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The widespread use of the Internet of Things (Iot) makes it possible to connect everything but having enough IP addresses is a fundamental requirement of this paradigm. All previous environmental monitoring systems in China are based on IPv4. In combination with the characteristics and requirements of China's atmospheric environment monitoring system, this paper develops a monitoring system based on IPv6 technology. Users can directly access the monitoring equipment through the IPv6 website to view data and configure operations. This paper first introduces the design and implementation of the software and hardware of the system, then introduces the simplification of IPv6 protocol, the transplantation of IPv6 protocol on ARM and the design and implementation of embedded Web server system. The experimental results show that the developed atmospheric environment monitoring system can realize continuous data acquisition based on IPv6 and provide data-driven support for environmental protection management and decision-making., (© 2022. The Author(s).)

Chinese companies in the computer and telecommunications sectors have been able to climb the value-added chain and become more innovative and globally competitive in part due to their ability to access global technology supply chains, many with a heavy US company presence. China's globally active players in these sectors are reliant to some degree on US technology inputs that have now been put in jeopardy by the increasing willingness of the US government to target Chinese firms, and weaponize US company dominance of key supply chains such as software, semiconductors, and semiconductor manufacturing equipment. This paper examines prospects for Chinese companies to continue to innovate and compete globally in the computer and telecommunications sectors given the worsening geopolitical tensions centering on the technology sector. [ABSTRACT FROM AUTHOR]

Landslides are one of the main geological disasters in the riparian zone of the Three Gorges Reservoir Area (TGRA) in China, causing massive losses in terms of industrial and agricultural production and people's lives and property. Due to the difficulty of data collection and lack of facade information, the remote sensing methods currently used for landslide mapping and detection are insufficient. The current landslide information acquisition method is mainly based on visual interpretation and automatic computer classification from aerial orthophotographs, which is subjective and time-consuming and yields low accuracy. New shipborne photogrammetry has unique advantages in small and steep slope landslide detection. In recent years, the deep learning technology represented by convolutional neural networks (CNNs) exhibits more powerful feature learning ability in image recognition than traditional machine learning technology. Meanwhile, transfer learning can make the network suitable for small datasets through fine-tuning, which could transfer the parameters of the pretrained model to the new model in a certain way, thereby increasing the learning efficiency of the model. In this paper, the training of multiple deep learning models is realized through transfer learning, and automatic identification of landslides based on shipborne images is achieved through decision-level fusion. The experimental results show that through the integration of decision-level fusion and transfer learning, it performs well in the classification of landslides based on shipborne images. [ABSTRACT FROM AUTHOR]

At present, percutaneous nephrolithotomy has become an option for hospitals increasingly to treat calculus. However, due to the certain risk of percutaneous nephrolithotomy, it may lead to postoperative complications. Analyzing the preoperative factors of septic shock after percutaneous nephrolithotomy can provide guidance for reducing the incidence of septic shock after PCNL. This article establishes a logistic regression model based on preoperative factors, uses statistical methods, and uses the professional statistical software SPSS to create a database and analyze the data. The method of classification data analysis was used to determine various influencing factors including patient factors, stone factor, and preoperative factors and combined B-ultrasound guidance and percutaneous nephrolithotomy to reduce the risk of surgery. The experimental results found that the percutaneous nephroscope laser lithotripsy guided by the smart Internet of Things can effectively perform lithotripsy; in this paper, the preoperative data of different complications are obtained, and the risk of surgery is closely related to the operation time. The operation time exceeds 90 minutes and causes the risk of severe bleeding after PCNL. It is twice the operation time of less than 90 minutes and 5 times the operation time of less than 60 minutes. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, with the continuous growth and improvement of the comprehensive strength of our national economy and the continuous improvement of our modern industrial machinery and equipment, the level of safety technology, production level, and quality management level see continuous steady improvement, resulting in the rapid economic growth of chemical industries in China in recent years. In recent years, chemicals have been widely used and demand is increasing. However, the physical properties of some chemical substances are very unstable and vary according to process conditions, making it difficult to control. At the same time, safety issues have become a major "red warning" for industrial enterprises. Various safety incidents have attracted social attention, and the chemical industry has also learned from them, sounding the safety alarm for itself and reviewing its own shortcomings, and taking improvement measures. Therefore, it is obviously necessary to always emphasize the concept of "safety first, prevention first, and comprehensive management" to prevent enterprise safety accidents. It is necessary to regularly investigate and analyze the situation of safety accidents to summarize and continuously improve. This is the most important thing for enterprises to prevent the continuous occurrence of safety accidents in a good and effective way. Regularly analyzing, summarizing, and improving safety issues are the best way to prevent accidents. Deep learning mainly relies on letting computers or other electronic devices perform iterative learning on a large number of data samples according to the rules defined by the program. The computer after learning will have the analysis ability similar to humans, but its analysis ability is limited to the range of the sample data to be learned. Artificial intelligence technology realizes an operation system that simulates human thinking through the definition of algorithms and programs. The use of deep learning and artificial intelligence in industrial enterprise safety production supervision can realize enterprise safety production supervision, early warning, and intelligent operation. Without the security of the production chain, a business cannot develop. Based on the basic theory of safety supervision and early warning in the field of enterprise safety production and related knowledge of artificial intelligence, this paper studies the work safety in the supervision and early warning mechanism abides by the selection principle of the management system, safety production indicators and indicator system, and conducts work based on the production work data of industrial plants. Reasonable safety supervision and early warning mechanism provide technical assistance for effectively controlling safety accidents in production and management measures to improve enterprise safety. [ABSTRACT FROM AUTHOR]

Due to the influence of regional climatic conditions, the development of cold ecological tourism agricultural park in the north is slow, its landscape planning and design research lags behind, and there are still some problems. With the development of electronic computers, the previously unachievable optimization of engineering structures is now possible, while the traditional optimization methods cannot meet the requirements of modern computing due to the implementation constraints. The emergence of algo for group intelligences has made some applications of engineering structural optimization possible. Group intelligence is the characteristic of a system in which the collective behavior of low-level individuals interacting locally with their environment forms an overall pattern of consistent functions. This paper presents a study of energy-efficient cold landscape design based on algo for group intelligences and analyzes the problems that arise in the ecological landscape design of cold urban settlements. Then countermeasures are proposed to solve the problems. The linear model is replaced by the determination of the weight of the evaluation index of the energy-saving design of frozen landscape. And take it as a new migration model. Then, based on the weight of the evaluation index, the group algorithm analysis in landscape design is proposed, which is a generalized definition of the standard model. The experimental results show that the algo for group intelligence outperforms another algorithm in terms of solving ability to the extent that the average optimization ability is improved by 13.45%, so the algo for group intelligence demonstrates its superior ability to take into account both local and global search. Therefore, the use of algo for group intelligences to build an energy-efficient society is a necessary path for China's development, and the construction of energy-efficient gardens is an important part of building an energy-efficient society, and the application of energy-efficient concepts to modern landscape design is an inevitable trend in the development of urban landscaping. [ABSTRACT FROM AUTHOR]

Over the past decade, the call to foster computational thinking (CT) in every child has received considerable attention. However, there is little understanding of whether children are developmentally ready to think computationally and what specific CT concepts and skills can be developed at various ages. This study explored the developmental and gender differences in CT skills of 197 Grade 4–6 students (aged 9–13) before being exposed to instruction and investigated the age–gender interaction effects on their CT acquisition in an intervention combining both programming and non‐programming (unplugged) activities. Results show that students' CT skills followed a developmental progression before instruction. Gender difference across ages was insignificant in conditionals, logical operators, pattern recognition and generalization skills. Additionally, students of different ages developed CT differently during the intervention, and their CT acquisition was unaffected by gender. Implications for practice and research in CT education were discussed. Lay Description: What is currently known about the topic: Learning progressions for computational thinking have been defined in K‐12 curricula globally.Engaging in programming and non‐programming (unplugged) activities can increase students' CT skills.Controversial results have been reported on the age–gender interaction effects on CT development.Lack of understanding on children's actual progressive development of CT. What this paper adds: A developmental progression was found in CT skills of students aged 9–13. Students aged 12 and above had some a priori understanding of CT.No gender difference across ages existed in conditionals, logical operators, pattern recognition and generalization skills before instruction.Students aged 9–11 benefitted the most from an intervention combining both programming and unplugged activities compared to older students. Students' CT acquisition in the intervention was unaffected by gender.Given the intervention, students aged 9 and above developed conditionals, logical operators and pattern recognition skills. However, generalization skill was developed only for students aged 12 and above. Implications for practice and policy: The findings lay the foundation for designing evidence‐based age‐appropriate CT learning progression and pedagogies.Age‐appropriate learning content: Given appropriate learning experiences, students aged 9 and above may be ready to learn certain CT concepts and skills, including conditionals, logical operators and pattern recognition.CT pedagogies: Using programming and unplugged activities to develop CT requires careful consideration of students' cognitive development and gender needs. [ABSTRACT FROM AUTHOR]

Cong, W.; Guo, J., and Yu, H., 2019. On-line analysis of marine water quality data based on large electronic computer. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 47–53. Coconut Creek (Florida), ISSN 0749-0208. In order to make full use of marine resources, develop marine economy and build a powerful marine country, in recent years, the state has focused on the on-line analysis technology of marine water. In order to solve the problems of rapid change of marine environment and difficulty in the detection and analysis of water quality data, this paper presents an online analysis method of marine water quality data based on large electronic computer after comparing various methods of marine water quality detection in China and abroad. This method uses a variety of marine water quality sensors to collect marine water quality data synthetically, and then uses multi-sensor fusion technology to fuse the collected water quality data. Finally, it combines multi-sensor D-S evidence theory data fusion technology with computer to analyze water quality data synthetically online. It is concluded that this method can effectively improve the ability of marine pollution early warning and marine ecological environment protection by real-time comprehensive analysis and research of marine water quality data. [ABSTRACT FROM AUTHOR]

With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images. [ABSTRACT FROM AUTHOR]

Owing to the current development trend of globalization and informatization, traditional transportation technologies and means can no longer meet the needs of economic and social development. Thus, intelligent transportation will be an inevitable and revolutionary choice for the development of the transportation industry. As a subsystem of the intelligent transportation system, the electronic nonstop toll collection (ETC) system provides a good solution to the problems of traffic congestion, environmental pollution, energy consumption, and low toll operation efficiency caused by expressway toll collection. This high-technology system is utilized in many fields, such as radio communication and computer and automatic control in the transportation field. China's expressway ETC systems have evident effects on congestion reduction at toll stations. Although the advantages of ETC lanes are significant, and China actively implements ETC-handling policies, some drivers are reluctant to install ETC on-board systems or they have negative experiences after handling them. According to a survey, existing literature lacks research on the factors affecting user satisfaction with ETC. On the basis of the questionnaire data of ETC user experience in Shanghai and Beijing, we comprehensively investigate the reasons of banking, ETC equipment, and drivers' personal factors and travel characteristics to establish an ordinal logistics model. Through cluster analysis, drivers in the two cities are divided into three groups, and the significance of related variables is further analyzed. This research can help us understand the degree of influence of different factors on user satisfaction with ETC to a certain extent. Moreover, effective methods and measures can be used to promote the popularity of ETC vehicle equipment and the utilization rate of ETC-dedicated lanes. [ABSTRACT FROM AUTHOR]

At present, the pressure on China's economic development is increasing day by day due to the profound changes in the internal and external environment. The global economic pattern is undergoing significant changes in terms of the external environment. Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term. Meanwhile, it will also accelerate sustainable economic development. In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the Internet of things (IoT) industry. Based on the gray correlation degree, the high- and new-tech industries under the development of the IoT industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated. The results show that the development of the IoT industry has driven the development of the high- and new-tech industry. The gray correlation between the development of the IoT industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation. The average output share of the electronic computer and office equipment manufacturing industries is 47.09%. The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%. Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively. The results have significant value in the research on the impact of the development of the IoT industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization. [ABSTRACT FROM AUTHOR]

The University Computer Foundation (UCF) course, which is a compulsory theoretical course for freshmen, especially science and engineering department students, lays the foundation for more advanced courses. However, the UCF course covers a wide variety of concepts, including the computer hardware, which are difficult for freshmen to comprehend. In recent years, virtual reality (VR) has become prevalent in many educational settings. To improve the teaching of the UCF course, we developed a VR program about introductory computer hardware and invited freshmen from two universities in China to take part in the experiment as participants. Four aspects of computer contents, namely the evolutionary history of computers, the computer components, the computer assembly, and the workflow of computer hardware, are displayed in the program. During the experiment, students' behavioral data were recorded. Then, the data were analyzed and the reasons were speculated for the differences among groups of various categories. From the experiment, the relationships between the students' behavior and their groups were found, and we demonstrated that our VR program is effective at attracting the students' curiosity and increasing their understanding of computer hardware in the UCF course. The study concludes with suggestions for practitioners and researchers in the field of VR for university education. [ABSTRACT FROM AUTHOR]

Data mining is a common basic function in humans and many other intellectual systems. The popularization of computers has created a good research environment for the advanced application of remote sensing satellites. With China's economic and population growth, the demand for food is also increasing. Obtaining the planting area is one of the most important tasks in calculating harvest costs. However, traditional cost analysis methods lack information. The traditional remote sensing image classification method is based on the pixel classification method, which cannot effectively extract the spatial texture information in an image. The pixel-based classification method also has the problem of salt and pepper phenomenon in the classification results, which generates a large number of invalid broken patches and ultimately leads to low classification accuracy. Here, we investigated the application of remote state system mapping for the extraction of the planting area. We analyzed the basic principles of remote image processing and remote image sharing methods and introduced an audio system and optical algorithm. Here, two separation methods are selected, long-distance traction measurements are performed in three training areas, and the two results are compared and analyzed. Experimental results show that the method of objective orientation increases the working speed compared with the method of manuscript translation. The benefit of speed measurement becomes more apparent in both seasons as the measuring range increases. Computer automatic classification and the method of artificial visual interpretation are tested sequentially, and the total time taken is 49 and 13.5 days, respectively. [ABSTRACT FROM AUTHOR]