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China eliminated rural poverty under current poverty standards in 2020. However, compared with rural poverty, urban poverty in China has been somewhat neglected. This paper aims to discover the changes and determinants of multidimensional urban poverty in Shandong Province, a representative province in Eastern China. Using a nationally representative panel dataset, the China Family Panel Studies, and the Dual Cutoff method, this study creates a multidimensional poverty index with four dimensions and 11 indicators to measure urban poverty in Shandong Province. This paper discovers that while the incidence of multidimensional urban poverty in Shandong Province decreased from 47.62% in 2010 to 36.45% in 2018, the intensity of multidimensional poverty only decreased from 41.27% to 37.25%, which indicates the inadequacy of urban anti-poverty efforts in Shandong Province. This paper also uses logistic regression to identify the determinants of multidimensional urban poverty. The findings suggest that income, health, drinking water, and durable goods are the main determinants of multidimensional urban poverty in Shandong Province. Based on these findings, this study provides targeted recommendations for future urban anti-poverty policies in Shandong Province. [ABSTRACT FROM AUTHOR]

Abstract: There has been much debate on the topic of whether stricter environmental regulations can promote environmental performance and economic performance at the same time. Different researchers have used different indicators to measure environmental performance and economic performance in their empirical studies. However, it is a surprise that few studies have checked the relationship between environmental regulation and eco-efficiency, as the latter is widely regarded as a quite powerful tool of considering ecology and economy together. In this study the background is the implementation in 2003 of the Stricter Discharge Standard (SDS) in Shandong Province’s Pulp and Paper Industry (SPPI), compared to the national standards of China. The stricter regulations were intended to promote corporate change from passive management to active control and from end-of-pipe treatment to cleaner production. This study investigated the eco-efficiency trends of SPPI from 2001 to 2008 in three fields: water efficiency, energy efficiency and environment efficiency. A “de-linking” and “re-linking” tool was used to attain a further evaluation. The study showed that with the implementation of stricter regulation most of the efficiency indicators (except CO2 emission and energy consumption) had achieved significant improvements, and the overall environmental performance trends of SPPI showed it to be more sustainable. However, the study also found that it was not enough to address a single indicator in the environmental regulation of the pulp and paper industry. More holistic eco-efficiency indicators need to be further considered and introduced to the industry as the next step to create true sustainable development. [ABSTRACT FROM AUTHOR]

Maintaining and improving ecological resilience is of great practical significance for the Yellow River Basin to reduce potential ecological risks and deliver sustainable development. Based on the essential characteristics of evolutionary resilience, this paper developed an ecological resilience index system of "resistance-recovery-reconstruction-renewal" and calculated the ecological resilience of 75 prefecture-level cities in the Yellow River Basin from 2007 to 2021 with the improved TOPSIS method. Then the spatiotemporal evolution characteristics of ecological resilience were analyzed using the gravity center-standard deviation ellipse, Dagum Gini coefficient decomposition, and spatial autocorrelation analysis. Furthermore, the dynamic spatial Durbin model (DSDM) was used to investigate the influencing factors of ecological resilience. The main results are as follows: (1) The ecological resilience of the Yellow River Basin showed an overall fluctuating upward trend, and the average annual growth rate in the downstream region was larger than in the upstream and midstream regions. (2) Cities with similar levels of ecological resilience were distributed in a "large settlement, small scattered" pattern. The center of gravity shifted to the southeast, and the spatial distribution exhibited a "northwest-southeast" pattern and a trend towards an "east–west" pattern. The primary source of spatial differences was the intensity of transvariation. (3) The ecological resilience in the Yellow River Basin showed significant spatial clustering, with the H–H clustering area shifting from the Hubao-Eyu urban agglomeration to the Shandong Peninsula urban agglomeration, and the L–L clustering area mainly distributed around the Central Plains city cluster. (4) The ecological resilience of the Yellow River Basin exhibited significant snowball, spillover, and siphon effects in time, space, and space-time dimensions, respectively. In the short and long term, population density and openness significantly positively affected the ecological resilience of local and surrounding cities. Urbanization had a long-term effect on ecological resilience without a short-term effect. GDP per capita and industrial structure only imparted a significant positive influence on local ecological resilience. The negative spatial spillover of the intensity of financial investment in technological innovation gradually turned into a positive effect. [ABSTRACT FROM AUTHOR]

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain. [ABSTRACT FROM AUTHOR]

The health of young people is crucial for the future and development of a nation. It is the collective responsibility and imperative mission of society to ensure the holistic well-being, both physically and mentally, of young individuals. Therefore, it is essential to thoroughly comprehend the factors that influence their health in order to expedite the exploration of effective solutions. The objective of this study is to comprehend the mechanisms that underlie the correlation between physical exercise behavior and psychological resilience among teenagers, while also examining the mediating role played by social sensitivity and need to belong. So put forward the hypothesis: (1) physical exercise behavior can positively predict the psychological resilience. (2) Social sensitivity and need to belong plays a mediating role between physical exercise behavior and psychological resilience. (3) Social sensitivity and need to belong plays a chain mediating role between physical exercise behavior and psychological resilience. Using the cluster sampling method, a total of 1106 students (with an average age of 15.7 and a standard deviation of 0.598) who met the requirements were surveyed from Shandong Province in China. Standard scales were utilized to assess Physical Exercise Behavior, Psychological Resilience, Social Sensitivity, and Need to Belong. For data analysis, Pearson's correlation analysis and bias-corrected percentile Bootstrap method were sequentially conducted. (1) The present study did not find any significant methodological bias, and the observed correlations between physical exercise behavior, psychological resilience, social sensitivity, and need to belong were all statistically significant; (2) Based on the self-determination theory, this study elucidates the relationship between physical exercise behavior and psychological resilience among teenagers. The findings indicate that physical exercise behavior positively predicts the need to belong and psychological resilience, while negatively predicting social sensitivity. Similarly, social sensitivity negatively predicts the need to belong and psychological resilience. Moreover, the need to belong directly and positively predicts psychological resilience. Importantly, all hypotheses proposed in this paper were empirically supported. (3) The indirect effect of the path mediated by social sensitivity is 0.009, while the indirect effect of the path mediated by need to belong is 0.033. Additionally, the combined indirect effect of both social sensitivity and need to belong as mediating variables is 0.014. (4) The cumulative sum of all these indirect effects amounts to 0.056. Based on the self-determination theory, we propose a chain mediation model, specially, physical exercise behavior can significantly positively predict psychological resilience, among which, social sensitivity and need to belong play a significant mediating role between Physical exercise behavior and psychological resilience. In addition, the adoption of good physical exercise behavior can enhance the psychological resilience of adolescents by diminishing social sensitivity and augmenting the need to belong. [ABSTRACT FROM AUTHOR]

The health of young people is crucial for the future and development of a nation. It is the collective responsibility and imperative mission of society to ensure the holistic well-being, both physically and mentally, of young individuals. Therefore, it is essential to thoroughly comprehend the factors that influence their health in order to expedite the exploration of effective solutions. The objective of this study is to comprehend the mechanisms that underlie the correlation between physical exercise behavior and psychological resilience among teenagers, while also examining the mediating role played by social sensitivity and need to belong. So put forward the hypothesis: (1) physical exercise behavior can positively predict the psychological resilience. (2) Social sensitivity and need to belong plays a mediating role between physical exercise behavior and psychological resilience. (3) Social sensitivity and need to belong plays a chain mediating role between physical exercise behavior and psychological resilience. Using the cluster sampling method, a total of 1106 students (with an average age of 15.7 and a standard deviation of 0.598) who met the requirements were surveyed from Shandong Province in China. Standard scales were utilized to assess Physical Exercise Behavior, Psychological Resilience, Social Sensitivity, and Need to Belong. For data analysis, Pearson's correlation analysis and bias-corrected percentile Bootstrap method were sequentially conducted. (1) The present study did not find any significant methodological bias, and the observed correlations between physical exercise behavior, psychological resilience, social sensitivity, and need to belong were all statistically significant; (2) Based on the self-determination theory, this study elucidates the relationship between physical exercise behavior and psychological resilience among teenagers. The findings indicate that physical exercise behavior positively predicts the need to belong and psychological resilience, while negatively predicting social sensitivity. Similarly, social sensitivity negatively predicts the need to belong and psychological resilience. Moreover, the need to belong directly and positively predicts psychological resilience. Importantly, all hypotheses proposed in this paper were empirically supported. (3) The indirect effect of the path mediated by social sensitivity is 0.009, while the indirect effect of the path mediated by need to belong is 0.033. Additionally, the combined indirect effect of both social sensitivity and need to belong as mediating variables is 0.014. (4) The cumulative sum of all these indirect effects amounts to 0.056. Based on the self-determination theory, we propose a chain mediation model, specially, physical exercise behavior can significantly positively predict psychological resilience, among which, social sensitivity and need to belong play a significant mediating role between Physical exercise behavior and psychological resilience. In addition, the adoption of good physical exercise behavior can enhance the psychological resilience of adolescents by diminishing social sensitivity and augmenting the need to belong. [ABSTRACT FROM AUTHOR]

In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m. [ABSTRACT FROM AUTHOR]

Developing rural e-commerce is seen as an effective measure to revitalize the economies of rural communities and is beginning to spread in several countries. However, conditions in rural communities are limited and lack the necessary elements for its promotion. Alibaba Group's Taobao Villages, a remarkable achievement of China's rural e-commerce development, has demonstrated strong vitality and is receiving increasing attention. Therefore, this research study into a Taobao Village, which has been developing in Shandong Province for more than a decade, conducts an in-depth analysis of its development history to provide a reference for rural e-commerce promotion. This paper proposes an 'internal innovation & external support' analytical framework for presenting the growth process of the rural community, which can explain how to sustain longterm operations and enhance e-commerce competitiveness. In addition, the paper confirms that the development of a Taobao Village is led by local-villager entrepreneurs, and this rural development strategy can encourage a diversity of entrepreneurs to participate in entrepreneurship and competition, all of which are worthy of further research and replication. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme weather disasters under global climate change has greatly impacted agricultural production, caused crop yield and price fluctuations, affected the increase of farmers' income, and hindered the development of the rural economy. As a typical mountainous region in North China, Shandong Province is a major peanut-planting province. In recent years, the peanut planting area in Shandong Province has remained relatively stable at around 10 million hectares (approximately 6.67 million hectares), accounting for approximately 14% of the national peanut planting area. Affected by meteorological disaster risks and consequent price risks, peanut farmers in Shandong Province are faced with double threats of output and price. This study is aiming to analyze the income risks faced by peanut planting farmers in the northern regions of China and to develop a comprehensive understanding of the factors influencing their income variability. The study may investigate various aspects especially the market price fluctuations, socioeconomic factors affecting peanut farming and the correlation of the peanut pricing and the local insurance. The current policy-based peanut insurance makes it difficult to fully disperse risks and guarantee stable production with the permission of relevant government department. Therefore, based on the peanut yield and price data of Shandong Province from 1991 to 2017 and following the logical context of "meteorological disaster - yield risk - price risk - income loss - income insurance", this paper uses the Copula method to calculate the premium rate of peanut income insurance and finds that under the guaranteed level of 70%-90%, the pure premium rate is between 1.5%-4.15%. Compared with the current cost insurance with a flat rate of 4% in Shandong Province, the income insurance rate is generally lower than the current rate. Secondly, there are regional differences in income insurance rates. In addition, at a higher level of protection, the income insurance rate is higher. [ABSTRACT FROM AUTHOR]

As the largest carbon emitter in China's manufacturing sector, the low-carbon transition of the steel industry is urgent. CO2 capture, utilization, and storage (CCUS) technology is one of the effective measures to reduce carbon emissions in steel industry. In this paper, a comprehensive assessment model of source–sink matching-levelized cost in China's steel industry is constructed to evaluate the potential, economy, and spatial distribution of CCUS retrofits of blast furnaces in the BF-BOF steel industry. The results show that, if no extra incentive policy is included, the levelized cost of carbon dioxide (LCOCD) of 111 steel plants with a 420.07 Mt/a CO2 abatement potential ranges from −134.87 to 142.95 USD/t. The levelized cost of crude steel (LCOS) range of steel plants after the CCUS retrofits of blast furnaces is 341.81 to 541.41 USD/t. The incentives such as carbon market and government subsidies will all contribute to the early deployment of CCUS projects. The CCUS technology could be prioritized for deployment in North China, Northwest China, and East China's Shandong Province, but more powerful incentives are still needed for current large-scale deployment. The research results can provide references for the early deployment and policy formulation of CCUS in China's steel industry. [ABSTRACT FROM AUTHOR]

Based on China's green energy development strategy, this paper constructs a basic model of recycling and a channel expansion model for the circular economy foundation of Zaozhuang, Shandong Province, China. Through numerical simulation, it is found that each member of the supply chain should control the rate of price adjustment, otherwise it will cause market disruption. The model is controlled based on a chaos control method. Then, based on the fuzzy comprehensive evaluation method, an early warning system for the circular economy of Zaozhuang City is constructed. It is found that the economic development of Zaozhuang is a serious warning, resources are moderate warning, and the environment is not in an alarm state. In addition to paying attention to energy conservation and emission reduction of enterprises, the government should pay attention to creating awareness of energy conservation and emission reduction in society, and strengthen the technological investment in reducing pollutant emissions. This paper provides a strategic reference for the circular economy model in Zaozhuang, Shandong, China. [ABSTRACT FROM AUTHOR]

In order to realize rapid and accurate evaluation of drinking water quality, a small-scale water quality detection instrument is designed in this paper that can detect two representative water quality parameters: the permanganate index and total dissolved solids (TDS). The permanganate index measured by the laser spectroscopy method can achieve the approximate value of the organic matter in water, and the TDS measured by the conductivity method can obtain the approximate value of the inorganic matter in water. In addition, to facilitate the popularization of civilian applications, the evaluation method of water quality based on the percent-scores proposed by us is presented in this paper. The water quality results can be displayed on the instrument screen. In the experiment, we measured the water quality parameters of the tap water as well as those after the primary and secondary filtration in Weihai City, Shandong Province, China. The testing results show that the instrument can quickly detect dissolved inorganic and organic matter, and intuitively display the water quality evaluation score on the screen. The instrument designed in this paper has the advantages of high sensitivity, high integration, and small volume, which lays the foundation for the popularity of the detection instrument. [ABSTRACT FROM AUTHOR]

• Fracture evolution in dual gob-side entries varies spatially and temporally. • A formula to calculate arbitrary dip rock layer cutting angles was derived. • A technology based on the fracture evolution's turning points was proposed. • Differential roof cutting-anchor grouting boosts stability in dual gob-side entries. The coal pillar-free gob-side entry retention technology (GER), which enhances resource recovery rates and reduces the amount of tunnel excavation, has been applied in multiple mining areas in China. However, cases of implementing gob-side entry retention on both sides of a single working face are rare. The asynchronous extraction of long-distance working faces leads to significant differences in the stress environment of roadways on both sides, and uniform technical means are poorly adapted to the control of surrounding rock on both sides, affecting the safe and efficient production. This paper, taking the dual gob-side entry retention on both sides of the 61001 W working face with thick mudstone roofs in Zhao Guan Coal Mine, Shandong Province, as the engineering background, utilizes a combination of theoretical calculations, numerical simulations, and on-site detailed monitoring to analyze the uncoordinated deformation characteristics of the upper part bulging and the lower part concaving of the two roadways. It clarifies the differentiated spatiotemporal evolution law of the number of fractures and the development turning points at different depths of the surrounding rock of the two entries, revealing the differential deformation mechanism of the surrounding rock of the two entries caused by stress state and fracture development differences. A "cutting roof to relieve pressure-multilevel continuous anchor-grouting" combined control technology system is proposed. By cutting off the stress transmission of the roof through drilling and blasting, and then reconstructing the integrity of the surrounding rock and improving the rock strength with the help of anchor-grouting. With the application of this new technology, the deformation of the surrounding rocks of the two entries was reduced by more than 60 % and 29 %, respectively, and the maximum crack depth of the roof was controlled within 3 m, significantly enhancing the stability of the roadways. The research results of this paper provide a reference for the control of surrounding rock in gob-side entry retention under similar conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: Most management methods for poultry farming currently rely on human labor. Such management is labor-intensive and inefficient, especially in identifying poultry growth stages. Given the lack of a high-precision artificial method for chick's day-age detection, a high-performance day-age classification and detection model for chicks was proposed based on artificial intelligent techniques. This method can detect and classify chicks in six different living stages from 1 to 32 day-ages, and the accuracy is 95.2%, superior to other current ones. In order to apply this method in practical scenarios, it has been deployed into an application based on the IOS system, which can recoganize the day-age of chicks by capturing real-time photos. The system is currently deployed in Rizhao City, Shandong Province, China. It helps chicken farm staff automatically detect the behavior of chickens, whose excellent working effect proves the robust availibility of the proposed method. Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices. [ABSTRACT FROM AUTHOR]

This paper introduces a method of combining phase space reconstruction and symbolic dynamics to study the causality between urbanization and economic growth at different regional levels in Shandong Province and finds that there is a strong positive relationship between urbanization and economic growth from China, indicating that the development of urbanization can drive the economic growth. Then, according to the results of correlation analysis between respective subvariables belonging to urbanization and economic growth and the principle of "deleting strong and reserving weak," the paper selects the independent variable and dependent variable to explore the hidden causal mechanisms that drive the developing of urbanization and economic growth from China. The results show that (1) the pattern causality between the independent variable and the dependent variable is dominated by dark causality at the provincial level; (2) the pattern causality between the independent variable and the dependent variable is dominated by dark causality at the Jinan economic circles and the Lunan economic circles, but the positive causality is dominated at the Jiaodong economic circles; (3) the types of causality between the same evaluation index and PU in different regions are different, and furthermore the degrees of positive, negative, and dark causality are different at two levels and three regions. The conclusion shows that although there is an obvious positive interaction between urbanization and economic growth, the influences of many factors are neither positive nor negative causality, but dark causality, which plays an important role in developing urbanization and economic growth. This work is useful for studying the law of causality between urbanization and economic growth, and this interesting result can be extended to other economic events. [ABSTRACT FROM AUTHOR]

With a focus on the Cu isotope geochemistry of chalcopyrite, this paper analyzed the Cu isotope geochemistry of the Qibaoshan crypto-explosive breccia-type Cu–Au polymetallic ore district in Wulian, Shandong Province, North China Craton (NCC). Combined with the results of the in situ sulfur isotope analysis of sulfides, a certain reference and evidence for the study of the genetic mechanism of the epithermal-porphyry Cu polymetallic metallogenic system were provided. The results of the in situ isotope analysis show that the δ34S values of the main sulfides in the Qibaoshan Cu–Au polymetallic ore district range from −6.81‰ to +3.82‰ and are likely to be attributed to the mixing of the derived mantle with the surrounding sedimentary rock assimilation. The ore-forming mechanism may be related to the progressive cooling and transition of the earliest hydrothermal fluids that were dominated by H2S under relatively reducing conditions, followed by a gradual transition from oxidation to reduction. The Cu isotopic composition of the sulfides in ores (δ65Cu = +0.169‰–+0.357‰) decreases with depth, which is likely caused by the upward transport of heavier Cu isotopes. The upper part of the crypto-explosive breccia pipe in the Qibaoshan area may be relatively more gaseous, resulting in the enrichment of δ65Cu. As the gas phase decreases and the liquid phase increases with depth, the δ65Cu value gradually decreases. This indicates the transition from a low-temperature phyllic alteration to a high-temperature K-feldspar alteration. Large, concealed pluton intrusions or orebodies may be present at a depth of the Qibaoshan area. The heavy δ65Cu characteristic is a potential indicator for tracing the fluid activity of the porphyry system and searching for Cu mines. The results provide a reference for the study of the genetic mechanisms of the epithermal-porphyry Cu polymetallic metallogenic system. [ABSTRACT FROM AUTHOR]

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction. [ABSTRACT FROM AUTHOR]

This paper explored the transmission mechanism of the public sense of gain (PSG) and YB conservation and development, suggested to take the PSG as a better regulation and guidance for the sound development of the Yellow River Basin. Based on the data of CSS2019, this paper tried to exam the PSG at the opening stage of Chinese major strategy on YB Conservation and Development. An evaluation index system including 5 factor dimensions and 25 indices was constructed, and the normal distribution was used to fitting the survey data, after that, the dominance matrix method was applied to make decision based on PROMETHEE?. The empirical analysis results indicated that the PSG of the middle and upper reaches of the Yellow River was lower than the overall level of China, while that of Henan and Shandong in the lower Yellow River were both higher than its national average; and the public generally had a higher sense of gain on government public service and social civilization, however, the PSG on individual self-fulfillments of Henan and Shandong and that on economic situations of the middle and upper reaches of the Yellow River were both at a lower level. [ABSTRACT FROM AUTHOR]

Many deep mining mines in southwestern Shandong Province of China are covered with thick loose layers. When mining near the loose layers, there is a risk of water and sand inrush, which threatens the personal safety of miners. The prediction of sudden water and sand inrush is difficult due to the comprehensive influence of many factors, and the influencing factors are fuzzy and random. To solve this problem, in this paper, a new risk assessment method of water and sand inrush based on comprehensive weight and cloud model was proposed. Seven factors are selected as indexes: the aquifer thickness, the thickness ratio of sand layer to clay layer, the thickness of bottom clay layer, the coal seam thickness, the percentage of core recovery, the geological structure, and the bedrock thickness. The assessment index system is established, and the index is divided into three grades. A comprehensive weighting method, which combines analytic hierarchy process (AHP), entropy weight method (EWM), and minimum entropy principle, is used to reasonably assign the weight of index. Based on the cloud generator equation, the membership function is obtained. The assessment result of the assessment object is obtained by combining the membership degree and the weight of index. The comprehensive weight-cloud model assessment method is applied to the risk assessment of water and sand inrush in the 6311-2 working face in the sixth mining area of Baodian Coal Mine. According to the assessment results, the following conclusions can be drawn: (1) the bedrock thickness and the coal seam thickness are the main factors of water and sand inrush under loose layer mining; (2) the assessment results obtained by the comprehensive weight-cloud model method are consistent with the actual situation. The assessment method can provide scientific reference for the safe mining under the thick loose layer in the deep mines of southwest Shandong. [ABSTRACT FROM AUTHOR]

The dendrolites composed of light gray massive micrite limestone occur at the top of the Cambrian Zhangxia Formation in Houziyu Section, Zibo, Shandong Province. Because it is very difficult to study the genesis of microbial rocks through "the function of diagenesis filter," this paper analyzes its genetic mechanism by characterizing the sedimentary fabric and formation environment of dendrolites and it is of great significance to study microbial carbonate deposited in geological history in oil exploration. The Cambrian Zhangxia Formation in the Houziyu section is a marine deposit. The macroscopic shrub-like fabric of centimeter scale can be seen on the surface of the tree-shaped stone, with small biological burrows and remnants of calcareous mud components after filling. The microstructure observation shows that the main fabric of the dendrolites is a dark micrite matrix, bright calcite cement, clastic particles, and a calcified cyanobacteria biological community. Detailed supplementary information on the diversity of calcified microorganisms in dendrolites has been made through the study of various types of calcified cyanobacteria and calcified residual substances in the microbial membrane of calcified cyanobacteria in the dendrolites of the study area. It is clear that dendrolites are the product of the calcification of biofilms or microbial mats dominated by cyanobacteria. With the deepening of this kind of research, it is bound to raise the research of microbial carbonate to a new level in China and provide great help to find oil and gas resources in marine strata of microbial origin in China. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) is a three-dimensional model presenting the elevation of the Earth's surface, which can be obtained by the along-track or cross-track stereo images of optical satellites. This paper investigates the DSM extraction method using Gaofen-6 (GF-6) high-resolution (HR) cross-track images with a wide field of view (WFV). To guarantee the elevation accuracy, the relationship between the intersection angle and the overlap of the cross-track images was analyzed. Cross-track images with 20–40% overlaps could be selected to conduct DSM extraction. First, the rational function model (RFM) based on error compensation was used to realize the accurate orientation of the image. Then, the disparity map was generated based on the semi-global block matching (SGBM) algorithm with epipolar constraint. Finally, the DSM was generated by forward intersection. The GF-6 HR cross-track images with about 30% overlap located in Taian, Shandong Province, China, were used for DSM extraction. The results show that the mountainous surface elevation features were retained completely, and the details, such as houses and roads, were presented in valleys and urban areas. The root mean square error (RMSE) of the extracted DSM could reach 6.303 m, 12.879 m, 14.929 m, and 19.043 m in valley, ridge, urban, and peak areas, respectively. The results indicate that the GF-6 HR cross-track images with a certain overlap can be used to extract a DSM to enhance its application in land cover monitoring. [ABSTRACT FROM AUTHOR]

Plastic shed vegetable (PSV) cultivation has become an important vegetable production system in China, which accounts for about 1/4 of the total vegetable production of China. Shandong is the largest facility vegetable growing area in China. Continued years of high-input, high-output planting methods have brought some problems to the surrounding environment. Salinization has not only affected the soil and groundwater environment, but also the growth of vegetables. A comprehensive understanding of soil salinization in plastic shed vegetable fields is important for the sustainable development of the vegetable farming industry. In this paper, random survey sampling was used to collect soil samples from representative PSV production areas in Shandong Province, and the soil salinization status, acidification status, salt ion composition, and the main causes of soil salinity were studied in the survey area. Our study found that the salinized soils (mild, moderate, and severe levels) in the surveyed areas accounted for 40% of the total samples, and the total soluble salt (TSS) increased with the increase of the planting years. After continuous planting for 10 years, the soil pH showed a downward trend, indicating that salinization and acidification occurred simultaneously. A significant positive correlation was observed between TSS and soil organic matter (SOM) (r = 0.4773**). Principal component analysis found that , , , , Ca2+, K+, Mg2+, and Na+ had higher positive loads on principal component 1, the factor loads were 0.557–0.927, and the contribution rate reached 34.928%. Correlation analysis also showed that TSS was positively correlated with the concentrations of these ions (P < 0.01). The soil salinization status should be closely monitored and regulated by regulatory authorities to improve environment and soil health through balanced fertilization, thereby improving the yield and quality of vegetables. [ABSTRACT FROM AUTHOR]

Background: Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction. Methods: We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results: Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are shown in results section of this paper. Conclusions: The experiments show that attention-based LSTM is superior to other comparative models. Multivariate attention and temporal attention can greatly improve the prediction performance of the models. Among them, when all meteorological factors are used, multivariate attention performance is better. This study can provide reference for the prediction of other infectious diseases. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the Seq2Seq model understands each predictor variable. The hybrid model combining an RF with Seq2Seq network (RF-S2S) was evaluated and achieved a Pearson's correlation coefficient of 0.93, a Nash–Sutcliffe coefficient (NS) of 0.79, a Root Mean Square Error (RMSE) of 5.85 µg/m3, a Mean Absolute Error (MAE) of 4.50 µg/m3, and a Mean Absolute Percentage Error (MAPE) of 20.86%. Feature selection by an RF model improves the performance of the Seq2Seq model, reducing errors by 19.7% (RMSE), 20.3% (MAE), and 29.3% (MAPE), respectively. Carbon monoxide (CO) and PM10 are two common, important features influencing the prediction of NO2 concentrations in coastal areas of northern China. The results of RF-S2S models can capture general trends and disruptions more accurately than can long-short term memory (LSTM) models with and without feature selection. The decreasing tendency of NO2 from 2014 to 2020 illustrated by the empirical mode decomposition (EMD) method is one important obstacle to improving the RF-S2S prediction accuracy. An EMD-based RF-S2S model could help to perform the short-term forecast of NO2 concentrations efficiently. [ABSTRACT FROM AUTHOR]

The North Yellow Sea, located at the intersection of the Eurasian continent and North Pacific Ocean at mid-latitudes, is a sensitive area subjected to the joint actions of the ocean, land, and monsoons. On its southern shore, loess and paleosol sedimentary sequences were widely developed during the last interglacial period, which is of great significance for revealing patterns of climate change and dynamic conditions. In this paper, we focus on the Zhifu section (ZFS) on Zhifu Island within the Shandong Province of China. The optically stimulated luminescence (OSL) dating method was used to construct our chronological framework. Grain size and its endmember (EM) components were then analyzed; EM1 is a clay component EM, which represents a weak dynamic environment and strong weathering pedogenesis, while EM2 and EM3 are silt and very fine sand component EMs, respectively, representing a strong dynamic environment and weak weathering pedogenesis. Maximum EM1, mean grain size, clay content, and pH values occur in the paleosol layers (ZF4, ZF6, and ZF8), with minimum values in the loess layers (ZF5 and ZF7); EM3 values show the opposite pattern. This indicates that the ZF4, ZF6, and ZF8 layers represent warm and humid environments with abundant precipitation, where the East Asian summer monsoon (EASM) was enhanced, corresponding to Marine Isotope Stages (MIS) 5a, 5c, and 5e. In contrast, ZF5 and ZF7 represent sub-warm and humid environments with less precipitation, where the EASM was weakened, corresponding to MIS 5b and 5d. Among these stages, MIS5e is the warmest and wettest. These climatic events reveal the pattern of climate fluctuation over a ten-thousand-year timescale; they are synchronous with climate changes recorded in other geological repositories, such as cave stalagmites in southern China and sea-level fluctuations in the Yellow-Bohai Sea, which result from changes in global solar radiation. [ABSTRACT FROM AUTHOR]

Improving culture and tourism integration efficiency is an important way to promote the high-quality development of cultural tourism. According to the inherent requirements of high-quality development, this paper constructed an evaluation indicator system for culture and tourism integration efficiency. Then, the culture and tourism integration efficiency of 16 cities in Shandong Province, China during the period from 2010 to 2019 was measured with the benevolent DEA cross-efficiency model. On the basis of exploratory spatial data analysis and dynamic spatial Durbin model, we explored the spatio-temporal evolution characteristics and influencing factors of culture and tourism integration efficiency in Shandong Province. The results show that from 2010 to 2019, the culture and tourism integration efficiency in Shandong Province has experienced three stages of "rapid growth-rapid decline-stable rise period". The spatial pattern has changed from "high in the east and low in the west" to "high in the central and low in the north and south", and regions with high integration efficiency are mainly concentrated in Jiaodong Peninsula. The level of economic development significantly promotes the culture and tourism integration efficiency in local and neighboring cities in the short and long term, while policy environment has a significant negative impact. Traffic conditions and human capital only promote the culture and tourism integration efficiency in local cities. The level of information development and openness degree only have a long-term effect on the culture and tourism integration efficiency, without short-term effect. The research results are of great significance to improve the growth quality and sustainable development of cultural tourism in Shandong Province. Our work could provide a scientific basis for maximizing the allocation benefits of cultural and tourism resources in similar regions in the world. [ABSTRACT FROM AUTHOR]

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities. [ABSTRACT FROM AUTHOR]

GaoFen 7 (GF-7) is China's first submeter high-resolution stereo mapping satellite with dual-linear-array cameras and a laser altimeter system onboard for high-precision mapping. To further take advantage of the very high elevation accuracy of laser altimetry data and the high relative accuracy with stereo images, an innovative combined adjustment method for GF-7 stereo images with laser altimetry data is presented in this paper. In this method, two flexible and effective schemes were proposed to extract the elevation control point according to the registration of footprint images and stereo images and then utilized as vertical control in the block adjustment to improve the elevation accuracy without ground control points (GCPs). The validation experiments were conducted in Shandong, China, with different terrains. The results demonstrated that, after using the laser altimetry data, the root mean square error (RMSE) of elevation was dramatically improved from the original 2.15 m to 0.75 m, while the maximum elevation error was less than 1.6 m. Moreover, by integrating a few horizontal control points, the planar and elevation accuracy can be simultaneously improved. The results show that the method will be useful for reducing the need for field survey work and improving mapping efficiency. [ABSTRACT FROM AUTHOR]

Food security plays an important role in maintaining national stability and sustainable development of human society, and its research has become a hot issue at present. Shandong is the main grain producing area in China, and its grain production plays an important role in national food security. Accordingly, this paper is based on the county climate change, grain yield, sown area, fertilizer use, total power of rural machinery, and total population data in Shandong Province from 1995 to 2020. The evolution process of the food security pattern was studied by the methods of spatial analysis and comprehensive evaluation, the influencing factors of food security were quantitatively analyzed, and the adaptive countermeasures to alleviate the food security risks in this region were discussed. The results show that: Grain production increased by 30.62% from 1995 to 2020. The total population and per capita food availability also increased. Since 2000, more than a quarter of counties have experienced a high risk of food insecurity. The spatial agglomeration of grain production was enhanced, and the local agglomeration characteristics were significantly different. The average temperature in the growing season, the sown area, and the total power of agricultural machinery had a significant positive impact on grain production, while the annual average temperature had a significant negative impact on grain production. Improving the food supply system, strengthening the protection of cultivated land, improving the efficiency of fertilizer utilization, and increasing investment in agricultural science and technology can effectively alleviate food security risks. [ABSTRACT FROM AUTHOR]

China's food security has always been a high priority issue on the political agenda with rapid urbanization affecting agricultural land, and it is challenged by several factors, such as human activities, social politics and policy. Shandong is an important grain-producing province and the second most populous province in China. In this paper, the spatiotemporal characteristics of grain yield and their potential influencing factors were explored at the county level in Shandong by using panel data over a 19-year period. The location Gini coefficient (L-Gini) and exploratory spatial data analysis (ESDA) were used to study the spatial agglomeration characteristics of grain yield, and spatial regression methods (SRMs) were used to analyse the influencing factors. The results indicated that grain yield increased from 38.3 million metric tons to 53.2 million metric tons in 2000–2018, with a growth rate of approximately 28.0%. The increase in grain yield in Shandong was due to the driving effect of radiation from high-yield counties to surrounding moderate-yield counties. This revealed an upward trend of spatial polarization in Shandong's grain yield. In 2000–2018, the L-Gini and global Moran's I increased from 0.330 to 0.479 and from 0.369 to 0.528, respectively. The number of counties in high-high (HH) and low-low (LL) agglomeration areas increased, and the spatial polarization effect was significant. SRMs analysis showed that irrigation investment and non-grain attention have significant positive and negative effects on grain production, respectively. The spatial relationship between grain yield and its influencing factors was explored to provide a reference for formulating scientific and rational agricultural policies. [ABSTRACT FROM AUTHOR]

On 12–13 February 2016, a record-breaking rain–snow event during the passage of an extratropical cyclone occurred in Shandong Province, China, in which the 24 h precipitation totals at 48 of 123 national meteorological stations in Shandong Province broke their historical records for the month of February, and a further 25 stations recorded their second-largest February totals. This paper investigates the evolution of the mesoscale precipitation areas and the mechanisms responsible for the formation, organization, and maintenance of the mesoscale precipitation areas, using FY-2G satellite data, Doppler radar observations, and a Weather Research and Forecasting (WRF) model numerical simulation at 4 km grid spacing. The main results show that the comma head cloud of the cyclone developed from four echo strips. Intense precipitation was related to the mesoscale elongated precipitation areas (EPAs) of reflectivity >30 dBZ within the stratiform clouds. The formation and development of the EPAs coincided with the activities of a low-level shear line and an associated increase in frontogenesis. The simulated EPAs occurred in an environment of conditional instability (CI), inertial instability (II), and conditional symmetric instability (CSI). In the initial stage of the elongated rainfall areas (ERAs), rainfall was initiated by the frontal forcing in the presence of elevated CI, and II was generated by upright convection. During the development stage of the ERAs, the CI was absent, and condensational heating was enhanced. II occurred in the absence of upright convection, and it seems likely that the presence of II is a diabatic signature of the precipitation itself. Upper-level II intensified the convective systems by enhancing outflow aloft, and II caused the ERAs to organize. Thus, II played an important role in the organization and maintenance of the ERAs. The frontogenesis provided the dynamic condition for the release of the instability. Enhanced CSI and II intensified slantwise convection, and combining with enhanced frontogenesis, intensified the ERAs. The echo, ascent, and frontogenesis in snowfall areas were weaker than those in rainfall areas. [ABSTRACT FROM AUTHOR]

To explore the correlation between academic performance and learning motivation in English course under a corpus-data-driven blended teaching model, this study set research objects as 62 year-2020-enrolled undergraduate students majoring in English from a university in Jinan City, Shandong Province, eastern China. According to their previous frequencies of using information technology to learn English, these 62 students were divided into two groups: practice group with high frequency and control group with low frequency, with 31 students in each group. The two groups of students were taught 3 English lessons per week for a total of 15 weeks by the exact same teachers using a corpus-data-driven blended teaching model. The students' English academic performances were assessed by well-organized final tests, and their English learning motivations were measured by a motivation scale and questionnaires. The results show that the correlation coefficients between the average score of motivation questionnaires, intrinsic motivation factors, extrinsic motivation factors, and the average score of academic performances in practice group were 0.894, 0.682, and 0.724, respectively, while those in control group were 0.749, 0.836, and 0.904. In all the above correlation analyses, the significance level is 0.01, and all coefficient values are higher than critical value. Hence, there is a positive correlation between learning motivation and academic performance of the two groups of subjects. It is found that the corpus-data-driven blended teaching model has a significant impact on college students' English academic performance and learning motivation, and it has a positive effect on the improvement of their English academic performance and the cultivation of learning motivation. In general, the key to this teaching model lies in reasoning and acquisition by analyzing the language provided by the corpus, and the whole process of data-driven learning is student-centered. Students are exposed to a large number of authentic language knowledge and cultural information, which promotes the sensitivity to relevant points. The results of this paper provide a reference for further research on the analysis of the correlation between academic performance and learning motivation in English course under the corpus-data-driven blended teaching model. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]

Late Mesozoic granites are present throughout the Jiaodong Peninsula in eastern China, but the petrogenesis of these granites, the magma sources, and the geodynamic setting of the magmatism remain unclear. This paper presents new zircon U–Pb geochronological and Lu–Hf isotopic and whole‐rock geochemical data for the Late Jurassic Queshan pluton in the Shandong Peninsula, an area that represents the boundary between the Jiaobei and Sulu ultrahigh‐pressure (UHP) terranes. Laser ablation–inductively coupled plasma–mass spectrometry zircon U–Pb dating yields Late Jurassic ages from 158 ± 1 to 153 ± 1 Ma for the pluton in addition to a small number of inherited zircons that yield ages that cluster at 2410, 1831–1776, 750, and 209–202 Ma. Zircons from different samples have similar 176Hf/177Hf values (0.281926–0.282255) and variably negative εHf (t) values that range from −26.5 to −15.1 (weighted mean of −22.9 ± 0.9). The corresponding two‐stage Hf model ages range from 2,869 to 2,154 Ma, with a weighted mean value of 2,645 ± 54 Ma. These data indicate that the magmas that formed the Queshan pluton were derived from multiple sources, including the lower crust of the North China (NCC) and Yangtze (YC) cratons, as well as collision‐related alkaline rocks. The whole‐rock geochemical compositions of the samples indicate that the granites are weakly peraluminous, are high‐K and calc‐alkaline with I‐type affinities, and have high contents of Sr, low contents of Y and Yb, high (La/Yb)N and Sr/Y values, and positive Eu anomalies (δEu = 1.20–1.65), indicating an adakitic affinity. This indicates that the Queshan granites formed from magmas generated by partial melting of a thickened region of the crust (>50 km) associated with eclogite, garnet amphibolite, or amphibolite residual material. The genesis of the Late Jurassic granites are also likely to have been influenced by the collisional orogeny between the NCC and YC and continued subduction of the Izanagi Plate. Thickening of the lower crust associated with this continental collisional event occurred before the magmatism, with further compression and thickening of the lower crust caused by the low‐angle subduction of the Izanagi Plate beneath northeastern China being the main geodynamic driver of the formation of the granites in this region. [ABSTRACT FROM AUTHOR]

Green has become the underpinning of High-quality development in China. Green development is inseparable from the support of institutional governance. This study uses MVC mode to build institutional governance mechanism, and constructs institutional governance system and model of regional green development. The empirical results show the following: institutional governance has great influence on industrial green development. From the perspective of China's provincial industrial green development, Shandong Province has been maintaining the first position of governance investment. Green governance is better in southern China than in the northern provinces. And the institutional governance intensity of each province is not evenly distributed. Therefore, China's industrial green development still needs to further strengthen the system governance of northwest and northeast regions. Finally, this paper aims to improve the green development level of China's regional industrial economy through the construction of institutional governance system. It provides institutional environment for the development of green innovation. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [ABSTRACT FROM AUTHOR]

Colletotrichum has numerous host range and distribution. Its species are important plant pathogens, endophytes and saprobes. Colletotrichum can cause regular or irregular depressions and necrotic lesions in the epidermal tissues of plants. During this research Colletotrichum specimens were collected from Mengyin County, Shandong Province, China. A multi-locus phylogenetic analysis of ITS, GAPDH, CHS-1, ACT, TUB2, CAL and GS sequence data combined with morphology, revealed a new species and two known species, viz. C. mengyinense sp. nov., C. gloeosporioides and C. pandanicola, belonging to the C. gloeosporioides species complex. The new species is described and illustrated in this paper and compared with taxa in the C. gloeosporioides species complex. [ABSTRACT FROM AUTHOR]

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy. [ABSTRACT FROM AUTHOR]

Effectively treating industrial SO 2 emissions depends on the synergy of different factors from the industrial SO 2 generation source to the end of treatment. Applying a whole process treatment perspective, this paper decomposes industrial SO 2 emissions into six specific driving factors in three whole process treatment dimensions (i.e. source prevention, process control, and end-of-pipe treatment), and economic scale. A temporal index decomposition analysis (Temporal-IDA), attribution analysis (AA), and spatial index decomposition analysis (Spatial-IDA) methods are then applied to quantify each dimension's treatment effect and its spatial differences. The empirical study across 30 regions in China using data from 2005 to 2015 shows that: (1) The end-of-pipe treatment is the dominant dimension for decreasing industrial SO 2 emissions, followed by process control. The contribution of source prevention to reduce industrial SO 2 emissions has begun to appear, however, there remains room for further improvement; (2) End-of-pipe treatment strength and energy intensity are key factors in reducing industrial SO 2 emissions; Inner Mongolia, Henan, and Shandong are the main contributors; (3) The treatment emphasis are different among regions; as such, there are different treatment effects across the three dimensions of the whole process treatment. Regions can be classified into four categories: the Leading type , Process-dependent type , End-dependent type, and Lagging type. Based on the empirical results, this paper identifies the policy implications of promoting whole process treatment on China's industrial SO 2 emissions. • We study China's industrial SO 2 emissions from a whole process treatment perspective. • Both temporal and spatial index decomposition analysis methods are used. • End-of-pipe treatment and process control majorly reduce industrial SO 2 emissions. • Industrial SO 2 emissions whole process treatment performances vary among regions. [ABSTRACT FROM AUTHOR]

The objectives of this paper were to develop, evaluate and apply the remote sensing based on models of ammonia nitrogen (NH3-N) and total phosphorus (TP) for the Yi and Shu River in Linyi Economic Development Zone, Shandong Province, China. The scenes of Landsat-8 OLI satellite data were used to establish the relationship between the planetary reflectance and the measured data for five years (i.e., 2014-2017). The five spectral bands (i.e., Coastal, Blue, Green, Red, NIR) were used to obtain the most suitable modeling factor from the different band combinations. The liner regression analysis showed that red band both had highest correlation with NH3-N and TP in Yi and Shu River. For the Yi River, the co-efficient of determination (R2) of red band was 0.541 for the NH3-N model and 0.498 for the TP model. For the Shu River, the co-efficient of determination (R2) of red band was 0.643 for the NH3-N model and 0.565 for the TP model. The best-fit Models were validated with measured data and found that the errors of the NH3-N and TP models of the Yi River were 34.93% and 29.33%, respectively, and while 33.66% and 32.48% for the Shu River. With the models, the distribution diagrams of NH3-N and TP were generated, which can be assisted analysis of spatial and temporal for Yi and Shu River. The results showed that the established models could indicate the trend of NH3-N and TP in rivers that was validated by field visit. The proposed approach in this paper has satisfactory validity and practicability, thus could be reference for study on NH3-N and TP in water based on remote sensing. [ABSTRACT FROM AUTHOR]

• Analyzed the cause of secondary explosion accident in Hushan Gold mine in China. • Clarified the direct and indirect causes of the accident. • Calculated and verified the direct cause of the secondary explosion accident. • Put forward the suggestions for preventing secondary explosion accidents in mines. Explosion accidents often occur during mining, especially sudden secondary explosions, which pose a serious threat to the life and property safety of employees. Based on the development process and consequences of typical secondary explosion accidents in Hushan Gold Mine, Qixia City, Shandong Province, China, this paper uses accident analysis model and cone measurement calculation method to determine the direct cause of secondary explosion accidents. The main conclusions are as follows: The HFACS-CM model is used to analyze the causes of secondary explosion from five aspects: external environment, organizational influence, unsafe leadership, the prerequisite conditions of unsafe behavior and unsafe behavior. It is found that the CO ignition explosion in the mining process is the direct cause of secondary explosion. Then, combined with the experimental data of the cone measuring instrument, the field CO amount was calculated, and the M CO ≥ 101.4 m3 was obtained, indicating that the CO production did reach the condition of explosion range, and the analysis results of the model were verified. Through HFACS-CM model analysis and calculation verification, the key causes of secondary explosion accidents in such mines are found and lessons are learned, which provides an effective reference for the effective prevention of deflagration accidents in the field of mine safety. [ABSTRACT FROM AUTHOR]

In this paper, we present a traffic cyber physical system for urban road traffic signal control, which is referred to as UTSC-CPS. With this proposed system, managers and researchers can realize the construction and simulation of various types of traffic scenarios, the rapid development, and optimization of new control strategies and can apply effective control strategies to actual traffic management. The advantages of this new system include the following. Firstly, the fusion architecture of private cloud computing and edge computing is proposed for the first time, which effectively improves the performance of software and hardware of the urban road traffic signal control system and realizes information security perception and protection in cloud and equipment, respectively, within the fusion framework; secondly, using the concept of parallel system, the depth of real-time traffic control subsystem and real-time simulation subsystem is realized. Thirdly, the idea of virtual scene basic engine and strategy agent engine is put forward in the system design, which separates data from control strategy by designing a general control strategy API and helps researchers focus on control algorithm itself without paying attention to detection data and basic data. Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. The standard protocol conversion middleware is skillfully designed, which decouples the field equipment from the system software and achieves the universality and reliability of the control strategy. To further demonstrate the advantages of the new system, we have carried out a one-year practical test in Weifang City, Shandong Province, China. The system has been proved in terms of stability, security, scalability, practicability and rapid practice, and verification of the new control strategy. At the same time, it proves the superiority of the simulation subsystem in the performance and simulation scale by comparing the different-scale road networks of Shunyi District in Beijing and Weifang City in Shandong Province. Further tests were conducted using real intersections, and the results were equally valid. [ABSTRACT FROM AUTHOR]

The green development of coastal urban agglomerations, which are strategic core areas of national economic growth in China, has become a major focus of both academics and government agencies. In this paper, China's coastal urban agglomeration is taken as the research area, aiming at the serious air pollution problem of coastal urban agglomeration, geographic information system (ArcGIS10.2) spatial analysis and the spatial Dubin model were applied to National Aeronautics and Space Administration atmospheric remote sensing image inversion fine particulate matter (PM2.5) data from 2010–2016 to reveal the temporal and spatial evolution characteristics and Influence mechanism of PM2.5 in China's coastal urban agglomerations, with a view to providing a reference value for coordinating air pollution in the coastal cities of the world. From 2010–2016, the PM2.5 concentration in China's coastal urban agglomerations decreased as a whole, and large spatial differences in PM2.5 concentration were observed in China's coastal urban agglomerations; the core high-pollution areas were the Beijing–Tianjin–Hebei, Shandong Peninsula, and Yangtze River Delta urban agglomerations. Large spatial differences in PM2.5 concentration were also observed within individual urban agglomerations, with higher PM2.5 concentrations found in the northern parts of the urban agglomerations. Significant spatial autocorrelation and spatial heterogeneity were observed among PM2.5-polluted cities in China's coastal urban agglomerations. The northern coastal urban agglomerations formed a relatively stable and continuous high-pollution zone. The spatial Dubin model was used to analyze the driving factors of PM2.5 pollution in coastal urban agglomerations. Together, meteorological, socioeconomic, pollution source, and ecological factors affected the spatial characteristics of PM2.5 pollution during the study period, and the overall effect was a mixed effect with significant spatial variation. Among them, meteorological factors were the greatest driver of PM2.5 pollution. In the short term, the rapid increase in population density, industrial emissions, industrial energy consumption, and total traffic emissions were the important driving factors of PM2.5 pollution in the coastal urban agglomerations of China. [ABSTRACT FROM AUTHOR]

Background: Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared. Methods: Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Results: By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively. Conclusions: Comparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number. [ABSTRACT FROM AUTHOR]

With the depth increasing and the improvement of mining mechanization of the coal mine, heat hazard is becoming more and more serious, the ice cooling system is gradually promoted and applied in China for recent years, but the cooling capacity and energy efficiency ratio (EER) test and analysis for such system is seldom reported. The cooling capacity and EER of the refrigerating equipment of an ice cooling system of a coal mine are tested and analyzed in Shandong Province in this paper. The test result indicates that the refrigerating capacity and EER are lower and both the pre-cooling units and the ice making system have a large variable range. It points out that it should not evaluate the ice cooling system by the test data only in this mine but expect more test data and analysis of the ice cooling system in other coal mines to get a more scientific conclusion. And the test, analysis and comparison for different kinds of cooling system are also expected to explore suitable cooling system while a full consideration of the geological conditions and other factors of the mine are taken into consideration. [ABSTRACT FROM AUTHOR]

Frequency drop due to loss of massive generation is a threat to power system frequency stability. Under-frequency load shedding (UFLS) is the principal measure to prevent successive frequency declination and blackouts. Based on traditional stage-by-stage UFLS scheme, a new continuous UFLS scheme is proposed in this paper to shed loads proportional to frequency deviation. The characteristic of the proposed scheme is analyzed with a closed-form solution of frequency dynamics. Frequency threshold and time delay are added to make the proposed scheme practical. A line-by-line scheme based on precise load control is introduced to implement the continuous scheme for systems without enough continuously controllable loads. The load shedding scale factor of the proposed scheme is tuned with an analytical method to achieve adaptability to different operating conditions. The adaptability of the proposed scheme is validated with 39-bus New England model and simplified Shandong Power Grid of China. [ABSTRACT FROM AUTHOR]

Complexity modelling of economic efficiency and growth potential is increasingly essential for countries and provinces. Evaluating the monetary flows, kinetic energy (efficiency) and potential capacity (resilience) provides crucial information for economic development. In the paper, the authors analyze growth opportunities for the Chinese economy from a system science point of view, using the perspective of information entropy, based on the input-output tables. Over the past four decades of reform and opening-up, China has made remarkable progress in its economic development. In 2007, China's GDP was at its fastest pace in history at 14.2% growth. However, after the financial crisis in 2008, the global economy experienced a downward trend and China's economic development also settled on a medium-low level of development. The traditional perspective is to rank regional development only based on GDP growth, whereas here, the authors advocate another evaluation method based on efficiency and potential growth. Unbalanced regional economic development has become problematic and has become a barrier for sustainability of China's economy. The results of the research indicate firstly that China's regional development in 2007 and 2012 has been unequal between the provinces. Secondly, the authors found that Shandong province had significantly higher indicators for efficiency and potential growth than others in the same circumstances. Authors observe that provinces tend to carry out industrial policies and adjust the structure of industry on a local level. This analysis demonstrates that the spatial imbalance of efficiency and potential of economic development under the perspective of provincial-level regions. From the perspective of industry, it indicates that the supply chain is too short, mainly focusing on the mining and processing of resources and minerals in the original upstream industry chain, while the downstream is not fully utilized. These represent some unique insights yielded through this type of analysis that authors advocate applying more broadly. [ABSTRACT FROM AUTHOR]

Environmental taxes can play an important role in reducing pollution issues. However, they also have an impact on the entire economy. This paper uses panel data from Shandong Province in China from 2005 to 2016 to analyze the impact of pollutant charges on the economic and environmental performances. The results show that the payment transfer of pollutant charges can influence the output but the effects varies with regions and time. Pollutant charges increase the resource utilization efficiency and promote economic growth. After controlling output and industry structure, pollutant charges on waste gas improve the air quality. This study confirms the existence of the environmental tax double dividend and provides a reference for new environmental protection taxes. • The effects of pollutant charges on economics and environment are tested. • There are heterogeneity of the effect of pollutant charges on industry structure. • Pollutant charges improves the resource utilization efficiency in Shandong. • Pollutant charges promotes economic growth and improves the environmental quality. [ABSTRACT FROM AUTHOR]

This paper collects eight years of data from 2007 to 2014 from seven major types of registered seaport enterprise s in Shandong Province in China, by using the production function method to calculate seaport enterprise s' capacity utilization performance, and further analyze and compare difference between different types of seaport enterprise s. In terms of capacity utilization distribution, the current overcapacity phenomenon is more concentrated in large and medium-sized state-owned seaport enterprise s. In the new round of de-capacity progress, state-owned seaport enterprise s will face greater pressure. In the 13th Five Year Plan period, actively and properly handle this overcapacity issue existing in seaport enterprise swill be significant for economy to maintain a medium and high growth speed. [ABSTRACT FROM AUTHOR]