieving coordinated development of green financial resources among regions is essential for reducing carbon emission intensity not only locally but also in neighbouring areas. These findings underscore the need for a holistic approach to green finance policy implementation to ensure widespread and equitable carbon reduction across China.

Public perceptions of Artificial Intelligence (AI) are often skewed, and current AI education faces the risk of being overly abstract in its ethical discussions. Based on a review of academic discussions on AI education, this study proposes a teaching practice framework utilizing the "AI Painting" project as a medium. The framework is designed to build a path of learning that progresses from "experiential cognitiontechnical principlesproject practiceethical reflection." Through the dual-spiral structure of technological practice and ethical reflection, this approach fosters the simultaneous development of students' computational thinking, innovative consciousness, and social responsibility. By participating in this project, students gradually shift from being mere users of technology to thinkers about technology, ultimately developing a rational understanding of AI technologies and critical thinking. This process aligns with the core goal of secondary school AI educationcultivating future citizens with digital literacy.