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Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare; in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.

Abstract Global human‐induced warming has intensified water circulation in the atmospheric environment and altered the streamflow generation regime. The VIC hydrological model approach for impact assessment of climate change and human activities mainly focuses on variations in streamflow, but ignores other critical flooding characteristics induced by extreme streamflow, especially bivariate flooding characteristics. In this work, the copula functions are employed to structure the flooding risk under the shared socioeconomic pathway (SSP) across the Yellow River basin (YRB). This is based on the multi‐model ensemble (MME) and Delta downscaling outputs (Delta‐MME) of the CMIP6 global climate models (GCMs), as well as the flooding characteristics simulated by VIC hydrological model. Compared to the reference period (1995–2014), Delta‐MME reveals a significant warming and humidifying trend under three SSPs over the YRB. Despite uncertainties originating from climate variables and hydrological model, multiple findings underscore the substantial influence of climate change on the flooding generation regime in YRB. This includes: (a) an increase in the streamflow under all SSPs; (b) a larger flooding peak (Q) and volume (W) under SSP585, with Q and W at the Huayuankou hydrologic station (HYK) increasing by 52.7% and 44.8%, respectively; (c) an advancement in the bivariate flooding risk, particularly in SSP585 where flooding co‐occurrence return period at HYK may be more than 50 times earlier. This study underscores that the urgent need to enhance social resilience to climate change in the YRB.

Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R2) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R2 of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion.

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians’ writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.

Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects.

Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape.

Climate change has a significant impact on forest ecosystems worldwide, but it is unclear whether forest responses to climate fluctuations are homogeneous across regions. In this study, we investigated the impact of climatic fluctuations on forest growth in a complex terrain, in Anhui Province of China, using Enhanced Vegetation Index (EVI) data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), while considering the impact of terrain characteristics and forest types. Our regional-scale analysis found that the forest response to climatic drivers in Anhui Province is not homogeneous, with only 69% of the forest area driven by temperature (TEM), while 11% is precipitation (PRE) driven and 20% is solar radiation (SWD) driven. We also found with random forest models that terrain traits (elevation and slope) contributed significantly (29.47% and 27.96%) to the spatial heterogeneity of forest response to climatic drivers, with higher elevation associated with a stronger positive correlation between the EVI and temperature (p < 0.001), a weaker positive correlation between the EVI with precipitation (p < 0.001), and a stronger negative correlation between the EVI with solar radiation (p < 0.01), while forest type contributed the least (4.21%). Our results also imply that in a warmer and dryer climate, some forest patches may switch from TEM driven to PRE driven, which could lead to a decrease in forest productivity, instead of an increase as predicted by existing climate models. These results highlight the heterogeneous response of forests within close vicinity to climate fluctuations in a complex terrain, which has important implications for climate-related risk assessments and local forest management.

The application of artificial intelligence (AI) technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial intelligence capability (AIC), management (AIM), driven decision making (AIDDM), and firm performance. Based on the resource-based view (RBV) and existing findings, this paper constructs a higher-order model of AIC and suggests a research model of e-commerce firm AIC and firm performance. We collected 394 valid questionnaires and conducted data analysis using partial least squares structural equation modeling (PLS-SEM). As a second-order variable, AIC was formed by three first-order variables: basic, proclivity, and skills. AIC indirectly affects firm performance through creativity, AIM, and AI-driven decision making. Firm creativity, AIM, and AIDDM are essential variables between AIC and firm performance. Innovation culture (IC) positive moderates the relationship between firm creativity and AIDDM as well as the relationship between AIDDM and firm performance. Environmental dynamism (ED) positive mediates the connection between AIM and AIDDM. Among the control variables, firm age negatively affects firm performance, and employee size does not. This study helps enterprises leverage AI to improve firm performance, achieve a competitive advantage, and contribute to theory and management practice.

The alterations in vegetation cover in the Three North Protection Forest Project area influence its ecological and environmental management. It helps to study soil erosion, environmental change, and ecosystem protection to provide basic data support. Based on the Google Earth Engine cloud platform, this paper selects MODIS 3Q1 data from 2000–2020 and uses the image element dichotomous model to estimate the fractional vegetation cover (FVC) of the Three North Protection Forest Project area, evaluates the stability and temporal and spatial variation of FVC, investigates the coupling relationship between the FVC and temperature and rainfall through evaluation indexes such as the slope of inter-annual variation and partial correlation coefficient, and then analyzes the influence of land use changes on the FVC. The results show that the fractional vegetation cover of the Three North Protection Forest Project area as a whole has increased significantly over the past 20 years, the medium and high vegetation coverage areas have reached 36.4%, the high vegetation FVC has increased from 20.66% in 2000 to 21.59% in 2020, and the vegetation activity is increasing. The vegetation cover is significantly affected by the topographic effect, with the vegetation cover improving relatively well at slopes between 5–13° and elevations ranging from 2000–2500 m. The overall positive correlation between vegetation and temperature and vegetation and precipitation in the study area was 54.08% and 70.04%, respectively, and precipitation was the key factor influencing vegetation growth in the research region. Human activities have a stronger effect on vegetation construction than destruction, and this study contributes to the evaluation of the benefits of the Three North Protection Forest Project and the understanding of human influence on environmental change.

Healthcare insurance frauds are causing millions of dollars of public healthcare fund losses around the world in various ways, which makes it very important to strengthen the management of medical insurance in order to guarantee the steady operation of medical insurance funds. Healthcare fraud detection methods can reduce the losses of healthcare insurance funds and improve medical quality. Existing fraud detection studies mostly focus on finding normal behavior patterns and treat those violating normal behavior patterns as fraudsters. However, fraudsters can often disguise themselves with some normal behaviors, such as some consistent behaviors when they seek medical treatments. To address these issues, we combined a MapReduce distributed computing model and association rule mining to propose a medical cluster behavior detection algorithm based on frequent pattern mining. It can detect certain consistent behaviors of patients in medical treatment activities. By analyzing 1.5 million medical claim records, we have verified the effectiveness of the method. Experiments show that this method has better performance than several benchmark methods.

Vegetation dynamics in arid and semi-arid regions have an important impact on carbon cycle, water cycle, and energy exchange at local, regional, and global scales. Therefore, it is of great significance for scientists to grasp the changes of vegetation cover in arid and semi-arid regions timely and accurately. Based on this, the applicability of ESTARFM model in the complex terrain area of arid and semi-arid Xinjiang was explored using Landsat and MODIS data fusion, and the overall change characteristics of vegetation cover (FVC) and the distribution and change patterns of different terrains in the study area in the past 15 years were analyzed by combining the dimidiate pixel model, unary linear regression and digital elevation model. The results show that: (1) the NDVI data fused by ESTARFM Model has high consistency with the real NDVI data, and it can be used for subsequent FVC estimation. (2) From 2006 to 2020, the inter FVC was at a high level as a whole, and the average annual FVC showed a weak increasing trend in fluctuation; there are obvious differences in spatial distribution, which is characterized by high distribution in the north and low in the south. (3) The improved area of vegetation cover in the study area is greater than the degraded area, accounting for 52.3% and 47.7% respectively; (4) In the elevation range of 2000 to 3500 m, the FVC showed a slight degradation trend on 25° to 45° slopes and south and southeast slopes, and the rest showed a slight improvement trend. ESTARFM-based model enables monitoring of vegetation cover changes in complex terrain areas of the arid and semi-arid regions in Xinjiang over a long time series. The overall FVC level in the study area is high, and there both are serious degradation and improvement phenomena.

This paper intends to clarify that the spatial and temporal evolutionary patterns of regional vegetation and their relationship with climate form a premise of ecological conservation and environmental governance, and play an important role in maintaining regional ecosystem balance and promoting sustainable development. Based on measured data collected from NDVI remote sensing products and meteorological stations, NDVI variation in Anhui Province from 2001 to 2019 was determined through trend analysis and measurement methods involving coefficient of variation and Hurst index; in addition, the response to climatic factors was also explored. It was concluded that, firstly, in terms of spatiotemporal analysis, the interannual variation of NDVI in Anhui Province showed an increasing trend with a rate of 0.024/10 a, while the monthly variation showed a weak bimodal pattern, with the highest value in August and the lowest value in January. Furthermore, NDVI in Anhui Province showed significant spatial heterogeneity, with high values concentrated in mountainous regions in southern Anhui and Dabie Mountain region, and low values concentrated in the hilly areas of Jianghuai and areas along the Yangtze River. At the same time, the overall spatial variation of NDVI showed an increasing trend, and the areas with extremely significant and significant improvement in vegetation coverage accounted for 54.69% of the total area of Anhui Province. Secondly, in terms of the analysis on variation characteristics, the variation of NDVI in Anhui Province was generally stable, with an average CV coefficient of variation of 0.089, which, however, was quite different in different regions; meanwhile, the future trend of NDVI variation in the study areas was mostly in a random manner. Thirdly, the response of NDVI in Anhui Province to climatic factors showed significant spatial heterogeneity. NDVI was found to be positively correlated with precipitation and negatively correlated with temperature; in general, the impact of precipitation on NDVI was greater than that of temperature. In the 19 years studied, NDVI in Anhui Province showed an increasing trend; and climate, topography and human activities led to heterogeneous spatial distribution of vegetation. Therefore, in the future, the evolutionary trend of vegetation will be relatively random, and NDVI will be more greatly affected by temperature, than by precipitation.

Monitoring the lake waterbody area in the Qinghai–Tibet Plateau (QTP) is significant in dealing with global climate change. The latest released Landsat-9 data, which has higher radiation resolution and can be complemented with other Landsat data to improve imaging temporal resolution, have great potential for applications in lake area extraction. However, no study is published on identifying waterbodies and lakes in large-scale plateau scenes based on Landsat-9 data. Therefore, we relied on the Google Earth Engine (GEE) platform and selected ten waterbody extraction algorithms to evaluate the quantitative evaluation of waterbody and lake area extraction results on the QTP and explore the usability of Landsat-9 images in the relationship between the extraction accuracy and the algorithm. The results show that the random forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average lake waterbody area extraction error is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithm. The linear regression coefficients of NDVI and reflectance of Landsat-8 and Landsat-9 data are close to 1, and R2 is more significant than 0.91. At the same time, the overall accuracy difference of water extraction between the two data is not better than 1.1%. This study proves that Landsat-9 and Landsat-8 data have great consistency, which can be used for collaborative analysis to identify plateau waterbodies more efficiently. With the development of cloud computing technologies, such as Gee, more complex models, such as RF, can be selected to improve the extraction accuracy of the waterbody and lake area in large-scale research.

Cloud and snow identification in remote sensing images is critical for snow mapping and snow hydrology research. Aimed at the problem that the semantic segmentation model is prone to producing blurred boundaries, slicing traces and isolated small patches for cloud and snow identification in high-resolution remote sensing images, the feasibility of combining DeepLab v3+ and conditional random field (CRF) models for cloud and snow identification based on GF-1 WFV images is studied. For GF-1 WFV images, the model training and testing experiments under the conditions of different sample numbers, sample sizes and loss functions are compared. The results show that, firstly, when the number of samples is 10,000, the sample size is 256 × 256, and the loss function is the Focal function, the model accuracy is the optimal and the Mean Intersection over Union (MIoU) and the Mean Pixel Accuracy (MPA) reach 0.816 and 0.918, respectively. Secondly, after post-processing with the CRF model, the MIoU and the MPA are improved to 0.836 and 0.941, respectively, compared with those without post-processing. Moreover, the misclassifications such as blurred boundaries, slicing traces and isolated small patches are significantly reduced, which indicates that the combination of the DeepLab v3+ and CRF models has high accuracy and strong feasibility for cloud and snow identification in high-resolution remote sensing images. The conclusions can provide a reference for high-resolution snow mapping and hydrology applications using deep learning models.

Drought causes a major constraint on plant growth, development, and crop productivity. Drought stress enhances the synthesis and mobilization of the phytohormone abscisic acid (ABA). Enhanced cellular levels of ABA promote the production of reactive oxygen species (ROS), which in turn induce anion channel activity in guard cells that consequently leads to stomatal closure. Although Cyclophilins (CYPs) are known to participate in the biotic stress response, their involvement in guard cell ABA signaling and the drought response remains to be established. The Arabidopsis thaliana gene ROC3 encodes a CYP. Arabidopsis roc3 T-DNA mutants showed a reduced level of ABA-activated S-type anion currents, and stomatal closure than wild type (WT). Also, roc3 mutants exhibited rapid loss of water in leaf than wild type. Two complementation lines of roc3 mutants showed similar stomatal response to ABA as observed for WT. Both complementation lines also showed similar water loss as WT by leaf detached assay. Biochemical assay suggested that ROC3 positively regulates ROS accumulation by inhibiting catalase activity. In response to ABA treatment or drought stress, roc3 mutant show down regulation of a number of stress responsive genes. All findings indicate that ROC3 positively regulates ABA-induced stomatal closure and the drought response by regulating ROS homeostasis and the expression of various stress-activated genes.

Understanding historical and future drought patterns is crucial to acclimation and the mitigation of drought. The negative impact of China’s droughts on the social economy has attracted attention; however, there is still no comprehensive or long-term monitoring pattern for future droughts. Here we evaluated the precipitation and temperature simulation capability of Coupled Model Intercomparison Project Phase 6 (CMIP6) and evaluated the temporal and spatial pattern of droughts during 1961–2099 across China. The results show that the multi-model ensemble mean (MME) is more representative of the observed precipitation and temperatures across China than the single climate model. China experienced an overall drying trend in the historical period. After 1991, the drought frequency (DF), drought duration (DD), and drought intensity (DI) in the northwest of the Inland River Basin and in the Yangtze River Basin increased significantly. Compared with the historical period, China will suffer more frequent drought events, although the DD and DI will be weakened under SSP1-2.6 and SSP2-4.5, while China will experience longer DD and more serious drought events under SSP3-7.0 and SSP5-8.5. The Hai River Basin and Huai River Basin are expected to have more serious drought trends in summer. Compared with historical periods, the drought trend will increase by 2.9–5.7 times and 1.1–4.2 times, respectively. The results can be used for decision making regarding future drought control.

Plant stomata which consist of a pair of guard cells, are not only finely controlled to balance water loss as transpiration and CO2 absorption for photosynthesis, but also serve as the major sites to defend against pathogen attack, thus allowing plants to respond appropriately to abiotic and biotic stress conditions. The regulatory signaling network for stomatal movement is complex in nature, and plant peptides have been shown to be involved in signaling processes. Arabidopsis secreted peptide PIP1 was previously identified as an endogenous elicitor, which induced immune response through its receptor, RLK7. PIP1-RLK7 can activate stomatal immunity against the bacterial strain Pst DC3118. However, the molecular mechanism of PIP1 in stomatal regulation is still unclear and additional new factors need to be discovered. In this study, we further clarified that PIP1 could function as an important regulator in the induction of stomatal closure. The results showed that PIP1 could promote stomata to close in a certain range of concentrations and response time. In addition, we uncovered that PIP1-RLK7 signaling regulated stomatal response by activating S-type anion channel SLAC1. PIP1-induced stomatal closure was impaired in bak1, mpk3, and mpk6 mutants, indicating that BAK1 and MPK3/MPK6 were required for PIP1-regulated stomatal movement. Our research further deciphered that OST1 which acts as an essential ABA-signaling component, also played a role in PIP1-induced stomatal closure. In addition, ROS participated in PIP1-induced stomatal closure and PIP1 could activate Ca2+ permeable channels. In conclusion, we reveal the role of peptide PIP1 in triggering stomatal closure and the possible mechanism of PIP1 in the regulation of stomatal apertures. Our findings improve the understanding of the role of PIP1 in stomatal regulation and immune response.

Objective This study aimed to investigate the effects of enrichment resources (a perch, dustbath, and nest) layout in furnished laying-hen cages (FC) on exterior quality of eggs. Methods One hundred and sixty-eight (168) Hy-Line Brown laying hens at 16 weeks of age were randomly distributed to four treatments: small furnished cages (SFC), medium furnished cages type I (MFC-I), medium furnished cages type II (MFC-II), and medium furnished cages type III (MFC-III). Each treatment had 4 replicates or cages with 6 hens for SFC (24 birds for each SFC) and 12 hen/cage for MFC-I, -II, and -III (48 birds for each MFC-I, -II and -III). Following a 2-week acclimation, data collection started at 18 weeks of age and continued till 52 weeks of age. Dirtiness of egg surface or cracked shell as indicators of the exterior egg quality were recorded each week. Results The results showed that the proportion of cracked or dirty eggs was significantly affected by the FC type (p

An important factor influencing corporate finance and economic growth in China lies in its government sponsored industrial policies. Examining China’s five-year plans during 1991–2010, we find that state-owned firms in government supported industries enjoy faster growth in initial public offerings and higher offer prices. Further, they enjoy faster growth in loans granted by major national banks. However, this preferential access to capital by state-owned firms appears to be achieved at the expense of non-state-owned firms which are crowded out. Government support induces more investment but also brings more overinvestment, which mainly comes from the non-state sector. Finally, supported industries have higher stock market returns and cash flow growth that dampen when state ownership increases.

This study was conducted to compare the effects of layout of furniture (a perch, nest, and sandbox) in cages on behavior and welfare of hens. Two hundred and sixteen Hyline Brown laying hens were divided into five groups (treatments) with four replicates per group: small furnished cages (SFC), medium furnished cages type I (MFC-I), medium furnished cages type II (MFC-II), and medium furnished cages type III (MFC-III) and conventional cages (CC). The experiment started at 18 week of age and finished at 52 week of age. Hens’ behaviors were filmed during the following periods: 8:00 to 10:00; 13:00 to 14:00; 16:00 to 17:00 on three separate days and two hens from each cage were measured for welfare parameters at 50 wk of age. The results showed that feeding and laying of all hens showed no effect by cage type (p>0.05), and the hens in the furnished cages had significantly lower standing and higher walking than CC hens (p0.05). The hens in MFC-I, −II, and −III showed a significant higher socializing behavior than SFC and CC (p

Carbonate reservoirs, especially dolomite reservoirs, contain large reserves of oil and gas. The complex diagenesis is quite challenging to document the dolomite reservoirs formation and evolution mechanism. Porosity development and evolution in dolomite reservoirs primarily reflect the comprehensive effect of mineral dissolution/precipitation during dolomitization. In this study, multicomponent multiphase flow and solute transport simulation was employed to investigate dolomitization in the deep carbonate strata of the Tarim Basin, Northwest China, where active exploration is currently under way. One- and two-dimensional numerical models with various temperatures, fluid compositions and hydrodynamic characteristics were established to quantificationally study dolomitization and its effect on porosity. After determining the main control factors, detailed petrologic characteristics in the studied area were also analyzed to establish four corresponding diagenetic numerical models under different sedimentary environments. These models enabled a systematic analysis of mineral dissolution/precipitation and a quantitative recovery of porosity evolution during various sedimentation-diagenesis processes. The results allowed for a quantitative evaluation and prediction of reservoir porosity, which would provide a basis for further oil and gas exploration in deep carbonate reservoirs.

This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its accessible node subset is globally reachable. The other is a consensus robust Kalman filter to estimate the system state based on the system matrix estimations and covariances. It is proved that the mean square estimation error of each sensor is upper-bounded by the trace of its covariance. An explicit sufficient stability condition of the algorithm is further provided. A numerical simulation is given to illustrate the results.

This paper investigates the influence of the economic value added (EVA) performance evaluation, issued in 2010 by the State-owned Assets Supervision and Administration Commission of the State Council, on the value of the cash holdings of central state-owned enterprises (CSOEs). We find that EVA performance evaluation has some influence on the overinvestment of CSOE cash holdings and significantly increases the value of CSOE cash holdings compared with the cash holdings of local state-owned enterprises. The greater value of CSOE cash holdings derives from underinvestment modification and overinvestment restraint. The value of cash holdings increases more for companies with better accounting performance. Thus, the EVA performance evaluation policy increases CSOE efficiency. This study contributes to the emerging literature related to cash holdings and the economic consequences of the EVA performance evaluation policy. It expands the literature related to investor protection in countries experiencing economic transition.

This study aimed to propose a mapping framework with entropy-based metrics for validating the effectiveness of the transition between International Classification of Diseases 10th revision (ICD-10)-coded datasets and a new context of ICD-11. Firstly, we used tabular lists and mapping tables of ICD-11 to establish the framework. Then, we leveraged Shannon entropy to propose validation methods to evaluate information changes during the transition from the perspectives of single-code, single-disease, and multiple-disease datasets. Novel metrics, namely, standardizing rate (SR), uncertainty rate (UR), and information gain (IG), were proposed for the validation. Finally, validation results from an ICD-10-coded dataset with 377,589 records indicated that the proposed metrics reduced the complexity of transition evaluation. The results with the SR in the transition indicated that approximately 60% of the ICD-10 codes in the dataset were unable to map the codes to standard ICD-10 codes released by WHO. The validation results with the UR provided 86.21% of the precise mapping. Validation results of the IG in the dataset, before and after the transition, indicated that approximately 57% of the records tended to increase uncertainty when mapped from ICD-10 to ICD-11. The new features of ICD-11 involved in the transition can promote a reliable and effective mapping between two coding systems.

This paper focuses on the effect of relative performance evaluation (RPE) on top managers’ compensation in Chinese public firms. Overall, we find no evidence of an RPE effect or any asymmetry in firms’ use of RPE. The results obtained using Albuquerque’s (2009) method are similar to those obtained using traditional methods. In addition, we find that RPE is used more in non-SOEs than in SOEs. This may be due to the regulation of compensation, various forms of incentives and the multiple tasks of managers in SOEs.

Using a sample of state-owned enterprises (SOEs) listed on the Shanghai and Shenzhen Stock Exchanges during the 1999–2009 period, we investigate the effects of overemployment on executives’ pay-for-performance sensitivity (PPS) and analyze how the behavior of firms with high/low PPS affects the number of surplus employees. We find the existence of a redundant workforce significantly weakens PPS and the role of accounting measures in performance assessment. In contrast to prior literature, we find that higher PPS is associated with a stronger incentive to lay off redundant employees and to limit future employee numbers. We also find that weaker government intervention strengthens managerial control over the future size of the workforce. Finally, our findings suggest that a heavier government policy burden on SOEs leads to lower tax rates and more government gains.

Since the opening of China’s securities market, there have been a number of bull and bear cycles. This paper discusses how executives use the market timing approach to manage earnings in different cycles to maximize firm value. We find that Chinese listed companies choose to release more earnings during bull markets and this phenomenon is more evident in companies that are more profitable and have higher valuations. We also find that executives who do not release more earnings during bull markets are more likely to be dismissed.

Firms in China have faced high political costs during China’s economic transition, because they are affected by macroeconomic policies. However, research to date has offered no consistent conclusions on the relationship between political costs and earnings management in China. This study tests whether real estate firms attempt to decrease earnings during periods of macroeconomic control, using variables related to the national real estate market as proxies for political costs. We find that political costs are negatively related to earnings management in listed real estate firms. In addition, we find that non-state-owned enterprises utilized more income-decreasing accruals during this period. Our results are consistent with the political costs hypothesis.

The extant literature offers extensive support for the significant role played by institutions in financial markets, but implicit regulation and monitoring have yet to be examined. This study fills this void in the literature by employing unique Chinese datasets to explore the implicit regulation and penalties imposed by the Chinese government in regulating the initial public offering (IPO) market. Of particular interest are the economic consequences of underwriting IPO deals for client firms that violate regulatory rules in China’s capital market. We provide evidence to show that the associated underwriters’ reputations are impaired and their market share declines. We further explore whether such negative consequences result from a market disciplinary mechanism or a penalty imposed by the government. To analyze the possibility of a market disciplinary mechanism at work, we investigate (1) the market reaction to other client firms whose IPO deals were underwritten by underwriters associated with a violation at the time the violation was publicly disclosed and (2) the under-pricing of IPO deals undertaken by these underwriters after such disclosure. To analyze whether the government imposes an implicit penalty, we examine the application processing time for future IPO deals underwritten by the associated underwriters and find it to be significantly longer than for IPO deals underwritten by other underwriters. Overall, there is little evidence to suggest that the market penalizes underwriters for the rule-violating behavior of their client firms in China. Instead, the Chinese government implicitly penalizes them by imposing more stringent criteria on and lengthening the processing time of the IPO deals they subsequently underwrite.

Regulatory agencies may, whether outside of set rules or within their discretion, depart from the original goals or principles set for enforcing the rules, which we term selective enforcement. Taking China, a country in transition, as an example, and using cases and large-sample tests, we present empirical evidence of selective enforcement. The results show that the China Securities Regulatory Commission (CSRC) takes into account whether companies violating the rules have a state-owned background and the strength of that background when investigating and punishing non-compliance. After controlling for the degree of violation, state-owned-enterprises (SOEs) are punished less severely than private companies; and the higher the hierarchy of the SOE in question, the less severe the punishment. It also takes longer for SOEs to be punished. We also find that companies that violate the rules less seriously have a greater tendency to apply for refinancing than those that violate the rules more seriously. This may be because the severity of the violation can affect listed companies’ expectations of obtaining refinancing. The analysis and conclusions of this study prove useful in understanding the causes and consequences of selective enforcement in transition economies.

Online health communities (OHCs) should utilize health-care knowledge for enhancing online patient support. To examine the use of existing OHCs to identify the challenges and strategies of enhancing online patients’ decision-making support, we conducted a descriptive study to evaluate the information availability, user availability and knowledge usability in 100 carefully-selected health-related websites. On the basis of criteria for effective OHCs, we used three evaluation instruments for health-care professionals to review and score the websites. Questionnaire results were examined from the perspective of information, user and knowledge support. Results corroborate that over 80% of the websites facilitate effective social functions, whereas only 33% provide health-care decision-making services to online patients. Approximately 46% of them satisfy four or five effective OHCs’ criteria. Three of them only offer the functions of patients’ charts and journals to support health data management. Although the existing OHCs are facilitated with good social interaction and support, only a few can assist patients in making effective health-care decisions, not to mention properly using health-care knowledge support.

The management of future financial risk on the part of managers and changes in firm finances are two of the fundamental reasons for upward and downward rigidity of wages. The proxy variable for firm financial risk is volatility, the past performance of which is among the principal indicators of wage rigidity. In firms whose current performance is on the upswing, the greater the volatility in past performance, the smaller the elasticity ratio and the more acute the upward rigidity; the more stable past performance, the larger the elasticity ratio and the more acute the upward elasticity. In firms in which current performance is declining, greater past performance volatility leads to a larger elasticity ratio and more acute downward rigidity, whereas more stable such performance leads to a smaller elasticity ratio and more acute downward rigidity.

In a changing transition economy, Chinese government regulations that adopt the relatively simple bright line rule formula are enforceable in practice. Taking the early reform-oriented policies of the China Securities Regulatory Commission (CSRC) as an example, we find that the CSRC did not consider local enthusiasm for reform when allocating IPO resources because of the high enforcement costs involved. We also find that CSRC listed company regulations were enforced due to the lower costs involved in verifying regulatory violations, and that listed companies that completed the reform process were given priority in public refinancing. We present empirical evidence supporting the theoretical basis for the hypotheses outlined above. We also conclude that companies that completed the reform process in 2005 were of significantly higher quality and that the SEO regulation did not affect stock market efficiency. These findings enhance our understanding of the efficiency of government regulation in a transition economy.

In plants, innate immune responses are initiated by plasma membrane-located pattern recognition receptors (PRRs) upon recognition of elicitors, including exogenous pathogen-associated molecular patterns (PAMPs) and endogenous damage-associated molecular patterns (DAMPs). Arabidopsis thaliana produces more than 1000 secreted peptide candidates, but it has yet to be established whether any of these act as elicitors. Here we identified an A. thaliana gene family encoding precursors of PAMP-induced secreted peptides (prePIPs) through an in-silico approach. The expression of some members of the family, including prePIP1 and prePIP2, is induced by a variety of pathogens and elicitors. Subcellular localization and proteolytic processing analyses demonstrated that the prePIP1 product is secreted into extracellular spaces where it is cleaved at the C-terminus. Overexpression of prePIP1 and prePIP2, or exogenous application of PIP1 and PIP2 synthetic peptides corresponding to the C-terminal conserved regions in prePIP1 and prePIP2, enhanced immune responses and pathogen resistance in A. thaliana. Genetic and biochemical analyses suggested that the receptor-like kinase 7 (RLK7) functions as a receptor of PIP1. Once perceived by RLK7, PIP1 initiates overlapping and distinct immune signaling responses together with the DAMP PEP1. PIP1 and PEP1 cooperate in amplifying the immune responses triggered by the PAMP flagellin. Collectively, these studies provide significant insights into immune modulation by Arabidopsis endogenous secreted peptides.

Endoplasmic reticulum (ER)-associated degradation (ERAD) plays key roles in controlling protein levels and quality in eukaryotes. The Ring Finger Protein 185 (RNF185)/membralin ubiquitin ligase complex was recently identified as a branch in mammals and is essential for neuronal function, but its function in plant development is unknown. Here, we report the map-based cloning and characterization of Narrow Leaf and Dwarfism 1 (NLD1), which encodes the ER membrane-localized protein membralin and specifically interacts with maize homologs of RNF185 and related components. The nld1 mutant shows defective leaf and root development due to reduced cell number. The defects of nld1 were largely restored by expressing membralin genes from Arabidopsis thaliana and mice, highlighting the conserved roles of membralin proteins in animals and plants. The excessive accumulation of ß-hydroxy ß-methylglutaryl-CoA reductase in nld1 indicates that the enzyme is a membralin-mediated ERAD target. The activation of bZIP60 mRNA splicing-related unfolded protein response signaling and marker gene expression in nld1, as well as DNA fragment and cell viability assays, indicate that membralin deficiency induces ER stress and cell death in maize, thereby affecting organogenesis. Our findings uncover the conserved, indispensable role of the membralin-mediated branch of the ERAD pathway in plants. In addition, ZmNLD1 contributes to plant architecture in a dose-dependent manner, which can serve as a potential target for genetic engineering to shape ideal plant architecture, thereby enhancing high-density maize yields. [ABSTRACT FROM AUTHOR]