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This paper explores whether it is feasible to use the RGB color information in images of wheat canopies that were exposed to low temperatures during the growth season to achieve fast, non-destructive, and accurate determination of the severity of any freeze injury it may have incurred. For the study presented in this paper, we compared the accuracy of a number of algorithmic classification models using either meteorological data reported by weather services or the color gradation skewness-distribution from high-definition digital canopy images acquired in situ as inputs against a reference obtained by manually assessing the severity of the freeze injury inflicted upon wheat populations at three experimental stations in Shandong, China. The algorithms we used to construct the models included in our study were based on either K-means clustering, systematic clustering, or naïve Bayesian classification. When analyzing the reliability of our models, we found that, at more than 85%, the accuracy of the Bayesian model, which used the color information as inputs and involved the use of prior data in the form of the reference data we had obtained through manual classification, was significantly higher than that of the models based on systematic or the K-means clustering, which did not involve the use of prior data. It was interesting to note that the determination accuracy of algorithms using meteorological factors as inputs was significantly lower than that of those using color information. We also noted that the determination accuracy of the Bayesian model had some potential for optimization, which prompted us to subject the inputs of the model to a factor analysis in order to identify the key independent leaf color distribution parameters characterizing wheat freeze injury severity. This optimization allowed us to improve the determination accuracy of the model to over 90%, even in environments comprising several different ecological zones, as was the case at one of our experimental sites. In conclusion, our naïve Bayesian classification algorithm, which uses six key color gradation skewness-distribution parameters as inputs and involves the use of prior data in the form of manual assessments, qualifies as a contender for the development of commercial-grade wheat freeze injury severity monitoring systems supporting post-freeze management measures aimed at ensuring food security., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper studied an integrated process planning and scheduling problem from a machining workshop for large-size valves in a valve manufacturing plant. Large-size valves usually contain several key parts and are generally produced in small-series production. Almost all the parts need to be manufactured in the same workshop at the same time in the plant. Facilities have to handle various items in one order, including different models, sizes, and types. It is a classical NP-hard problem on a large scale. An improved NSGA-II algorithm is suggested to obtain satisfactory solutions for makespan and manufacturing costs, which involve large optimization parameters and interactions. A two-section encoding method and an inserting greedy decoding method are chosen to enable the algorithm. The dynamic population update strategy based on dynamic population update and the adaptive mutation technique depending on the population entropy changing rate are selected for enhancing both the solution quality and population diversity. The methodology was successfully implemented in a real-life case at a major valve machining workshop operated by Yuanda Valve Company in China. By taking into account realistic factors and restrictions that have been identified from a real-world manufacturing setting, this technique aids in bridging the knowledge gap between present IPPS research and practical valve production implementations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper's method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence., Competing Interests: the authors have declared that no competing interests exist., (Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error(RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hu, Cheng. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Jiamu Hu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

China is gradually transitioning from the "tunnel construction exploration era" to the "tunnel high-quality construction and operation era", and the maintenance demand of highway tunnels has increased sharply. Therefore, there is an urgent need for an evaluation method to evaluate the service reliability of highway tunnels, so as to provide reference for tunnel maintenance personnel to carry out maintenance work. Taking highway tunnels as the research object, this paper extracts three parameters, including length, maximum width and fractal dimension, from the binary image of highway tunnel lining cracks. The standard for dividing the length of the highway tunnel section is 500m as the tunnel section, and a section disease sample space including multiple highway tunnels is constructed. The EM clustering algorithm was used to determine the number of graded grades of disease, and the relative Euclidean distance was used as the evaluation index to divide the safety grade of the tunnel into five grades: normal, degraded, inferior, deteriorated and hazardous. The partial least squares method is used to establish the lining service reliability evaluation formula and verify the residual of each sample point in the sample space. The smaller the average value of the residual, the better fitting effect of the established evaluation formula. The service reliability evaluation method proposed in this paper is applied to engineering practice and compared with the expert scoring method and the national standard method, which proves that the evaluation method in this paper has the advantages of strong visibility, simple evaluation method, and is conducive to engineering practice., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2023 Dai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, we propose a new freight mode to describe how the designed HSR freight train serve for express delivery. We introduce the functions of the hubs and design the hybrid hub-and-spoke network of road-rail intermodal transportation from the perspective of planners, which features as single allocation rule and configures varying levels of hubs. The problem is accurately described by a mixed integer programming model with the objective to minimize total construction cost and total operation cost. We develop a hybrid heuristic algorithm based on a greedy strategy to obtain the levels of hubs, customer allocation and cargo routing. Taking the example of HSR freight network consisting of 50 cities in China, numerical experiments are conducted on the basis of forecasting data from the real-life express market to obtain the hub location schemes. The validity of the model and the performance of the algorithm are verified., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Most image content modelling methods are designed for English description which is different form Chinese in syntax structure. The few existing Chinese image description models do not fully integrate the global features and the local features of an image, limiting the capability of the models to represent the details of the image. In this paper, an encoder-decoder architecture based on the fusion of global and local features is used to describe the Chinese image content. In the encoding stage, the global and local features of the image are extracted by the Convolutional Neural Network (CNN) and the target detection network, and fed to the feature fusion module. In the decoding stage, an image feature attention mechanism is used to calculate the weights of word vectors, and a new gating mechanism is added to the traditional Long Short-Term Memory (LSTM) network to emphasize the fused image features, and the corresponding word vectors. In the description generation stage, the beam search algorithm is used to optimize the word vector generation process. The integration of global and local features of the image is strengthened to allow the model to fully understand the details of the image through the above three stages. The experimental results show that the model improves the quality of Chinese description of image content. Compared with the baseline model, the score of CIDEr evaluation index improves by 20.07%, and other evaluation indices also improve significantly., Competing Interests: The authors have declared that no competing interests exist.

The transportation industry has entered a new stage from quantity expanding to structure optimization, quality and efficiency improvement, and from respective governance to integrative development. This indicates that the traditional corridor mode allocation dominated by quantity equilibrium can no longer meet the requirements of the new stage. In this paper, we propose a multi-entity programming model based on the economic equilibrium between supply and demand. It not only ensures the economic equilibrium in the market, but also maximizes the social benefits of the whole system, thereby realizing the sustainable development of the transportation system. Also, the Globalsearch algorithm and intlinprog algorithm are designed to solve the problem. The actual case of Beijing-Shanghai corridor shows that the model and algorithms are effective, providing decision support for the optimal allocation of regional transport network resources., Competing Interests: The authors have declared that no competing interests exist.

In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust. [ABSTRACT FROM AUTHOR]

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has. [ABSTRACT FROM AUTHOR]

The development of China's manufacturing industry has received global attention. However, research on the distribution pattern, changes, and driving forces of the manufacturing industry has been limited by the accessibility of data. This study proposes a method for classifying based on natural language processing. A case study was conducted employing this method, hotspot detection and driving force analysis, wherein the driving forces industrial development during the "13th Five-Year plan" period in Jiangsu province were determined. The main conclusions of the empirical case study are as follows. 1) Through the acquisition of Amap's point-of-interest (POI, a special point location that commonly used in modern automotive navigation systems.) data, an industry type classification algorithm based on the natural language processing of POI names is proposed, with Jiangsu Province serving as an example. The empirical test shows that the accuracy was 95%, and the kappa coefficient was 0.872. 2) The seven types of manufacturing industries including the pulp and paper (PP) industry, metallurgical chemical (MC) industry, pharmaceutical manufacturing (PM) industry, machinery and electronics (ME) industry, wood furniture (WF) industry, textile clothing (TC) industry, and agricultural and food product processing (AF) industry are drawn through a 1 km× 1km projection grid. The evolution map of the spatial pattern and the density field hotspots are also drawn. 3) After analyzing the driving forces of the changes in the number of manufacturing industries mentioned above, we found that manufacturing base, distance from town, population, GDP per capita, distance from the railway station were the significant driving factors of changes in the manufacturing industries mentioned above. The results of this research can help guide the development of manufacturing industries, maximize the advantages of regional factors and conditions, and provide insight into how the spatial layout of the manufacturing industry could be optimized., Competing Interests: The authors have declared that no competing interests exist.

Although R&D internationalization plays an important role in enterprises' globalization, few studies explore the mechanism of R&D internationalization and emerging market companies' innovation, or the relationship between R&D internationalization, domestic technology alliances and absorptive capacity. How does the R&D internationalization of emerging market enterprises affect the innovation of those enterprises? Under fierce market competition, do absorption capacity and domestic technology alliances have a significant impact on enterprise innovation? From the perspective of the knowledge-based view, this paper studies 185 enterprises undergoing R&D internationalization in China from 2012 to 2017, using high-dimensional Poisson fixed effects model, we use instrumental (HDFE IV) estimation to explain the impact of R&D internationalization on the innovation of the parent company and the mechanism behind it. The study finds that R&D internationalization positively promotes the parent company's innovation, and domestic technology alliances and absorptive capacity play a partial mediator role in R&D internationalization. In the face of fierce market competition, domestic technical alliances play a significant role in promoting enterprise innovation, while absorptive capacity plays a negative role in promoting enterprise innovation with the moderating effect of market competition., Competing Interests: The authors have declared that no competing interests exist.

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration ([Formula: see text]), root mean square errors of calibration (RMSEC), determinant coefficients of prediction ([Formula: see text]), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ([Formula: see text] = 0.907, RMSEC = 0.425%, [Formula: see text] = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ([Formula: see text] = 0.888, RMSEC = 0.446%, [Formula: see text] = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

Circuity, defined as the ratio of the shortest network distance to the Euclidean distance between one origin–destination (O-D) pair, can be adopted as a helpful evaluation method of indirect degrees of train paths. In this paper, the maximum circuity of the paths of operated trains is set to be the threshold value of the circuity of high-speed train paths. For the shortest paths of any node pairs, if their circuity is not higher than the threshold value, the paths can be regarded as the reasonable paths. With the consideration of a certain relative or absolute error, we cluster the reasonable paths on the basis of their inclusion relationship and the center path of each class represents a passenger transit corridor. We take the high-speed rail (HSR) network in China at the end of 2014 as an example, and obtain 51 passenger transit corridors, which are alternative sets of train paths. Furthermore, we analyze the circuity distribution of paths of all node pairs in the network. We find that the high circuity of train paths can be decreased with the construction of a high-speed railway line, which indicates that the structure of the HSR network in China tends to be more complete and the HSR network can make the Chinese railway network more efficient. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. [ABSTRACT FROM AUTHOR]

Chinese rare earth export policies currently result in accelerating its depletion. Thus adopting an optimal export trade selection strategy is crucial to determining and ultimately identifying the ideal trading partners. This paper introduces a multi-attribute decision-making methodology which is then used to select the optimal trading partner. In the method, an evaluation criteria system is established to assess the seven top trading partners based on three dimensions: political relationships, economic benefits and industrial security. Specifically, a simple additive weighing model derived from an additive utility function is utilized to calculate, rank and select alternatives. Results show that Japan would be the optimal trading partner for Chinese rare earths. The criteria evaluation method of trading partners for China's rare earth exports provides the Chinese government with a tool to enhance rare earth industrial policies.

Variation of streamflow and sediment load in the Yellow River basin has received considerable attention due to its drastic reduction during the past several decades. This paper presents a detailed investigation on the changes of streamflow and sediment load from 1952 to 2011 using monthly observations at four gauging stations along the Yellow River. The results show significant decreasing trends for both streamflow and sediment load at all four gauging stations over the past 60 years. The wavelet transform demonstrated discontinuous periodicities from 1969 to 1973 and after 1986 due to the construction of large reservoirs and implementation of numerous soil and water conservations practices. The sediment rating curves with the power-law function was applied to investigate the relationship between discharge and sediment load. The results indicate distinct variations of the relationship between streamflow and sediment and implied significant hydro-morphological changes within different periods. The reducing sediment supply from the source region and the increased erosive power of the river are detected at Lanzhou station, while the decrease of the transport capacity at Toudaoguai is caused by severe siltation. Significant changes in the relationship between streamflow and sediment load are found at Huayuankou and Gaocun stations, which are largely induced by evident sediment income and trapping effects of large reservoirs. It is estimated that numerous reservoirs have strongly altered the regime and magnitude of streamflow and trapped large amount of sediment, leading to severe siltation and evident reduction of their total volumes. A decrease in precipitation, incoming water from the upper reaches, soil and water conservation measures as well as water consumption contribute most to the significant reduction of streamflow. The decrease of sediment load mainly resulted from various soil and water conservation measures and trapping in reservoirs from 1986 to 2011. [ABSTRACT FROM AUTHOR]

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. [ABSTRACT FROM AUTHOR]

Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses' dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter's performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation. [ABSTRACT FROM AUTHOR]

In this work we introduce a system pipeline for the analysis of earth’s electromagnetic field that is used to analyse precursors to earthquakes. Data gathered by the Swarm satellites are used to present the utility of our system. Our objective is to provide a streamlined method to analyze electromagnetic data over a region and investigate the relationship of precursory signals to seismic events. The process follows three distinct stages: data extraction, data pre-processing and anomaly detection. The first stage consists of the region selection and data extraction. The second stage consists of four different pre-processing methods that address the data sparsity problem and the cause of artificial anomalies. The last stage is the Anomaly Detection (AD) of the Swarm satellite data, over the investigated region. The different methods that are implemented are known to perform well in the field of AD. Following the presentation of our system, a case study is described where the seismic event of 6.2 Mw is in Ludian, China and occurred on 3rd August 2014. The event is used to present the usefulness of our approach and pinpoint some critical problems regarding satellite data that were identified. [ABSTRACT FROM AUTHOR]

Development of easily defoliating sugarcane varieties is urgently needed to facilitate efficient mechanical harvesting, reduce production costs, and increase sugar yield in China. In order to quantify the defoliation characteristics of sugarcane, we investigated eight traits in two field experiments with a range of sugarcane varieties at maturity. The length (LSR) and angle (ASR) of the sheath ruptured from the stalk, defoliation force (DF), and self-defoliation (SD) were the traits with the greatest contribution to the quantitative assessment of sugarcane defoliation based on a principal component analysis, which accounted for more than 75% of the cumulative variability. A small set of traits, namely SD, ASR, and DF measured at the 10th leaf were selected as predictors. Using these predictors, 37 out of 38 sugarcane cultivars were correctly classified into three groups (easy, difficult and intermediate in terms of ease of defoliation) that had been clustered based on six traits. These simplified measurements will be applied to screen new lines in the sugarcane breeding program in China. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou City, China. Methods: Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. Results: Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou City. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01). Conclusions: The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention. [ABSTRACT FROM AUTHOR]

The purpose of this study is to recognize the contamination characteristics of trace metals in soils and apportion their potential sources in Northern China to provide a scientific basis for basic of soil environment management and pollution control. The data set of metals for 12 elements in surface soil samples was collected. The enrichment factor and geoaccumulation index were used to identify the general geochemical characteristics of trace metals in soils. The UNMIX and positive matrix factorizations (PMF) models were comparatively applied to apportion their potential sources. Furthermore, geostatistical tools were used to study the spatial distribution of pollution characteristics and to identify the affected regions of sources that were derived from apportionment models. The soils were contaminated by Cd, Hg, Pb and Zn to varying degree. Industrial activities, agricultural activities and natural sources were identified as the potential sources determining the contents of trace metals in soils with contributions of 24.8%–24.9%, 33.3%–37.2% and 38.0%–41.8%, respectively. The slightly different results obtained from UNMIX and PMF might be caused by the estimations of uncertainty and different algorithms within the models. [ABSTRACT FROM AUTHOR]

We investigate the economic hubs and authorities of the world trade network (WTN) from to , an era of rapid economic globalization. Using a well-defined weighted hyperlink-induced topic search (HITS) algorithm, we can calculate the values of the weighted HITS hub and authority for each country in a conjugate way. In the context of the WTN, authority values are large for countries with significant imports from large hub countries, and hub values are large for countries with significant exports to high-authority countries. The United States was the largest economic authority in the WTN from to . The authority value of the United States has declined since , and China has now become the largest hub in the WTN. At the same time, China's authority value has grown as China is transforming itself from the “factory of the world” to the “market of the world.” European countries show a tendency to trade mostly within the European Union, which has decreased Europe's hub and authority values. Japan's authority value has increased slowly, while its hub value has declined. These changes are consistent with Japan's transition from being an export-driven economy in its high economic growth era in the latter half of the twentieth century to being a more mature, economically balanced nation. [ABSTRACT FROM AUTHOR]

Multiple comprehensive in situ bio-optical investigations were conducted from 2005 to 2010 and covered a large variability of total suspended matter (TSM) in Lake Taihu to calibrate and validate a TSM concentration estimation model based on Medium Resolution Imaging Spectrometer (MERIS) data. The estimation model of the TSM concentration in Lake Taihu was developed using top-of-atmosphere (TOA) radiance of MERIS image data at band 9 in combination with a regional empirical atmospheric correction model, which was strongly correlated with the in situ TSM concentration (r2 = 0.720, p<0.001, and n = 73). The relative root mean square error (RRMSE) and mean relative error (MRE) were 36.9% and 31.6%, respectively, based on an independent validation dataset that produced reliable estimations of the TSM concentration. The developed algorithm was applied to 50 MERIS images from 2003 to 2011 to obtain a high spatial and temporal heterogeneity of TSM concentrations in Lake Taihu. Seasonally, the highest and lowest TSM concentrations were found in spring and autumn, respectively. Spatially, TSM concentrations were high in the southern part and center of the lake and low in Xukou Bay, East Lake Taihu. The lake topography, including the water depth and distance from the shore, had a significant effect on the TSM spatial distribution. A significant correlation was found between the daily average wind speed and TSM concentration (r2 = 0.685, p<0.001, and n = 50), suggesting a critical role of wind speed in the TSM variations in Lake Taihu. In addition, a low TSM concentration was linked to the appearance of submerged aquatic vegetation (SAV). Therefore, TSM dynamics were controlled by the lake topography, wind-driven sediment resuspension and SAV distribution. [ABSTRACT FROM AUTHOR]

Background: The Beijing Declaration on food safety and security was signed by over fifty countries with the aim of developing comprehensive programs for monitoring food safety and security on behalf of their citizens. Currently, comprehensive systems for food safety and security are absent in many countries, and the systems that are in place have been developed on different principles allowing poor opportunities for integration. Methodology/Principal Findings: We have developed a user-friendly analytical tool based on network approaches for instant customized analysis of food alert patterns in the European dataset from the Rapid Alert System for Food and Feed. Data taken from alert logs between January 2003 - August 2008 were processed using network analysis to i) capture complexity, ii) analyze trends, and iii) predict possible effects of interventions by identifying patterns of reporting activities between countries. The detector and transgressor relationships are readily identifiable between countries which are ranked using i) Google's PageRank algorithm and ii) the HITS algorithm of Kleinberg. The program identifies Iran, China and Turkey as the transgressors with the largest number of alerts. However, when characterized by impact, counting the transgressor index and the number of countries involved, China predominates as a transgressor country. Conclusions/Significance: This study reports the first development of a network analysis approach to inform countries on their transgressor and detector profiles as a user-friendly aid for the adoption of the Beijing Declaration. The ability to instantly access the country-specific components of the several thousand annual reports will enable each country to identify the major transgressors and detectors within its trading network. Moreover, the tool can be used to monitor trading countries for improved detector/transgressor ratios. [ABSTRACT FROM AUTHOR]