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Durability is one of the technical bottlenecks restricting fuel cell electric vehicle development. As a result, significant time and resources have been invested in research related to this area worldwide. Current durability research mainly focuses on the single cell and stack levels, which is quite different from the usage scenarios of actual vehicles. There is almost no research on developing durability test cycles on the fuel cell system level. This paper proposes a universal model for developing a durability test cycle for fuel cell system based on the China automotive test cycle. Large-scale comparison tests of the fuel cell systems are conducted. After 1000 h test, the output performance degradation of three mass-produced fuel cell system is 14.49%, 9.59%, and 4.21%, respectively. The test results show that the durability test cycle proposed in this paper can effectively accelerate the durability test of the fuel cell system and evaluate the durability performance of the fuel cell system. Moreover, the methodology proposed in this paper could be used in any other test cycles such as NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Procedure), etc. And it has comprehensive application value and are significant for reducing the cost of durability testing of fuel cell systems and promoting the industrialization of fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

In the process of global urban development, there are urgent ecological security and environmental pollution problems, green development is the fundamental way for urban sustainable development, economic transformation and mitigation of ecological and environmental problems. Based on the panel data of 283 cities at prefecture level and above in China from 2003 to 2017, this paper analyzes spatial–temporal evolution characteristics of urban green development efficiency (UGDE) and the peer effects of UGDE between cities of different grades. It is found that during the study period, in terms of temporal evolution, the average UGDE in China increased from 0.47 in 2003 to 0.61 in 2017, with a cumulative growth rate of 29.79%, showing a rising trend in general. In terms of spatial evolution, the number of low-efficiency cities and medium-efficiency cities continued to decrease. The eastern region has always been the main distribution area of higher-efficiency cities and high-efficiency cities; in the central region, UGDE in most cities improved significantly; in the western region, UGDE has always lagged behind that in the eastern and central regions. In addition, the center of gravity of UGDE presented a trend of northwest migration in general, with a total displacement of 100.07 km, and UGDE showed a spatial dispersion trend. The empirical results indicate that the improvement of UGDE in large cities has a driving effect on that in neighboring medium cities and small cities through the positive peer effect, and the growth of UGDE in medium cities has a promoting effect on that in neighboring small cities through the positive peer effect; the increase of UGDE in medium cities has a positive peer effect on that in neighboring large cities, and the growth of UGDE in small cities has a positive peer effect on that in neighboring medium cities; UGDE promotes each other between large cities through the positive peer effect. [ABSTRACT FROM AUTHOR]

As the carrier of enterprise water resources management disclosure, water information disclosure is a means of expression of enterprises' environmental responsibility. First, a corporate water information disclosure quality evaluation index system and evaluation method are established, and with the help of the projection tracing method of accelerated genetic algorithm, 27 paper companies in China are selected as a sample and the disclosure quality level is analyzed empirically. Then, the analysis is carried out in terms of three changes in vertical trends, horizontal trends and changes in laws, regulations and policies, and the results show that Chinese paper and paper product enterprises have low quality of water information disclosure, weak disclosure content and low voluntary disclosure. Finally, feasible suggestions are made based on the evaluation of disclosure issues. [ABSTRACT FROM AUTHOR]

City reputation is a valuable asset for the local economy and firms in the contemporary society. However, the impact of city reputation on micro-level firms has been largely overlooked by the literature. This paper uses the National Civilized City (NCC) policy in China as a quasi-natural experiment to enhance city reputation. We employ the DID approach to investigate the relationship between city reputation and corporate risk-taking. The result shows that corporate risk-taking significantly increases following the NCC policy adoption. Moreover, information asymmetry can strengthen the positive impact of city reputation on corporate risk-taking. Channel tests show that city reputation improves financial condition and decreases default risk, leading to improved risk-taking tolerance. Overall, our paper indicates that city reputation is an important mechanism to improve corporate financial performance, providing empirical evidence for local governments to pursue the NCC title. [ABSTRACT FROM AUTHOR]

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent. [ABSTRACT FROM AUTHOR]

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change. [ABSTRACT FROM AUTHOR]

Industrial ecology (IE) is the process of constructing industrial-ecological systems (IES) with the realization of the mutual benefits for industrial system (IS) and ecological system (ES). Therefore, the IE level depends on the IES development and the relationship between IS and ES. This paper calculated the development index of ES and IS to account for IES development and adopted the grey Lotka–Volterra to analyze the relationship between IS and ES. Choosing the π-shaped curve area in the Yellow River basin of China as the study area, this paper analyzed its IE level and influence factors from 2005 to 2019. Findings include: (1) The development level of IES in the Area presented significant spatial differentiation and could be classified into three levels. (2) IS and ES remained in the stage of isolated development or mutual restriction. (3) IE was found to be positively correlated with scientific innovation, economic development, openness degree, pollution control, and industrial structure while negatively correlated with urbanization, resource consumption, and government influence. [ABSTRACT FROM AUTHOR]

This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human–environment interaction hypothesis. Applying survey data from users of China's three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms' multimodal recognition and governance capabilities exert significant negative moderating effects on merchants' information behavior, while it shows no apparent moderating effect on users' information behavior; users' emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected. [ABSTRACT FROM AUTHOR]

Due to the increased frequency of extreme weather events and the implementation of the China's dual-carbon target, thermal power companies have been under pressure to construct green infrastructure and to actively pursue low-carbon transformation in response to stricter environmental regulations. This research thus selects 30 listed thermal power enterprises in China as study objects and assesses their green investment efficiency in the low-carbon transition process using three-stage DEA evaluation model with environmental regulation as an exogenous variable. Based on this, a benchmark regression model is used to corroborate the relationship between environmental regulation and green investment. Simultaneously, we carry out analysis to compare the correlation between thermal power firms' green investment efficiency and their focus on green investments. The results show in terms of total efficiency that environmental regulation significantly improves the total efficiency of 80% of thermal power enterprises compared to the absence of this exogenous variable. With the addition of environmental regulation, firms' total efficiency declines gradually in general from 2018 to 2022, with the mean value of efficiency falling by 0.068. In terms of stage-specific efficiency, the efficiency of the green investment stage of the majority of firms is between 0.3 and 0.6, which is much lower than that of the operational stage and the market performance stage. In terms of sub-indicator efficiency, both green investment efficiency and social donation efficiency among thermal power enterprises show obvious polarization, with 30% of them having an efficiency of 1 and 30% less than 0.1. In terms of green investment focus, thermal power unit renovation has a more obvious role in boosting the green investment efficiency of thermal power enterprises than do wind power and photovoltaic projects. Therefore, both governmental departments and thermal power enterprises need to take active measures in order to achieve green transformation from the perspective of green investment efficiency. Through the segmentation of important projects of green investment, this paper provides a reasonable investment direction reference for the sustainable transformation of China's thermal power industry. It also provides a rich and novel theoretical basis for the Chinese government to further improve the relevant environmental protection laws and regulations of thermal power industry. [ABSTRACT FROM AUTHOR]

The colloidal borescope, using colloidal particle motion, is used to monitor the flow velocities and directions of groundwater. It integrates advanced techniques such as microscopy, high-speed photography, and big data computing and enjoys high sensitivity at the micron level. However, In the same well, the groundwater flow velocity monitored by colloidal hole mirror is varies greatly from that obtained by conventional hydrogeological monitoring, such as pumping test. In order to solve this problem, the stability catcher and stratified packer are designed to control the interference of the vertical flow in drilling, and to monitor the flow velocity and direction of groundwater velocity at the target aquifer and target fracture. Five wells with different aquifers and different groundwater types were selected for monitoring in south-central China. The instantaneous velocity and direction are converted into east–west component and north–south component, the average velocity and direction is calculated according to the time of 10 min, and the particle trajectory diagram is established. Based on these results, it proposed a concept of cumulative flow velocity. Using curve-fitting equations, the limits of cumulative flow velocities as the monitoring time tends to infinity were then calculated as the actual flow velocities of the groundwater. The permeability coefficient of aquifer is calculated by using the fissure ratio of aquifer, hydraulic slope and flow velocity, and compared with the permeability coefficient obtained by pumping test. The results are as follows: (1) The variation coefficient of the instantaneous flow velocity measured at the same depth in the same well at different times is greater than that of the time average flow velocity and greater than that of the cumulative flow velocity. The variation coefficient of the actual velocity is the smallest, indicating that the risk of using the actual flow velocity is lower. (2) The variation coefficient of the flow rate monitored at different depths in the same well is mainly controlled by the properties of the aquifer. The more uniform water storage space in the aquifer, the smaller the variation coefficient. (3) The comparison between the permeability coefficient obtained by monitoring and the permeability coefficient obtained by pumping test shows that the flow of structural fissure water controlled by planar fissure is more surface flow, and the results are consistent. When the groundwater flow is controlled by pores and solution gaps, the flow channel is complicated, which is easy to produce turbulent flow, and the result consistency is poor. (4) According to different research accuracy requirements, different monitoring and calculation methods can be selected for different aquifers and groundwater types. Researches show that, the permeability coefficient calculated for the actual flow velocity in well DR01 is the same as that calculated for the pumping test. The aquifer characteristics reflected by the coefficient of variation of the actual flow velocity in the same aquifer are more realistic. The pumping test method obtains the comprehensive parameters of a certain aquifer, and this method can be used to monitor a certain fissure. In this paper, the new technology developed for monitoring, and the new algorithm established for data processing, can accurately obtain the flow velocity and direction of groundwater, using capsule hole mirror monitoring method. The key parameters of hydrogeology can be obtained by using one well, which can reduce the time and cost input and improve the work efficiency. [ABSTRACT FROM AUTHOR]

In order to quantitatively calculate the critical depth and critical load of mines affected by rock burst, and to achieve effective prevention and control of rock burst in coal mines, this paper proposes a mechanical model for predicting the occurrence of rock burst in coal mine roadways based on catastrophe theory. Additionally, a theoretical calculation formula for initiating rock burst is derived. The first step was to establish a mechanical analysis model, which directly correlated with the in-situ stress, physical and mechanical characteristics of the coal-rock mass, and engineering structural parameters. Following this, a mechanical instability criterion was derived for the key load-bearing circle within the surrounding rock of the roadway. In the final step, the critical depth and load for rock burst initiation were verified for 25 distinct coal mines in China that were prone to rock burst hazards. The research results demonstrate that the discrepancy between the theoretically calculated critical depth and the actual measured statistical values was less than 35%. In addition, the difference between the theoretically determined critical depth and the value calculated by Pan Yishan was less than 32%. Notably, the ratio of the theoretically calculated critical load to the uniaxial compressive strength of the coal-rock mass ranged from 0.38 to 1.93. This aligns with empirical data on rock burst occurrences, as set out in the engineering classification standards for rock masses. These research outcomes substantiated the practical utility of the proposed theory, thereby laying a robust theoretical groundwork for the quantitative control of rock burst. [ABSTRACT FROM AUTHOR]

Aiming at the current low degree of mechanization of pomegranate sprouting tiller pruning in China, all relying on manual pruning, this paper designs a PLC-based pomegranate sprouting tiller removal machine. This machine adopts the identification method of wireless map transmission, the sprouting tiller removal method of multi-cylinder cooperative operation, and the MCGS configuration to realize the interaction between the user and the system, which realizes the displacement and angle compensation of the end-effector under complex conditions to realize the all-around accurate removal of the pomegranate sprouting tiller. The performance test and finite element analysis showed that the device could remove up to 74.62% of sprouting tillers, and the damage rate was as low as 18%. This meets the requirements of pomegranate plantations for the removal of emergent tillers. [ABSTRACT FROM AUTHOR]

Old urban community renovation is an important task of urban renewal in China. In order to ensure the quality and efficiency of renovation work, the government requires a method to assess the priority for old urban community renovation. This paper proposes an evaluation model from a more comprehensive perspective. It establishes the evaluation index system with CIPP model. The method of Cloud-VIKOR is selected to construct the evaluation model. Finally, selects nine case communities to verify the evaluation model. The results show that the evaluation index system covers the whole process of renovation project and the evaluation indexes proposed in the existing research. The priority decision result of 9 case communities is basically consistent with the actual renovation sequence and does not change greatly due to the fluctuation of decision-making mechanism coefficient or the evaluation index weight. This evaluation model can help decision-makers diagnose and optimize the renovation project. [ABSTRACT FROM AUTHOR]

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China. [ABSTRACT FROM AUTHOR]

The application of industrial robots is considered a significant factor affecting environmental pollution. Selecting industrial wastewater discharge, industrial SO2 emissions and industrial soot emissions as the evaluation indicators of environmental pollution, this paper uses the panel data model and mediation effect model to empirically examine the impact of industrial robots on environmental pollution and its mechanisms. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. (2) Industrial robots can reduce environmental pollution by improving the level of green technology innovation and optimizing the structure of employment skills. (3) With the increase in emissions of industrial wastewater, industrial SO2, and industrial dust, the impacts generated by industrial robots are exhibiting trends of a "W" shape, gradual intensification, and progressive weakening. (4) Regarding regional heterogeneity, industrial robots in the eastern region have the greatest negative impact on environmental pollution, followed by the central region, and the western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the emission reduction effect of industrial robots after 2013 is greater than that before 2013. Based on the above conclusions, this paper suggests that the Chinese government and enterprises should increase investment in the robot industry. Using industrial robots to drive innovation in green technology and optimize employment skill structures, reducing environmental pollution. [ABSTRACT FROM AUTHOR]

This study uses a two-step approach to construct a multi-period double-difference model and introduces a quasi-natural experiment of the Broadband China pilot policy to investigate whether household financial market participation at the urban level is affected by the digital economy, which is significant for promoting Chinese households' shift from savings to investment and alleviating the long-standing problem of insufficient household financial market participation in China. In terms of direct impact, the digital economy increases the household financial market participation rate of urban residents by 3.26%, and increases the financial market participation rate of highly financially literate households by 2.14%; in terms of indirect impact, the development of the digital economy increases the total number of household smart Internet devices by 8.27%, and similarly increases the attention to household financial information by a significant 4.22%, which further positively influences the household financial market participation rate. This paper also evaluates the individual and regional differences of the digital economy on household financial market participation, and the estimated causal effect of the digital economy on household financial market participation is purer, which expands the scope of research on the digital economy and household financial market participation, and provides a certain reference basis and policy inspiration for the government to promote the construction of the digital economy. [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]

Climate change has become an unavoidable problem in achieving sustainable development. As one of the major industries worldwide, tourism can make a significant contribution to mitigating climate change. The main objective of the paper is to assess the development level of low-carbon tourism from multi-aspect, using the Yellow River Basin as an example. Firstly, this study quantified tourism carbon dioxide emissions and tourism economy, and analyzed their evolution characteristics. The interaction and coordination degree between tourism carbon dioxide emissions and tourism economy were then analyzed using the improved coupling coordination degree model. Finally, this study analyzed the change in total factor productivity of low-carbon tourism by calculating the Malmquist–Luenberger productivity index. The results showed that: (1) the tourism industry in the Yellow River Basin has the characteristics of the initial environmental Kuznets curve. (2) There was a strong interaction between tourism carbon dioxide emissions and tourism economy, which was manifested as mutual promotion. (3) The total factor productivity of low-carbon tourism was increasing. Based on the above results, it could be concluded that the development level of low-carbon tourism in the Yellow River Basin has been continuously improved from 2000 to 2019, but it is still in the early development stage with the continuous growth of carbon dioxide emissions. [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]

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 carbonate rock formations have obvious dual media characteristics, fracture development and good physical conditions, which are the main seepage channels and storage spaces for gas after the reconstruction of underground gas storage. The carbonate strata of the Ordovician system are important natural gas reservoirs in the eastern area of Sulige Gas Field in the Ordos Basin, and the identification and characterization of their fractures are of great significance for the modeling of fractures in the later stage and the improvement of the operation scheme of the gas storage. At present, there is little research on fractures, which restricts exploration and development. Therefore, taking the 39–61 gas storage reservoir in the eastern area of Sulige Gas Field in the Ordos Basin as the research object, this paper identifies and studies the characteristics of the fractures by core, microscopic, conventional logging curves, and imaging logging identification. The results show that the fracture length ranges from 5 to 15 cm and the width ranges from 0.1 to 3 mm. The fracture angles are mostly between 75° and 90° and the main direction is NW–SE. In conventional logging curves, porosity logging has a good response to fractures, while resistivity logging has a general response to fractures; In layers with more developed fractures, natural gamma values are mostly higher than 40API, rock volume density is less than 2.8 g/cm3, neutron porosity is greater than 12.5%, and acoustic time difference is greater than 160 μ s/m. This study is of great significance for improving the identification of carbonate fractures, enriching the relevant theories, and providing guidance for the construction of carbonate gas storage. [ABSTRACT FROM AUTHOR]

There are urgent ecological and environmental problems in the process of the utilization of urban construction land, promoting green utilization of construction land is conducive to urban sustainable development and high-quality economic development. Based on the panel data of 108 prefecture-level and above cities in the Yangtze River Economic Belt, China from 2003 to 2017, this paper uses the super-efficiency SBM model to measure the green efficiency of urban construction land (GEUCL), analyzes its spatial–temporal evolution characteristics, and constructs the spatial autoregressive model to study its spatial spillover effects from the perspective of urban hierarchy. It is found that, in terms of temporal variation, the average efficiency value shows a fluctuating upward trend during the study period, rising from 0.27 in 2003 to 0.39 in 2017, the cumulative growth rate is 44.44%, with an average annual growth rate of 3.14%. In terms of spatial distribution characteristics, during the study period, the number of medium-efficiency and high-efficiency cities increases significantly, while the number of low-efficiency cities decreases sharply; high-efficiency cities always present scattered distribution, while medium-efficiency cities change from scattered distribution to agglomeration distribution. In addition, GEUCL has significantly positive spatial spillover effects between neighboring cities of different grades and between neighboring cities of the same grade, among them, the increase of GEUCL in higher-grade cities has significantly positive spatial spillover effects on that in adjacent lower-grade cities; the increase of GEUCL in lower-grade cities has significantly positive spatial spillover effects on that in neighboring higher-grade cities; GEUCL has significantly positive spatial spillover effects between neighboring cities of the same grade. [ABSTRACT FROM AUTHOR]

Green innovation plays a crucial role in transforming economic models and achieving sustainable development in enterprises. As an important embodiment of artificial intelligence technology, how industrial robots can effectively promote the green transformation of enterprises has become an important issue. This paper examines the impact and mechanisms of industrial robot adoption on corporate green innovation, as well as its heterogeneous effects. Using data from Chinese listed companies from 2007 to 2019, we find that industrial robot adoption has a significant positive impact on corporate green innovation, enhancing both its quantity and quality. Furthermore, our mechanism study reveals that industrial robot adoption can promote corporate green innovation by improving productivity and environmental management capabilities. Additionally, we investigate the moderating effects of various factors and conclude that the positive impact of industrial robot adoption on green innovation is more pronounced among the state-owned enterprises, enterprises with the intense market competition, as well as enterprises located in regions with higher carbon emissions intensity. This paper contributes to enrich the research on industrial robots and corporate green innovation, and provides a reference to improve environmental management and achieve a low-carbon economy in emerging markets. [ABSTRACT FROM AUTHOR]

There are urgent ecological and environmental problems in the process of the utilization of urban construction land, promoting green utilization of construction land is conducive to urban sustainable development and high-quality economic development. Based on the panel data of 108 prefecture-level and above cities in the Yangtze River Economic Belt, China from 2003 to 2017, this paper uses the super-efficiency SBM model to measure the green efficiency of urban construction land (GEUCL), analyzes its spatial–temporal evolution characteristics, and constructs the spatial autoregressive model to study its spatial spillover effects from the perspective of urban hierarchy. It is found that, in terms of temporal variation, the average efficiency value shows a fluctuating upward trend during the study period, rising from 0.27 in 2003 to 0.39 in 2017, the cumulative growth rate is 44.44%, with an average annual growth rate of 3.14%. In terms of spatial distribution characteristics, during the study period, the number of medium-efficiency and high-efficiency cities increases significantly, while the number of low-efficiency cities decreases sharply; high-efficiency cities always present scattered distribution, while medium-efficiency cities change from scattered distribution to agglomeration distribution. In addition, GEUCL has significantly positive spatial spillover effects between neighboring cities of different grades and between neighboring cities of the same grade, among them, the increase of GEUCL in higher-grade cities has significantly positive spatial spillover effects on that in adjacent lower-grade cities; the increase of GEUCL in lower-grade cities has significantly positive spatial spillover effects on that in neighboring higher-grade cities; GEUCL has significantly positive spatial spillover effects between neighboring cities of the same grade. [ABSTRACT FROM AUTHOR]

Digital transformation and green innovation are powerful initiatives to achieve carbon peaking, carbon neutrality targets and high-quality economic development. Using a sample of high energy-consuming listed enterprises from 2012 to 2021, a double fixed-effect model is constructed to verify the effect of green innovation on the carbon emission reduction performance of high energy-consuming enterprises, and digital transformation is used as a moderating variable to analyze the inner mechanism of green innovation affecting the carbon emission reduction performance of high energy-consuming enterprises under the effect of digital transformation. The empirical results show that green innovation can significantly improve the carbon emission reduction performance of energy-consuming enterprises, while digital transformation positively moderates the effect of green innovation on the carbon emission reduction performance of energy-consuming enterprises. When considering the industry heterogeneity, the moderation effect of digital transformation is significant in the chemical raw materials and chemical products manufacturing industry and the electricity and heat production and supply industry, but the petroleum processing and coking and nuclear fuel processing industry, the non-metallic mineral products industry, the ferrous metal smelting and rolling processing industry and the non-ferrous metal smelting and rolling processing industry are not yet significantly affected by green innovation and digital transformation. The findings of the study provide empirical evidence to promote the improvement of carbon emission reduction performance of energy-intensive enterprises in China and to achieve the "double carbon" target. [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 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]

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China. [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]

Large Scale Natural Draft Cooling Tower has become a hot topic in China because it is an important part of the nuclear power plant, and its environmental impacts include shading, solar energy loss, water deposition and salt deposition. In China, there is no built large-scale natural draft cooling tower of nuclear power plant. Therefore, model prediction becomes an effective way to solve this problem. This paper introduces the basic principles and structure of SACTI (Seasonal and Annual Cooling Tower Impact) model. SACTI is a cooling tower assessment model developed by Argonne National Laboratory, USA. A comparative case study between China's Pengze Nuclear Power Plant and the US Amos Power Plant is also presented. Calculations were carried out for the Pengze and Amos power plants, and the results showed that the maximum value of salt deposition at the Pengze plant was about 166.5 kg/(km2-month) at a distance of 800 m from the cooling tower. The maximum value of salt deposition at the Amos plant was about 92.85 kg/(km2-month) at a distance of 600 m from the cooling tower. Conclusions show that the research work can provide a useful solution in future work, the simulation results of the SACTI model have a potential mean in the absence of monitoring data. This research provides a way to generate simulation data through SACTI program in the design process of nuclear power plant cooling tower, and designers can use these data to determine how the cooling tower will affect the natural environment and manage within an appropriate range to reduce the impact on the environment. [ABSTRACT FROM AUTHOR]

20-inch Large area photomultiplier tube based on microchannel plate (MCP-PMT) is newly developed in China. It is widely used in high energy detection experiments such as Jiangmen Underground Neutrino Observatory (JUNO), China JinPing underground Laboratory (CJPL) and Large High Altitude Air Shower Observatory (LHAASO). To overcome the poor time performance of the existing MCP-PMT, a new design of large area MCP-PMT is proposed in this paper. Three-dimensional models are developed in CST Studio Suite to validate its feasibility. Effects of the size and bias voltage of the focusing electrodes and MCP configuration on the collection efficiency (CE) and time performance are studied in detail using the finite integral technique and Monte Carlo method. Based on the simulation results, the optimized operating and geometry parameters are chosen. Results show that the mean ratio of photoelectrons landing on the MCP active area is 97.5%. The acceptance fraction of the impinging photoelectrons is close to 100% due to the emission of multiple secondary electrons when hitting the MCP top surface. The mean transit time spread (TTS) of the photoelectrons from the photocathode is 1.48 ns. [ABSTRACT FROM AUTHOR]

Nowadays environmental issues have been of great concern to the world, among which the problem of global warming caused by greenhouse gas emissions is particularly prominent. All countries in the Kyoto Protocol and the Paris Agreement have committed to control greenhouse gas emissions, and China, as the largest carbon emitter, has assumed a heavier burden. China has been striving to develop low-carbon technologies such as hydrogen, nuclear, wind, and solar energy, but the most attention should be paid to CCUS, which many scholars have high expectations that CCUS can help China reduce emissions to some extent. Therefore, this paper presents a prediction that CCUS can reduce 3.8% of carbon emissions for China in 2040 when CCUS emission reductions increase at a rate of 30%. The power and chemical industries could reduce carbon emissions by 2.3% and 17.3%, respectively. [ABSTRACT FROM AUTHOR]

Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future. [ABSTRACT FROM AUTHOR]

As an important part of urban public open space, pocket parks have become an important activity place for the elderly in the context of the aging society in China. With the pocket parks in Nanjing, Jiangsu province, China as research object, this paper set six landscape features to be studied, namely, Height of trees, Green color richness, Stratification of green landscapes, Green space ratio, Leisure facilities, and Water landscape. The elderly respondents with different demographic characteristics, such as age, gender, education level and residential type, were subjected to the picture stimulation experiment whose results were then statistically analyzed. The results indicate that gender and residential type exert certain influence on the elderly's visual impact assessment of pocket park landscape. To be specific, the male elderly prefer the pocket park landscape with 3-6 m high trees, medium green space ratio, and more leisure facilities; the female elderly are in greater favor of pocket park landscapes with 0-3 m high trees, five or more colors, three or more layers; the elderly who live with their families prefer pocket park landscapes with medium green space ratio and more leisure facilities; to the elderly who live alone, pocket park landscapes with trees which are 0–3 m high, five or more colors, and medium leisure facilities are more attractive. This study can provide valuable reference for pocket park design in China. [ABSTRACT FROM AUTHOR]

The Yellow River Basin is a key ecological barrier and commercial zone in China, as well as an essential source of energy, chemicals, raw materials, and fundamental industrial foundation, the achievement of its carbon peaking is of great significance for China's high-quality development. Based on this, we decomposed the influencing factors of carbon dioxide emissions in the Yellow River Basin using the LMDI method and predicted the carbon peaking in the Yellow River Basin under different scenarios using the STIRPAT model. The results show that (1) the energy intensity effect, economic activity effect and population effect play a positive role in promoting carbon emissions during 2005–2020. The largest effect on carbon emissions is the population size effect, with a contribution rate of 65.6%. (2) The STIRPAT model predicts that the peak of scenarios "M–L", "M–M" and "M–H" will occur in 2030 at the earliest. The "M–H" scenario is the best model for controlling carbon emissions while economic and social development in the Yellow River Basin. The results of this paper can provide a theoretical basis for the development of a reasonable carbon peak attainment path in the Yellow River Basin and help policy makers to develop a corresponding high-quality development path. [ABSTRACT FROM AUTHOR]

Previous scientific research on reservoirs of the Deng IV Member in the Gaoshiti-Moxi area, the main production area of the Anyue gas field with reserves of tens of billions of cubic metres, has focused on karst palaeogeomorphology reconstruction, the facies distribution on platform margins, and their effects on creating favourable reservoirs. However, the quality of microbial carbonate reservoirs is also closely related to their original depositional environments on both inner and marginal platforms. Therefore, this paper attempts to reveal favourable microbial carbonate reservoir characteristics and the sedimentary effects on their distribution and prediction based predominantly on a synthetic analysis of the sequence stratigraphy and depositional facies. The results show that favourable reservoirs of the Deng IV Member are classified into three types according to their reservoir spaces: fracture-cavity, pore-cavity and pore reservoirs. Secondary dissolution pores and cavities are primary reservoir spaces developed mainly in nonskeletal grain dolomites with sparry cements, thrombolites, and stromatolites. The physical properties of the fracture-cavity and pore-cavity types of reservoirs are better than those of pore reservoirs and have porosities between 1 and 5% and permeabilities between 0.01 × 10–3 and 1 × 10–3 μm2. Vertically, favourable reservoirs are developed mainly in parasequence set 6 (PSS6) and PSS7 and are laterally distributed in well zone MX9-MX19-MX1 for the fracture-cavity type, MX105-MX110-GS20 for the pore-cavity type and MX17-MX107-MX41-MX102-GS102 for the pore type. Moreover, depositional effects on reservoirs in terms of depositional sequences, seismic facies, microfacies and microfacies associations indicate that to some extent, the fracture-cavity type of reservoir is constrained by the top boundaries of PSS7, PSS2, parasequence 17 (PS17) and PS14; the pore-cavity type of reservoir is constrained by the top boundaries of PSS7, PSS4, PS18 and PS12; and the pore type of reservoir is constrained by the top boundaries of PSS7, PSS6, PSS3, PSS2, PS18, PS17, PS14, and PS12. Seismic facies associated with shoals and mound-flat complexes are related to the facies distributions of pore cavities and pore reservoirs. MA1, MA3, MA7, and MA8 are predominant microfacies associations of favourable reservoirs of the Deng IV Member in the Gaoshiti-Moxi area. The above results are significant for further petroleum exploration and exploitation of ultradeep microbial carbonate reservoirs in this area. [ABSTRACT FROM AUTHOR]

Nowadays environmental issues have been of great concern to the world, among which the problem of global warming caused by greenhouse gas emissions is particularly prominent. All countries in the Kyoto Protocol and the Paris Agreement have committed to control greenhouse gas emissions, and China, as the largest carbon emitter, has assumed a heavier burden. China has been striving to develop low-carbon technologies such as hydrogen, nuclear, wind, and solar energy, but the most attention should be paid to CCUS, which many scholars have high expectations that CCUS can help China reduce emissions to some extent. Therefore, this paper presents a prediction that CCUS can reduce 3.8% of carbon emissions for China in 2040 when CCUS emission reductions increase at a rate of 30%. The power and chemical industries could reduce carbon emissions by 2.3% and 17.3%, respectively. [ABSTRACT FROM AUTHOR]

As an important part of urban public open space, pocket parks have become an important activity place for the elderly in the context of the aging society in China. With the pocket parks in Nanjing, Jiangsu province, China as research object, this paper set six landscape features to be studied, namely, Height of trees, Green color richness, Stratification of green landscapes, Green space ratio, Leisure facilities, and Water landscape. The elderly respondents with different demographic characteristics, such as age, gender, education level and residential type, were subjected to the picture stimulation experiment whose results were then statistically analyzed. The results indicate that gender and residential type exert certain influence on the elderly's visual impact assessment of pocket park landscape. To be specific, the male elderly prefer the pocket park landscape with 3-6 m high trees, medium green space ratio, and more leisure facilities; the female elderly are in greater favor of pocket park landscapes with 0-3 m high trees, five or more colors, three or more layers; the elderly who live with their families prefer pocket park landscapes with medium green space ratio and more leisure facilities; to the elderly who live alone, pocket park landscapes with trees which are 0–3 m high, five or more colors, and medium leisure facilities are more attractive. This study can provide valuable reference for pocket park design in China. [ABSTRACT FROM AUTHOR]

Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future. [ABSTRACT FROM AUTHOR]

Based on the literature on the factors influencing port competitiveness, this paper evaluates the competitiveness of Chinese ports along the Belt and Road based on Porter's diamond model. The evaluation system includes four primary indicators, including port infrastructure, port operation scale, port hinterland economic level and port development potential, and 13 secondary indicators. Then, based on the entropy-TOPSIS method, an evaluation model of port competitiveness is constructed. Finally, 15 seaports along China's "Belt and Road" are used to evaluate competitiveness, and a comprehensive ranking of port competitiveness is obtained. The advantages and shortcomings of each port are pointed out according to the evaluation results to provide a reference for the long-term development of the competitiveness of ports along the "Belt and Road" in China. [ABSTRACT FROM AUTHOR]

Despite being a clean and renewable energy source, photovoltaic (PV) power generation faces severe challenges in operation due to its strong intermittency and volatility compared to the traditional fossil fuel power generation. Accurate predictions are therefore crucial for PV's grid connections and the system security. The existing methods often rely heavily on weather forecasts, the accuracy of which is hard to be guaranteed. This paper proposes a novel GBDT-BiLSTM day-ahead PV forecasting model, which leverages the Teacher Forcing mechanism to combine the strong time-series processing capabilities of BiLSTM with an enhanced GBDT model. Given the uncertainty and volatility inherent in solar energy and weather conditions, the gradient boosting method is employed to update the weak learner, while a decision tree is incorporated to update the strong learner. Additionally, to explore the correlation between photovoltaic power output and historical time-series data, the adaptive gradient descent-based Adam algorithm is utilized to train the bidirectional LSTM model, enhancing the accuracy and stability of mid- to long-term time-series predictions. A prediction experiment, conducting with the real data from a PV power station in Sichuan Province, China, was compared with other methods to verify the model's effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Public sports venues serve as a crucial material medium for human athletic activities. Research on their spatial distribution holds profound implications for optimizing spatial layout and promoting human sports endeavors. This paper takes interest points of sports venues in China as research samples, employing GIS spatial analysis tools, mathematical statistics, and Geo-detector as research methods. It systematically investigates the spatial distribution characteristics and causes of sports venues in China, aiming to provide insights and references for the optimization of spatial arrangement, sustainable development, and relevant policy formulation of sports venues in the country. The results indicate that: (1) In terms of overall distribution characteristics, sports venues in China present a multi-centric agglomeration in geographical space. (2) From a regional distribution perspective, the spatial layout of these venues reveals a pattern of "more in the south, less in the north," "dense in the southeast, sparse in the northwest," and a coastal strip distribution. (3) Regarding spatial correlation, the hot and cold spot partitions of sports venues largely align with the "Hu-Line", with their spatial distribution tending towards a positively correlated pattern of high–high clustering and low–low clustering. (4) There is evident heterogeneity in factors affecting sports venues' spatial distribution. The distribution results from the interactive coupling of multiple factors, where the interaction between any two factors offers stronger explanatory power for the spatial layout of the venues. [ABSTRACT FROM AUTHOR]

China's economic growth has reached a new plateau. It is no longer appropriate to use the old economic growth model, which relied on labor, land resources, mineral resources, and other economic considerations. Under the background of artificial intelligence, high-quality economic development is an inevitable trend. A new financial paradigm called "digital finance" integrates financial services with information technologies. Digital financial technology is thought to be a crucial foundation for fostering high-quality and sustainable economic and social development since it may offer more economic entities reduced cost of capital and more realistic financial service skills than in traditional financial models. In the era of artificial intelligence, how to reasonably release the momentum of digital finance for China's sustained economic growth has become a hot topic of discussion at this stage. This paper studies the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. Relevant metrics were also calculated. The findings revealed that: The benchmark regression result of digital finance on the efficiency of the green economy was 0.4685 before adding the main restrictions; the benchmark regression result of digital finance on the efficiency of the green economy was 0.2243 after adding the main constraints. As a result, data finance had a favorable impact on the effectiveness of the green economy. [ABSTRACT FROM AUTHOR]

Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger's health as well as the public's comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients' narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields. [ABSTRACT FROM AUTHOR]

In this paper, the adoption of SO2 emission tax rates higher than the legal minimum standard is regarded as a noteworthy policy reform in China (quasi-natural experiment), and a spatial Difference-in-Differences (Spatial-DID) model is constructed to test the direct effects (local effects) and indirect effects (spatial spillover effects) of SO2 emission tax policy reform on PM2.5 concentrations in the atmosphere of 285 China's cities. The estimation and calculation results of the Spatial-DID model show that the SO2 emission tax policy reform can significantly reduce local PM2.5 concentration and significantly promote PM2.5 concentration in surrounding areas. The results of heterogeneity analysis show that the SO2 emission tax policy reform can produce a relatively more beneficial spatial spillover effect in eastern cities and higher administrative level cities, while the pollutants emission rights trading and the reform of NOx emission tax rates can produce beneficial spatial spillover effects when cooperating with the reform of SO2 emission tax rates. The results of the mediation effect analysis show that the higher SO2 emission tax rate can aggravate the surrounding PM2.5 pollution by promoting the aggregation level of industrial production factors and the industrial SO2 emission intensity in the surrounding areas, which can support the existence of the pollution heaven effect. [ABSTRACT FROM AUTHOR]

The rise of China as a scientific research superpower has been frequently discussed in media and literature. However, past analyses are usually based on the geographical database and they ignore how the millions of emigrated Chinese students, who are now being considered the major research workforce in many countries, affect their academic outputs. Here we quantitatively analyze the contribution of Chinese scholars in physical science around the globe by their publications in a country's papers from 2010 to 2021 as well as their citations. Contrary to common perception, we find that increasing the number of Chinese scholars does not correlate with the net publication growth or decline in their host countries before the Chinese population exceeds a critical value. On the other hand, increasing Chinese authors in a paper improves its citations. The phenomena, though anomalous, are observed in many subfields of physics across the globe. Our analysis suggests that although Chinese scholars do not change the perceived publication capabilities of many countries but may have reshaped their research culture as well as workforce distributions. The results would be valuable for R&D, higher education, and immigration policymakers. [ABSTRACT FROM AUTHOR]