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With the opening of the Stock Connect programs, the mainland China and Hong Kong stock markets are becoming more closely linked. In this paper, we develop a China's stock market risk early warning system. The proposed early warning system consists of three components. First, we use value at risk (VaR) to identify the stock market risk in which stock market risk is divided into multiple categories instead of two categories. Second, we construct a comprehensive indicator system in which basic indicators, technical indicators, overseas return rate indicators, and macroeconomic indicators are considered simultaneously. Third, we use four machine learning models, namely long short-term memory (LSTM), gate recurrent unit (GRU), multilayer perceptron (MLP), and EXtreme Gradient Boosting algorithm (XGBoost), to predict China's stock market risk. Experimental results show that: (1) Considering the macroeconomic indicators and basic indicators of Shanghai Composite Index (SSEC), ShenZhen Component Index (SZCZ) and Hang Seng Index (HSI) can significantly improve the performance of predicting China's stock market risk. (2) The opening of SH-HK Stock Connect program improves the predictive performance, but the opening of SZ-HK Stock Connect program decreases the predictive performance. (3) The indicators related to Hong Kong become more important after the SZ-HK Stock Connect program. [ABSTRACT FROM AUTHOR]

This transition from gathering to cultivation is a significant aspect of studying early agricultural practices. Fruit trees are an essential component of food resources and have played a vital role in both ancient and modern agricultural production systems. The jujube (Ziziphus jujuba Mill.), with its long history of cultivation in northern China, holds great importance in uncovering the diet of prehistoric humans and understanding the origins of Chinese agricultural civilization. This paper focuses on the domestication of jujube by analyzing the morphology of jujube stones found in three Neolithic sites in northern China's Qi River basin, Zhujia, Wangzhuang, and Dalaidian. The measurements of these jujube kernels are compared with those found in other areas of northern China, as well as modern jujube kernels that were collected. The measurements revealed that the length-to-diameter (L/D) ratio of sour jujube kernels ranged from 1.36 to 1.78, whereas the L/D ratio of cultivated jujube stones varied between 1.96 and 4.23. Furthermore, jujube stones obtained from Zhujia and Wangzhuang sites exhibit pointed ends and possess an elongated oval or narrow oval shape overall, which is indicative of clearly artificial domestication traits. Therefore, this study suggests that jujube was selected and cultivated as an important food supplement in the Qi River basin no later than around 6200 BP. [ABSTRACT FROM AUTHOR]

Currently, a standardized design and calculation specification for suction drum foundations has yet to exist in China. The engineering design currently depends mainly on the subjective understanding and engineering experience of the designers, which can be considered somewhat blind and subjective. In this paper, we utilize the offshore wind power project in Yangjiang City, Guangdong Province, as our case study. Building upon domestic and international research results, relevant investigations, design specifications, and engineering applications in related fields, we conduct a systematic study on the design calculation and construction control technology of the suction drum foundation. The document presents the design calculation and inspection of the suction drum foundation. Building on this foundation, we propose a sinking feasibility analysis method and a parameter value method for the suction drum foundation calculation. We also examine the suction drum foundation construction process, examining its control parameters, technology, and standards. Finally, based on the measured data from six four-barrel guided frame platform suction drum foundations that were successfully installed, the proposed design and control method are evaluated, and their effectiveness is verified. The results of this study can provide valuable references for the design and construction of similar suction drum foundation platforms. [ABSTRACT FROM AUTHOR]

In recent years, transformative technological advances have reshaped countless sectors, including higher education, leaving a lasting and indelible mark. Artificial intelligence (AI) and the Internet of Things (IoT) coming together has proven to be an influential driving force for the development of creative solutions to the ever-evolving challenges faced by educational institutions. Traditional classroom teaching methods have encountered unprecedented hurdles with the widespread integration of the Internet into education. China's commitment to "governing the nation by law" has elevated the need for legal expertise. As society evolves, pedagogical approaches must adapt. Information and communication technology trends, combined with the rise of the IoT, are reshaping education and offering tools to bolster campus security, monitor vital assets, and enrich classroom accessibility. This paper proposes an AI-based hybrid model and an IoT-infused teaching approach personalized for legal education in colleges and universities, employing the decision tree algorithm. The study's focal point is the development of legal professionals possessing the robust capabilities and qualities society seeks, using teaching methods compatible with modern information technology. The foundation of this endeavor lies in the hybrid teaching mode, specifically Internet-based legal research. Within this platform, educators can disseminate instructional materials, videos, and exercises, with the option to enlist teaching assistants to aid in managing student learning. Comparing varied and traditional teaching classes, the passing rates were 96.75% and 95.36%, respectively. Data analysis using decision trees identifies factors influencing these rates. The study also evaluates students' accuracy in five in-class exercises; in the mixed class, correctness rates were 96.4%, 93.5%, 82.5%, 64.2%, and 62.8%. The proposed method is compared with other machine learning algorithms (SVM, KNN, LR, RF) and Decision Trees to evaluate the educational impact of the mixed teaching approach comprehensively. [ABSTRACT FROM AUTHOR]

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the XGBoost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the XGBoost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, XGBoost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the XGBoost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naïve Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores the transformative potential of the integrated framework in revolutionizing sports medical services. [ABSTRACT FROM AUTHOR]

Encryption chips are specialized integrated circuits that incorporate encryption algorithms for data encryption and decryption, ensuring data confidentiality and security. In China, the domestic SM4 algorithm is commonly utilized, as opposed to the international AES encryption algorithm. These widely implemented encryption standards have been proven to be difficult to crack through crypt analysis methods Currently, power consumption side-channel attacks are the most prevalent method. They involve capturing power consumption data during the encryption process and subsequently recovering the encryption key from this data. The two leading methods are Differential Power Analysis (DPA) and machine learning techniques. DPA does not necessitate prior knowledge but relies heavily on the number of power consumption curves. With only 50 power consumption data points, the accuracy is a mere 80%. Machine learning methods require prior knowledge, achieving an accuracy rate above 95% with only 30 power traces, albeit with training times typically exceeding 15 min. In this paper, a distributed energy analysis attack approach was presented based on Correlation Power Analysis (CPA). The power consumption data was divided into 16 subsets, with each subset corresponding to 8 bytes of the key. By training each subset separately, the 8-byte key's corresponding power consumption data is reduced to only 100 dimensions, resulting in a 76% decrease in cracking time and a 3% improvement in cracking accuracy rate.This article also trains a more complex 256 classification model to directly crack the final key, achieving a success rate of 28% in cracking 128-bit passwords with only 1 power trace [ABSTRACT FROM AUTHOR]

Shenfu Dongsheng coal field is a cross-century energy base which is developed and constructed in China. In recent years, some mines have successively entered to the coal seam of the second layer. Due to the reasons of early mining, many coal pillars are left in the coal seam of the first layer, resulting in the phenomenon of strong ore pressure in the mining range before and after the coal pillar in the lower coal seam and even causing the buckling accident. In order to solve such safety problems, this paper takes the 22,307 working face in Bulianta coal mine as the research object, adopts physical similarity simulation experiment and theoretical analysis to systematically study the overlying rock characteristics and abnormal ore pressure manifestation mechanism of shallow and close coal seam in different working stages. The results show that the roof overburden of the key layer in the lower group bends and sinks when the coal pillar is mined, resulting in the activation and instability of the "masonry beam" structure formed by the roof of the upper coal seam. When the coal pillar is discharged, the residual concentrated coal pillar and the room type coal pillar are unstable under the action of high supporting stress, resulting in shear failure of the inter-layer rock in the upper part of 22,307 working face, causing the strong dynamic pressure of the working face to appear and then leading to the buckling accident. The working resistance of the support in each stage is obtained by establishing the structure diagram of the overlying rock under each stage and the corresponding mechanical structure model. Finally, the working resistance required by the support in the mining stage under the goaf is 16,692.6 kN, the working resistance required by the support in the coal pillar stage is 19,692.6 kN, the working resistance required by the support in the mining stage under the concentrated coal pillar is 13,150.6 kN, and the working resistance required by the support in the coal pillar stage is 19,215.6 kN. [ABSTRACT FROM AUTHOR]

Underground coal mining leads to land subsidence, which, in turn, results in damage to buildings and infrastructure, disturbs the original ecological environment, and hinders the sustainable development of coal mining cities. A reasonable estimation of land subsidence, on the other hand, is the foundation for building protection, land reclamation, and ecological environment reconstruction. However, when we applied the existing land subsidence estimation theory to the deep mining areas of the Ordos coalfield in western China, there was a significant deviation between the estimations and the measurements. To explain such unusual case, we propose using the overburden's average GSI (Geological Strength Index) value instead of the compressive strength (UCS) of rock specimens for a better representation of the overburden's overall properties. By using on-site subsidence monitoring results and historical data, we provided evidence which supports that the overburden's average GSI value has a much greater impact on subsidence rates than the UCS. Subsequently, we investigated the relationship between three typical overburden's GSI values and the subsidence rates via a calibrated numerical model, revealing the variation patterns of maximum surface subsidence when the overburden's average GSI value is set at 30, 50, and 75, respectively. Finally, on the basis of the measured and simulated results, we discussed a non-conventional strip mining method for mining subsidence control in the deep mining areas of the Ordos coalfield in western China, and explained why it is possible and what are the significant advantages behind. The proposed methods, findings, and suggestions in this paper are therefore quite helpful for researchers and engineers who wish to estimate and control the mining-induced land subsidence, as well as for those who are particularly interested in the study of environment science related to land subsidence. [ABSTRACT FROM AUTHOR]

The formation of Carlin-type gold deposits is poorly constrained, mainly due to the lack of accurate ages for gold mineralization. The Youjiang basin, SW China, represents the world's second-largest Carlin-type gold province after NV, USA. In this paper, we use hydrothermal apatite U–Th–Pb dating to systemically determine the mineralization ages of five Carlin-type gold deposits, namely, Shuiyindong, Jinfeng, Zimudang, Linwang, and Jinya, in the Youjiang basin. The hydrothermal apatite is characterized by intimate intergrowth with gold-bearing sulfides, presence of primary fluid inclusions, irregular morphology and cathodoluminescence zoning, and MREE-enriched composition patterns, indicating that it is clearly associated and coeval with gold mineralization. The concentrations of lattice-bound U and Th in the apatite are highly variable, making it suitable for in situ secondary ion mass spectrometry (SIMS) U–Th–Pb dating. The dating results of hydrothermal apatite from the five deposits show that they formed at ca. 150–140 Ma, contemporary with the widespread felsic magmatism and associated W–Sn mineralization to the east of the Youjiang basin, probably in response to slab breakup of the Paleo-Pacific oceanic plate beneath the South China continental plate. Our case story suggests that hydrothermal apatite could be a robust chronometer for age dating of sedimentary rock-hosted gold mineralization worldwide. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

Fritillaria taipaiensis P. Y. Li is one of the biological sources for Fritillariae Cirrhosae Bulbus. Its bulbs are widely used for treating respiratory diseases such as pneumonia, bronchitis and influenza. Cultivated F. taipaiensis suffers from many diseases during its growing season. Leaf spot is a destructive disease that is increasingly affecting F. taipaiensis and can cause an incidence of up to 30% in severe cases. Leaf spot inhibits the growth of F. taipaiensis by causing disease spots on the surface of leaves. In severe cases, these spots can result in leaf desiccation and blackspot formation at the lesion site, leading to a decrease in photosynthesis. Leaf spot has shown little benefit, and it can even result in a reduced yield of bulbs and the death of plants. According to previous studies, Alternaria alternata has been identified as the pathogen of leaf spot in many medicinal plants, but the main pathogens of the leaf spot of F. taipeiensis remains uncertain. In this paper, five isolates from diseased leaves of F. taipaiensis were isolated and purified and the pathogenicity test showed that isolates B-5 and B-7 induced leaf spot symptoms on healthy F. taipaiensis leaves. Integrating multiple phylogenetic analyses of rDNA using Internal transcribed spacer region (ITS), Beta-tubulin (TUB2), RNA polymerase II second largest subunit (RPB2) and Translation elongation factor 1-alpha (TEF1-a) primers, strain B-5 and strain B-7 were eventually identified as Didymella segeticola and A. alternata. This is also the first report on the pathogens that cause leaf spot in F. taipaiensis in China. [ABSTRACT FROM AUTHOR]

There are many types of atmospheric heat engines in land-air systems. The accurate definition, calculation and interpretation of the efficiency of atmospheric heat engines are key to understanding energy transfer and transformation of land-air systems. The atmosphere over the Qinghai-Tibet Plateau (QTP) in summer can be regarded as a positive heat engine. The study of the heat engine efficiency is helpful to better understand land-air interaction and thermal-dynamic processes on the QTP. It also provides a new perspective to explain the impact of the QTP on the climate of China, East Asia and even the world. In this paper, we used MOD08 and ERA5 reanalysis data to calculate the atmospheric heat engine efficiency, surface heat source and atmospheric heat source on the QTP in summer (May to September) from 2000 to 2020. The average atmospheric heat engine efficiency on the QTP in summer from 2000 to 2020 varies between 1.2% and 1.5%, which is less than 1.6%; the heat engine efficiency in summer is higher than that in June, July and August; the Qaidam Basin is the region with the highest atmospheric heat engine efficiency, followed by the western QTP. The mean surface heat source on the QTP in summer from 2000 to 2020 is 96.0 W m−2, the atmospheric heat source is 90.7 W m−2, and the release of precipitation condensation latent heat is the most important component of the atmospheric heat source on the QTP in summer. There is a strong and significant positive correlation between the atmospheric heat engine efficiency and the surface heat source on the QTP in summer. The precipitation con densation latent heat is the most important component of the atmospheric heat source in summer and can reflect the precipitation process. There is a strong and significant negative correlation between the atmospheric heat engine efficiency and the atmo spheric heat source on the QTP in summer. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

The gravity flow sedimentation of lakes is characterized by diversity and can vary greatly on a small spatial scale, leading to extremely complex diagenesis. This complexity makes it difficult to accurately classify them using conventional diagenetic methods. This paper focuses on the study of gravity flow sedimentary units in the Dongying, the diagenetic facies were classified into five different types by using a novel principal component analysis method. Based on this, the diagenetic evolution of rocks was characterized, and the reservoir quality influence factors were clarified. First, detailed characterizations were conducted on the compositional characteristics of the two main reservoir types (feldspathic sandstone and lithic feldspathic sandstone), along with their diagenetic processes, including compaction, cementation, dissolution, and metasomatism. Then, the diagenetic control factors of gravity flow sandstone were clarified. It was found that the compaction and cementation had the greatest impact on rock porosity, had the presence of a large amount of clay cement leading to pore deterioration. Finally, a principal component analysis model was used to identify five types of diagenetic facies in the study area and characterize their distribution patterns, providing evidence for a deeper understanding of the sedimentary evolutionary sequence. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

Due to the periodic changes of climate, cracks are widely developed in expansive soils, leading to the destruction of soil integrity, the deterioration of physical strength, and eventually the instability of the expansive soil slope and other disasters. In this paper, a deep learning-based modeling method was proposed for soil crack networks characterization through steps of segmentation, quantification and simulation. Inspired by the U-Net convolutional neural network, a dilated convolution module was added to the backbone to enhance the crack segmentation capability and a subpixel edge detection algorithm was followed for accurate crack edge detection. Then, a deterministic or stochastic method was designed for crack network simulation. A case study of an expansive soil slope on the bank of Wadong Main Canal in the Pi-Shi-Hang Irrigation District, China, was conducted. Results show that the dilated U-Net model gained 0.24 and 0.25 improvement in F1-score and IoU (Intersection over Union) comparing to the conventional segmentation method (Otsu) and crack edge precision was further improved by 5.38%. The performances of proposed method including the image labeling, effect of crack thickness and environmental conditions, etc., are also explored. To validate the simulated crack network, crack areas at different depths were measured through X-ray images. Comparing with the simulation result, mean error rate of 1.05% was achieved. Thus, the proposed method can efficiently characterize soil crack networks using the field acquired crack images. [ABSTRACT FROM AUTHOR]

The instability control of deep mining roadway is currently dominated by ejection and grouting, as well as floor support, which contradicts the economic and excavation efficiency of production. Taking the failure site of Xin'an Coal Mine in China as an example, this paper systematically analyzes the failure characteristics of supporting components, crack propagation, and surrounding rock deformation in situ, and the impact of failure on the rights and interests of staff, enterprise benefits, and low-carbon environmental protection. Then, a three-way continuous anchorage theory and the derived control technology are proposed based on the exploration of the instability mechanism. Besides, the UDEC Trigon model is adopted to reproduce the instability process and demonstrate the effect of the proposed control technology. According to the subsequent industrial test, the plastic deformation developing from shallow to deep parts leads to the bending, subsiding, swelling, and deformation of rock mass within 5.5 m of the roof, and the surrounding rock failure mainly features the shear failure in the middle of the tail of the supporting components and anchorage relaxation. Meanwhile, it wrecks huge havoc on the rights and interests of staff, enterprise benefits, and CO 2 emission. Following the smooth application of the new technology to roadway excavation, the roof subsidence and two side displacements are reduced by 91.18% and 58.5%, respectively, compared with the original scheme, the rock mass fractures within 1.71 ~ 3.92 m of the roof is effectively sealed, the evolution depth of bed separation is down by 78.6%, and the excavation efficiency rises by 50% while ensuring the supporting cost. In addition, the defects of the new technology in engineering application are also discussed. Accordingly, the R&D and application of this new technology provide a positive reference for the efficiency control of deep mining roadway. Highlights: A three-way continuous anchorage theory and relevant technique are proposed. Multiscale original position analyses are conducted on the failure features of deep coal roadway. The impact of surrounding rock instability on sustainability was considered for the first time. The new scheme reduces roof subsidence by 91.2% and sides' displacement by 58.5%. [ABSTRACT FROM AUTHOR]

Hard roof's 3D breaking characteristics is vital for mining pressure control. However, the research concerning a longwall face that employs an innovative roof-cutting technology via a chainsaw arm machine (RCTCAM) is scant. To address this gap, this paper thoroughly analyzes RCTCAM's core equipment and roof-cutting procedures. Firstly, the thin elastic plate theory and the superposition principle were applied to establish the first and periodic break mechanical models of hard roofs using the RCTCAM. These mechanical models elucidated the distribution laws of the first and third principal bending moments in a hard roof. Following this, based on tensile failure criteria and crack expansion criteria, the study uncovered that the hard roof's first break transitioned from an 'O–X' shape to a 'U–Y' shape, while its periodic break morphed from a 'C–ン' shape to an 'L–ノ' shape. This transformation led to a unique phenomenon. The rotation of arc-shaped triangular plates formed during the 'O–X' and 'C–ン' -shaped breaks resulted in severe damage to a gob-side roadway. However, a longwall face employing the RCTCAM did not produce such plates, effectively avoiding the associated damage. By implementing the RCTCAM in Workface 8311 of the Yanya Mine in China, the stress peaks of the coal pillars were reduced on average by 22.8%, and the deformation of the roadway decreased by 27.8%. These findings underline the success of the RCTCAM in achieving pressure relief for a gob-side roadway. Highlights: It was given that the theoretical calculation method for deflection, bending moment, and stress of a hard roof with the RCTCAM under the first and periodic breaks. When with the RCTCAM, hard roof's first break exhibits a transition from an 'O-X' shape to an 'U-Y' shape, while its periodic break changes from a 'C-ン' shape to an 'L-ノ' shape. Due to rotation of arc-shaped triangular plates formed in the 'O-X' and 'C-ン'-shaped breaks, a gob-side roadway bear severe damage. However, a longwall face with the RCTCAM has no such plates, thus avoiding damage. With the RCTCAM in Workface 8311 of the Yanya Mine, China, the stress peaks of a gob-side roadway's coal pillars dropped averagely by 22.8%, indicating that the RCTCAM achieve pressure relief for a gob-side roadway. [ABSTRACT FROM AUTHOR]

In recent years, the escalating global concerns surrounding climate change have placed a growing emphasis on achieving dual objectives: reducing carbon emissions and achieving carbon neutrality. Businesses and organizations are under mounting pressure to align their operations with these crucial environmental goals. This paper introduces the concept of an enterprise carbon performance evaluation index system (ECPIS) to strike a balance between economic development and environmental protection and enhance overall enterprise management and development strategies. The ECPIS framework is constructed using machine learning and advanced data mining techniques, particularly support vector machines (SVM). Its core purpose is to provide enterprises with a systematic tool to gauge, analyze, and enhance their carbon performance, addressing dual carbon objectives. ECPIS development hinges on data mining techniques, extracting insights from diverse data sources to construct a comprehensive system that accommodates these dual carbon goals' intricacies. Its methodology includes data collection, preprocessing, feature selection, and data mining algorithms to unveil vital patterns and relationships within data. It conforms to international standards, establishing a tailored carbon performance index system aligned with China's national conditions. It validates carbon-related enterprise data and employs data mining's association rules to uncover pertinent carbon performance information. The results obtained from ECPIS are auspicious, boasting an experiential accuracy rate of 97.5%. This level of accuracy surpasses that achieved by other algorithms like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Naïve Bayes (NB), and Logistic Regression (LR). ECPIS stands out by considering various factors, including carbon emissions reduction, energy consumption, supply chain efficiency, and financial performance indicators. This multifaceted approach enables enterprises to gain a comprehensive understanding of their carbon performance and identify areas for improvement. [ABSTRACT FROM AUTHOR]

This paper explores cycles in innovative outcomes corresponding with the timing of political turnover. Using data on local government officials and firm level innovation activities in China, firm innovation is found to be negatively associated with a turnover of local political leaders. We examine several potential explanations and find evidence supporting the premise that political turnover reduces firms' incentives to innovate until the uncertainty is resolved. This paper also indicates that local political turnover significantly inhibits firms' research and development investment, government subsidies, and expansion decisions, leading to less innovative outcomes. Moreover, reductions in innovation are greater in cities with higher levels of government expenditure or intellectual property rights trials, or in smaller firms or non-state-owned enterprises during the rotation of local government leaders. [ABSTRACT FROM AUTHOR]

Using deep learning in remote sensing interpretation could reduce a lot of human and material costs. Semantic segmentation is the main method for this task. It can automatically outline the objects and it has recently achieved great success in remote sensing images. However, in the appliance of remote sensing interpretation, the accuracy of contour largely determines the evaluation of remote sensing interpretation. Though the current loss functions reflect the segmentation performance, they could not guide the model to optimize itself toward a more precise contour. This paper proposed an exactly defined contour loss (CL) for remote sensing interpretation with Residual Attention U-Net (RA U-Net) as the main framework. The RA U-Net uses the residual attention module as the skip connection layer. It enhances the judgment of U-Net. In CL, image processing methods are used to extract the contours of the foreground. And elements-sum and elements-subtract operations are used to transfer the contour information to a matrix of the same size as label images. Then, these matrices would be the weights for CE. By assigning different weights for different elements in different regions, this function will guide the model to reach a balance between accurate segmentation results and precise contours. The experiment on open datasets shows a good performance. The proposed model was also trained on the Construction Disturbance Dataset collected from Jiang Xi Province, China. The dataset was labeled manually. The evaluation enhanced a lot on the Construction Disturbance Dataset and the IoU on two datasets increased 1 % to 2 % when using CL as the loss function. This paper also compared the proposed method with other state-of-the-art methods and the results showed extensive effectiveness. [ABSTRACT FROM AUTHOR]

This paper aims to explore the multidimensional poverty of the migrant workers. This paper also analyzed the migrant worker's household registration differences and multi-period analysis of the migrant workers. China Family Panel Studies (CFPS), a household and social tracking survey data conducted by the Institute of Social Science Survey, was used in this article. Income, health, education, social security, living standards, and urban intergration were considered Multidimensional Poverty. This paper combines the A-F multidimensional poverty method and Foster's duration analysis method to construct a long-term multidimensional poverty index. Our results show that the long-term multidimensional poverty situation of the migrant workers is not severe but is mainly concentrated in certain dimensions and specific indicators. However, the long-term multidimensional poverty of the migrant workers is a persistent problem. Although the situation is gradually improving, the long-term negative impact of multidimensional poverty on individuals is increasingly prominent. [ABSTRACT FROM AUTHOR]

According to the existing literature on public management, many factors affect popular satisfaction with the government (referred to as "government satisfaction"), one of which is the use of e-government. Particularly, many e-government proponents regard increasing government transparency as an important way to improve government satisfaction. To test the validity of this assertion, this paper uses an Ordinary Least Squares (OLS) regression model to analyze survey data on individuals' e-government usage frequencies in China and their satisfaction with the local government during the COVID-19 pandemic. We find that people can rationally and consciously evaluate governments and make decisions by using the government performance information that they know about. This conscious and rational process is also mixed with the impact of some irrational and emotional factors, which is consistent with the concept of the "framing effect." This paper identifies local government transparency as the mediating variable in the relationship between the use of e-government and government satisfaction and public knowledge of local government performance in COVID-19 outbreak control as the moderating variable in the relationship between local government transparency and government satisfaction. Since the mediating effect of local government transparency is significantly different from those of overall and central government transparency, government transparency at different levels plays a role of the above-mentioned "frame." [ABSTRACT FROM AUTHOR]

This paper aims to show a possible path to address the introduction of intimate digital technologies through a phenomenological and postphenomenological perspective in relation to Japanese and Chinese contexts. Digital technologies are becoming intimate, and, in Japan and China, there are already many advanced digital technologies that provide digital companions for love relationships. Phenomenology has extensive research on how love relationships and intimacy shape the subjects. At the same time, postphenomenology provides a sound framework on how technologies shape the values and meanings we have. Thus, this paper introduces two digital technologies in Japan and China (Love Plus and XiaoIce chatbot), and it analyses according to the elements proposed by phenomenology and postphenomenology. In conclusion, this paper shows how digital companions like Love Plus and XiaoIce chatbot change who we are and the values and meanings we have according to the phenomenological and postphenomenological framework. These entities might not be human, but they shape who we are as human beings and the meanings and value we give to love. [ABSTRACT FROM AUTHOR]

These days, digital art has substantially improved painting art in China's new media period through modern technology and specialized software, which has generated various digital artworks with painting effects, demonstrating their aesthetic, commercial, and cultural worth. As a result, designers must actively investigate intelligently linked and content-diverse techniques to innovate and improve digital media art. This investigation will allow the extraction of pivotal role of digital media communication and drives visual art to new heights in the modern digitized era. Based on this motivation, this paper tries to analyze the application of new media digital technology in painting creation using big data. The major goal is to obtain insights into artists' behavior using behavioral habits and a morphological algorithm in digital painting. The study investigates the design process of digital painting by analyzing the habitual forms displayed by diverse painting user groups and integrating them with user experience design concepts. Two experimental methodologies are used to investigate painting user behavior: the interview method and the observation method. The observation approach collects comprehensive user behavior data to develop a habit form model that includes a both virtual and real activity. This model employs a hierarchical scheme emphasizing various behavior aspects and concepts throughout user interactions and painting system operations. The interview method, on the other hand, captures the psychological activities involved in user behavior decision-making. The study discovers and decomposes the traits specific to these characters by defining painted characters, developing their behavior sets, and making interactive decisions. The learned information and experience are then applied in interactive decision-making processes. Based on these findings, prototype development includes explicit and implicit interaction prototypes, with usability tests determining whether specific roles are included in the design. Throughout the study process, fuzzy mapping associations are built, and behavior mappings are labeled with varying relationship proportions, providing critical data for the habit form model's construction. The paper uses new digital media technology to create paintings and experiments with 50 randomly selected persons. Forty-four participants are well-known authors on major painting websites, with the remaining six being interviewed as professional painters. [ABSTRACT FROM AUTHOR]

In recent years, there have been several sports-related injuries and even unexpected fatalities due to the rapid growth of China's sports industry and increasing fitness issues. The prevalence of sports injuries is rapidly rising because of the intense competition among athletes and the increased physical load caused by players' long-term training. The difficulty in the sports' world is the identification and evaluation of risk factors associated with athletes. To address these issues, this paper develops a cognitive evaluation model for risk assessment of injuries in sports using big data to understand and determine the likelihood of injuries. The model is used to prevent the issues faced by running athlete's injury and thereby lowers the risk of overall risk. This paper also analyzes the injury factors while running by establishing an estimation model based on the radial basis function (RBF) neural network and explains how to compare RBF neural networks and risk level evaluation using certain criteria. The estimation model helps to identify and judge the risk factors of athletes. It designs an algorithm for Gaussian distribution, which predicts the output of the model. It discusses how the error may be calculated, which measures the neural network model's overall performance in forecasting sports' injury outcomes using a particular dataset. Finally, it designs the RBF Neural Network Learning algorithm, which generates the leaned network parameters to evaluate the reliability of risk cognitive model for athletes based on big data. The Lagrange and Bayesian models are selected for comparison. The results indicate that this model has a higher evaluation effect and accuracy as compared to other models. For this purpose, the comparison is made using 25 athletes. The proposed model's evaluation time is the lowest in comparison to the existing approaches. After examining the impact of cognitive evaluation, it has been reflected that the proposed model can be utilized to assess the different injury types for different parts of athletes. The highest single injury degree is 38.76%, while the highest composite injury degree is 40.05%. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper examines the roles of digital finance development in household income, consumption, and financial asset holding from an extreme value theory perspective. Three types of extreme pairs (Min to Min, Max to Max, and Max to Min) are constructed, corresponding to the three aspects of the economic welfare of digital finance: fairness, efficiency, and their trade-off. Using panel data from the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) and China Family Panel Studies (CFPS) over time span 2014–2018, this paper models the block maxima and minima of variables by fitting them with generalized extreme value (GEV) distribution. The binary expansion testing (BET) is used to detect the nonlinear dependence between digital finance and household economic variables. The tail quotient correlation coefficient (TQCC) is used to quantify the tail dependencies. The results show that: (1) digital finance has significant fairness effects in reducing poverty, increasing consumption, and promoting financial asset holding; (2) digital finance shows effects of promoting incentives and efficiency in household income and financial asset holding, but this effect is relatively limited in household consumption; (3) digital finance generally increases efficiency without harming fairness in terms of all cases of household income and consumption, and most of the cases regarding household financial asset holding; (4) the positive spatial externality of digital finance exists for all household economic variables; and, for pairs regarding household income and consumption, the wider the scope, the greater the spatial spillover effect. The result of this paper implies many novel policy implications. [ABSTRACT FROM AUTHOR]

The excavation of large-scale lacustrine soft soil in natural state in the Chengmenshan Copper mine, Jiujiang, China, is a challenging work and is illustrated in this paper. A geometrical model of the lacustrine soft soil was built based on data from more than 150 boreholes. A series of tests were conducted on the natural soft soil including compaction, consolidation and common test, and also remolded samples containing water contents of 10%, 13%, 16%, 21%, 25%, 30% and 37% were tested. Results showed that the cohesion and friction angle decrease rapidly with the increase in water content, but the consolidation pressure played a positive role to the increase in shear strength. To properly manage the soil properties and optimize the surcharge program, settlement and uplift during the infill construction were numerically analyzed by means of SIGMA/W. Based on the test and numerical simulation results, a long term in situ improvement method was proposed and performed. Finally, drilling sampling tests, settlement monitoring and slope stability analysis were conducted to evaluate the overall effects of the surcharge. The properties of the soft soil were improved greatly under the infill surcharge, especially the cohesion and frictional increased by 279.3% and 152.8% and almost reached the value of remolded soil after consolidation under pressure 300 kPa. The settlement rate decreased with the surcharge duration. The safety factor of slope after improvement also met the design requirement. Furthermore, the method proposed in this paper which have given consideration to economic conservation and environmental protection is significant for the soft soil excavation projects. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the "air pollution complex" was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades. [ABSTRACT FROM AUTHOR]

Improving total factor carbon emission efficiency (TCE) is the key to achieving carbon emission reduction targets while ensuring economic growth. In this paper, the global Malmquist index based on the SBM model is used to measure TCE of 283 cities in China from 2011 to 2019. On this basis, this paper uses the spatial econometric model and intermediary effect model to empirically analyze the impact of industrial co-agglomeration on TCE and its transmission mechanism. Furthermore, considering the differences in geographical location and resource endowment among regions, this paper analyzes the heterogeneous effect of industrial collaboration agglomeration on TCE in different regions and cities. The results show that: (1) Industrial co-agglomeration can improve TCE, and its main transmission channel is technological innovation. (2) Industrial co-agglomeration has a positive spatial spillover effect. Industrial co-agglomeration in one region can improve the TCE in the surrounding regions. (3) Industrial co-agglomeration of cities with different geographic locations and resource endowments has a heterogeneous effect on TCE. Regarding geographical heterogeneity, the industrial co-agglomeration in the eastern region has the greatest promoting effect on TCE, followed by the central region. However, the impact of industrial co-agglomeration in the western region on TCE is not significant. Regarding resource endowment heterogeneity, the industrial co-agglomeration in non-resource-based cities has a greater promoting effect on TCE than that in resource-based cities. [ABSTRACT FROM AUTHOR]

The paper presents a novel statistical approach for analyzing the daily coronavirus case and fatality statistics. The survival discretization method was used to generate a two-parameter discrete distribution. The resulting distribution is referred to as the "Discrete Marshall–Olkin Length Biased Exponential (DMOLBE) distribution". Because of the varied forms of its probability mass and failure rate functions, the DMOLBE distribution is adaptable. We calculated the mean and variance, skewness, kurtosis, dispersion index, hazard and survival functions, and second failure rate function for the suggested distribution. The DI index demonstrates that the proposed model can represent both over-dispersed and under-dispersed data sets. We estimated the parameters of the DMOLBE distribution. The behavior of ML estimates is checked via a comprehensive simulation study. The behavior of Bayesian estimates is checked by generating 10,000 iterations of Markov chain Monte Carlo techniques, plotting the trace, and checking the proposed distribution. From simulation studies, it was observed that the bias and mean square error decreased with an increase in sample size. To show the importance and flexibility of DMOLBE distribution using two data sets about deaths due to coronavirus in China and Pakistan are analyzed. The DMOLBE distribution provides a better fit than some important discrete models namely the discrete Burr-XII, discrete Bilal, discrete Burr-Hatke, discrete Rayleigh distribution, and Poisson distributions. We conclude that the new proposed distribution works well in analyzing these data sets. The data sets used in the paper was collected from 2020 year. [ABSTRACT FROM AUTHOR]

The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China's large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development. [ABSTRACT FROM AUTHOR]

This paper uses patterned structure to study the efficiency improvement of the practical application of BIM technology, and innovates the patterned structure method based on this. The research focus of this paper is to build a new assembly component library based on BIM model and algorithm optimization, improve its use and management efficiency, and use the core technology to design the simulation analysis model of BIM in construction, and give timely feedback of experimental information. Analyze the existing assembly structure design process, design standards and design methods, propose a BIM-based assembly structure design method, change the design idea from the whole to the component elements, and use the design method for prefabricated components. Taking the W prefabricated component library as a starting point, select the prefabricated components of the component library for assembly design to form a BIM model with a complete structure. Adjust and optimize the BIM model, and finally form a reasonable design plan. This BIM type of product includes production, transportation and assembly construction. Prefabricated buildings have obvious advantages, its comprehensive benefits are higher than other buildings, and the technical content is relatively high, through innovative prefabricated buildings, the modernization level of the building can be improved. Therefore, on the basis of in-depth investigation and study of the development status of China's construction industry, this paper studies the impact of different factors on engineering costs, including engineering schemes, engineering construction modes, etc., based on a large number of actual engineering operations and application cases, so as to achieve the purpose of reducing cost costs, improving project income, and improving project quality. [ABSTRACT FROM AUTHOR]

Tourism is the pillar industry of many cities, and it is also an important key point to promote urban development and maintain urban vitality. At present, the analysis of urban tourism activity in China can better assist the research of regional economic development and promote the orderly development of regional economy. Many scholars have carried out the analysis in this respect. As a new and growing field, artificial intelligence also plays an important role in urban tourism. With the continuous development of science and technology, and the human intelligence field of human research is also developing. New artificial intelligence products continue to emerge. The workload of most artificial intelligence may exceed the manual workload. In order to continuously update artificial intelligence, individuals effectively combine data mining and artificial intelligence, and combine many knowledge disseminated by the network with artificial intelligence technology to create an advanced knowledge network model. This paper uses the OPTICS-based clustering algorithm to analyze the clustering of photographs on the Flickr website and obtain information about tourism activities in Chinese cities. With the help of visualization software to visualize the experimental data and verify the experimental results introduced in this article, city tourism activities can be recommended to the destination. At present, many scholars have studied the application of improved density clustering algorithm in the field of biology and image analysis, but there are still some gaps in the development of tourism. This paper can make some contributions to the related fields. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

Collecting feedback from students through course, program and other evaluation questionnaires has become a costly and time consuming process for most colleges. Converting to data collection through the internet, rather than completion on paper, can result in a cheaper and more efficient process. This article examines several research questions which need to be answered to establish that results collected by the two modes of administration are equivalent. Data were gathered for a program evaluation questionnaire from undergraduate students at a university in Hong Kong. Students were able to choose between completion on paper or through the internet. In six of the seven Faculties the number of responses through each mode was roughly the same. Students in the Engineering Faculty favored the internet. Scores on the 14 out of 18 scales in the instrument showed small differences by mode of response, which became smaller still with controls for pertinent demographic variables. The main response question addressed in the study was whether there was any difference in the way respondents to the two modes interpreted the questions. The study demonstrated the equivalence of the two data sets by showing that both could be fitted to a common model with structural equation modeling (SEM). Five levels of tests of invariance further confirmed the comparability of data by mode of administration. This study, therefore suggests that changing to internet collection for course and program evaluations will not affect the comparability of ratings. [ABSTRACT FROM AUTHOR]

Microbiota from herbivore rumen is of great interest for mining glycoside hydrolases for lignocellulosic biomass biorefinement. We previously isolated a highly active but poorly thermostable xylanase (LXY) from a rumen fluid fosmid library of Hu sheep, a local high-reproductive species in China. In this study, we used a universal enzyme-engineering strategy called SpyTag/SpyCatcher molecular cyclization to improve LXY stability via isopeptide-bond-mediated ligation. Both linear and cyclized LXY (L- and C-LXY, respectively) shared similar patterns of optimal pH and temperature, pH stability, and kinetic constants (km and Vmax). However, the C-LXY showed enhanced thermostability, ion stability, and resilience to aggregation and freeze–thaw treatment than L-LXY, without compromise of its catalytic efficiency. Circular dichroism and intrinsic and 8-anilino-1-naphthalenesulfonic acid-binding fluorescence analysis indicated that the cyclized enzyme was more capable of maintaining its secondary and tertiary structures than the linear enzyme. Taken together, these results promote the cyclized enzyme for potential applications in the feed, food, paper pulp, and bioenergy industries. [ABSTRACT FROM AUTHOR]

The motivation for analyzing the impact of deep neural networks on enterprise performance is mainly due to the continuous deepening of enterprise information construction, shifting from traditional paper-based data acquisition methods to electronic data management. The data generated by the sales, production, logistics and other links of enterprises is also becoming increasingly large. How to scientifically and effectively process these massive amounts of data and extract valuable information has become an important issue that enterprises need to solve. The continuous and stable growth of China's economy has promoted the development and growth of enterprises, however, it has also made enterprises face a more complex competitive environment. The question of how to improve the performance of enterprises to enhance their competitiveness in the market has become a major issue to be addressed in the face of fierce competition and to ensure the long-term development of enterprises. In this paper, based on the research of firm performance evaluation, deep neural network is introduced to analyse the influence of ambidextrous innovation and social network on firm performance, and the theories of social network, ambidextrous innovation and deep neural network are sorted out and analysed, and a deep neural network-based firm performance evaluation model is established, and finally the sample data is obtained using crawler technology, and then the response values are analysed. Innovation and the improvement of the mean value of social networks are helpful to firm performance. [ABSTRACT FROM AUTHOR]

To solve various extraction-related problems existing in the deep Jurassic mines in the Inner Mongolia-Shaanxi (IM-S) mining area, this paper puts forward a method to control roof water hazards based on the sedimentary facies pattern of the aquifers. The problems we addressed are: high volumes of goaf water, mixing of coal-based water in the stope, the proximity of the upper coal seam to water-enriched aquifers, and the inability for safe excavation and stoping. We first investigated the spatial distribution patterns of the aquifers and their related storage properties. A zone model of water yield compatible with the hydrogeological characteristics of a deep Jurassic mine was evaluated for the aquifers in the roof of the 3–1 coal mine, and a "low-position and lateral interception" control method was proposed based on the water yield zonation. The results demonstrated that the depositional environment for the sandstone aquifer in the first member of the Zhiluo Formation had a channel bar paleoenvironment that was characterized by a varying water table. This proved to be the key layer for preventing and controlling the water hazards. The method described in this paper effectively mitigated the water hazards associated with that aquifer in the study area and thus provide a theoretical and practical basis for controlling mine water hazards in the IM-S mining area. [ABSTRACT FROM AUTHOR]

'Environment, Social and Governance' (ESG) considerations have received increasing attention as components that inform responsible investments in mining and global material supply. In ESG, governance variables have been mostly regarded as internal firm structures and broad national institutional strength, neglecting closer observations on economic regulation and national raw material politics. Improved governance data to support ESG evaluations should include historical and current relations between public governance bodies and private-sector firms. This paper examines the role of competition regulation agencies in weighing out and mediating relations between the State and multinational private-sector mining firms to unpack components that affect governance in mining. It does so by commenting on empirical data of the purchase held in 2018 of SQM shares—the world's second largest lithium raw materials supplier—by Tianqi Lithium—one of the largest lithium materials producers in China. The paper discusses the place of equity markets in politics of nation-making and economic regulation in mining. It argues that beyond internal corporate structures, the governance criteria in the ESG profile of mining companies are largely affected by domestic economic regulation maturity, domestic political geography histories of private equity and imageries of corporate shares and natural resources. Furthermore, this paper argues that the effective governance of lithium supply in Chile is affected by political tensions between economic regulation and contractual regimes in mining. [ABSTRACT FROM AUTHOR]

This paper redefines the connotation of China's human development in the context of the new development concept and high-quality development, and constructs the China Human Development Index (CHDI) indicator system accordingly. Then, based on the inequality adjustment model and DFA model, the human development level of each region in China from 1990 to 2018 is measured, and the spatial and temporal evolution characteristics of China's CHDI and the current situation of regional imbalance are analyzed accordingly. Finally, LMDI decomposition technique and spatial econometric model were used to study the influencing factors of China's human development index. The results show that: (1) The weights of the CHDI sub-index estimated by the DFA model have good stability, and it is a relatively good objective weighting method. (2) Compared with the HDI, the CHDI in this paper can better reflect the level of human development in China. (3) China's human development has made great achievements and has basically achieved the leap from the low human development level group to the high human development level group. However, there are still significant gaps between regions. (4) From the results of LMDI decomposition, the livelihood index is the most important driving index of CHDI growth in each region. From the results of spatial econometric regressions, there is a strong spatial autocorrelation of China's CHDI among the 31 provinces. GDP per capita, financial education expenditure per capita, urbanization rate, and financial health expenditure per capita are the main influencing factors of CHDI. Based on the above research findings, this paper proposes a scientific and effective macroeconomic policy with important reference value for promoting the high-quality development of China's economy and society. [ABSTRACT FROM AUTHOR]

This paper measures the human development indices of 31 inland provinces (municipalities) in China in a continuous time series during 2000–2017 according to the 2010 HDI compilation method. It uses a geographically and temporally weighted regression model for conducting an empirical study on the effects of R&D investment and network penetration on human development in each province (municipality) of China. There is significant spatial and temporal heterogeneity in the impact of R&D investment and network penetration on human development across provinces (municipalities) in China due to differences in resource endowments and economic and social development. For R&D investment, eastern provinces (municipalities) have mostly positive effects on human development, and central regions have mostly weak positive or negative effects. In contrast, western provinces (municipalities) show different development paths, with weak positive effects in the early stage and significant positive effects after 2010. Most provinces (municipalities) show a continuous and increasing positive effect for network penetration. The marginal contributions of this paper are mainly in improving the shortcomings in research perspectives, empirical methods, and research data in the study of human development influencing factors in China relative to the study of HDI itself in terms of measurement or application dimensions. This paper constructs a human development index for China, analyzes its spatial and temporal distribution, and explores the impact of R&D investment and network penetration on human development in China, with the hope of providing lessons for China and developing countries to promote the level of human development and cope with the pandemic. [ABSTRACT FROM AUTHOR]

Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China's traditional villages. [ABSTRACT FROM AUTHOR]

With the gradual increase of international willingness to reach the carbon peak and carbon neutrality, this paper decomposes the influencing factors of China's carbon emission changes from 2008 to 2019 using the Logarithmic Mean Divisia Index model (LMDI), and analyzes the contribution amount and rate of each influencing factor. The results found that: for the whole country, the cumulative growth of carbon emissions during the study period is about 416,484.47 (104 tons), among which the economic growth effect plays a major role in promoting, with a cumulative contribution rate of 284.16%; The increase in regulation intensity and the optimization of industrial structure, however, suppress carbon emissions well, with a cumulative contribution rate of about – 199.21% and − 64.75%, respectively, during the study period. For economic regions, the cumulative influence direction of each driver is the same as that of the whole country, while the population size effect in the northeast economic region and the regulation input effect in the eastern coastal economic region act in the opposite direction from other economic regions, and the carbon emission reduction direction of the energy intensity effect varies from one economic region to another. Accordingly, this paper proposes policy recommendations to enhance regulatory intensity, optimize industrial and energy consumption structure, implement localized emission reduction strategies, and promote synergistic emission reduction in economic zones. [ABSTRACT FROM AUTHOR]

Background: Gout is the most common inflammatory arthritis with an increasing prevalence and incidence across the globe. We aimed to provide a comprehensive and systematic knowledge map of gout research to determine its current status and trends over the past decade. Methods: Publications on gout research were obtained from the Web of Science Core Collection (WOSCC) database. Bibliometric R, VOSviewer, and Citespace were employed to analyze the eligible literature. Results: A total of 5535 publications concerning gout research between 2012 and 2021 were included. Most publications and citations both numerically came from China. The strongest international cooperation belonged to the USA. The University of Auckland was the most productive institution with a leading place in research collaboration. The prime funding agency was the National Natural Science Foundation of China. Most papers were published in Clinical Rheumatology. Annals of the Rheumatic Diseases achieved the highest number of citations, H-index and IF, which showed the most excellent comprehensive strength. The individual author with the most paper authorship was Dalbeth Nicola with 241 publications and 46 H-index. Keywords and co-citation analysis discovered that pathological mechanism remains the future hotspot in gout research. It may involve gout connection with gut microbiota, NLRP3 inflammasome, xanthine oxidase, and urate-transporter ABCG2. In addition, besides metabolic diseases, the relationship between gout and heart failure may need more attention. Conclusion: This study clarified the current status and research frontier in gout over the past decade, which would provide valuable research references for later researchers. Key Points •We disclosed the current status and frontier directions of gout over the past 10 years worldwide. •We identified future hotspots of gout research, including gout connection with gut microbiota, NLRP3 inflammasome, xanthine oxidase, and urate-transporter ABCG2. •We discovered that the relationship between gout and heart status would be the research frontier. [ABSTRACT FROM AUTHOR]

Constructing salt cavern gas storage in the ultra-deep strata more than 2000 m in depth is an important strategic technologic development in China. A primary issue is that higher temperature affects the creep behaviors of the surrounding rock. In this paper, triaxial creep tests of rock salt under multi-stage temperatures were carried out to study the influence of temperature on the creep behaviors of rock salt. Synchronous acoustic emission monitoring was performed throughout the creep test to study the microcracks characteristics of rock salt during creep at different temperatures. The results show that the duration of transient creep decreases with the increase of temperature. The steady-state creep rate increases with the increase of temperature. Temperature rise prompts the creep of rock salt to enter the volume dilatation stage. By analyzing the acoustic emission energy/counts, it is found that the microcracks generated during the creep at high temperature are few, but there are more at low temperature. Microcracks are generated intensively during rising temperatures. By analyzing the characteristics of the acoustic emission spectrum, it is found that when the temperature is low, the cracks generated are mainly intergranular cracks with a larger size, and they are mainly intragranular cracks with a smaller size when the temperature is high. The research in this paper is helpful to further understand the creep properties of ultra-deep rock salt and provides an important basis for the design, construction, and operation of gas storage salt caverns. Highlights: The influence of temperature on the creep of rock salt was studied by performing multistage temperature creep tests. The microcracks characteristics of rock salt during creep at different temperatures were studied using acoustic emission technology. The duration of transient creep phase decreases with the increase of temperature. The steady-state creep rate increases exponentially with the increase of temperature. [ABSTRACT FROM AUTHOR]

As one of the common physical materials in Chinese archaeological excavations, ancient bronzes are an essential basis for studying the development of Chinese bronze culture, which is of great significance for exploring the development law of ancient human civilization and reconstructing ancient human society. As China's infrastructure advances, the number of bronzes unearthed by archaeological excavations continues to increase. However, environmental damage to artefacts is very complex, whether the buried environment of the artefacts or the above-ground environment when the artefacts are unearthed, leading to different health problems for the excavated bronzes. A scientific assessment of these bronzes needs to be carried out prior to extraction to inform staff how they should be extracted, moved, and transported and how they should be restored afterwards. In response to the above problems, this paper takes excavated bronzes from archaeological sites as the research object and, by analysing and studying the relevant industry standards and the disease characteristics of bronzes, establishes a three-tier indicator framework for assessing the health of bronzes in a layer-by-layer refinement and proposes quantitative indicators with typical correlations. Through extensive research and testing, we screened out efficient, non-destructive, convenient and reliable assessment and testing methods and assessment models that combine subjective and objective aspects suitable for archaeological sites. On this basis, the paper achieves a scientific and practical assessment of the health status of bronzes excavated from archaeological sites. After repeated experiments, a set of comprehensive methods for quickly and conveniently assessing the health status of excavated bronzes was proposed for the first time and successfully applied to the archaeological excavation site of Sanxingdui site in Guanghan City, Sichuan Province, China. [ABSTRACT FROM AUTHOR]

Chinese calligraphy is an important part of Chinese national culture and art and part of the essence of Chinese national culture. Stone calligraphy is one of the important elements of Chinese calligraphy art. Stone carved calligraphy characters have high cultural and artistic value. Therefore, accurately recognizing stone carved calligraphy characters are of great importance. Stone carved calligraphy can identify hard-to-preserve stone calligraphy paper materials in electronic data that can be preserved for a long time, thereby offering important reference materials for the study of the historical development of Chinese calligraphy art. Moreover, with the development of science and technology and the investment of China in cultural and artistic undertakings, computer-aided calligraphy character recognition technology is also constantly improving, and its application in calligraphy recognition is becoming increasingly extensive. This article aims to study a method of stone inscription calligraphy recognition based on convolutional neural networks. In this paper, we use an image recognition and optimization method consisting of a convolutional neural network to carry out an experiment with stone inscription calligraphy characters. It was concluded that the recognition accuracy of stone calligraphy characters by the convolutional neural network reached 99.2%, indicating that this stone calligraphy character recognition method based on a convolutional neural network has a good ability to recognize stone calligraphy characters. [ABSTRACT FROM AUTHOR]