Ministry of Civil Affairs

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time-lag effects, which are the most important mechanism of climate-vegetation interactive effects. Extensive studies focused on large-scale vegetation-climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time-lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time-lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time-lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the time-lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time-lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time-lag effects; (iii) for the area with a significant change trend (for the period 1982-2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time-lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.

Abstract The way in which the NE Tibetan Plateau uplifted and its impact on climatic change are crucial to understanding the evolution of the Tibetan Plateau and the development of the present geomorphology and climate of Central and East Asia. This paper is not a comprehensive review of current thinking but instead synthesises our past decades of work together with a number of new findings. The dating of Late Cenozoic basin sediments and the tectonic geomorphology of the NE Tibetan Plateau demonstrates that the rapid persistent rise of this plateau began ~8 ± 1 Ma followed by stepwise accelerated rise at ~3.6 Ma, 2.6 Ma, 1.8–1.7 Ma, 1.2–0.6 Ma and 0.15 Ma. The Yellow River basin developed at ~1.7 Ma and evolved to its present pattern through stepwise backward-expansion toward its source area in response to the stepwise uplift of the plateau. High-resolution multi-climatic proxy records from the basins and terrace sediments indicate a persistent stepwise accelerated enhancement of the East Asian winter monsoon and drying of the Asian interior coupled with the episodic tectonic uplift since ~8 Ma and later also with the global cooling since ~3.2 Ma, suggesting a major role for tectonic forcing of the cooling.

Social vulnerability emphasizes the different burdens of disaster losses within and between places. Although China continuously experiences devastating natural disasters, there is a paucity of research specifically addressing the multidimensional nature of social vulnerability. This article presents an initial study on the social vulnerability of the Yangtze River Delta region in China. The goal is to replicate and test the applicability of the place-based Social Vulnerability Index (SoVI®) developed for the United States in a Chinese cultural context. Twenty-nine variables adapted from SoVI® were collected for each of the 134 analysis units in the study area. Using principal components analysis, six factors were identified from the variable set: employment and poverty, education, poor housing quality, minorities, family size, and housing size—factors similar to those identified for the United States. Factor scores were summed to get the final SoVI® scores and the most and least vulnerable study units were identified and mapped. The highest social vulnerability is concentrated in the southern portions of the study area—Jingning, Suichang, Yunhe, Lanxi, Pan’an, and Shengsi. The least socially vulnerable areas are concentrated southwest, west, and northwest of Shanghai. Limitations of replication are discussed along with policy-relevant suggestions for vulnerability reduction and risk mitigation in China.

Motor performance is improved by stimulation of the agonist muscle during movement. However, related brain mechanisms remain unknown. In this work, we perform a functional magnetic resonance imaging study in 21 healthy subjects under three different conditions: (1) movement of right ankle alone, (2) movement and simultaneous stimulation of the agonist muscle or (3) movement and simultaneous stimulation of a control area. We constructed weighted brain networks for each condition by using functional connectivity. Network features were analyzed using graph theoretical approaches. We found that (1) the second condition evokes the strongest and most widespread brain activations (5147 versus 4419 and 2320 activated voxels); and (2) this condition also induces a unique network layout and changes hubs and the modular structure of the brain motor network by activating the most “silent” links between primary somatosensory centers and the motor cortex, particularly weak links from the thalamus to the left primary motor cortex. Significant statistical differences were found when the strength values of the right cerebellum (P<0.001) or the left thalamus (P=0.006) were compared among the three conditions. Over the years, studies reported a small number of projections from the thalamus to the motor cortex. This is the first work to present functions of these pathways. These findings reveal mechanisms for enhancing motor function with somatosensory stimulation, and suggest that network function cannot be thoroughly understood when weak ties are disregarded.

Abstract Artificial intelligence (AI) provides new opportunities for K-12 English as foreign language (EFL) teachers to improve their teaching. To address the emerging trend of integrating AI into teaching, this study investigated EFL teachers’ perceptions, knowledge, and behavioral intention to use AI to support teaching and learning of English in middle schools. This study combined relevant aspects of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technological Pedagogical and Content Knowledge (TPACK) as the theoretical basis. A survey was conducted in an AI education demonstration district in China. This survey adopted a 5-point Likert scale which was developed from previous research and the interview records of EFL teachers. A total of 470 valid responses were collected. The reliability and validity of the scale were satisfied with eight constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), AI language technological knowledge (AIL-TK), AI technological pedagogical knowledge (AI-TPK), AI-TPACK, and Behavioral Intention (BI). The results showed that the EFL teachers were positive with regard to the measured factors. PE, SI, AIL-TK, and AI-TPACK had significant positive predictive power on BI; and EE, FC, AI-TPK had indirect effects on BI. The complex interrelations were mapped out to provide educators and policymakers with a theoretically grounded scheme to foster teachers’ BI to use AI in teaching.

Parkinson's disease (PD) patients have difficulty in initiating movements. Previous studies have suggested that the abnormal brain activity may happen not only during performance of self-initiated movements but also in the before movement (baseline or resting) state. In the current study, we investigated the functional connectivity of brain networks in the resting state in PD. We chose the rostral supplementary motor area (pre-SMA) and bilateral primary motor cortex (M1) as "seed" regions, because the pre-SMA is important in motor preparation, whereas the M1 is critical in motor execution. FMRIs were acquired in 18 patients and 18 matched controls. We found that in the resting state, the pattern of connectivity with both the pre-SMA or the M1 was changed in PD. Connectivity with the pre-SMA in patients with PD compared to normal subjects was increased connectivity to the right M1 and decreased to the left putamen, right insula, right premotor cortex, and left inferior parietal lobule. We only found stronger connectivity in the M1 with its own local region in patients with PD compared to controls. Our findings demonstrate that the interactions of brain networks are abnormal in PD in the resting state. There are more connectivity changes of networks related to motor preparation and initiation than to networks of motor execution in PD. We postulate that these disrupted connections indicate a lack of readiness for movement and may be partly responsible for difficulty in initiating movements in PD.

The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.

Abstract Crop residue retention (RR) is a recommended practice in China and globally. However, comprehensive assessment of changes and mechanisms affecting crop production and soil processes with RR and thus identifying systems of sustainable residues management are not widely studied. A national meta‐analysis was conducted to assess changes in 24 indicators (related to soil quality, soil nutrients, crop yield, and environmental impacts) along with their relationships under RR through 4,910 comparisons from 278 publications across China's croplands. Positively, RR significantly increased crop yield (7.8%), soil organic carbon (SOC) pool (12.3% to 36.8%), soil nutrient reserves (1.9% to 15.2%), soil temperature (6.7%), and water contents (5.9%) and improved soil structure when compared with residue removal ( P &lt; .05). Negatively, RR may increase soil acidification and significantly increase emissions of greenhouse gases (by 31.7%, 130.9%, and 12.2% for CO 2 , CH 4 , and N 2 O). Nonetheless, the negative effects can be alleviated, and the positive effects can be strengthened by adopting RR in conjunction with appropriate crops, specific farming practices, and avoiding more than 10 years of consecutive use. The results indicated that a higher decomposition of native and newly added organic matters, induced by RR and attendant changes in soil physical properties, could enhance the dynamics of SOC, microbial biomass, soil nutrients, and the final increase in crop yield and greenhouse gases emissions. Thus, the sustainability of RR‐based system could be enhanced by a careful choice and adoption of integrated farming practices. Proper RR management strategies could offer a climate‐smart solution to ensure food security and sustain soil productivity.

Droughts, which are characterized by multiple dimensions including frequency, duration, severity, and onset timing, can impact tree stem radial growth profoundly. Different dimensions of drought influence tree stem radial growth independently or jointly, which makes the development of accurate predictions a formidable challenge. Measurement-based tree-ring data have obvious advantages for studying the drought responses of trees. Here, we explored the use of abundant tree-ring records for quantifying regional response patterns to key dimensions of drought. Specifically, we designed a series of regional-scaled "natural experiments," based on 357 tree-ring chronologies from Southwest USA and location-matched monthly water balance anomalies, to reveal how tree-ring responds to each dimension of drought. Our results showed that tree-ring was affected significantly more by the water balance condition in the current hydrological year than that in the prior hydrological year. Within the current hydrological year, increased drought frequency (number of dry months) and duration (maximum number of consecutive dry months) resulted in "cumulative effects" which amplified the impacts of drought on trees and reduced the drought resistance of trees. Drought events that occurred in the pregrowing seasons strongly affected subsequent tree stem radial growth. Both the onset timing and severity of drought increased "legacy effects" on tree stem radial growth, which reduced the drought resilience of trees. These results indicated that the drought impact on trees is a dynamic process: even when the total water deficits are the same, differences among the drought processes could lead to considerably different responses from trees. This study thus provides a conceptual framework and probabilistic patterns of tree-ring growth response to multiple dimensions of drought regimes, which in turn may have a wide range of implications for predictions, uncertainty assessment, and forest management.

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county-level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county-level social vulnerability. The results demonstrate that the dynamic characteristics of China's county-level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering-scattering-gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.

ABSTRACT Drought is a major natural hazard that can have devastating impacts on regional agriculture, water resources and the environment. To assess the variability and pattern of drought characteristics in the Huang‐Huai‐Hai ( HHH ) Plain, the daily Standardized Precipitation Evapotranspiration Index ( SPEI ) is developed based on daily meteorological data in this study. The daily SPEI data are used, including Annual Total Drought Severity ( ATDS ), Annual Total Drought Duration ( ATDD ) and Annual Drought Frequency ( ADF ), which were calculated from 1981 to 2010 at 28 meteorological stations. We used the indices ( ATDS , ATDD and ADF ), Hovmöller diagrams and the reliable no parameter statistical methods of the Mann–Kendall test to assess the variability and pattern of drought characteristics for the period from 1981 to 2010 in the HHH plain. The results suggested that severe drought occurred in the 1980s, the late 1990s and the early 2000s, severe drought events occurred in 1981, 1986, 1997 and 2002. Decreasing trends for both ATDS and ATDD were found, and the drought situation did not worsen under global warming during the past 30 years, and the drought situation is alleviating in the entire HHH plain. The northeast and southwest regions of the HHH plain have suffered from more severe drought, and the north region is prone to drought. The results of the study can provide a scientific understanding for the adoption of countermeasures of regional defence against drought and also may serve as a reference point for drought hazard vulnerability analysis.

An emerging consensus suggests that large igneous provinces (LIPs) are a significant driver of dramatic global environmental and biological changes, including several Phanerozoic mass extinctions, leading to plausible links with geological time scale (GTS) boundaries. LIP-induced environmental changes are now being identified in the Precambrian record, suggesting potential for the use of LIPs to define natural pre-Phanerozoic GTS boundaries. There is now opportunity for more systematic integration of the sedimentary and LIP records. Here we provide maps of generalized LIP distributions through time, and a compilation of LIP ages, approximate areal extents, and potential links (both robust and speculative) with secular environmental changes and GTS boundaries.

China is a country that is significantly affected by and sensitive to global climate change. Floods are one of the major natural disasters in China, and they occur with high frequency and wide impact in the country, causing serious losses. Since the 1990s, they have become more frequent. China has made remarkable achievements in flood risk management, but the problems and challenges of this in the context of climate change and urbanization are still serious and require in-depth analysis and targeted adaptations. During the summer of 2020, southern China suffered from catastrophic flooding; however, the losses from this flooding were much lower than those of previous major floods. Herein, the flood disasters of the Yangtze River Basin in China in 1998 and 2020 are compared and analyzed from atmospheric, hydrological, socioeconomic, and disaster-loss perspectives and the reasons behind the observed differences are examined and discussed. The findings indicate that risk-management capabilities, such as engineering defense capabilities, environmental recovery capabilities, forecasting and early-warning capabilities, and emergency response capabilities, have achieved remarkable results. The results show that disaster loss has been largely reduced because of China's achievements in disaster risk reduction measures. The problems and challenges faced by China's flood risk management are analyzed, and detailed watershed comprehensive flood risk management recommendations are put forward to reduce the losses caused by flooding.

This article analyzes the role of dynamic economic resilience in relation to recovery from disasters in general and illustrates its potential to reduce disaster losses in a case study of the Wenchuan earthquake of 2008. We first offer operational definitions of the concept linked to policies to promote increased levels and speed of investment in repair and reconstruction to implement this resilience. We then develop a dynamic computable general equilibrium (CGE) model that incorporates major features of investment and traces the time-path of the economy as it recovers with and without dynamic economic resilience. The results indicate that resilience strategies could have significantly reduced GDP losses from the Wenchuan earthquake by 47.4% during 2008-2011 by accelerating the pace of recovery and could have further reduced losses slightly by shortening the recovery by one year. The results can be generalized to conclude that shortening the recovery period is not nearly as effective as increasing reconstruction investment levels and steepening the time-path of recovery. This is an important distinction that should be made in the typically vague and singular reference to increasing the speed of recovery in many definitions of dynamic resilience.

It was supposed that auxetic structure with negative Poisson's ratio (NPR) expands under stretch and could enhance the screw-bone fixation. In this study, the novel auxetic structure based bone screws were designed, and mechanical properties and fixation strength were evaluated. Auxetic unit cells (A1–A6) were introduced into the design of screw bodies after a mechanical evaluation. Tubular auxetic structures (TA1–TA6), auxetic screws (AS1–AS6) and one non-auxetic screw (NS) were manufactured using 3D-printing. The fabrication process well reproduced the original designs despite the some mismatch in the macro and micro morphologies. Tensile tests on specimens were conducted experimentally and computationally. The relationship between NPR and fixation strength of the screws was investigated by computationally bone-pullout test. Among all screw designs, AS2 generated the largest stiffness and strength, and better NPR, AS5 produced the highest NPR, and smallest stiffness and strength. Maximal pullout force within low-, mid- and high-density bone was shown in AS5 (399.39 N), AS6 (561.07 N) and AS2 (1185.93 N) respectively. It was concluded that varying auxetic structures altered the screw's mechanical properties especially its functional properties. The bone-screw fixation could be improved by auxetic structures while other design factors should also be taken in account.

Maps of population exposure, vulnerability and risk to natural hazards are useful tools for designing and implementing disaster risk mitigation programs in China. The ranking of provinces by relative risk to natural hazards would provide a metric for prioritizing risk management strategies. Using provinces as our study unit, from the perspectives of hazard exposure, susceptibility, coping capacity and adaptive capacity, this study first constructed China’s disaster risk index for five types of major natural hazards: earthquakes, floods, droughts, low temperatures/snow and gale/hail. Then, the relative risk level at the provincial scale in China was assessed. Finally, the hotspots with the highest hazard exposure, vulnerability and risk were identified. The results showed that high exposure was a significant risk driver in China, whereas high vulnerability, especially social vulnerability, amplified the risk levels. Similar to the population exposure to disasters, the relative risk levels in the southwestern, central and northeastern regions of China were significantly higher than those in the eastern, northern and western regions. The high-risk regions or hotspots of multi-hazards were concentrated in southern China (less-developed regions), while the low-risk regions were mainly distributed in the eastern coastal areas (well-developed regions). Furthermore, a nonlinear relationship existed between the disaster risk level and poverty incidence as well as per capita GDP, demonstrating that disaster losses in middle-income areas are likely to increase if economic policies are not modified to account for the rising disaster risk. These findings further indicated that research on disaster risk should focus not only on hazards and exposure but also on the vulnerability to natural disasters. Thus, reducing vulnerability and population exposure to natural hazards would be an effective measure in mitigating the disaster risk at hotspots in China.

Disaster risk reduction (DRR) activities have given growing attention to building community resilience, but the effects of such efforts on community resilience are still under-investigated, especially in China where the concept of community resilience has only just emerged. Using the Communities Advancing Resilience Toolkit Assessment Survey, data on self-perceived community resilience were collected in 2017 from a post-disaster Chinese rural community in Yingxiu Town, which was the epicenter of the Wenchuan earthquake (Magnitude = 8.0) in the year 2008. Linear regression analyses were conducted to explore the correlations between residents' DRR behaviors and perceived community resilience with the control of their socio-demographic characteristics including age, ethnicity, gender, education, income level, employment status and marital status. Results indicate that residents who volunteered for DRR activities received geological disaster education, participated in evacuation drills, and reported higher income levels had a perception of higher community resilience. Practice research is suggested to help clarify the cause and effect of DRR work on the enhancement of community resilience to disasters in China and abroad. Attention is also called to the development of a Chinese indigenous community resilience concept and assessment instrument.

Automatic seizure prediction promotes the development of closed-loop treatment system on intractable epilepsy. In this study, by considering the specific information exchange between EEG channels from the perspective of whole brain activities, the convolution neural network (CNN) and the directed transfer function (DTF) were merged to present a novel method for patient-specific seizure prediction. Firstly, the intracranial electroencephalogram (iEEG) signals were segmented and the information flow features of iEEG signals were calculated by using the DTF algorithm. Then, these features were reconstructed as the channel-frequency maps according to channel pairs and the frequency of information flow. Finally, these maps were fed into the CNN model and the outputs were post-processed by the moving average approach to predict the epileptic seizures. By the evaluation of cross-validation method, the proposed algorithm achieved the averaged sensitivity of 90.8%, the averaged false prediction rate of 0.08 per hour. Compared to the random predictor and other existing algorithms tested on the Freiburg EEG dataset, our proposed method achieved better performance for seizure prediction in all patients. These results demonstrated that the proposed algorithm could provide an robust seizure prediction solution by using deep learning to capture the brain network changes of iEEG signals from epileptic patients.

In China the family is still the major welfare provider for old people in rural areas. Although the implementation of this role has varied significantly, in different historical periods, owing to social and economic changes in the rural environment, the core functions of the family have remained the same, that is, the provision of welfare for dependants, particularly for the aged. In the more traditional China, providing care for the aged was indeed assumed to be a paramount function of the family. Whereas, following the founding of the PRC in 1949, the welfare function of the family was reduced, as a result of the collectivization of the rural economy, which meant a part of family responsibilities being shared by collective organizations. However, after more than twenty years’ experience of agricultural collectivization, China embarked on a course of further rural economic reform in the early 1980s, replacing the commune system with one of private production based on the family unit. As a result, rural welfare responsibilities were shifted back from the commune to the family, which became solely responsible for providing support for its dependent members. This paper attempts to set out the real situation with regard to family support for rural old people in China. The first section offers a brief introduction to the declining family status of rural old people as a consequence of socio‐economic change. The second section reviews the implications of rural economic reform for the (declining) status of old people with regard to family support, focusing on patterns of rural old age dependency and the changing roles of family caregivers. Lastly, cases of family support disputes and community responses are presented, drawing on findings from fieldwork conducted by the author between 1995 and 1996 in three rural localities in China.

Human-assisting intelligent systems demand certain methods to precisely predict motorized limb joint angles. This paper presents the application of deep-recurrent neural networks (RNNs), which is a type of neural network for processing sequential data, for predicting the knee joint angle in real-time. This model is created based on a combination of electromyographic (EMG) signals, (with electrodes being placed on three leg muscles), and inertial measurements of the upper and lower legs. The data collected from different subjects when they performed different gaits were used to construct the model, which was evaluated in a real-time setting. The proposed RNN model based on fusion information contains a balance between computational complexity and prediction accuracy. Results on a microcontroller show that, within a predicted horizon of 50 ms, the model has a low prediction error of ±2.93 degrees.