Container terminals, handling over 80% of global trade, face a "triple dilemma": complex multi-equipment coupling, frequent disturbances, and stringent international carbon mandates like the IMO policies and EU ETS. Traditional optimization methods struggle with real-time adaptation and low-carbon compliance. Digital Twin (DT) technology offers a new paradigm for virtual–physical mapping and carbon efficiency management. This paper systematically reviews port digital twins and intelligent scheduling, constructing a four-dimensional framework: architecture, problems, technologies, and applications. Findings indicate that DTs are characterized by bidirectional closed-loop interaction, with a four-layer architecture becoming the mainstream paradigm. While scheduling models for multi-equipment collaboration are maturing, the research frontier has shifted toward DT-driven uncertainty rescheduling and low-carbon strategies, such as equipment ratio optimization and charging management. Core technologies—data fusion, virtual modeling, and reinforcement learning—are increasingly robust. Practical implementations in major hubs like Qingdao and Yangshan ports demonstrate that DT solutions significantly enhance operational efficiency and reduce emissions. Ultimately, DT technology provides a critical pathway for terminals to navigate international carbon regulations and achieve green, high-quality development.

Urban renewal has entered a new stage where stock development is as important as quality improvement. At this stage, the urban renewal in policy new towns and ordinary new districts shows clearly distinguishable structural differences in terms of institutions, resources, and implementation. This study selects Qianhai and Xiongan as cases of policy new towns, and selects Bao 'an of Shenzhen and the main urban area of Baoding as ordinary new towns with the same geographic location, level and development period. The core focus of the study is the difference of urban renewal between policy new areas and ordinary new areas and which path will the next renewal take. Many documents indicate that policy-based new areas, leveraging their policy advantages, can shorten the decision-making process and gather information. In contrast, ordinary new areas have deficiencies in information acquisition and departmental collaboration, and urban renewal in these areas is prone to fluctuations and restrictions. For this purpose, we propose a differentiated urban renewal path centered on information governance, providing operational guidance for transitioning from pilot experience to large-scale promotion.

In recent years, China's population aging has accelerated, requiring higher standards for an aging-friendly society. As a key part of urban renewal, supporting farmers' markets in old communities, due to long construction time, aging facilities and poor management, can hardly meet the elderly's demands for safe, convenient and comfortable shopping.Taking these farmers' markets as the research object, this paper constructs an aging-friendly evaluation system with 4 criterion-level indicators (environmental comfort, spatial safety, layout rationality and functional convenience) and 18 indicator-level indicators. Using the analytic hierarchy process (AHP) to determine indicator weights, it conducts empirical analysis on three typical case markets in Guanshan Subdistrict, Hongshan District, Wuhan through field research and questionnaires.Results show that spatial safety and related indicators are the primary dimensions for aging-oriented renovation; indicators such as barrier-free design, acoustic environment and resting facilities perform poorly, revealing shortcomings in current aging-friendly construction. Based on the evaluation, the paper puts forward improvement strategies including acoustic environment control, resting facility allocation and business format optimization, aiming to improve market conditions, enhance aging-friendly level and provide references for aging-friendly practice in urban renewal.

Visible Light Communication (VLC) in 6G ultra-dense networks is bottlenecked by LED modulation bandwidth against massive access demands. While OMA and NOMA suffer from rigid partitioning and SIC instability , this study employs Rate-Splitting Multiple Access (RSMA) optimized via a Knowledge-Assisted Differential Evolution (KA-DE) solver. The framework navigates non-convex optimization surfaces by integrating physical priors. Simulations confirm RSMA's superiority in sum-rate and fairness. Notably, the solver maintains a 0.209 coefficient of variation under 15 dB burst shadowing, proving resilience to stochastic blockages. At high SNR, the architecture achieves a 7.182 bps/Hz gain over gradient-based baselines prone to local optima. By compressing latency to 61.40 ms, the framework effectively balances spectral efficiency and speed. This research provides critical engineering validation for next-generation all-optical networks

This paper focuses on advancing multi-sensor fusion calibration techniques for autonomous vehicles, with the goal of enhancing vehicular safety and reliability across diverse operational environments.Centering on LiDAR, cameras, and auxiliary sensors, this paper first analyzes their performance attributes and then designs dedicated intrinsic and extrinsic parameter calibration methodologies. Additionally, a natural-scene-based site selection strategy for calibration is proposed. Intrinsic calibration determines each sensors' internal parameters, and extrinsic calibration establishes the transformation relationship between each sensor and the global coordinate system. Experimental results show that multi-sensor fusion can improves the accuracy of environmental perception, especially in target detection, positioning, and scene reconstruction. This paper shows that high-precision calibration is the foundation for efficient multi-sensor fusion, and is important to improve the safety and enhance the performance of autonomous vehicles. Future research efforts will focus on improving calibration algorithms and exploring new fusion approaches to handle complex dynamic scenarios.

With rapid urbanization in China, issues such as urban flooding, water scarcity, and water pollution have become increasingly prominent. The sponge city concept offers systematic solutions to these challenges. Community rain gardens, as a key implementation of this concept at the neighborhood scale, utilize ecological design to enable natural rainwater accumulation, infiltration, and purification. This study explores the ecological design strategies and key technical features of permeable paving and bioretention ponds in rain gardens, based on case studies of three representative projects in China: the Shenzhen Guangming Art Center, the Qinhuang Avenue in Xixian New Area, and the Qingshan Lake Greenway in Hangzhou. Findings indicate that permeable paving design must address site-specific challenges, such as balancing bearing capacity with permeability, employing composite infiltration on collapsible loess, and integrating blind pipes with cleanout openings to prevent clogging. Bioretention pond designs also vary considerably: multi-layer media with storage modules in public landscapes, L-shaped waterproof walls with impermeable geotextiles in road medians, and terraced inverted trapezoidal trenches with mixed fillers in ecological corridors. The findings aim to provide theoretical references and practical guidance for sponge community construction in China.

This paper proposes an integrated "Prediction-to-Control" data-driven scheduling framework for building and campus-level energy management. The framework first employs quantile forecasting and uncertainty estimation to obtain multi-step future load intervals, rather than single-point predictions, so that the variability and distributional characteristics of demand can be explicitly captured. These probabilistic forecasts are then embedded into constrained scheduling problems, where the "constraint violation probability" (e.g., peak power limits, thermal comfort bounds, and electricity cost objectives) is directly controlled through chance-constrained formulations. By transforming uncertainty into tractable constraints, the method enables a systematic trade-off between operational cost and reliability. Compared with traditional point forecast–based control strategies, the proposed approach is more robust under distribution shifts such as extreme weather conditions or occupancy fluctuations. In addition, it provides an interpretable risk–cost relationship, allowing operators to adjust risk tolerance in a transparent manner while maintaining stable and efficient system performance.

This paper focuses on an integrated photovoltaic storage and charging station for grid carbon reduction. It outlines the "source-storage-load" coordination characteristics of the system, including the synergy among photovoltaic generation, battery energy storage, and electric vehicle charging facilities, as well as its integration with multi-energy systems, and systematically reviews the carbon reduction mechanisms, including PV replacing thermal power, energy storage for peak shaving and valley filling, and V2G reverse power supply. Uncertainty handling methods (e.g., forecasting techniques, robust optimization, and rolling optimization) and coordinated scheduling strategies (e.g., multi-time-scale optimization and multi-objective optimization methods) are also examined. This study not only provides a comprehensive reference for the low-carbon planning and operation of photovoltaic-storage-charging systems in the context of new power systems but also proposes fusion strategies that combine methods addressing the same problem to achieve a more comprehensive solution, supporting the transition towards a more sustainable, low-carbon energy future.

The dynamic processes in estuarine and coastal regions are relatively complex in themselves, and the shoreline morphology has also always been in a state of change. Accurately predicting the dynamic geomorphic evolution of this region is of great significance for coastal engineering construction, disaster prevention and mitigation, and ecological protection. With the continuous development of computer technology, numerical simulation has already been able to effectively break through the spatial and temporal limitations of field observations and physical models, and has gradually become a core method for carrying out research on medium- and long-term geomorphic evolution. This paper systematically reviews the research progress in the field of numerical simulation of dynamic geomorphic evolution in estuarine and coastal regions, and focuses on discussing the frontier methods adopted to solve the two major technical bottlenecks of model "spatial span" and "temporal span". From the spatial dimension, it expounds multidimensional nested models and multi-type-grid cross-scale coupling technologies, and at the same time analyzes their advantages in taking into account both the large-scale background dynamic field and local high-resolution topographic feedback. From the temporal dimension, it provides a detailed analysis of the medium- and long-term simulation acceleration mechanisms represented by the morphological acceleration factor method (MAF), as well as the corresponding parameter constraint conditions. Finally, combined with application cases of typical estuaries at home and abroad, this paper summarizes the achievements made by multiscale coupling models in empirical research on geomorphic evolution, and also looks ahead to future trends in intelligent simulation involving multiphysical-process coupling, dynamic optimization of parameters, and the integration of digital twins and machine learning.