This paper reviews the research progress of sustainable pavement materials, focusing on the urgent need to reduce carbon emissions and improve the recyclability of road construction materials. Traditional materials such as asphalt and cement contribute significantly to greenhouse gas emissions due to their reliance on fossil fuels. This study explores innovative solutions including bio-based asphalt substitutes, recycled aggregates, and industrial by-products that comply with circular economy principles and offer similar or better mechanical properties. Integrating these materials into road construction can not only solve environmental problems but also support global climate governance efforts. Despite the progress made, challenges remain, such as the need for a comprehensive life cycle assessment framework and improved performance evaluation systems. This paper emphasizes the importance of building a multi-dimensional collaborative innovation research framework to overcome the technological bottleneck and promote the sustainable development of green infrastructure.

With the increasing penetration rate of new energy in the power generation system, the uncertainty of its output brings great challenges to the stable operation of the power system. It is imperative to build an efficient and reliable energy storage system. Hybrid energy storage can satisfy the task of wave calming under the demand of multiple scenarios. This paper focuses on the hybrid energy storage system composed of a supercapacitor and lithium battery, mainly introduces its characteristics and topology, and discusses the key role in calming new energy fluctuations and peak cutting and valley filling. The internal power distribution strategy of hybrid energy storage is sorted out, and it is clear that the optimization goal should balance economy and reliability, and there should be corresponding optimization goals in different scenarios. It points out that a variety of topologies should be built in the future, and calls for the active application of new wide-band gap materials and multi-sided collaborative configuration combined with intelligent optimization algorithms to promote the more efficient and stable application of hybrid energy storage systems in the new power system.

This study uses molecular dynamics simulations to systematically explore the effects of Ti content, temperature, and shear strain rate on the shear deformation behavior of CoCrFeNiCuTix high-entropy alloys. The results show that, at the same temperature and shear strain rate, the shear modulus increases with the increase in Ti content, while the shear strength remains unaffected. For the equiatomic CoCrFeNiCuTi high-entropy alloy, both the shear modulus and shear strength decrease linearly as the temperature rises. However, as the shear strain rate increases, the shear modulus remains mostly unchanged, but the shear strength shows a significant increase.

To improve the intermodal service at Qingdao Jiaodong Airport, addressing operational challenges such as fuzzy passenger demand layering and insufficient cross-modal coordination, and to solve the core issues of supply-demand mismatches and a single pricing mechanism in the air-rail intermodal ticketing system, this study proposes a personalized ticketing optimization strategy based on user profiling. First, through extensive survey data, the study analyzes the personal attributes and travel characteristics of the surveyed passengers. Then, using the K-means clustering algorithm, the study clusters passengers' multidimensional features and determines the optimal number of clusters through the elbow method and silhouette coefficient method. This leads to the establishment of differentiated user labels: economy-class passengers, business-class passengers, and leisure-class passengers. The market segmentation research on passenger groups shows that these three distinct groups perceive the bottlenecks of intermodal services differently, especially exhibiting significant layering features in the key dimensions of time sensitivity and price sensitivity. The results provide a comparative scheme for improving the air-rail intermodal ticketing service at Qingdao Jiaodong International Airport, offering differentiated service strategies for each passenger group. Through responsive demand and resource optimization, this study has significant practical implications for enhancing passenger experience and strengthening the market competitiveness of the service.

Improved YOLO11-based shaft-hole parts detection method In the assembly scene of shaft-hole parts, there are often occlusions and changes in the viewing angle, making real-time accurate detection of the target part there are certain difficulties, this paper is based on the structure of the YOLO11 network, respectively, through the introduction of the RepViT module, the edge information-based feature fusion module EFI and P-EfficientHead detection head, a multi-module fusion improved YOLO11 network for shaft hole assembly scenarios is proposed, and finally experimentally verified on the Pascal VOC dataset with, mAP50 and mAP50-95 are improved by 3.7% and 4%, and the precision and recall are improved by 2.9% and 2.5%, respectively, and the validation on the homemade dataset is also obtained with better results, and the final results show that the proposed multi-module fusion to improve YOLO11 network in this paper has better performance.

With the deepening development of communication technology, the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields. This paper mainly studies the implementation of automatic modulation recognition using deep learning as a computing tool, focusing on CNN neural network and LSTM neural network, and conducting simulation experiments on public data sets. Based on the original CNN neural network, this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network. The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into deep learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.

As a primary medium for emotional expression, human facial expressions carry rich informational value. Recent advancements in residual networks and attention mechanisms have broadened their application in expression classification, yet challenges persist in suboptimal key feature extraction and complex model training. To address these issues, this study proposes a novel facial expression recognition method integrating residual networks with attention mechanisms. The framework employs ResNet50 as the backbone network for feature extraction, enhanced by the Convolutional Block Attention Module (CBAM) to autonomously learn and prioritize critical features. Further innovations include reconstructing residual modules within the backbone network to optimize feature extraction and introducing a CAM-adjusted CBAM-ERF mechanism to mitigate neuronal suppression in specific regions, thereby accelerating network convergence and classification efficiency. Experimental results demonstrate the proposed residual network achieves 73.45% and 96.97% accuracy on the FER2013 and CK+ datasets, respectively.

Photocatalytic CO₂ reduction has emerged as a promising strategy to simultaneously mitigate climate change and meet growing energy needs by transforming CO₂ into useful fuels and chemicals using solar energy. This review systematically explores the key principles and latest progress in heterogeneous photocatalytic CO₂ conversion systems. The working mechanisms of CO₂ photoreduction are analyzed, with particular emphasis on high-performance photocatalysts responsive to both UV and visible light. To improve catalytic performance, various approaches for boosting the activity and product selectivity of these materials are discussed. Additionally, current challenges in photocatalytic CO₂ reduction are outlined, along with potential research directions to overcome these barriers. The findings presented here offer valuable insights for designing more effective photocatalytic systems, ultimately advancing CO₂ utilization technologies.

Moore's Law states that the number of transistors in an integrated circuit doubles approximately every two years. Due to the physical limits of silicon transistors, there is a need for a material with better development potential than silicon to make transistors, in order to develop more advanced integrated circuits. Looking back at the literature, research has been going on for decades, and currently the promising materials are gallium and germanium, which have different properties; gallium compounds are well suited for semiconductors, Gallium nitride can withstand higher electric fields and conduct electrons more efficiently than silicon. Compared to other materials, germanium transistors offer a cost-effective solution and can be mass-produced efficiently. In general, people are bound to work out more significant integrated circuit. Gallium transistors and germanium transistors will certainly take over part of the transistor body market, but will not be able to completely replace silicon as the main material for integrated circuits.

This paper analyzes the key technologies of power batteries for new energy vehicles. By using data search and theoretical analysis methods, the concept and development status of power batteries are elaborated. Then, combined with the development paths of power battery performance testing technology, power battery pack matching technology, power battery charging technology, etc., in-depth exploration is carried out to summarize the application characteristics of various technologies, analyze the key points of different technologies, and achieve the comprehensive application of various technologies to support the development process of power batteries and create a good environment for the development of new energy vehicles. The following conclusion can be drawn: the flexible application of key technologies in power batteries has injected new development momentum into new energy vehicles and provided certain references for related research.