The intensive development of urban space provides a good opportunity for the development of underground engineering. At the same time, it also demands higher requirements in terms of underground construction technology. In this paper, combined with the advantages of the shield method and shallow tunneling method, a mobile support technology suitable for soft soil tunneling is proposed, and its control effect on ground deformation is also studied. Numerical analysis and field tests show that this technique has a strong ability to control ground deformation. This technique has remarkable advantages in urban short-distance crossing engineering and tunnel construction in districts where there is a strict surface subsidence demand, and as such, it is worth applying and popularizing.

In order to improve public health by more effectively curbing the prevalence of chronic non-communicable diseases, the Chinese Academy of Engineering organized a major advisory project in 2015: the Strategic Study on the Health Economics Applied to Policymaking for the Prevention and Control of Chronic Non-Communicable Diseases. The study demonstrated health economics to be an important decision-making tool that may be applied to the prevention and control of chronic non-communicable diseases while they remain at a primary stage. However, a lack of understanding of the importance of health economics application and limitations in mastering and using the methods of health economics restrict the application of this tool in policymaking for the prevention and control of chronic non-communicable diseases. We suggest that multi-subject participation in health economics studies should be improved in future, big data related to health economics should be accumulated and applied to policymaking for the prevention and control of chronic non-communicable diseases, and the strategic framework of health economics should be developed and applied to policymaking for the prevention and control of chronic non-communicable diseases.

In order to solve the problem of residents' difficulties in affording expensive medical treatment, we build a community-hospital concept model of a secondary medical service system, and establish a function model and a referral mechanism for patients' preferences. Taking Jiading District in Shanghai as an example, we make use of the AnyLogic system modeling software and demonstrate how this model can be used to study the effect of different medical cooperation modes and medical reform policies through the introduction of actual case data. Our results show that promoting the quality of primary medical care services and increasing the reimbursement ratio of medical insurance can effectively improve the efficiency of the whole medical service system.

Artificial Intelligence (AI) aims to simulate information storage and processing mechanisms and other intelligent behaviors of a human brain, so that the machine has a certain level of intelligence. With the rapid development of the new generation of information technology, such as the Internet, big data, cloud computing, and deep learning, researches and applications of AI have made and are making important progresses. In this paper, the historical integration and evolution of computer science, control science, brain-inspired intelligence, human brain intelligence, and other disciplines or fields closely related to AI are analyzed in depth; then it is pointed out that the research results on the structure and functional mechanism of brain from neuroscience, brain science and cognitive science provide some important inspirations for the construction of an intelligent computing model. Moreover, the drives and developments of AI are discussed from the aspects of logic model and system, neuron network model, visual nerve hierarchy mechanism, etc. Finally, the development trend of AI is prospected from the following five aspects: the computational theory of the Internet, the integration of AI calculus and computation, the model and mechanism of brain-inspired intelligence, the impetus of AI to neuroscience, and the algorithm design of feedback computation and the energy level of the control system.

The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate two-dimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.

Ethylene production by the thermal cracking of naphtha is an energy-intensive process (up to 40 GJ heat per tonne ethylene), leading to significant formation of coke and nitrogen oxide (NO_x), along with 1.8–2 kg of carbon dioxide (CO₂) emission per kilogram of ethylene produced. We propose an alternative process for the redox oxy-cracking (ROC) of naphtha. In this two-step process, hydrogen (H₂) from naphtha cracking is selectively combusted by a redox catalyst with its lattice oxygen first. The redox catalyst is subsequently re-oxidized by air and releases heat, which is used to satisfy the heat requirement for the cracking reactions. This intensified process reduces parasitic energy consumption and CO₂ and NO_x emissions. Moreover, the formation of ethylene and propylene can be enhanced due to the selective combustion of H₂. In this study, the ROC process is simulated with ASPEN Plus® based on experimental data from recently developed redox catalysts. Compared with traditional naphtha cracking, the ROC process can provide up to 52% reduction in energy consumption and CO₂ emissions. The upstream section of the process consumes approximately 67% less energy while producing 28% more ethylene and propylene for every kilogram of naphtha feedstock.

Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering are presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist towards the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.

The internal flow field study of car compartments is an important step in railroad vehicle design and optimization. The flow field profile has a significant impact on the temperature distribution and passenger comfort level. Experimental studies on flow field can yield accurate results but carry a high time and computational cost. In contrast, the numerical simulation method can yield an internal flow field profile in less time than an experimental study. This study aims to improve the computational efficiency of numerical simulation by adapting two simplified models—the porous media model and the porous jump face model—to study the internal flow field of a railroad car compartment. The results provided by both simplified models are compared with the original numerical simulation model and with experimental data. Based on the results, the porous media model has a better agreement with the original model and with the experimental results. The flow field parameters (temperature and velocity) of the porous media model have relatively small numerical errors, with a maximum numerical error of 4.7%. The difference between the numerical results of the original model and those of the porous media model is less than 1%. By replacing the original numerical simulation model with the porous media model, the flow field of subway car compartments can be calculated with a reduction of about 25% in computing resources, while maintaining good accuracy.