Research on key technologies for AI-assisted design of high-performance optoelectronic materials

Artificial intelligence technology is profoundly changing the research and development paradigm of high-performance optoelectronic materials. This paper systematically summarizes the core methods of AI-assisted design, namely, revealing the structure-activity relationship of materials through multi-scale modeling and intelligent computing; using high-throughput virtual screening and reverse design to break the traditional trial-and-error limitations; using a combination of dynamic performance prediction and defect analysis to optimize its stability; relying on cross-modal data fusion to mine implicit rules. It is applied to the discovery of perovskite materials, band regulation of organic materials, analysis of device attenuation mechanisms and composite electrode optimization scenarios. The results show that this technology can significantly accelerate the development cycle of new materials, enhance the photoelectric conversion efficiency and device life, and is a new driving force for the new generation of optoelectronic devices

UKF Prediction Assisted UAV Sensing and Communication Optimization Design for Air-Ground Networks

This paper studies the joint design of sensing and communication in air-ground networks, where multiple unmanned aerial vehicles (UAVs) cooperate to assist the base station in sensing and serving mobile users. To improve the sensing accuracy, we propose an unbiased Kalman filter (UKF) based UAV sensing scheme. The performance of communication and sensing is characterized by the downlink communication rate and the Cramer-Rao lower bound (CRLB) of the user location estimation, respectively. To maximize the downlink communication rate while accurately tracking the user’s location, this involves solving the user association and UAV trajectory optimization problems. In particular, we formulate the joint optimization problem as a mixed integer non-convex optimization problem, which is difficult to solve. To address this problem, we first derive the CRLB in a closed form based on the user’s state prediction. Then, the original problem is decomposed into two sub-problems, one corresponding to the optimization of user association and the other one about the optimization of UAV trajectory. An iterative algorithm is introduced to optimize the two sub-problems alternately using a relaxation-based method and a continuous convex approximation method. Comparisons with existing algorithms verify that our designed scheme can achieve superior performance improvements in user tracking and communication

Research on the Impact of Interpersonal Relationships on the Safety Behaviors of Construction Workers

In order to comb out the relationship between interpersonal relationship and construction workers’ safety behavior and analyze its influencing mechanism, based on accident causation theory, conformity motivation theory and planned behavior theory, a theoretical model of the mechanism of interpersonal relationship affecting construction workers’ safety behavior was established. A meta-analysis method was used to systematically analyze 135 independent samples from 46 domestic empirical studies. To clarify the influence mechanism of various interpersonal relationships on the safety behavior of construction workers. The results show that: in interpersonal relationship, the correlation coefficients between family relationship and safety behavior of construction workers are 0.613, and the correlation coefficients between emotional exhaustion and job satisfaction and safety behavior of construction workers are 0.564 and 0.479 respectively. The correlation coefficient between workers’ team-group relationship and construction workers’ safety behavior is 0.522, the correlation coefficient between safety atmosphere and construction workers’ safety behavior is 0.671, and the coefficient between safety communication and construction workers’ safety behavior is 0.622. The correlation coefficient between leadership relationship and construction workers’ safety behavior is 0.26, the correlation coefficient between safety management and construction workers’ safety behavior is 0.527, and the correlation coefficient between safety incentive and construction workers’ safety behavior is 0.492. The conclusion of this study can provide a theoretical basis for the subsequent research on the safety behavior of construction workers, and also provide a management basis for construction managers

A Study on the Spatial Prototype of Traditional Chinese Academy Architectures Based on Historial Image Analysis

The academy is a historically educational and cultural establishment that dates back to the Tang Dynasty in ancient China as a private school. The traditional academy architecture is the academy’s material vehicle. The ancient document “Chronicles of the Academy” contains detailed information about the historical academy. History images are included in these records, intuitively depicting the arrangement of the buildings and their relationship to one another and providing great assistance for the study of the architecture of the academy. Nonetheless, there is still a need for the academic community to make extensive use of the academy’s architectural archives. Many historical images of the academy are used as primary source material in this research. By identifying and interpreting the historical images, the spatial layout prototype A of the academy with “the lecture hall in the middle and the ancestral temple on top” was finally analyzed, and two variations of prototype A were analyzed

Application of Multi-objective Stochastic User Equilibrium Model in Traffic Network Optimization

When choosing routes in a transportation network, travelers not only consider travel time but also pay attention to multiple factors such as travel time reliability and traffic costs. Traditional single-objective traffic assignment models struggle to fully reflect the complex decision-making behavior of travelers. Based on multi-objective optimization theory and the stochastic user equilibrium framework, this paper proposes an improved multi-objective stochastic user equilibrium model. Taking travel time and travel time reliability as core objectives, the model overcomes the limitations of traditional models in handling multi-objective decisions by introducing a non-compensatory decision-making mechanism and Pareto optimality theory. The improved model can more accurately describe travelers’ route choice behavior, providing more scientific decision support for transportation network planning and management

Singapore’s Policies in Environmental Reform on Transport Sector

Singapore’s high vehicle density contributes significantly to GHG emissions, hindering its Paris Agreement goals. This study evaluates three transport policies: (1) Vehicle Quota System (VQS) and Electronic Road Pricing (ERP) to limit ownership and congestion; (2) urban integration to reduce travel demand; and (3) low-carbon transitions, including EV incentives and carbon taxes. These policies align with Grubb’s framework—modifying behaviors, optimizing governance, and fostering innovation. Results show reduced vehicle use, better public transport, and gradual EV adoption. Challenges like luxury car preferences, land constraints, and low carbon pricing remain. To meet its 2030 emissions target (36% reduction from 2005 levels), Singapore must strengthen carbon pricing, boost R&D, and improve policy coherence. The case offers lessons for compact cities globally, emphasizing integrated governance and adaptive strategies for sustainability

Exploration of Waterlogging Early Warning Technology for Municipal Drainage Systems under Extreme Rainfall

This paper takes the prevention and control of urban flooding due to extreme rainfall in Shenzhen as the research object, focuses on the demands of municipal drainage projects and urban flooding control projects, and constructs a full-chain solution of “early warning technology - project implementation - resilience enhancement”. Through the collaborative innovation of multi-source heterogeneous perception networks (liDAR + fiber optic sensors) and physical-AI hybrid models (SWMM-STGCN architecture), a technological breakthrough has been achieved with a flood prediction period of ≥30 minutes and a prediction accuracy NSE of >0.89, and it has also promoted the transformation of drainage engineering systems towards intelligence and resilience. Empirical research shows that this system has shortened the emergency response time during the 2024 flood season in Luohu District by 63.4% and reduced direct economic losses by 87.4%. From the perspective of supervision, a smart water management assessment matrix with 137 indicators was pioneered. Based on early warning information, a three-level resilience improvement path of “real-time regulation - facility coordination - planning and decision-making” was constructed. Eventually, a policy-technology-governance integration model supporting Shenzhen’s smart water management goals during the 14th Five-Year Plan was formed. It provides a reference template that combines academic innovation with practical engineering value for drainage engineering and urban flood control engineering

Degradation Mechanism and Performance Evaluation of Composite Chlorine Dioxide Blockage Relieving Agent

Composite chlorine dioxide blockage relieving agent is a new type of well blockage relieving agent. It has strong abilities to oxidatively degrade polymers, kill microbial cells, dissolve sulfides, and dissolve inorganic salt minerals, which can effectively relieve blockages in crude oil extraction channels. This paper introduces the properties of chlorine dioxide and its blockage relieving mechanism, and evaluates its performance through viscosity reduction experiments, corrosion inhibition tests, clay stability tests, and bactericidal experiments

DMTCANet: Dual-Branch Multiscale CNN and Token Cross Attention Fusion for Hyperspectral and LiDAR Data Classification

Advancements in remote sensing (RS) technology have highlighted the potential of jointly classifying Hyperspectral Images (HSI) and Light Detection and Ranging (LiDAR) data, leveraging the rich spectral information of HSI and the precise 3D structural details of LiDAR. While this combination improves classification accuracy, it presents challenges due to differences in data dimensions and semantic levels. Existing deep learning approaches often struggle to effectively extract features and capture interactions between these heterogeneous sources, and traditional CNNs suffer from limited receptive fields and detail loss in complex multi-scale scenarios. To address these issues, we propose DMTCANet, a novel joint classification network that combines a dual-branch multi-scale CNN with token cross-attention (TCA) fusion. The network incorporates a multi-scale hybrid convolution module to process HSI and LiDAR data, expanding the receptive field and capturing local and global information. A TCA fusion encoder further enhances deep interactions between the two data modalities, overcoming the limitations of insufficient feature integration. Experimental results on Trento, Houston2013, and MUUFL datasets demonstrate the effectiveness of DMTCANet, outperforming existing methods

New Energy Vehicle Power System Fault Detection and Vehicle Engineering Support

New energy vehicle power system fault detection and vehicle engineering guarantee is an important topic in the current automobile industry. With the popularity of new energy vehicles, its safety and reliability problems have become increasingly prominent. This paper summarizes the key points of fault detection of the power system of new energy vehicles, including battery management system, thermal management system and fault diagnosis technology, etc., and discusses the necessary measures to ensure the engineering protection of new energy vehicles, such as improving the safety management mechanism, ensuring product quality and safety, and improving the efficiency of the monitoring platform. By analyzing the existing technologies and methods, the improvement direction is proposed to improve the safety and operation efficiency of new energy vehicles