National Bureau of Statistics of China

Gross domestic product (GDP) summarizes a vast amount of economic information in a single monetary metric that is widely used by decision makers around the world. However, GDP fails to capture fully the contributions of nature to economic activity and human well-being. To address this critical omission, we develop a measure of gross ecosystem product (GEP) that summarizes the value of ecosystem services in a single monetary metric. We illustrate the measurement of GEP through an application to the Chinese province of Qinghai, showing that the approach is tractable using available data. Known as the "water tower of Asia," Qinghai is the source of the Mekong, Yangtze, and Yellow Rivers, and indeed, we find that water-related ecosystem services make up nearly two-thirds of the value of GEP for Qinghai. Importantly most of these benefits accrue downstream. In Qinghai, GEP was greater than GDP in 2000 and three-fourths as large as GDP in 2015 as its market economy grew. Large-scale investment in restoration resulted in improvements in the flows of ecosystem services measured in GEP (127.5%) over this period. Going forward, China is using GEP in decision making in multiple ways, as part of a transformation to inclusive, green growth. This includes investing in conservation of ecosystem assets to secure provision of ecosystem services through transregional compensation payments.

In bridging the technology gap with the OECD nations, developing economies have access to three avenues of technological advance: domestic R&D, technology transfer, and foreign direct investment. This paper examines the contributions of each of these avenues, as well as their interactions, to productivity within Chinese industry. Based on a large data set for China's large and medium-size enterprises, the estimation results show that in-house R&D significantly complements technology transfer—whether of domestic or foreign origin. Foreign direct investment, which we assume is an important channel of proprietary technology transfer, does not facilitate the transfer of market-mediated foreign technology.

This research, which investigates a set of fundamental relationships in the R&D literature, is based on an unusually rich set of panel data covering the population of China's large and medium-size manufacturing enterprises. Using a recursive three-equation system, we investigate the determinants of firm-level R&D intensity, the process of knowledge production, and the impact of innovation on firm performance. Several results stand out. Overall, the statistical relationships within the model are surprisingly robust, including the contributions of R&D expenditure to new product (NP) innovation, productivity, and profitability. The roles of firm size, market concentration, and profitability in driving R&D effort parallel to those found in the US literature. We find that new product (NP) innovation accounts for approximately 12% of the total returns to R&D. Also, returns to industrial R&D in China appear to be at least three to four times the returns to fixed production assets.

Importance: The increasing use of cesarean delivery is an emerging global health issue. Prior estimates of China's cesarean rate have been based on surveys with limited geographic coverage. Objective: To provide updated information about cesarean rates and geographic variation in cesarean use in China. Design, Setting, and Data Sources: Descriptive study, covering every county (n = 2865) in mainland China's 31 provinces, using county-level aggregated information on the number of live births, cesarean deliveries, maternal deaths, and perinatal deaths, collected by the Office for National Maternal & Child Health Statistics of China, from 2008 through 2014. Exposures: Live births. Main Outcomes and Measures: Annual rate of cesarean deliveries. Results: Over the study period, there were 100 873 051 live births, of which 32 947 229 (32.7%) were by cesarean delivery. In 2008, there were 13 160 634 live births, of which 3 788 029 (28.8%) were by cesarean delivery and in 2014 there were 15 123 276 live births, of which 5 280 124 (34.9%) were by cesarean delivery. Rates varied markedly by province, from 4.0% to 62.5% in 2014. Despite the overall increase, by 2014 rates of cesarean delieries in 14 of the nation's 17 "super cities" had declined by 4.1 to 17.5 percentage points from their earlier peak values (median, 11.4; interquartile range, 6.3-15.4). In 4 super cities with the largest decreases, there was no increase in maternal or perinatal mortality. Conclusions and Relevance: Between 2008 and 2014, the overall annual rate of cesarean deliveries increased in China, reaching 34.9%. There was major geographic variation in rates and trends over time, with rates declining in some of the largest urban areas.

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

We ask how export demand shocks associated with the Asian financial crisis affected Chinese exporters. We construct firm-specific exchange rate shocks based on the precrisis destinations of firms' exports. Because the shocks were unanticipated and large, they are a plausible instrument for identifying the impact of exporting on firm productivity and other outcomes. We find that firms whose export destinations experience greater currency depreciation have slower export growth and that export growth leads to increases in firm productivity and other firm performance measures. Consistent with “learning-by-exporting,” the productivity impact of export growth is greater when firms export to more developed countries.

OBJECTIVE: To measure the association of China's universal two child policy, announced in October 2015, with changes in births and health related birth characteristics. DESIGN: National, descriptive before-and-after comparative study. SETTING: Every county in 28 of 31 provinces of mainland China. PARTICIPANTS: Births included in two national databases: 67 786 749 births from county level monthly aggregated data between January 2014 and December 2017; and 31 786 279 deliveries from individual level delivery information records between January 2015 and December 2017. MAIN OUTCOME MEASURES: Monthly mean number of births and mean proportion of multiparous mothers and mothers aged 35 and over, preterm deliveries, and caesarean deliveries. RESULTS: The study had two phases: the baseline period (up to and including June 2016, nine months after the policy announcement) and the effective period (from July 2016 to December 2017). The estimated number of additional births attributable to the new policy between July 2016 and December 2017 was 5.40 million (95% confidence interval 4.34 to 6.46). The monthly mean percentage of multiparous mothers and mothers aged 35 and over increased by 9.1 percentage points (95% confidence interval 6.4 to 11.7) and 5.8 percentage points (5.2 to 6.4), respectively. This increase in older mothers, however, was not associated with a concurrent increase in the overall rate of preterm birth. The monthly mean caesarean delivery rate among multiparous mothers increased by 1.2 percentage points (0.8 to 1.6) from 39.7% to 40.9%, and decreased by 3.0 percentage points (-3.5 to -2.5) among nulliparous mothers from 39.6% to 36.6%. CONCLUSIONS: Since its announcement in October 2015, the universal two child policy has been associated with a rise in births in China and with changes in health related birth characteristics: women giving birth have been more likely to be multiparous, and more likely to be aged 35 and over. No evidence of concurrent worsening outcomes (that is, premature births) was seen.

We c0nsider in detail probit and logistic regression models when some of the predictors are measured with error. For normal measurement errors, the functional and structural maximum likelihood estimates (MLE) are considered; in the functional case the MLE is not generally consistent. Non-normality in the structural case is also considered. By an example and a simulation, we show that if the measurement error is large, the usual estimate of the probability of the event in question can be substantially in error, especially for high risk groups. Some key words: Probit regression; Logistic regression; Functional models; Structural models; Measurement errors. 3

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.

This paper designs a smart urban environment monitoring system based on the wireless network of ZigBee to complete the real-time collection of urban environment information. The system consists of the basic monitoring network and the remote receiving terminal. The basic monitoring network connects the streetlights as routes and the taxis as nodes. After dynamically organizing the network, each node is assigned with an address as the only identity in the network. Then, the system designed conducts the simulation experiment to prove that it could meet the needs and send the collected information to the designated terminal in the form of message according to the setting. The sensor organized through the wireless network of ZigBee could inspire the infrastructure construction of the smart city. With the network, a smarter and more comfortable society could be well offered to people.

The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.

The fall armyworm, Spodoptera frugiperda, is a species native to the Americas and has spread to many countries in Africa and Asia in recent years. Proactive actions for potential invasion of S. frugiperda to China coordinated by government agencies and agricultural extension systems resulted in timely detection in January 2019 in Yunnan province neighboring onto Myanmar. The extensive monitoring in southern provinces of China since February 2019 resulted in dynamic tracking of S. frugiperda spreading to 13 provincial regions in China within 4 months by May 10, 2019, which is crucial for timely management actions in the fields. The first detections of S. frugiperda (corn strain) in China were confirmed using cytochrome oxidase subunit 1 (CO1) and triosephosphate isomerase (Tpi) genes molecular marker method. In addition to S. frugiperda, larvae of three other noctuid species with similar morphological appearance (S. litura, S. exigua and Mythimna separata) can occur simultaneously and cause similar damage in cornfields in southern China. Thus, we can use both morphological and molecular marker methods to compare larval stages of four noctuid species. Further, we discuss the risk of potential spread of invasive S. frugiperda to other regions and impact on corn production in China.

The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.

In the context of big data and the Internet of Things, with the advancement of geospatial data acquisition and retrieval, the volume of available geospatial data is increasing every minute. Thus, new data-management architecture is needed. We proposed a building information model (BIM) big data-storage-management solution with hybrid storage architecture based on web virtual reality geographical information system (WebVRGIS). BIM is associated with the integration of spatial and semantic information on the various stages of urban building. In this paper, based on the spatial distribution characteristics of BIM geospatial big data, a data storage and management model is proposed for BIM geospatial big data management. The architecture primarily includes Not only Structured Query Language (NoSQL) database and distributed peer-to-peer storage. The evaluation of the proposed storage method is conducted on the same software platform as our previous research about WebVR. The experimental results show that the hybrid storage architecture proposed in this research has a lower response time compared to the traditional relational database in geospatial big data searches. The integration and fusion of BIM big data in WebVRGIS realizes a revolutionary transformation of city information management during a full lifecycle. The system also has great promise for the storage of other geospatial big data, such as traffic data.

The distributed energy system is used as the prototype of the energy Internet, including a variety of forms of energy networks, plenty of distributed equipment and energy storage equipment composed of energy flow, and real-time communication and data volume of information systems. As an important energy system that is closely related to people's lives, its security and stability is one of the cores of its development. With the access of a large number of distributed devices, the structure of the power system has changed greatly. The addition of various forms of energy network, distributed equipment, and energy storage equipment has made it more difficult for the energy Internet to achieve the coordination among and control over these devices. Regardless of the fluctuation of the power load and the sudden change of the thermal load, problems such as energy network failure and demand will affect the security and stability of the energy Internet. Traditional energy systems are independent of one another, while integrated energy systems include subsystems, such as the power system, thermal system, and natural gas system, which can complement one another in planning and operation. To improve the utilization rate of all kinds of energy, reduce the waste of energy, and cut the emission of pollutants, it is crucial to realize the economic utilization of energy as well as the safe and stable operation of the energy Internet.

(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 × 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

BACKGROUND: As one of only a handful of countries that have achieved both Millennium Development Goals (MDGs) 4 and 5, China has substantially lowered maternal mortality in the past two decades. Little is known, however, about the levels and trends of maternal mortality at the county level in China. METHODS: Using a national registration system of maternal mortality at the county level, we estimated the maternal mortality ratios for 2852 counties in China between 1996 and 2015. We used a state-of-the-art Bayesian small-area estimation hierarchical model with latent Gaussian layers to account for space and time correlations among neighbouring counties. Estimates at the county level were then scaled to be consistent with country-level estimates of maternal mortality for China, which were separately estimated from multiple data sources. We also assessed maternal mortality ratios among ethnic minorities in China and computed Gini coefficients of inequality of maternal mortality ratios at the country and provincial levels. FINDINGS: China as a country has experienced fast decline in maternal mortality ratios, from 108·7 per 100 000 livebirths in 1996 to 21·8 per 100 000 livebirths in 2015, with an annualised rate of decline of 8·5% per year, which is much faster than the target pace in MDG 5. However, we found substantial heterogeneity in levels and trends at the county level. In 1996, the range of maternal mortality ratios by county was 16·8 per 100 000 livebirths in Shantou, Guangdong, to 3510·3 per 100 000 livebirths in Zanda County, Tibet. Almost all counties showed remarkable decline in maternal mortality ratios in the two decades regardless of those in 1996. The annualised rate of decline across counties from 1996 to 2015 ranges from 4·4% to 12·9%, and 2838 (99·5%) of the 2852 counties had achieved the MDG 5 pace of decline. Decline accelerated between 2005 and 2015 compared with between 1996 and 2005. In 2015, the lowest county-level maternal mortality ratio was 3·4 per 100 000 livebirths in Nanhu District, Zhejiang Province. The highest was still in Zanda County, Tibet, but the fall to 830·5 per 100 000 livebirths was only 76·3%. 26 ethnic groups had population majorities in at least one county in China, and all had achieved declines in maternal mortality ratios in line with the pace of MDG 5. Intercounty Gini coefficients for maternal mortality ratio have declined at the national level in China, indicating improved equality, whereas trends in inequality at the provincial level varied. INTERPRETATION: In the past two decades, maternal mortality ratios have reduced rapidly and universally across China at the county level. Fast improvement in maternal mortality ratios is possible even in less economically developed places with resource constraints. This finding has important implications for improving maternal mortality ratios in developing countries in the Sustainable Development Goal era. FUNDING: National Health and Family Planning Commission of the People's Republic of China, China Medical Board, WHO, University of Washington Center for Demography and Economics of Aging, Bill & Melinda Gates Foundation.

BACKGROUND: China is one of the few Countdown countries to have achieved Millennium Development Goal 5 (75% reduction in maternal mortality ratio between 1990 and 2015). We aimed to examine the health systems and contextual factors that might have contributed to the substantial decline in maternal mortality between 1997 and 2014. We chose to focus on western China because poverty, ethnic diversity, and geographical access represent particular challenges to ensuring universal access to maternal care in the region. METHODS: In this systematic assessment, we used data from national census reports, National Statistical Yearbooks, the National Maternal and Child Health Routine Reporting System, the China National Health Accounts report, and National Health Statistical Yearbooks to describe changes in policies, health financing, health workforce, health infrastructure, coverage of maternal care, and maternal mortality by region between 1997 and 2014. We used a multivariate linear regression model to examine which contextual and health systems factors contributed to the regional variation in maternal mortality ratio in the same period. Using data from a cross-sectional survey in 2011, we also examined equity in access to maternity care in 42 poor counties in western China. FINDINGS: Maternal mortality declined by 8·9% per year between 1997 and 2014 (geometric mean ratio for each year 0·91, 95% CI 0·91-0·92). After adjusting for GDP per capita, length of highways, female illiteracy, the number of licensed doctors per 1000 population, and the proportion of ethnic minorities, the maternal mortality ratio was 118% higher in the western region (2·18, 1·44-3·28) and 41% higher in the central region (1·41, 0·99-2·01) than in the eastern region. In the rural western region, the proportion of births in health facilities rose from 41·9% in 1997 to 98·4% in 2014. Underpinning such progress was the Government's strong commitment to long-term strategies to ensure access to delivery care in health facilities-eg, professionalisation of maternity care in large hospitals, effective referral systems for women medically or socially at high risk, and financial subsidies for antenatal and delivery care. However, in the poor western counties, substantial disparity by education level of the mother existed in access to health facility births (44% of illiterate women vs 100% of those with college or higher education), antenatal care (17% vs 69%) had at least four visits), and caesarean section (8% vs 44%). INTERPRETATION: Despite remarkable progress in maternal survival in China, substantial disparities remain, especially for the poor, less educated, and ethnic minority groups in remote areas in western China. Whether China's highly medicalised model of maternity care will be an answer for these populations is uncertain. A strategy modelled after China's immunisation programme, whereby care is provided close to the women's homes, might need to be explored, with township hospitals taking a more prominent role. FUNDING: Government of Canada, UNICEF, and the Bill & Melinda Gates Foundation.