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Sustainable intensification (SI) of agriculture can produce more food to meet the demand of a growing population while considering ecosystem health. The current SI estimation framework ignores the complex coupling between input and output intensity of arable land. A method for coupled analysis of arable land input intensity and output intensity based on sliding windows is proposed. By calculating the correlation coefficient and partial correlation coefficient between input intensity and output intensity in different value ranges as the order parameter, the phase transition and the influence process of input intensity on output intensity can be explained. Meanwhile, a python-based framework is developed.An application of the method was made to reveal the interaction process between annual provincial input intensity and output intensity in mainland China. Researchers in many fields may benefit from the method by obtaining a fast way to analysis the coupling relationship between driving and dependent variables in complex systems. • New method for SI estimation is presented. • The order parameter of the coupling relationship between input and output intensity is calculated based on sliding windows. • Analysis of coupling relationships between driving and dependent variables in complex systems.

Grasslands, which cover approximately 40 % of the global land surface, are crucial to the global carbon cycle due to their substantial carbon storage capacity and sensitivity to environmental changes. Net primary productivity (NPP) serves as a critical indicator of ecological function, representing the net carbon input to terrestrial ecosystems. Accurately quantifying and understanding grassland NPP, particularly below-ground net primary productivity (BNPP), and its responses to environmental changes are essential for assessing carbon sequestration potential and predicting future dynamics under varying environmental conditions. However, this remains challenging at large spatial scales. In our study, we integrated observations of above-ground net primary productivity (ANPP) with satellite-derived total net primary productivity (MODIS NPP) products across 508 individual locations in the diverse grasslands of Northern China. Plot-level BNPP was derived by subtracting field-measured ANPP from MODIS NPP and has been rigorously validated using field-measured BNPP from 68 ground sites. We then explored how climate, soil characteristics, topography, and grassland types influence ANPP and BNPP using machine learning models. Our results indicate that ANPP and BNPP averaged 154 g m−2 and 273 g m−2, respectively, exhibiting significant variations across different grassland types. Furthermore, national mapping revealed regional disparities, with higher ANPP and BNPP values in the northeast compared to the southwest of Northern China. Climate was identified as the predominant driver of ANPP, explaining 41 % of the variance, followed by topography (26 %) and soil conditions (25 %). Conversely, topography had the most substantial impact on BNPP, accounting for 44 % of the explained variance, with climate and soil each contributing 25 %. These findings underscore the vulnerability of ANPP to climate change impacts such as droughts and temperature increases, and highlight how landscape features, such as elevation, slope, and aspect significantly influence BNPP, with crucial implications for soil carbon inputs and sequestration. Overall, this comprehensive study advances our understanding of the spatial patterns and environmental determinants of ANPP and BNPP in Northern China’s grasslands. It offers valuable insights for developing sustainable management practices and for the preservation and effective management of these crucial ecosystems, especially in the context of ongoing environmental changes.

Improving nitrogen (N) management are crucial for food security and ecological protection in China and globally. Assessing the contribution and influence characteristics of N input components on crop growth is a key component. There are challenges in conducting such assessments at large regional scales, particularly in developing models that fit regional N inputs and crop N uptake. An improved scheme that fit regional N inputs and crop N uptake with consideration of spatial heterogeneity of cultivation conditions was proposed. Based on the division of homogeneous cultivation conditions zones, linear models and Random Forest models were developed to assess the contribution and influence characteristics of N input components on crop growth at the provincial scale in mainland China. And synthetic N contribution rate (SNCR) and soil fertility N contribution (SFNC) were innovatively proposed to represent the contribution of fertilizer and soil fertility to crop growth. The results showed that the overall N use efficiency ranged from 35 % to 55 % in 1985–2020, and higher NUE could be seen in major grain producing provinces. The SNCR generally declined, with a spatial pattern higher in the southwest, northwest, and northeast regions. The SFNC showed an increasing trend, with a spatial pattern higher in the northeast than southeast than west. For each N input component, Synthetic N input was critical in the northwest, southwest regions and north China, positively affecting crop N uptake. The improved scheme, which incorporated considerations for spatial heterogeneity, demonstrated superior accuracy compared to the model fitted by year, increasing from 0.27 (0.11) to 0.42 (0.47). Over the past 35 years, the crop N uptake and N surplus of mainland China had experienced a process of extensive-unsustainable-sustainable-conservative pattern changes. The response of crop N uptake and N use efficiency to the regulation of N inputs in typical regions was simulated, providing reference for provincial N inputs regulation in China. This study can provide support for the design of N management strategies in China to reduce N pollution, and the method can provide guidance for other regions to assess N use in different scales.

Abstract Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.

The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, there is a lack of understanding of the impact of environmental conditions and human activity on the vegetation’s carbon sequestration ability, especially in highly urbanized areas. For example, effective vegetation management methods can enhance vegetation Gross Primary Productivity, while emissions of air pollutants like O3, CO, NO2, and PM2.5 can suppress it. This paper mainly explores the factors influencing vegetation carbon sequestration capacity across different regions of Beijing. Based on remote sensing data and site observation data, this paper analyzed the spatiotemporal variation trend of Annual Gross Primary Production (AGPP) and the influence of environmental factors and human activity factors on GPP in Beijing from 2000 to 2020 by using the Theil−Sen’s slope estimator, Mann−Kendall trend test, and comparing Geographically Weighted Regression method (GWR) and Geographically and Temporally Weighted Regression method (GTWR). GWR is a localized multiple regression technique used to estimate variable relationships that vary spatially. GTWR extends GWR by adding temporal analysis, enabling a comprehensive examination of spatiotemporal data variations. Besides, we used land use cover data to discuss the influence of land use cover change on AGPP. The results showed that the spatial distribution pattern of GPP in Beijing was higher in the northwest and lower in the southeast, and it showed an overall upward trend from 2000 to 2020, with an average annual growth rate of 14.39 g C·m−2·a−1. From 2000 to 2020, excluding the core urban areas, the GPP of 95.8% of Beijing increased, and 10.6% of Beijing showed a trend of significant increase, concentrated in Mentougou, Changping, and Miyun. GPP decreased in 4.1% of the regions in Beijing and decreased significantly in 1.4% of the areas within the sixth ring. The areas where AGPP significantly decreased were concentrated in those where land use types were converted to Residential land (impervious land), while AGPP showed an upward trend in other areas. CO and NO2 are the main driving forces of GPP change in Beijing. O3 and land surface temperature (LST) also exert certain influences, while the impact of precipitation (PRE) is relatively minor. O3 and CO have a positive impact on AGPP as a whole, while LST and NO2 generally exhibit negative impacts. PRE has a positive impact in the central area of Beijing, while it has a negative impact in the peripheral areas. This study further discusses opinions on future urbanization and environmental management policies in Beijing, which will promote the carbon peak and carbon neutrality process of ecological space management in Beijing. Besides, this study was conducted at the urban scale rather than at ecological sites, encompassing a variety of factors that influence vegetation AGPP. Consequently, the results also offer fresh insights into the intricate nexus between human activities, pollutants, and the GPP of vegetation.

Abstract Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand–supply relationships. In this study, we first investigated the source code of CLUMondo, providing a complete, detailed mechanism of this model. We found that the featured function of CLUMondo—balancing demands and supplies in a many-to-many mode—relies on a parameter called conversion order. The setting of this parameter is a manual process and requires expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. Comparative experiments demonstrated the validity and effectiveness of the proposed automated method. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting in CLUMondo-BNU v1.0. This study facilitates the application of CLUMondo and helps to exploit its full potential.

The health issues of China’s older adult population in rural areas have been receiving increasing attention with the continuous expansion of the nation’s ageing population and the continuous promotion of urban–rural integration. The impact of the new rural social pension insurance (NRSPI) on the health of the rural older adult population, the mechanism of its action and how old-age service can be improved and optimised according to the health needs of the rural older adult population are urgent and realistic challenges. Based on survey data from the China Health and Retirement Longitudinal Study in 2015 and 2018, this study applies a multivariate ordered logistic regression model to explore the impact mechanism and effect of the NRSPI on the older adult population health in rural China while controlling for endogeneity. The results show that participation in the NRSPI can significantly improve the health of the rural older adult population at a 1% level. The results of the heterogeneity test reveal that the NRSPI has a significant impact on the self-reported health of the rural older adult at a 1% level, with a significantly positive impact on the mental and physical health of rural female older adult, whereas the impact on male older adult is not significant. The mediating effect test results show that medical services, food access and entertainment activities have a mediating effect on the new rural social endowment insurance. The results of the moderating effect test indicate that the NRSPI regulates 7.8% of the effect of physical health on mental health and 10.7% of the effect of mental health on physical health. Based on these findings, this study proposes to strengthen the construction of healthy lifestyle guidance and emotional support systems while improving the NRSPI’s participation rate and treatment level to meet the diverse health service needs of different older adult groups.

A composite index based on selected indicators is a widely used tool for guiding, monitoring, and evaluating a society’s level of sustainability. However, determining the weight of each indicator is typically a methodologically problematic and highly controversial process. This paper proposes a weightless strategy for constructing composite sustainability indices based on the mathematical optimization concept of Pareto fronts. The core idea is to model each indicator as an individual objective and explore Pareto fronts within the resulting multi-objective solution space. In practice, a total of 24 typical implementations of the strategy were realized to represent four categories with varying parameter settings, i.e., straightforward/hierarchical implementations with/without avoiding basic indicator accuracy issues. Comparative experiments demonstrated that a hierarchical approach utilizing the goodness of variance fit-based (GVF = 0.80) natural breaks to nullify accuracy problems is the most effective implementation. To demonstrate its usefulness, the strategy implemented using this approach was applied to analyze the world’s sustainability by revising the well-known sustainable society index. This study provides a novel paradigm of composite sustainability indices and represents the first assessment of world sustainability using multiple criteria (indicators) without weights.

Ensuring global food security and sustainable development remains a top priority at present [...]

The artificial noise (AN) shielded frequency hopping (FH) secure communication can counteract electromagnetic interference and wiretapping, but its carrier frequencies change rapidly within a wide bandwidth which will raise the level of carrier frequency offset and phase noise, yielding AN cancellation performance degradation.To tackle this, the signal distortion caused by frequency and phase mismatch (FPM) was mathematically modeled, the AN cancellation performance was evaluated via signal-to-noise ratio, and the system secrecy was scaled via secrecy capacity.On basis of this, the optimal power allocation scheme for AN and confidential signal was designed, and the scheme was expanded according to the FPM level and the propagation channel quality.Numerical simulations verify that FPM can significantly degrade the noise cancellation performance and system secrecy, and to counteract this, the power ratio of AN to confidential signals should decrease with an increasing FPM.

The synthesis of upconversion nanoparticles (UCNPs) by flame aerosol is of great significance to realize industrial large-scale production of UCNPs and develop nanotechnology further. Here, for the first time, we successfully fabricated the ultrasmall Y2O3:Yb3+/Ho3+ UCNPs by a self-build swirl flame spray pyrolysis (SFSP) method with a high production rate of ∼40 g$ \cdot $ h−1. These flame-made UCNPs are all pure cubic phases with an average ultrasmall size of ∼14 nm. Excited by 980 nm laser, the synthesized UCNPs show bright green (2F4, 5S2 → 5I8) and relatively weak red (5F5 → 5I8) upconversion luminescence (UCL). Based on the UCL spectra of Y2O3:Yb3+/Ho3+ UCNPs, the optimal doping concentrations of 6 mol% Yb3+ and 0.1 mol% Ho3+ were determined to reach the most intense UCL. The dependence of UCL intensity and pump power was further analyzed, and it indicated that the green and red UCL are two-photon processes. In addition, the UCL properties with different synthesized conditions were also demonstrated. The UCL mechanism of these flame-made Y2O3:Yb3+/Ho3+ UCNPs were illustrated in detail. Our results prove that industrial large-scale production of continuous one-step synthesis of UCNPs by flame aerosol technology is completely feasible and deserves further study.

Abstract Watersheds are the basic unit of Earth's terrestrial systems and are therefore ideal units for the study of critical zones (CZs). However, grand challenges remain regarding the observation, modeling, and management of CZs and watershed systems. We synthesize the progress and breakthroughs associated with watershed science and CZ research in the Heihe River basin (HRB), a large‐scale endorheic river basin with unique mountain cryosphere‐oasis‐desert landscapes and prominent human‐nature competition for water resources. The HRB observation system consists of mountain cryosphere, agricultural oasis, and natural oasis observatories and is promoted by large‐scale comprehensive experiments to achieve multiscale observations. A watershed system model that couples ecohydrological models with socioeconomic models is developed to investigate the complex interactions among water, ecology, and socioeconomics in the HRB. The model is embedded in a decision support system to bridge science and decision‐making and to better serve river basin sustainability. Significant progress and breakthroughs have been made in CZ and watershed process research (e.g., cryospheric hydrological processes, ecological and hydrological interactions, and surface‐groundwater interactions) in the HRB. Nevertheless, observation and modeling of geochemical and geomorphological processes in the CZ have not been well addressed in integrated watershed studies of the HRB. In the future, new observation technologies, agent‐based models, machine learning, and data assimilation will benefit both watershed science and CZ science and help to address complexity and dynamics in the CZ at the river basin scale. Overall, the HRB has successfully demonstrated how an experimental river basin can link CZ science with watershed science.

This research investigates the transmission of Maoqiang opera, focusing on the iconic repertoire "Zhao Meirong Guan Deng" and its role in preserving cultural heritage in the Shandong Peninsula. The objective is to investigate the role and social function of Maoqiang Chinese opera in transmitting cultural heritage through education. The research site encompasses various academic institutions and community-based events in Jiaozhou City, where key informants, including Maoqiang opera practitioners and local authorities, provide insights into the transmission process. Data analysis involves qualitative methods, including interviews, observations, and document analysis, to elucidate the multifaceted role of "Zhao Meirong Guan Deng" in cultural transmission. Methodologically, the research employs a mixed-methods approach to capture the complexity of Maoqiang opera transmission and its societal impact. The research results highlight the enduring legacy of Maoqiang opera and the effectiveness of incorporating traditional repertoire into educational curricula, fostering an appreciation for intangible cultural heritage among diverse audiences. Suggestions for future research include exploring innovative transmission strategies, leveraging digital media, and strengthening collaboration between cultural institutions and educational stakeholders.

Information content is an important criterion for many image processing algorithms such as band selection and image fusion. Usually, information content is quantified by using information entropy (i.e., Shannon entropy); however, this is not a suitable measure because information entropy is independent of the spatial distribution of image pixels. Thus, improved information entropies and variants of information entropy have been developed. Among all the entropic measures, the discrimination ratio-based variant of information entropy (hereinafter DVIE) has recently been demonstrated to be the most effective. On the other hand, DVIE is the most inefficient measure in terms of computation time, which severely restricts its applications. To solve this problem, we present a three-strategy approach to efficiently compute the DVIE of an image. The first strategy is to use a simplified equation for DVIE. The second strategy is to selectively compute the two computationally intensive components of DVIE-intra-distance and extra-distance-based on the computational complexity. Only one distance was computed, and the other distance was derived based on the lookup table of average distances. The third strategy was to efficiently construct the lookup table based on geometric symmetry. We performed both validation and evaluation experiments to demonstrate that the proposed approach was not only valid for accurately computing DVIE, but it was also highly efficient. Our proposed approach saved more than 99% of the time taken for the original approach, without compromising the accuracy; therefore, DVIE was made applicable for processing images.

A segmented self-interference cancellation (SSIC) method is proposed for frequency-modulated continuous-wave phased-array radar for canceling wideband multipath self-interference (SI) signals. A radio frequency canceller based on the SSIC method that uses the time-frequency segmentation equivalence of the linear frequency-modulated (LFM) signal is designed that can convert wideband SI cancellation (SIC) into several narrowband SICs. Numerical and simulation results show that the proposed method can achieve better cancellation performance with lower hardware complexity than the conventional multi-tap SI cancellation (MTSIC) method. Furthermore, the superior performance of the SSIC method is verified by experiments with a 250 MHz LFM signal.

For active optical imaging applications where targets are irradiated by a laser beam, the backscattering characteristic influences the image quality. An outfield experimental investigation of kilometer scale was conducted in this study. Unmanned aerial vehicles with complex shapes and various surface materials were used as targets. Backscattered signals were obtained, while the targets were located at different attitudes and distances. Furthermore, the relationship between the amplitude of the backscattered signal and the target distance was deduced and summarized at close and far distances. The experimental results can be helpful in detecting complicated targets with laser illumination.

In physical layer security,considering imperfect time synchronization,a power allocation scheme was proposed to maximize the system secrecy capacity.By analyzing the residual interference power at the authorized receiver caused by imperfect time synchronization,the optimal power distribution method was given,and the relationship between the optimal interference power and the relative channel quality was discussed.Theory and simulation analysis show that when the synchronization error is small,the synchronization error consumes extra power to reach the preset secrecy capacity,and the interference power increases with its increase.As the synchronization error increases,the secrecy capacity of the system gradually fails to reach the preset target,and may even drop to zero.

Air pollution has become a major health risk in China. Here Zhang et al. report that maternal and neonatal exposure to particulate matter increases the risk of neonatal jaundice based on the study of 25,782 newborns born in China between 2014 and 2017.

Abstract Extreme high temperature (EHT) events have caused serious impact on society. In previous research, EHT indices seldom consider spatial aggregation. To conduct the comprehensive evaluation, the global EHT days from 1979 to 2017 have been evaluated in terms of high temperature duration, accumulative temperature intensities and magnitude of spatial aggregation based on ERA‐Interim 2 m air temperature data set. In addition, abnormal warming of polar and ocean surfaces has rarely been addressed in global‐scale studies. To address this deficiency, the indices of EHT days are normalized by maximum in history for each location. To solve the time discontinuity problem on EHT spatiotemporal distribution in previous studies, we utilized spatiotemporal scan statistics to detect EHT spatiotemporal clusters (EHT‐STCs), and clusters correspond to a certain spatiotemporal extent where the EHT events are aggregated. This method actualizes time‐continuous and spatiotemporal integral detection. The results show the following: 1. There are apparent changes in EHT‐STC spatial distribution after 1999, so 1999 can be recognized as the critical time node of EHT‐STC spatial transformation. 2. After 1999, the EHT‐STC number gradually increased, and the duration and spatial coverage of EHT‐STC gradually expanded. Overall, EHT‐STCs display a significant northward migration, with an average northward movement of 7.03° for each transition from 1979 to 2017. 3. There are differences in the extremity and transformation process in different regions. EHT‐STCs in Antarctica are concentrated before 1992, with the strongest EHT extremity; EHT‐STCs in the Arctic and middle‐low latitude continents are concentrated after 2010 and 1999 respectively, both with a strong EHT extremity. There is a similar EHT‐STC number in the middle‐low latitude ocean before and after 1999 with the weakest extremity. These results contribute to determining EHT causes and future trends in global climate change.

Food security is a worldwide challenge that is related to the basic human needs of sustainable development [...]

Abstract Remote sensing can provide near real-time and dynamic monitoring of drought. The drought severity index (DSI), based on the normalized difference vegetation index (NDVI) and evapotranspiration/potential evapotranspiration (ET/PET), has been used for drought monitoring. This study examined the relationship between the DSI and winter wheat yield for prefecture-level cities in five provinces of eastern China during 2001–2016. We first analyzed the spatial and temporal distribution of droughts in the study area. Then the correlation coefficient between drought-affected area and detrended yield of winter wheat was quantified and the impact of droughts of different intensities on winter wheat yield during different growth stages was investigated. The results show that incipient drought during the wintering period has no significant impact on the yield of winter wheat, while moderate drought in the same period can reduce yield. Drought affects winter wheat yield significantly during the flowering and filling stages. Droughts of higher intensity have more significant negative effects on the yield of winter wheat. Monitoring of droughts and irrigation is critical during these periods to ensure normal yield of winter wheat. This study has important practical implications for the planning of irrigation and food security.

Abstract Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location, scale, and shape was used for at-site precipitation forecasting. The results indicate that: (1) winter and spring precipitation processes at most stations are nonstationary, while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the study region; (2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation (PDO). The El Niño / Southern Oscillation (ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin; (3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and (4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation.

The Indian Ocean Region (IOR) has become one of the main economic forces globally, and countries within the IOR have attempted to promote their intra-regional trade. This study investigates the spatiotemporal evolution of the community structures of the intra-regional trade and the impact of determinant factors on the formation of trade community structures of the IOR from 1996 to 2017 using the methods of social network analysis. Trade communities are groups of countries with measurably denser intra-trade ties but with extra-trade ties that are measurably sparser among different communities. The results show that the extent of trade integration and the trade community structures of the IOR changed from strengthening between 1996 and 2014 to weakening between 2015 and 2017. The largest explanatory power of the formation of the IOR trade community structures was the IOR countries’ economic size, indicating that market remained the strongest driver. The second-largest explanatory power was geographical proximity, suggesting that countries within the IOR engaged in intra-regional trade still tended to select geographically proximate trading partners. The third- and the fourth-largest were common civilization and regional organizational memberships, respectively. This indicates that sharing a common civilization and constructing intra-regional institutional arrangements (especially open trade policies) helped the countries within the IOR strengthen their trade communities.

Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.

The vulnerability of vegetation ecosystems and hydrological systems in high-altitude areas makes their phenology more sensitive and their response to climate change more intense. The Qilian Mountains, an important geographic unit located in the northeastern Tibetan Plateau (TP), has experienced the more significant increases in temperature and precipitation in the past few decades than most areas of the TP. However, under such intense climate change, the temporal and spatial differences in phenology in the Qilian Mountains are not clear. This study explored the spatial and temporal heterogeneity of phenology in the Qilian Mountains from 1982 to 2014 and its response to three temperature indicators, including the mean daily temperature (Tmean), mean daily daytime temperature (Tmax), and mean daily nighttime temperature (Tmin). The results showed that (1) as the altitude rose from southeast to northwest, the multiyear mean of the start of the growing season (SOS) was gradually delayed mainly from 120 to 190 days, the multiyear mean of the end of the growing season (EOS) as a whole was advanced (from 290 to 260 days), and the multiyear mean of the length of the growing season (LGS) was gradually shortened (from 150 to 80 days). (2) In general, there was an advanced trend in the annual average SOS (0.2 days per decade), a delayed trend in the annual average EOS (0.15 days per decade), and an extended trend in the annual average LGS (0.36 days per decade) over the study period. However, there has been no significant phenological trend in recent years, especially for the SOS after 2000 and the EOS and LGS after 2003. (3) Higher preseason temperatures led to an advanced SOS and a delayed EOS at the regional scale. Moreover, the SOS and EOS were more triggered by Tmax than Tmin and Tmean. The LGS was significantly positively correlated with annual mean temperature (r = −0.82, p < 0.01).

The new wave of modern rail transit and the proposal of the Belt and Road Initiative (BRI) have complicated the business patterns of the rolling stock manufacturing industry (RSMI) and the export of rolling stock products, especially in the case of countries participating in the BRI. Based on the analysis of trade patterns—which focuses on the evolution of trade links, community structures, and intraregional export competitiveness—this study aims to explore the changes in the RSMI within the BRI region from 2003 to 2017. Sequential clustering was applied to the creation of a three-phase timeline. The network models of the cumulative trade of the rolling stock products and trades of two typical categories of products were constructed in each phase for the evolution analysis. Social network analysis methods, such as the analysis of network indices and community detection, were also applied. The results show that from 2003 to 2017, the connectivity of the rolling stock trade in this region significantly increased. China was the largest exporter, with increasing trade influence and technological strength. Ukraine and Russia were less competitive and highly mutually dependent. Czechia and Austria’s competitiveness remained prominent, but compared with China they lacked expansive vitality. South Korea was also an active and competitive country with strong technological prowess. These countries accounted for the majority of the exports, and were always at the center of their own separate communities, over which they maintained a sphere of influence. The grouping of countries far from any such spheres of influence changed frequently.

Er3+-sensitized upconversion nanoparticles (UCNPs) have attracted great attention due to their tunable upconversion (UC) emissions, low cytotoxicity, high resistance to photobleaching and especially multiple effective excitation wavelengths. However, detailed energy conversion between Er3+ and Tm3+ ions in Y2O3 UCNPs is still a problem, especially under multi-wavelength and variable pulse width excitation. In this work, we successfully fabricated a series of Er3+-sensitized Y2O3 nanocrystals by a spray flame synthesis method with a production rate of 40.5 g h−1. The as-prepared UCNPs are a pure cubic phase with a mean size of 14 nm. Excited by both 980 and 808 nm lasers, the tunable upconversion luminescence (UCL) from Er3+ ions was achieved by increasing the Er3+ doping concentration, co-doping Tm3+ ions and extending excitation pulse-width. The investigations of the lifetimes and the laser power dependence of UC emissions further support the proposed mechanism, which provides guidance for achieving effective color control in anticounterfeiting and multiplexed labeling applications. In addition, the red UC emission at about 5 mm beneath the tissue surface was observed in an ex vivo imaging experiment under the excitation of 808 nm laser, indicating that the Y2O3:Er3+/Tm3+ UCNPs have great prospects in further biological applications.

Product bilateral trade features can be organized and expressed in the Cartesian coordinate system by taking imports and exports as X and Y, which is similar to spatial visualization. Hence, geospatial expression and analysis methodologies can be applied in bilateral trade studies. In this paper, we propose a new digital trade feature map (DTFM) method for the visualization of bilateral trade features from a spatial perspective. The implementation process of DTFM can be summarized as feature extraction, visualization, and analysis. China–US bilateral trade data were used in several case studies. As the case studies show, the DTFM has the advantages of clear expression, easy operationalization and is highly extensible. Moreover, this method can provide a broader perspective for the understanding of trade features, i.e., in comprehensively considering the features of a specific product type and its neighbors. Furthermore, we propose an extensible DTFM application framework into which different trade features, different grid generation modes, and numerous spatial analysis models can be readily integrated.

Sustainable development appears to be the theme of our time. To assess the progress of sustainable development, a simple but comprehensive index is of great use. To this end, a multivariate index of sustainable development was developed in this study based on indicators of the United Nations Sustainable Development Goals (SDGs). To demonstrate the usability of this developed index, we applied it to Fujian Province, China. According to the China SDGs indicators and the Fujian situation, we divided the SDGs into three dimensions and selected indicators based on these dimensions. We calculated the weights and two indices with the entropy weight coefficient method based on collecting and processing of data from 2007 to 2017. We assessed and analyzed the sustainable development of Fujian with two indices and we drew three main conclusions. From 2007 to 2017, the development index of Fujian showed an increasing trend and the coordination index of Fujian showed a fluctuating trend. It is difficult to smoothly improve the coordination index of Fujian because the development speeds of Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice, and Strong Institutions) were low. The coordination index of Fujian changed from strong coordination to medium coordination from 2011 to 2012 because the development speed of the environmental dimension suddenly improved. It changed from strong coordination to medium coordination from 2015 to 2016 because the values of the development index of the social dimension were decreasing. To the best of our knowledge, these are the first SDGs-based multivariate indices of sustainable development for a region of China. These indices are applicable to different regions.

Spatiotemporal fusion is considered a feasible and cost-effective way to solve the trade-off between the spatial and temporal resolution of satellite sensors. Recently proposed learning-based spatiotemporal fusion methods can address the prediction of both phenological and land-cover change. In this paper, we propose a novel deep learning-based spatiotemporal data fusion method that uses a two-stream convolutional neural network. The method combines both forward and backward prediction to generate a target fine image, where temporal change-based and a spatial information-based mapping are simultaneously formed, addressing the prediction of both phenological and land-cover changes with better generalization ability and robustness. Comparative experimental results for the test datasets with phenological and land-cover changes verified the effectiveness of our method. Compared to existing learning-based spatiotemporal fusion methods, our method is more effective in predicting phenological change and directly reconstructing the prediction with complete spatial details without the need for auxiliary modulation.

Supersonic film cooling tangentially ejected through a half Laval nozzle set in a backward-facing slot was numerically simulated to investigate the structures of flowfield and the mechanisms of heat transfer after the slot. In particular, changes in flowfield near the turning point in the cooling effectiveness curve were studied. The turning point is defined by extrapolation of the cooling effectiveness curve using a power-law relationship. The point corresponds to the streamwise position where the hot mainstream is reaching the wall protected by the film coolant and to the disappearance of the unsmooth transition points in streamwise velocity profiles, where the growth rate of velocity in wall normal direction changes gradually from one state to another. The transition of velocity becomes smooth downstream of the turning point. The unsmooth transition point also exists in other profiles of flow parameters, such as the mole fraction of film gas and the total temperature of the fluid, which are indicators of the mixing extent between the mainstream and film coolants. Moreover, the unsmooth transition point is more evident in the film coolant of nitrogen than that of helium due to the slower drop rate of effectiveness in the current configuration of the supersonic film cooling.

VO2(B), VO2(M), and V2O5 are the most famous compounds in the vanadium oxide family. Here, their gas-sensing properties were investigated and compared. VO2(B) nanoflakes were first self-assembled via a hydrothermal method, and then VO2(M) and V2O5 nanoflakes were obtained after a heat-phase transformation in nitrogen and air, respectively. Their microstructures were evaluated using X-ray diffraction and scanning and transmission electron microscopies, respectively. Gas sensing measurements indicated that VO2(M) nanoflakes were gas-insensitive, while both VO2(B) and V2O5 nanoflakes were highly selective to ammonia at room temperature. As ammonia sensors, both VO2(B) and V2O5 nanoflakes showed abnormal p-type sensing characteristics, although vanadium oxides are generally considered as n-type semiconductors. Moreover, V2O5 nanoflakes exhibited superior ammonia sensing performance compared to VO2(B) nanoflakes, with one order of magnitude higher sensitivity, a shorter response time of 14⁻22 s, and a shorter recovery time of 14⁻20 s. These characteristics showed the excellent potential of V2O5 nanostructures as ammonia sensors.

In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.

Financial time series analyses have played an important role in developing some of the fundamental economic theories. However, many of the published analyses of financial time series focus on long-term average behavior of a market, and thus shed little light on the temporal evolution of a market, which from time to time may be interrupted by stock crashes and financial crises. Consequently, in terms of complexity science, it is still unknown whether the market complexity during a stock crash decreases or increases. To answer this question, we have examined the temporal variation of permutation entropy (PE) in Chinese stock markets by computing PE from high-frequency composite indies of two stock markets: the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). We have found that PE decreased significantly in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. One window started in the middle of 2006, long before the 2008 global financial crisis, and continued up to early 2011. The other window was more recent, started in the middle of 2014, and ended in the middle of 2016. Since both windows were at least one year long, and proceeded stock crashes by at least half a year, the decrease in PE can be invaluable warning signs for regulators and investors alike.

Big data application is still in its primary stage,and agricultural big data is the integrated products of many different subjects,so the insufficiency of big data specialists or experts is an important bottle-neck of big data application.The concept of agricultural big data,the processing technology and its application were briefly introduced.Combining the characteristics of the agricultural big data,aiming at the situation of agricultural big data talent lack,the importance of graduate students majored in agriculture big data was discussed.On this basis,the essence of training scheme and the training mode of graduate students were discussed.

Agricultural big data assets management is the prerequisite of developing big data of agriculture and countryside.Combined the knowledge of agricultural fields and the hot topics in big data industry,agricultural big data assets management was discussed.Furthermore,definitions and descriptions of the concepts were defined,and some features of agricultural big data assets were analyzed from the view of service value.Next,the facing challenge of agricultural big data assets management was discussed from the aspects of storage,quality,value,safety,open and personnel training.The present situation of agricultural big data assets was analyzed.At the end,some thinking and suggestions were proposed for agricultural big data assets management.