In this paper, the latest advancements of DeepSeek in Artificial General Intelligence (AGI) were reviewed, with a focus on innovations in large language models and reasoning technologies. Novel model structures and algorithmic designs were introduced in the newly developed DeepSeek-V3 architecture, , to implement a comprehensive optimization methodology which significantly enhanced training efficiency under constrained computing resources. In the reasoning system of DeepSeek-R1 framework, reinforcement learning (RL) was integrated with supervised fine-tuning (SFT) in an innovative way and breakthroughs in reasoning depth and logical coherence were achieved. Based on the innovations of DeepSeek, in the study, three critical challenges in AGI development were discussed: (1) the strategic role of open-source ecosystems in accelerating AGI progress, (2) the validity of neural scaling laws in the era of foundation models, and (3) practical approaches to implement industry-specific models through synergistic integration of general and domain-specific capabilities, demonstrated via DeepSeek-based implementations.

In recent years, with the emerging contaminants (ECs) in drinking water sources frequently detecting, water quality safety issues have become a focal point of widespread social concern. In 2024, significant progress has been achieved in preventing and controlling ECs in drinking water. This paper firstly systematically analysis the characteristics of ECs in drinking water, delving into their environmental impacts and potential risks to human health. Subsequently, it reviews the research progress of the latest technologies and materials designed to remove these ECs. Finally, it proffers strategic insights and recommendations aimed at effectively managing ECs in drinking water. Research indicates that the occurrence characteristics of ECs such as perfluorinated compounds, antibiotics, microplastics, and disinfection by-products in drinking water have attracted widespread attention. Traditional treatment technology coupled with advanced membrane technologies as well as the integration of artificial intelligence has significantly enhanced the efficiency of reducing ECs. Additionally, modified activated carbon adsorption, photocatalytic degradation, ultraviolet-assisted advanced oxidation, and functional membrane separation have demonstrated effectiveness in removing emerging contaminants. To further strengthen prevention and control efforts, a life-cycle-based approach is recommended. This includes dynamic screening of contaminants in water sources, development of high-efficiency removal processes and equipment, comprehensive environmental health risk assessments, and improvements to regulatory standards for drinking water quality. Continuous efforts in these areas aim to ensure safe water supply for residents while addressing ecological and public health challenges.

Cities play a key role in sustainable development, and it is of great significance to quantify and assess the environmental impact of urban built environment. In this paper, the feasibility of integrating city information model (CIM) and lifecycle assessment (LCA) were systematically analyzed; an environmental impact assessment framework for urban built environment was proposed; the potential application value of the framework in a city’s life cycle was explored, and the development prospect of the integration of CIM and LCA was discussed. The study can help promote sustainable cities.

Cross-border trips among bordered cities are becoming more and more common on a global scale. These trips are frequent and varied throughout the Guangdong-Hong Kong-Macao Greater Bay Area. Upgrading the transport facilities and services in the Greater Bay Area to meet the increasing demand for cross-border low-carbon transport is a key measure to promote the region's integrated development under China’s dual carbon goals. In this study, by reviewing relevant researches around the world, immigration and cross-border migration, and cross-border daily travel were discussed. Based on current research gaps and development potential, it was suggested that attention should be paid to micro-research on daily cross-border travel behaviors in the Greater Bay Area, to promote green travel habits and provide a basis for improving low-carbon transport facilities and policies. Daily cross-border traveling, an inevitable result of the development of cross-border regional integration, can promote more-dimensional and deeper integration process under the dural carbon goals.

With a developing national economy, the demand for green and low-carbon built environment has also increased significantly. However, the ever-growing number of motor vehicles has made exhaust emissions a major obstacle to urban greening and decarbonization. Taking Beijing for an example, the temporal distribution of motor vehicle exhaust emissions on road segments was estimated by using Computer programme to calculate emissions from road transport（COPERT） model. Based on various elements of built environment, the subjective and objective impact of these elements on motor vehicle emissions were analyzed through the Ordinary Least Squares (OLS) model and the entropy weight method. Finally, the comprehensive integration weighting method was employed to identify the significant factors influencing exhaust emissions. The results showed that the trend of exhaust emissions during weekdays and weekends was similar, both increasing first and then decreasing over time; but the average daily emissions on weekdays were significantly higher than those at weekends. Among the various built environment elements, road segment length was the most significant element affecting vehicle emissions. The study concluded with reasonable suggestions for promoting green and low-carbon built environment.

Understanding the travel behavior of children at different age is crucial for building child friendly communities. In this article, by taking Shuangliu District in Chengdu as an example, a survey on children's travel characteristics was conducted, and random forest model was employed to explore the nonlinear relationship between children's age and their daily travel behavior. The results show: (1) Children's age is the most significant factor affecting their travel behavior among various social demographic and family characteristics, especially the choice of travel companionship, the relative importance of which is 25.37%; (2) The mode of travel for children exhibited nonlinear characteristics as they grow, and became more independent and diverse; (3) The choice of travel companionship and destinations also showed nonlinear characteristics with age. Particularly after the age of 14, children tended to choose to travel alone or with friends, with a significant increase in their travel distance.

Active travel is a major form of physical activity for older adults as well as an important part of their daily transport. However, the non-linear relationship between their active travel and the built environment has been insufficiently revealed. In this study, by utilizing comprehensive travel survey data in Chengdu and multi-source big data, an interpretable machine learning framework (random forest & SHAP model) was constructed to systematically investigate the non-linear impact of community-level built environment factors on older adults’ active travel propensity. The results showed that accessibility to health facilities, the sidewalk index, and the green view index are the most important three factors of built environment affecting older adults’ active travel. Various elements of built environment had complex non-linear relationship with their active travel propensity of older adults, with a significant threshold effect. The effect of built environment indicators such as accessibility to health facilities and the green view index was asymmetric. Specifically, the inhibitory effect of low values far outweighed the promoting effect of high values. This study laid a theoretical foundation and provided scientific support to develop refined intervention strategies aiming at age-friendly cities.

Carbon dioxide (CO₂) geological sequestration is an effective way to control carbon dioxide emission. The fine particle content affects the porosity and permeability of reservoir, thus affecting the efficiency and safety of carbon dioxide injection and sequestration. Based on the H2A sandstone reservoir in Xijiang 23-1 oilfield, the pore structure of sandstone with different fine particle content was analyzed by CT scanning. Stress sensitivity tests of sandstone with different fine particle content were carried out. Stress sensitivity coefficient, permeability damage rate and permeability curvature were used to evaluate the stress sensitivity of sandstone. The results showed that with the increase of fine particle content, the permeability stress sensitivity of sandstone became stronger. The pore structure and permeability stress sensitivity of sandstone were analyzed comprehensively. It was shown that pore volume distribution was the main factor affecting the stress sensitivity of sandstone. In other words, the higher the frequency of small pore, the stronger the permeability stress sensitivity. Finally, based on the double strain Hooke model, a prediction model of porosity and stress of sandstone was proposed to predict the pore evolution of sandstone during the increase of effective stress. Guidance was provided for reservoir porosity and permeability analysis in the process of carbon dioxide geological sequestration.

Based on the requirements of the "dual carbon" goal for urban and rural construction and low carbon transformation in the construction industry, problems of multi-stakeholder collaborative governance in green buildings were analyzed, and policy objectives for multi-stakeholder synergistic governance over green buildings were proposed in terms of participation capacity, collaborative effort, and governance mechanism. Starting from the realistic dilemma faced by the multi-stakeholder synergistic governance over green buildings, the policy and advanced experience of green building development in foreign countries were compared and analyzed and insights were gained. policy suggestions of multi-stakeholder synergistic governance over green buildings under the "dual carbon" goal were put forward: enhancing multiple stakeholders' synergistic ability, safeguarding multiple stakeholders' synergistic interests, realizing multiple stakeholders' synergistic digital governance, and building a multiple stakeholders' synergistic industrial ecosystem, which will provide theoretical support and experience reference to accelerate the low-carbon transformation of the construction industry and realize China's "dual-carbon" goal.