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The early diagnosis and treatment of foreign body aspiration (FBA) can significantly improve the overall prognosis of children. There are significant differences in the epidemiology and clinical characteristics of FBA in different regions. Therefore, we conducted a real-world study in the western region of China with over 4000 patients. The aim of this study was to improve the understanding of FBA in terms of its types, the specific months of its occurrence, and the distribution of primary caregiver characteristics in western China. We collected the clinical and epidemiological data of children who were diagnosed with FBA in our hospital over the past 20 years through a big data centre. We matched the data of healthy children who underwent routine physical examinations at the paediatric health clinic during the same period to analyse the differences in the data of actual guardians. A total of 4227 patients from five provinces were included in this study. Foreign bodies were removed by rigid bronchoscopy in 99.4% (4202/4227) of patients, with a median age of 19 months and a median surgical duration 16 min. January was the most common month of onset for 1725 patients, followed by February, with 1027 patients. The most common types of foreign objects were melon peanuts, seeds and walnuts, accounting for 47.2%, 15.3%, and 10.2%, respectively. In the FBA group, the proportion of grandparents who were primary caregivers was 70.33% (2973/4227), which was significantly greater than the 63.05% in the healthy group (2665/4227) (P < 0.01). FBA most commonly occurs in January and February. More than 60% of FBAs occur between the ages of 1 and 2 years, and the incidence of FBA may be greater in children who are cared for by grandparents. A rigid bronchoscope can be used to remove most aspirated foreign bodies in a median of 16 min. [ABSTRACT FROM AUTHOR]

With the increasing enrollment of higher education in China, teaching quality evaluation is an urgent problem to be studied. Colleges and universities should also have their own set of teaching quality evaluation system with the evaluation activities, and thus pre-evaluate their teaching and ensure the teaching quality between the two teaching evaluations. At present, big data is more and more widely used, and distributing these big data to corresponding scores through content centric data center networks (CCDCNs) provides Quality of Experience (QoE) for English teaching quality assessment. Therefore, one of the challenges faced by the current network is to enhance QoE under content distribution. In this work, in order to solve this problem, we schedule the cached teaching feature vector to the corresponding score level. Three cache scheduling algorithms are constructed in CCDCNs. Firstly, an approximate dynamic algorithm is proposed, which has high complexity. Then, based on the characteristics of node centralization, we propose an improved approximate dynamic scheduling algorithm. Although the algorithm includes the scheduling of cached content and the scheduling of content transmission rate, it has low complexity in processing scheduling. In addition, we propose a cache scheduling algorithm based on deep reinforcement learning (DRL). Although the algorithm has high complexity, the scheduling accuracy is also high. Experiments show that the method proposed in this work can obtain higher QoE, and excellent performance in English teaching evaluation, about 7.8% improvement degree. [ABSTRACT FROM AUTHOR]

The "Yangtze River Economic Belt Development Strategy" is one of China's three major national development strategies. Enhancing the protection and quality of arable land in the Yangtze River Economic Belt (YEB) is pivotal for fostering regional growth. In this study, land use data spanning the years 1980 to 2020 in the YEB were extracted from the national land use database maintained by the Resource and Environment Data Center of the Chinese Academy of Sciences. Employing Geographic Information System (GIS) spatial analysis techniques and arable land change metrics, the study delineated the spatiotemporal characteristics of arable land alterations across the YEB for the period. Additionally, using grain output data at the prefecture level from 2011 to 2020, the paper calculated provincial grain output to analyze the impact of arable land changes over the last four decades on grain output. The findings revealed that: (1) From 1980 to 2020, the total arable land area in the YEB decreased by approximately 41,775 square kilometers, with the most significant decrease occurring in the downstream region. (2) From 1980 to 1990, the primary factor contributing to the decrease in arable land area was the expansion of water bodies, while from 1990 to 2020, the principal reason for the reduction in arable land area was the expansion of construction land. (3) From 1980 to 2020, the decrease in arable land area resulted in a net reduction of approximately 25.12 million tons in total grain output, with the largest decline observed in the downstream regions and the smallest decline in the upstream regions. (4) Consistent with the trends in arable land area reduction, the main reason for the decline in grain output from 1980 to 1990 was the expansion of water bodies encroaching upon arable land, whereas from 2000 to 2010, the primary cause of arable land reduction was the expansion of construction land areas. In conclusion, the research suggested that over the past four decades, the primary driver behind the reduction in arable land within the YEB has been the expansion of construction land areas. Particularly noteworthy was the period from 2000 to 2010, during which the impact of arable land reduction on grain output was most pronounced. This period coincided with the rapid economic development and accelerated urbanization process within the YEB. [ABSTRACT FROM AUTHOR]

The study of seismic ruptures is crucial for understanding major earthquake events. Historically, research on large earthquake rupture processes has required a long time to be published. However, recent advancements in earthquake early warning (EEW) systems have allowed for more rapid analyses of earthquake rupture inversions. The widespread and dense distribution of EEW stations provides in China an opportunity to study and invert rupture processes in near-real time. Two notable earthquakes were studied using this technology: the 2017 Jiuzhaigou MW 6.5 earthquake in Sichuan Province, China, and the 2022 Menyuan MW 6.7 earthquake in Qinghai Province, China. In both instances, numerous strong-motion sensors captured the seismic events and transmitted waveform data to data analysis centers in real time. Following automated site selection and preparation procedures, the rupture processes of these earthquakes were analyzed and released within 30 min of the event origin. This rapid response time demonstrates that the seismic rupture process determined through inversion using existing EEW systems can serve as a guide for emergency rescue work after earthquakes. [ABSTRACT FROM AUTHOR]

Based on the fifth‐generation reanalysis (ERA5) data of the European Centre for Medium Range Weather Forecasts (ECMWF), spring Mongolian cyclones were examined from 1950 to 2023. Then, based on the feature parameters of activity path information, machine learning method (k‐means) is used to classify the trajectories of spring Mongolian cyclone. Subsequently, the atmospheric circulation configurations of the three categories of Mongolian cyclones and their influence on the weather in China verify the reasonability of the classification method. Specifically: (1) eastward moving type, affecting Inner Mongolia and Northeast China (Clus‐1). The circulation background of the Clus‐1 cyclone displays a broad negative geopotential height anomaly centre over East Asia; (2) northeastward moving type, affecting northeast part of Northeast China (Clus‐2). The circulation background of the Clus‐2 cyclones shows a "positive–negative–positive" distribution of circulation anomalies in the European Plain, Lake Baikal and the Sea of Japan; (3) southeastward moving type, this type can move to the south of China (Clus‐3). The circulation background of the Clus‐3 cyclones is opposite to that of the Clus‐2 cyclones. The three categories of Mongolian cyclones will cause dust weather and strong winds in different regions of China, mainly affecting Inner Mongolia and Northeast China. [ABSTRACT FROM AUTHOR]

National holidays are associated with high mortality in some diseases, but little is known about patients undergoing peritoneal dialysis (PD). The research aimed to investigate the impact of national holidays on the health outcomes of PD patients. Over ten years, all episodes of unplanned hospitalization, death, and peritonitis in PD patients were collected in our center. Seven national holidays in China were chosen, and non-holiday days were selected as the control period. The effect of national holidays was observed by comparing the hospitalization, death, and peritonitis rates between holiday and non-holiday groups. There were 297 events in all holiday periods and 1247 in non-holiday periods. There is no significant difference in hospitalization rate between holiday and non-holiday groups (32.4% ± 6.4% vs. 29.2% ± 3.4%, p = 0.175). So is the death rate [6.3% (4.8–12.3%) vs.5.0% (4.2–8.9%), p = 0.324] and peritonitis rate [0.19 (0.13–0.53) vs. 0.22 (0.18–0.27), p = 0.445] between the two groups. Significant differences were observed in the distribution of peritonitis causes between the two groups (p = 0.017). The rate of secondary to other infections in the holiday group was significantly higher than in the non-holiday group (25.0 vs. 10.3%, p = 0.015). Our study suggested no national holiday effect on health outcomes of PD patients based on ten-year data in our center. [ABSTRACT FROM AUTHOR]

Background: Human brucellosis continues to be a great threat to human health in China. The present study aimed to investigate the spatiotemporal distribution of human brucellosis in China from 2004 to 2019, to analyze the socioeconomic factors, meteorological factors and seasonal effect affecting human brucellosis incidence in different geographical regions with the help of spatial panel model, and to provide a scientific basis for local health authorities to improve the prevention of human brucellosis. Methods: The monthly reported number and incidence of human brucellosis in China from January 2004 to December 2019 were obtained from the Data Center for China Public Health Science. Monthly average air temperature and monthly average relative humidity of 31 provincial-level administrative units (22 provinces, 5 autonomous regions and 4 municipalities directly under the central government) in China from October 2003 to December 2019 were obtained from the National Meteorological Science Data Centre. The inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield and gross pastoral product of 31 provincial-level administrative units in China from 2004 to 2019 were obtained from the National Bureau of Statistics of China. The temporal and geographical distribution of human brucellosis was displayed with Microsoft Excel and ArcMap software. The spatial autocorrelation and hotspot analysis was used to describe the association among different areas. Spatial panel model was constructed to explore the combined effects on the incidence of human brucellosis in China. Results: A total of 569,016 cases of human brucellosis were reported in the 31 provincial-level administrative units in China from January 2004 to December 2019. Human brucellosis cases were concentrated between March and July, with a peak in May, showing a clear seasonal increase. The incidence of human brucellosis in China from 2004 to 2019 showed significant spatial correlations, and hotspot analysis indicated that the high incidence of human brucellosis was mainly in the northern China, particularly in Inner Mongolia, Shanxi, and Heilongjiang. The results from spatial panel model suggested that the inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature (the same month, 2-month lagged and 3-month lagged), average relative humidity (the same month) and season variability were significantly associated with human brucellosis incidence in China. Conclusions: The epidemic area of human brucellosis in China has been expanding and the spatial clustering has been observed. Inner Mongolia and adjacent provinces or autonomous regions are the high-risk areas of human brucellosis. The inventory of cattle and sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature, average relative humidity and season variability played a significant role in the progression of human brucellosis. The present study strengthens the understanding of the relationship between socioeconomic, meteorological factors and the spatial heterogeneity of human brucellosis in China, through which 'One Health'-based strategies and countermeasures can be provided for the government to tackle the brucellosis menace. Author summary: Human brucellosis continues to be a great threat to human health in China. It was reported that human brucellosis incidence in China was 4.95/100,000 in 2021, surging from 16th place in 2000 to 5th place in 2021. The present study was designed to investigate the spatiotemporal distribution of human brucellosis in China from 2004 to 2019, to analyze the socioeconomic factors, meteorological factors, and seasonal effect affecting human brucellosis incidence in different geographical regions. The clear seasonal increase of reported human brucellosis cases between March and July, the spatial clustering situation and the spread of epidemic area from northern to the southern coast and southwestern regions were identified. The inventory of cattle and sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature (the same month, 2-month lagged and 3-month lagged), average relative humidity (the same month) and season variability played a significant role in the progression of human brucellosis. Spatial panel model was found to have good applicability and interpretability. As each factor plays a different role in different geographical regions, a comprehensive surveillance system for human brucellosis should be established in conjunction with the relevant risk factors to provide early warning of human and animal epidemics. [ABSTRACT FROM AUTHOR]

Based on the daily average temperature data of 2374 stations in China from 1993 to 2022 and the monthly average temperature data of European Centre for Medium‐Range Weather Forecasts (ECMWF) System5 in winter, this article analyses the intraseasonal variations of winter surface air temperature (SAT) over China by using seasonal empirical orthogonal function decomposition (S‐EOF) and compare the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S‐EOF1) is characterized by out‐of‐phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño‐Southern Oscillation (CP ENSO) can impact S‐EOF1. The second mode (S‐EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai‐Tibet Plateau's variation, while the other areas are characterized by the out‐of‐phase change in SATA. S‐EOF2 is closely related to Arctic oscillation (AO). The third mode is characterized by alternating changes over 3 months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S‐EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well. [ABSTRACT FROM AUTHOR]

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China. [ABSTRACT FROM AUTHOR]

In this study, the spatiotemporal variations in CH4 concentrations in China from 2003 to 2021 are investigated, and their trends are forecasted over the next decade. Based on the seventh edition standard product retrieved by the atmospheric infrared detector (AIRS) at an altitude of 500 hPa, we verified monthly CH4 products using observational data provided by the World Data Center for Greenhouse Gases (WDCGG) from six ground stations in and around China. The correlation coefficients (R values) between the two data sets ranged from 0.68 to 0.92, signifying the ability of AIRS inversion data to represent temporal and spatial changes in surface CH4 concentrations. Additionally, China was classified into three regions (steps) based on terrain, and the changes in CH4 concentrations were assessed from three perspectives: spatial distribution, interannual variation, and seasonal variation. The results revealed that the CH4 concentration decreased with elevation along a topographic gradient, with high-value areas located in the first and second steps, corresponding to the eastern Qinghai–Tibet Plateau, northern Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region. Over 19 years, the average increase in CH4 concentration has ranged from 65 to 175 ppb. In addition, the CH4 concentrations were higher during summer and autumn and lower during spring and winter. Finally, a SARIMA model was used to predict the near-surface CH4 concentration trend in China over the next ten years, which indicated a continued seasonal increase. [ABSTRACT FROM AUTHOR]

Accurate knowledge on spatiotemporal characteristics of historical precipitation extremes could provide great potential guidance for preventing hydrological‐related disasters caused by precipitation extremes in the future. On the basis of the fifth generation of atmospheric reanalysis precipitation data by the European Centre for Medium Range Weather Forecasts (ERA5, 0.25°, 1 hourly, 1950–2020) with high spatiotemporal resolutions, continuity and quality, this study analyzed the spatiotemporal characteristics of precipitation extremes over the Ganjiang River Basin and its surroundings during 1950–2020. The main conclusions include, but are not limited to, the following: (1) In general, precipitation extremes present increasing trends over most areas of the basin and its surroundings. For instance, areas showing upward trends of R10, SDII and PRCPTOT account for ~93.45%, ~66.36%, and ~88.18%, respectively. (2) The spatiotemporal variations of precipitation extremes over the Ganjiang River Basin and its surroundings show obvious northwest–southeast differences. For instance, precipitation extremes are increasing in the southeastern parts, but they are decreasing in the northwestern parts. (3) High‐value clusters are also identified in the southeast (e.g., R10, SDII, R95P and PRCPTOT, accounting for ~20.71%, ~20.72%, ~25.88%, and ~22.56%, respectively) and low‐value clusters in the northwest (e.g., Rx5day, SDII and R95P, accounting for ~18.05%, ~27.03%, and ~21.18%, respectively). (4) The spatiotemporal variations of precipitation extremes in both the southeast and northwest are quite stable. For example, regions with less than five abrupt change points of R10, SDII, and PRCPTOT account for 77.49%, 54.84%, and 81.74%, respectively. [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m 2) was successfully upscaled to the MODIS pixel scale (62 500 m 2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m -2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272587; H. Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to describe the clinical features of AAV and identify possible risk factors for end-stage renal disease (ESRD) and mortality in AAV based on the experience of a single center in China. Methods: A prospective cohort study of AAV was conducted based on data acquired by the Chinese Rheumatism Data Center (CRDC). The cohort involved 140 patients diagnosed with AAV in the Second Affiliated Hospital of Nanchang University from January 2013 to April 2022. Clinical characteristics and prognostic data were prospectively collected. The patients were divided into an ESRD group and a non-ESRD group, a death group and a survival group, Then, univariate and multivariate COX regression models were used to determine the risk factors associated with ESRD and mortality by AAV. Results: Multivariate Cox regression results showed that high initial serum creatinine (hazard ratio (HR) = 1.001, 95% confidence interval (CI): 1.000–1.002, P = 0.024), high initial Birmingham vasculitis activity score (BVAS) (HR = 1.081, 95% CI: 1.027–1.138, P = 0.003), and the need for dialysis treatment (HR = 4.918, 95% CI: 1.727–14.000, P = 0.003) were independent risk factors for the progression of ESRD in AAV patients. Multivariate Cox regression results showed that alveolar hemorrhage (HR = 3.846, 95% CI: 1.235–11.973, P = 0.020), interstitial lung disease (HR = 4.818, 95% CI: 1.788–12.982, P = 0.002), and low initial estimated glomerular filtration rate (EGFR) (HR = 0.981, 95% CI: 0.968–0.995, P = 0.009) were independent risk factors for the prediction of death in AAV patients. Conclusion: These findings suggest that high initial serum creatinine, a high initial BVAS score, and the need for dialysis were independent risk factors for the progression of ESRD in AAV patients. Alveolar hemorrhage, interstitial lung disease, and low initial EGFR were independent risk factors for death. Key Points • The risk factors for ESRD in AAV determined in this study are high initial serum creatinine, a high initial BVAS score, and the need for dialysis. • The risk factors for mortality in AAV are alveolar hemorrhage, interstitial lung disease, and low initial EGFR. [ABSTRACT FROM AUTHOR]

Water shortages have always been the primary bottleneck for the healthy and sustainable development of the ecological environment on the Loess Plateau (LP). Proper water resource management requires knowledge of the spatiotemporal characteristics of precipitation frequency. This paper employed the gridded precipitation dataset obtained from the China Meteorological Data Service Centre to present a spatially explicit characterization of precipitation frequencies in tandem with their return periods on the LP based on the L-moment method. The 60% and 80% of the mean annual precipitation from 1981 to 2010 were synonymous with severe and moderate droughts, respectively. Droughts occurred more frequently in the northwest than in the southeast of the LP. Moreover, the frequencies of moderate drought showed a slight difference throughout the area, while those of severe droughts demonstrated considerable differences between the northwestern arid zone and the southeastern semi-humid zone. The maps associated with various return periods of precipitation deficits can be used to produce drought risk maps together with drought vulnerability maps. These findings could also provide useful information for drought management, water resource management and the development of food security policies. [ABSTRACT FROM AUTHOR]

With the continuous deepening and development of tobacco manufacturing informatization construction, tobacco manufacturing enterprises have the characteristics of strong production capacity, high degree of production automation, and advanced enterprise-level management information system. In order to cooperate with the market and brand strategy of the tobacco industry, the State Tobacco Monopoly Administration has carried out serious strategic thinking on informatization and customized the overall plan and strategic goals. In recent years, China's informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. In the deployment and development of mobile services, it is often local and municipal mobile companies that, in order to meet the different needs of the market, immediately carry out corresponding services, resulting in one service for each municipal subsidiary in the same province or one platform for a class of services. Situation is good for quickly meeting user needs at that time. The exchange and communication of enterprise personnel information are becoming more and more frequent, and the collection of enterprise information through mobile has become a new way of enterprise information collection. The work orders, process parameters, documents, notifications, and instructions generated by the planning layer are sent to the production control layer to respond, guide, and trigger the production site events. The ultimate goal of mobile data collection is to gather the collected data into the enterprise production data center, which plays an important role in mobile devices, mobile performance, mobile planning, mobile balancing, mobile prediction, and so on. Based on the unified mobile application data collection method, this paper analyzed and analyzed the design of application architecture in the tobacco enterprise platform and further described the business functions, security performance, and system characteristics of the platform. [ABSTRACT FROM AUTHOR]

With the accelerating pace of China's electricity market reform, the construction of the electricity spot market has been put on the agenda. However, as the number and scope of market participants gradually expand, the market-oriented transaction power continues to rise, and the intraprovincial and interprovincial transaction varieties are increasingly abundant. How to design an intelligent and powerful system that can meet the performance requirements of high concurrency and high-frequency transactions in the future market is a major problem in power reform. Based on the research of theoretical research results, this paper builds the front-end interaction platform of the southern spot electricity system based on the regional center to provide a data declaration interface for market users, including market management, market declaration, market release, market evaluation, intelligent analysis, front-end data interface, security protection, and other functional modules, provide declaration information and some market evaluation results to the southern regional spot power system platform, and obtain clearance results and published information from this platform. [ABSTRACT FROM AUTHOR]

The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis. [ABSTRACT FROM AUTHOR]

The management and conservation of lakes should be conducted in the context of catchment because lakes collect water and materials from their upstream catchments. So the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP) with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with area from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream contributing area of each lake, and the inter-lake catchments which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e. lake body, topographic, climatic, land cover/use, soil & geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the daily time series of climatic variables are also extracted, which can be used to drive lumped hydrological models or machine learning models for hydrological simulation. The LCC-TP dataset contains fundamental information for analysing the impact of catchment-level characteristics on lake properties, which on the one hand can deepen our understanding on the drivers of lake environment change, and on the other hand, can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially-explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center https://doi.org/10.11888/Terre.tpdc.272026, Liu, 2022). [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of ozone (O 3) taken from the Copernicus Atmosphere Monitoring Service (CAMS) and the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) were intercompared and evaluated with ground and ozone-sonde observations over China in 2018 and 2019. Intercomparison of the surface ozone from CAMS and MERRA-2 reanalysis showed significant negative bias (CAMS minus MERRA-2, same below) at Tibetan Plateau of up to 80 µg/m3, and the average R 2 was about 0.6 across China. Evaluated with the ground observations from China National Environmental Monitoring Center (CNEMC), we found that CAMS and MERRA-2 reanalysis were capable of capturing the key patterns of monthly and diurnal variations of surface ozone over China except for the western region, and MERRA-2 overestimated the observations compared to CAMS. Vertically, the CAMS profiles overestimated the ozone-sonde from the World Ozone and Ultraviolet Radiation Data Center (WOUDC) above 200 hPa with the magnitude reaching up to 150 µg/m3, while little bias was found between the reanalysis and observations below 200 hPa. Intercomparison drawn from the vertical distribution between CAMS and MERRA-2 reanalysis showed that the negative bias appeared throughout the troposphere over China, while the positive bias emerged in the upper troposphere and lower stratosphere (UTLS) with high order of magnitude exceeding 100 µg/m3, indicating large uncertainties at higher altitudes. In summary, we concluded that CAMS reanalysis showed better agreement with the observations in contrast to MERRA-2, and the large discrepancy especially at higher altitudes between these two reanalysis datasets could not be ignored. [Display omitted]. [ABSTRACT FROM AUTHOR]

In order to obtain the evaluation and calculation method of indirect evaporative free cooling potential in data center, a new method for evaluating the annual indirect evaporative free cooling potential of data center was proposed based on the division of meteorological parameters of evaporative cooling air conditioning and the calculation method of free cooling potential. Based on the hours when there is a temperature difference between the ambient temperature and the return air (return water) temperature of the data center, the judgment limit of heat exchange efficiency of indirect evaporative cooling equipment and the supply air temperature limit of the data center were added. Combined with the efficiency and air supply limit provided by the standard, a new method was adopted to count the annual operation time distribution of indirect evaporation natural system in data centers of 31 major cities in China. The results show that the potential of indirect evaporative free cooling in the data center is related to the ambient air meteorological parameters, the heat exchange efficiency of indirect evaporative cooling equipment, and the required air supply temperature limit in the data center. Air side and water side indirect evaporative free cooling air conditioning system has great applicability in China's data center. [ABSTRACT FROM AUTHOR]

Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy consumption and environmental impact of DCs in the upcoming period. Machine learning algorithms have advantages in optimizing the operation of DCs to improve energy efficiency as they have shown the potential of discovering control strategies not found by traditional method, and producing working condition‐dependent control strategies. This study proposes ready‐to‐use machine learning methods with practical details to decrease the most commonly used energy efficiency metric—power usage effectiveness in DCs. We achieved an accurate prediction by properly selecting the features used in the proposed prediction models established by neural network, light gradient boosting machine, recurrent neural network, and random forests. The proposed approaches are implemented in one of the largest hyperscale DCs in China—Tencent Tianjin DC, to optimize the set points of controllable variables in the cooling system and to detect and adjust the unreasonable working conditions in the modular data centers. The lower bound of PUE reduction was 0.005 with the proposed approaches, leading to about 1500 MWh (0.24% of the total designed electricity consumption of this DC) of energy saved per year in this hyperscale DC. The proposed methods have the potential to be transferred to DCs of similar scale, and the framework of our work could serve as a guide for machine learning‐based optimization of environmental indicators in other complex product/service systems. [ABSTRACT FROM AUTHOR]

Utilizing the thermal stratification of reservoirs to obtain cold water for cooling green data centers (GDCs) is a new mode of energy conservation and emission reduction. However, global warming is expected to alter this phenomenon in deep reservoirs and thus may affect the digitalization process. While the frequency and intensity of extreme climate (EC) events are increasing under a changing climate, the impacts of these highly destructive events on thermal stratification and the related cascading effects on GDC cold-water supplies are still unclear. A two-lateral-dimensional thermohydrodynamic model was established to determine the characteristics of reservoir thermal stratification changes and its potential water environment impacts based on the heat extreme experienced by the Jinshuitan Reservoir, a large, pumped storage power station (PSPS) with a GDC in southeastern China. Fourteen different PSPS inlet/outlet schemes under 2 climate scenarios were designed, and two new indexes, namely, the risk index of cold-water shortage and the index of thermal stratification, were proposed to explore the impact of extreme climates on GDC water extraction. The results showed that compared to the present climate conditions, the reservoir experienced stronger thermal stratification with a 20.6 % average increase in thermal stability, a longer thermal stratification duration with a transitional trend from seasonal to permanent stratification, and a thinner cold-water layer with a 15-m average decrease. Changes in the thermal stratification of reservoirs under EC conditions will benefit some species, such as diatoms (except for early July and the period from mid-August to early October), green algae and cyanobacteria and the migration of warm-water fish, but adversely impact dissolved oxygen, diatoms and cold-water fish. Furthermore, the risk of cold-water shortages under EC conditions was consistently greater than that under average climate (AC) conditions (traditional assessment) when the elevation of pumped storage intake/outlet was less than 145 m. According to the traditional assessment method, the intake/outlet elevation of pumped storage reservoirs resulted in a 60.27 % increase in the risk of cold-water shortages during climate extremes. Furthermore, the demand for green cold-water withdrawal and optimal pumped storage power generation benefits were met when the PSPS intake/outlet elevation was 145 m. Beyond solving this specific case, this study elucidates the importance of linking the thermal stratification and data center of cold-water withdrawal to ECs. These findings provide new insights for increasing climate change resilience. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, more and more global attention has been paid to data centers' energy consumption and climate impact. Serving as the backbone of the digital world, the energy and carbon-intensive data centers have to find a green expansion way to fulfill their obligations in building a carbon-neutral society. Therefore, this paper proposes a net-zero carbon operation oriented data center expansion scheme for the central government to formulate incentive policies. The economic expansion model minimizes newly established data centers' investment and electricity consumption costs. Geographical location, on-site renewable utilization, and green certificate purchase are integrated and considered to achieve net-zero carbon operation by reducing total electricity consumption, increasing green power consumption, and offsetting inevitable carbon emissions. A case study in China is conducted, and results under different geographical location scenarios, power supply mix scenarios, and green certificates market scenarios are analyzed and compared. It is found that the zero-carbon operation of new data centers mainly relies on clean main-grid power, but a more decentralized layout and a provincial green certificate market can increase the contribution of on-site renewable energy. Lastly, four detailed policy implications for stimulating China's data center expansion are discussed. • A data center expansion scheme considering net-zero carbon operation is proposed. • Expansion model minimizes newly established data centers' expansion costs is built. • Three measures are integrated to achieve data centers' net-zero carbon operation. • Case study in China is conducted and core findings and key implications proposed. [ABSTRACT FROM AUTHOR]

The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis. Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences. The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance. [Display omitted] • E. coli 's resistance rates change with climate and Hu line. • Region-specific health strategies against antibiotic resistance is needed. • The model has a higher explanatory power. [ABSTRACT FROM AUTHOR]

Water clarity serves as a sensitive tool for understanding the spatial pattern and historical trend in lakes' trophic status. Despite the wide availability of remotely sensed data, this metric has not been fully explored for long-term environmental monitoring. To this end, we utilized Landsat top-of-atmosphere reflectance products within Google Earth Engine in the period 1984–2018 to retrieve the average Secchi disk depth (SDD) for each lake in each year. Three SDD datasets were used for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red / blue band ratio algorithm was applied to map SDD for lakes (>0.01 km 2) based on the first SDD dataset, where R2=0.79 and relative RMSE (rRMSE) =61.9 %. The other two datasets were used to validate the temporal transferability of the SDD estimation model, which confirmed the stable performance of the model. The spatiotemporal dynamics of SDD were analyzed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found the number of lakes with SDD <2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total areas of lakes with SDD of <0.5 and >4 m were the largest, both accounting for about 24.00 % of the total lakes. During 1984–2018, lakes in the Tibetan–Qinghai Plateau region (TQR) had the clearest water with an average value of 3.32±0.38 m, while that in the northeastern region (NLR) exhibited the lowest SDD (mean 0.60±0.09 m). Among the 10 814 lakes with SDD results for more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia–Xinjiang region (MXR), more than half of the total lakes in every other region exhibited significant increasing trends. In the eastern region (ELR), NLR and Yungui Plateau region (YGR), almost more than 50 % of the lakes that displayed increase or decrease in SDD were mainly distributed in the area range of 0.01–1 km 2 , whereas those in the TQR and MXR were primarily concentrated in large lakes (>10 km 2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset is freely available at the National Tibetan Plateau Data Center (10.11888/Hydro.tpdc.271571, Tao et al., 2021). [ABSTRACT FROM AUTHOR]

In today's economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the "Specification for Design of Data Center" in GB50174-2017 of China: economic factors, natural climate environment factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China. [ABSTRACT FROM AUTHOR]

Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004-2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0-0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984-2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01-1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571. [ABSTRACT FROM AUTHOR]

In China, stroke is a major cause of mortality, and long-term physical and cognitive impairment. To meet this challenge, the Ministry of Health China Stroke Prevention Project Committee (CSPPC) was established in April 2011. This committee actively promotes stroke prevention and control in China. With government financial support of 838.4 million CNY, 8.352 million people from 536 screening points in 31 provinces have received stroke screening and follow-up over the last seven years (2012–2018). In 2016, the CSPPC issued a plan to establish stroke centers. To shorten the pre-hospital period, the CSPPC established a stroke center network, stroke map, and stroke "Green Channel" to create three 1-h gold rescue circles, abbreviated as "1-1-1" (onset to call time <1 h; pre-hospital transfer time < 1 h, and door-to-needle time < 1 h). From 2017 to 2018, the median door-to-needle time dropped by 4.0% (95% confidence interval (CI), 1.4–9.4) from 50 min to 48 min, and the median onset-to-needle time dropped by 2.8% (95% CI, 0.4–5.2) from 180 min to 175 min. As of 31 December 2018, the CSPPC has established 380 stroke centers in mainland China. From 1 November 2018, the CSPPC has monitored the quality of stroke care in stroke center hospitals through the China Stroke Data Center Data Reporting Platform. The CSPPC Stroke program has led to a significant improvement in stroke care. This program needs to be further promoted nationwide. [ABSTRACT FROM AUTHOR]

Aerosol particles affect the Earth’s radiative balance and represent one of the largest uncertainties in climate research. The removal of clouds is the first step and critical in the aerosol retrievals. However, the cloud detection is still challenging. Here, a novel simplified cloud detection algorithm (SCDA) is proposed to identify the cloud and clear-sky over land and based on a simplified radiative transfer model (RTM). The fewer input bands, dynamic thresholds, and only one parameter to be modified are the main advantages of the algorithm, which can be applied to different satellite sensors. In this article, we apply SCDA to the Himawari-8 data in 2016 for preliminary analysis. The detection results are validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) data and the National Centers for Environmental Information (NCEI) ground-based observation data. We also compare the results with the Himawari-8 cloud products from the Japan Aerospace Exploration Agency (JAXA). Compared with CALIPSO VFM data and NCEI ground-based observation data, the correct rate of SCDA cloud detection result is 86.08% and 79.86%, which are higher than that of Himawari-8 cloud products (85.71% and 78.89%). The correct rate of SCDA clear-sky detection result is 88.33% and 87.85%, which are close to the correct rate of Himawari-8 clear-sky products (90.54% and 88.63%). The overall performance of the SCDA is comparable to that of the threshold method for JAXA Himawari-8 cloud products. Therefore, the SCDA can provide accurate cloud mask with only one threshold to be modified and few input parameters. [ABSTRACT FROM AUTHOR]

This article proposes a novel optimal sizing method for the energy station in the multienergy system (MES) integrated with data center. First, an overall framework of the energy station, data center, and multienergy loads is constructed to describe the interconnection of energy system and information system. Then, the dynamic voltage frequency scaling (DVFS) technique, the thermal inertia, and the deep coupling relationships of the electricity load, waste heat, cooling load, and workload in the data center are respectively modeled. On this basis, the optimal sizing model of the energy station is established, in which the data center can participate in the integrated demand response by the DVFS technique and thermal inertia. The capacity of energy station and the scheduling schemes of energy devices and servers are optimized simultaneously to coordinate the energy flow and information flow. Based on the quasi-steady-state assumption of thermal balance, the model is simplified to a mixed-integer quadratic program problem, and it is solved by Gurobi. Case studies based on a real-world test system in China illustrate that the integration of data center can be utilized to reduce the total cost of energy station planning in MES. [ABSTRACT FROM AUTHOR]

Background: China has made progress in tuberculosis control, but this disease remains a burden in many regions of China. We performed time-series analysis to examine changes in the rates of newly notified and newly smear-positive cases of tuberculosis in different regions of China from 1997 to 2018 and assessed the effect of the current control program. Methods: National and provincial notification data on tuberculosis from 1997 to 2018, which covers 31 provinces in the mainland of China, were extracted from the Chinese public health science data center. The annual percentage changes in newly notified and smear-positive cases were analyzed using a joinpoint regression method. Results: There were 18 646 672 newly notified tuberculosis cases from 1997 to 2018, with the greatest number in 2005. A total of 6 605 414 of these cases (35.42%) were smear-positive cases. The number of newly notified cases in China overall decreased (96.88–59.27 cases per 100 000) significantly during the most recent years. The decline during this period ranged from −3.9% (95% CI −5.7 to −2.9) in the western region to −4.3% (95% CI −4.8 to −3.7) in the eastern region. Most provinces had significant declines in newly notified and smear-positive cases, whereas the decline of newly smear-positive cases in Xinjiang was about half of that observed during the same period in China overall (−4.1% vs −9.9%). In addition to disparities in annual percentage changes, the rate of newly notified cases was higher in the western region than in the eastern and central regions. Conclusions: The burden of tuberculosis has been on declining throughout China during recent years, but tuberculosis in western China continues to be a public health emergency that needs to be urgently addressed. Effective prevention and control strategies are needed for regions with high disease burdens and those with increasing or unchanging numbers of newly notified and smear-positive cases of tuberculosis. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

• Performances of present water source heat pump (WSHP) unit were compared. • Three types of WSHP heating system using data center waste heat are proposed. • Thermal and energy characteristics of three proposed systems were studied. • Matching rule of data center waste heat, ending user and WSHP system is obtained. The number and scale of data centers (DCs) in China are constantly expanding with the advancement of networking and informatization. A large amount of waste heat is dissipated at its end. If this waste heat can be recovered, then achieving carbon neutrality in DCs can be effectively accelerated. At present, water source heat pump (WSHP) are widely used in air-cooled and liquid-cooled DCs of different sizes for waste heat recovery. This study analyzes the equipment type and heating capacity of different brands of WSHP units. Three WSHP heating systems are proposed according to the different locations of waste heat enhancement (SE, TDE, UE). The coupling and matching relationship between user-side heat load demand, DC waste heat load, and energy efficiency of WSHP units under different systems is also investigated. Moreover, the law of energy consumption and energy efficiency changes is analyzed. Thus, this study provides a reference for the DC waste heat recovery system design and equipment development. The results show that the coupling matching relationship between user-side heat load demand, DC waste heat load, and heating COP of WSHP units is Q. N / Q. S = C O P ′ / C O P ′ - 1 = φ , where φ ranges from 1.1962 to 1.3642. The heating COP of the SE system is the greatest when user-side heat load demand is between 0 and 120 kW. The heating COP of the TDE system is the highest when the user-side heat load demand is between 1750 and 70000 kW. Furthermore, the heating COP of the three WSHP heating system types is related to unit brand when user-side heat load demand is between 120 and 1750 kW. [ABSTRACT FROM AUTHOR]

• The two sets of switch conditions and three working modes for the unit applied to data center was proposed. • The energy-saving ratio of the unit in the actual data center for the whole year in China was simulated by DeST. • The energy consumption characteristics and its influencing factors of the unit were investigated. To reduce the power consumption of the cooling system in the data center effectively, a prototype of the integrated cooling unit with rotary booster and compressor was developed, and the operating characteristics were tested comprehensively according to its cooling requirements throughout the year. A data center model reflecting the characteristics of energy consumption and environmental requirements was established, the energy consumption characteristics and influencing factors of the model data center when the integrated unit was used to meet its yearly cooling requirements in the typical climate zones in China were simulated by DeST software based on the measured characteristics of the integrated cooling unit. The results showed that compared with the traditional computer room air conditioner, the energy-saving ratio of the integrated system can be as high as 39.55% for the whole year, and the energy-saving effect is remarkable. The energy-saving potential of the integrated unit becomes more significant while the heat transfer coefficient of the envelope structure becomes larger, and the load rate of IT equipment and the annually-averaged ambient temperature lower. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable (ECV) concerned widely. Due to its high spatial variability, it is costly to measure soil moisture at tens of kilometers scale. In this study, a ten-year (2002–2011) daily soil moisture dataset at 0.25° spatial resolution for Mainland China was produced through assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature (TB) data into a land surface model (LSM). The obtained soil moisture data was evaluated against soil moisture-measuring networks deployed in two wet areas and one dry area of the Tibetan Plateau. The results show that for the wet areas the accuracy of the soil moisture product obtained from the assimilation is considerably higher than that of both AMSR-E official soil moisture products and land surface simulation results, and for the dry area their accuracy is comparable to each other. The spatial pattern of the soil moisture from the new product is consistent with that of soil porosity from an independent survey-based dataset, further confirming the credibility of the new product. According to this product, the transition regions in China show stronger seasonal variation of soil moisture than dry and wet regions, and drier regions have stronger inter-annual variability of soil moisture than wetter regions, particularly during transitional seasons (spring and autumn). The soil moisture product is accessible at the National Tibetan Plateau Data Center. [ABSTRACT FROM AUTHOR]

To promote the intelligent and accurate management of river basins, especially large basins which involve many catchments, it is highly required to develop a useful platform to effectively coordinate arithmetic resources and data, and simultaneously help to make decisions based on the real-time calculation. In this study, a multi-centre cloud platform architecture called 3L4C was constructed, which includes a Cloud-edge-terminal Layer (3L), data centre, model centre, control centre, and customer-service centre (4C). Data fusion technology and an air-land-water coupled model were constructed. Based on HTML5, JavaScript, and Java, an integrated water environment management platform was created and applied to the Three Gorges Reservoir Basin, China. The platform was tested and successfully used for automatic water quality prediction, water environment pollution analysis and control, early warning of abnormal water quality, and emergency water pollution incident evaluation. This platform quickly and accurately forecasts and perfectly displays past, present and future state of the water environment, and offers beneficial support for management decisions in various water environment departments. • A cloud platform architecture for environmental management decision-making. • Three Gorges Reservoir Basin Water Environment Management Platform. • Integrate water environment data from multiple sources. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewables, load flexibility is crucial to balance the power system in China. Data centers are rapidly growing energy consumers and their flexible operations could make them a potential source of load flexibility. However, there have been no large-scale attempts to apply this concept in China. This paper utilizes a hybrid research method to explore the flexibility potentials, identifying key technical, financial, policy, and institutional barriers. The study quantifies the flexibility potential of data centers in China and provides solutions for addressing the identified solutions. • A hybrid research method of literature review, questionnaire, and semi-structured interviews is employed. • Potentials, barriers, and possible solutions for data centers in China to provide flexibility are analyzed in four parts. • Power flexibility potentials of data centers in China is quantified. [ABSTRACT FROM AUTHOR]

In recent years, water-side free cooling has been broadly applied in data centers to lessen the high cost of energy consumption. The energy-saving opportunities of free cooling have been recognized by numerous studies across the world. In this paper, the field test and energy performance evaluation were performed in an operational data center with water-side free cooling, located in Chongqing, China. Existing problems were found during tests, including the insufficient cooling capacity of towers in wintertime and the inability to activate partial-free cooling. The results of the tests show that the reduction in Power Usage Effectiveness (PUE) is much lower than the expected. To understand the marked discrepancies, the performance of the cooling tower was discussed in terms of the approach temperature and actual cooling capacity. The maximum cooling capacity in winter is only about 50 % of the rated. With the consideration of the cooling tower configuration performance in different climate conditions, the conservative energy effects were calculated in 34 provincial cities of China. To prolong the hours available, optimized operation schemes of cooling towers are presented. The available hours for free cooling could be increased from 730 h to 1300 h when operating at 67 % of rated flow rate, resulting in a decrease in the energy saving of chillers between 8.44 % and 21.05 %. [ABSTRACT FROM AUTHOR]

Serving as the information backbone of an increasingly digitalized world and energy-intensive, the data center industry has to seek low-carbon developing way to properly meet the rapidly rising service demand. This paper designs 45 kinds of configuration choices for each decision units by integrating three data centers, three cooling technologies, as well as five energy supply mixes. The model is formulated as a bi objective optimization problem for the decision maker to decide newly established data location and configuration with objective of reducing data center expansion costs and well as minimizing power utilization associated with carbon emissions from a country level. The pilot study in China under the scenarios of different carbon emissions limitation and propagation delay that China can reduce at least 25% of newly established data center related emissions from 2020 to 2023. It further shows that future expansion can be focus on Inner Mongolia and Ningxia for the north, Hubei for the middle, and Yunnan and Guangxi for the south. This paper provides a fresh perspective and makes a valuable attempt at the low-carbon development of data centers. • Carbon awareness based integrated optimization model for data center in China is proposed. • Layout, scale, cooling technology and power supply mix are simultaneously considered. • 25% of newly established data center electricity related emissions in China can be avoided. [ABSTRACT FROM AUTHOR]

Data center is a very important infrastructure to support the development of information technology, and its development and increment are very remarkable. However, with the rapid and large-scale development of data centers, the problem of energy consumption turns to be also very prominent. Under the background of global carbon peak and carbon neutrality, developing green and low-carbon data centers has become an inevitable trend. This paper reviews and analyzes the policies and their roles in promoting China's green development of data centers in the past 10 years, summarizes the current situation of the implementation of green data center projects in China and gives the changes of PUE limits of data centers under the policy constraints. Application of green technologies is an important measure for energy-saving and low-carbon development of data centers, so encouraging innovation and application of green technologies in data center is also a priority task in relevant policies. This paper points out the green and low-carbon technology system of data centers, further summarizes energy-saving and carbon-reducing technologies in IT equipment, cooling system, power supply and distribution system, lighting, intelligent operation and maintenance, and provides an outlook on the future green development of data centers. • China's green data center policy in recent 10 years has played an important role in promoting the development of green data centers. • Summarizes the status of green data centers under policy guidance. • Provides energy saving and carbon reduction technology path for data centers. • Prospects for the future development of green data centers. [ABSTRACT FROM AUTHOR]

Immunization Information Systems (IIS) are computerized population-based systems with individual-level vaccination-related information used to help ensure protection from vaccine preventable diseases. The Chinese Center for Diseases Control and Prevention (China CDC) conducted a survey of the 32 mainland China provincial CDCs to determine IIS coverage and implementation of key functions: individual vaccination records, vaccine management, cold-chain management, and school entry vaccination status verification. Twenty-seven IISs collectively managed 252 million immunization records, 43.8% for children under 6 years; 20 could exchange records with other IISs. The within-province duplicate-record rate varied from 0.3% to 4.0%, but compared with National Statistical Bureau's census estimates, 138.0% of births from 2012 to 2017 were represented in the IISs, implying significant across-province record duplication. China CDC should consider developing a national-level IIS center for data exchange and analysis. [ABSTRACT FROM AUTHOR]

It is important to cool data centers for improving IT equipment reliability through indoor temperature control. The air conditioning energy consumption is almost 38% of the total energy consumption. Free cooling in cool days is a promising technology for energy conservation. A pump-driven loop heat pipe (PLHP) system for free cooling was developed and installed in a data center project, which has a high energy efficiency ratio (EER) compared to air conditioners. The running performance of the PLHP system in the case project was tested and studied. The fitting rule of EER and outdoor temperature was obtained. The hourly energy savings and payback periods of the PLHP system for different cities of different climate regions in China were analyzed basing on the fitting formula compared to air conditioners. A national distribution map was developed to show the annual energy saving ratio in China. Results show that the PLHP system provides remarkable energy savings and the indoor temperature is remained at 18°C to 25°C. About 74.2% of Chinese cities lie in regions suitable for applications, and the annual energy saving ratio is over 30%. The average payback period for most cities in China is about 3.9 years. [ABSTRACT FROM AUTHOR]

The high energy consumption of data centers (DCs) has become a global issue that needs to be addressed, and as a result, their development of green rating systems (GBRSs) has been widely emphasized. Although GBRSs have been the subject of many studies for various building types, there has been few studies for DCs. This is the first study to comprehensively analyze GBRSs for DCs. The study established nine categories of standardized overall indicators and seven categories of standardized energy indicators, and furthermore reformatted and aligned the GBRS items accordingly. The study first compared GBRSs for DCs and other non-residential buildings to find out main concerns for a green DC, second compared different GBRSs for DCs to identify regional differences, and finally investigated GBRSs for DCs in China where digital infrastructure is booming. Power usage effectiveness (PUE) as a key indicator of DC energy efficiency was further analyzed. The results highlighted the importance of energy related criteria to DCs and discussed how to use the assessment criteria to appropriately guide and promote green DCs in future work. It also discussed the constraint for energy efficiency in DCs and the best practices that should be encouraged in the category of Innovation for green DCs. This study fills the gap in existing studies of GBRSs for DCs and contributes to the development of green buildings and the refinement of assessment criteria in DCs. [ABSTRACT FROM AUTHOR]

The volume of landfill gas (LFG) produced during municipal solid waste (MSW) decomposition in local landfills has increased rapidly in recent years. To promote circular economy (CE) applications through resource recovery, this article investigates the feasibility of utilizing LFG as a sustainable energy to power data centers (DCs) in Xiamen (China) and evaluates the implications of waste heat reuse on the energy consumption of DC. The objective of this study was to investigate the environmental impacts and economic benefits of this scheme based on a SFC model. With respect to its novelty, LFG is utilized as an energy supply to DCs, while the waste heat generated in the energy production is recovered. The socio-economic benefits generated in this process are also elaborated. It was found that a large number of MSW was completely recycled to generate energy. The LFG enterprise could benefit from energy sale, while the energy satisfied the annual demand for DC power consumption. Utilizing this energy output resulted in the reduction of primary energy and less CO₂ emissions. By reusing the waste heat for domestic heating, the energy efficiency could be improved, while primary energy could be further saved, avoiding more CO₂ emissions. This implies that utilizing LFG output and waste heat to power DCs not only facilitate resource recovery from unused resources to improve energy efficiency and obtain socio-economic benefits, but also promote the digitalization of DCs and sustainable development of urban cities in the framework of CE. [Display omitted] • LFG can be used to power data centers (DCs) in order to promote CE. • The recovery of waste heat from LFG generation reduced CO 2 emission of 8.46 × 108kg. • Within 15 years, 4.97 × 10 10 kg of MSW can generate 4.65 × 109 kW ⋅ h of electricity. • The recovery of waste heat benefited landfill operators and DC firms financially. [ABSTRACT FROM AUTHOR]

The study of the impact of high temperature and drought on the yield of major staple crops can provide important scientific support for the decision-making of agricultural sustainable development. Based on the temperature and precipitation data of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA 5 for northern China, this paper calculates three indexes, the standard precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) and the extreme degree-day (EDD), from 1979 to 2017. Monthly SPI and monthly SPEI were calculated at 1 – to – 12 month lags, and EDD was calculated per crop growth season. The yield of winter wheat, spring wheat and summer maize in each province of the study area from 1979 to 2017 was de-trended, and the relative fluctuation of the yield of the three crops was calculated. The change trends of SPI, SPEI and EDD were analysed using the Mann–Kendall test and Sen's slope. The single and interactive effects of high temperature and drought on crop yield were studied using multidimensional Copula function. The results show that: 1) Both high temperature and drought stress in northern China show an increasing trend. The drought trend in the study area detected based on SPEI was greater than the drought trend detected by SPI. The difference between SPEI and SPI in the winter wheat growing season was smaller than that in the spring wheat and maize growing seasons. 2) With the increase in EDD and the decrease of SPI/SPEI values, the probability of negative yield fluctuation gradually increased, and the probability of positive yield fluctuation gradually decreased. Under the same drought and high temperature conditions, the probability of yield fluctuation varies among different crops and different provinces. Drought has a greater impact on crop yield than high temperature. Both the single and interactive effects of drought and high temperature on yield are nonlinear. 3) Irrigation can effectively alleviate the impact of drought and high temperature on yield. In heavily irrigated provinces, the effects of both high temperature and drought on crop yield are not obvious. • The impact of high temperature and drought stress on yield is analysed by a probability model. • The effects of high temperature and drought on yield are non-linear. • Irrigation can offset some of the effects of high temperature and drought on yield. • SPEI may overestimate the impact of drought on yield while SPI may underestimate. [ABSTRACT FROM AUTHOR]

Loop thermosyphon system is an efficient cooling system for energy saving in data center. It is necessary to investigate the performance of the loop thermosyphon system filled with alternative refrigerants to prompt its application. In this paper, a R32 loop thermosyphon system is experimentally investigated in an enthalpy difference laboratory for its performance. R22 and R134a are also used for comparison. The results show that the optimal filling ratio of R22, R134a and R32 is the same (37.3%). At the optimal filling ratio, the flow stability of R32 is obviously better than those of R22 and R134a. When cold water temperatures are 10–22 °C, the cooling capacity of R32 is 5.4–15.6% higher than those of R22 and R134a. After controlling extractor fans, the energy efficiency ratio of R32 is 4.9–206.6% higher than those of R22 and R134a. Moreover, the free cooling potentials of the three refrigerants in five typical climate cities across China are analyzed and compared. Compared with R22 and R134a, R32 has 3.6–11.0% and 18.5–26.7% higher free cooling percentages and annual energy efficiency ratio, respectively; and its annual average partial power usage effectiveness is 0.0012–0.0017 lower than that of other two refrigerants. [ABSTRACT FROM AUTHOR]

• K-means clustering was used to map diurnal cycles of precipitation amount. • Precipitation events with different durations were mapped separately. • Results can characterize regions of irregular shapes and show more spatial details. • Regional characteristics of diurnal cycles of precipitation in China were revealed. The diurnal cycle of precipitation is a fundamental mode of climatic variability. Based on an hourly 0.1° × 0.1° gauge-satellite merged precipitation dataset, which is provided by the China Meteorological Data Service Center, this study used the K-means clustering algorithm to map the diurnal cycles of precipitation in summer over China. The obtained maps can objectively delineate irregular regions with similar patterns of diurnal cycles, and many of these regions resemble large-scale geomorphic units such as the North China Plain and the valley of the Yarlung Zangbo River, which verifies the importance of macroterrain for diurnal cycles of precipitation. In addition to total precipitation, precipitation events with different durations, which are often related to different types of precipitation (e.g., convective precipitation and stratiform precipitation), were analyzed separately. Compared with the previous studies using predefined region boundaries, the main advantage of this study is that the obtained maps can show more spatial details, through which we can get intuitive and in-depth understandings of the regional characteristics and spatial patterns of the diurnal cycles of precipitation over China. [ABSTRACT FROM AUTHOR]

In data centers, a large number of IT sever racks with different heat dissipation rates are arranged in multiple rows, leading to much higher per-unit heat release and therefore more uneven temperature distribution than in general buildings. This paper chooses a small-scale data center located in China to analyze the thermal environment for the operation of these critical equipments. The wind speed and temperature in the data center were measured, and a corresponding computer room model was established via computational fluid dynamics (CFD) simulation software. Based on it, the characteristics of flow field and temperature field of the data center were analyzed. The optimization methods for problem of uneven temperature distribution and chaotic air supply in the data center were proposed and verified by evaluation index. Furthermore, the return temperature index(RHI) increased from 0.918 to 0.93, the return heat index(RTI) increased from 0.222 to 0.342. • The thermal environment of a small-scale data center in Wuhan, China was measured. • The data center model was established via numerical simulation software. • The optimized schemes for the thermal environment in the data center were proposed. • Optimization of data center can provide reference for construction and operation. [ABSTRACT FROM AUTHOR]

• An overview of utilization of mined underground space over the world is presented. • Classification, development, advantages and identification of mined underground space are explored. • Future demands for mined underground space and directions of development and utilization are discussed. The mined underground space is formed by the exploitation of resources and minerals. On the basis of review analysis of experiences of utilization of mined underground space over the world, suitable conditions of surrounding rocks for mined underground space are clarified to various functions of development and utilization of mined underground space. A method is proposed for identifying and estimating the scale of mined underground space, and this is used to obtain an estimate of the volume of available underground space in mines in China and to construct distribution maps of mined underground space available in the country. In order to improve comprehensive utilization of mined underground space, combined with current foundation of underground space utilization and future strategic demands, nine types of functional utilization are proposed systematically, including underground pumped-storage power stations, deep underground medicine and rehabilitation, the storage of strategic energy and resource reserves, underground data centers, domestic and industrial waste disposal, intelligent vertical parking systems, cutting-edge scientific facilities, underground agriculture, and underground ecological cities. [ABSTRACT FROM AUTHOR]