Evolving patterns of agricultural production space in China:A network-based approach

The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Mixed Integer Robust Programming Model for Multimodal Fresh Agricultural Products Terminal Distribution Network Design

The low efficiency and high cost of fresh agricultural product terminal distribution directly restrict the operation of the entire supply network.To reduce costs and optimize the distribution network,we construct a mixed integer programmingmodel that comprehensively considers tominimize fixed,transportation,fresh-keeping,time,carbon emissions,and performance incentive costs.We analyzed the performance of traditional rider distribution and robot distribution modes in detail.In addition,the uncertainty of the actual market demand poses a huge threat to the stability of the terminal distribution network.In order to resist uncertain interference,we further extend the model to a robust counterpart form.The results of the simulation show that the instability of random parameters will lead to an increase in the cost.Compared with the traditional rider distribution mode,the robot distribution mode can save 12.7%on logistics costs,and the distribution efficiency is higher.Our research can provide support for the design of planning schemes for transportation enterprise managers.

A multiscale adaptive framework based on convolutional neural network:Application to fluid catalytic cracking product yield prediction

Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.

Thoughts on Establishment of the Agricultural Products Quality and Safety(Standards) Cooperation Center in Asia-Pacific Region

The establishment of agricultural products quality and safety cooperation mechanism and the research demonstration center in the Asia-Pacific reg coordinating each other, is not only of great ening cooperation with countries in Asia-Pacific region and for agricultural production safety of those countries in Asia- Pacific region, but also very necessary for our country to build a responsible image in the quality and safety of agricultural products, protection of the benefit of agriculture-related enterprises, increasing exports, ensuring imported food safety and China＇s national food security, as Well as nice exploration of establishing the global agricultural＇ products quality and safety cooperation center.

‘Partly'globalized networks and driving mechanism in resource-based state-owned enterprises:A case study of J Group

In the context of economic globalization,while multinational enterprises from developed countries occupy a high-end position in the global value chain,enterprises from developing countries are often marginalized in the world market.In China,resource-based state-owned enterprises(SOEs)are tasked with the mission of safeguarding resource security,and their internationalization development ideas and strategic deployment are significantly and fundamentally different from those of other non-state-owned enterprises and large multinational corporations.This study provides ideas for the globalization policies of enterprises in developing countries.We consider J Group in western China as a case and discuss its productive investment and global production network development from 2010 to 2019.We found that J Group was‘Partly'globalized,and there are multiple core nodes with the characteristics of centralized and decentralized coexistence in the production network;in addition,the overall layout centre shifted to Southeast Asia and China;however,its global production was restricted by the enterprise's investment security considerations,support and restrictions of the home country,political security risk of the host country,and sanctions from the West.These findings provide insights for future research:under the wave of anti-globalization and'internal circulation as the main body',resource SOEs should consider the potential risk of investment,especially keeping the middle and downstream industrial chain in China as much as possible.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

A Study of Optimum Switching Problem for Production Systems Considering Efficiency, Delivery Time and Green Evaluation

This paper aims to derive the optimal switching strategy for production system considering efficiency, delivery time and green evaluation. Nowadays more and more manufacturing and logistics systems not only pursue better work efficiency, but also focus on green energy evaluation issues. Cost reduction and shortening of delivery time are always important management issues in pursuit of efficiency and optimization of the entire production system because of global production competition. In a market situation where customer needs change in various ways, in particular, due to inadequate quality, changes in the local environment, natural disasters and so on. Therefore, prompt planning of management measures such as switching work processes and changing production methods has become an important issue. On the other hand, since the Paris Agreement came into effect, the construction of an environment-friendly production system has been required as an approach to environmental problems such as global warming. In this paper, we propose an optimum switching model of production systems considering efficiency, delivery time and green evaluation using a green evaluation index (GEC: Green Energy Coefficient). We also discuss the optimal switching strategy by numerical observation.

A Worsted Yarn Virtual Production System Based on BP Neural Network

Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.

Assessment for Production and Operation Ability of Medium and Small-sized Enterprises Based on Neural Network

In order to improve production and operation ability of medium and small-sized enterprises, an assessment-index system of production and operation ability was proposed, and a corresponding assessment model was established based on BP neural network. The conjunction weights of the neural network were continuously modified from output layer to input layer in the process of neural network training to reduce the errors between the anticipated and actual outputs. The results from an example show that this method is reliable and feasible. The production and operation abilitv of an enterorise with assessed result of 0.833 is fairly oowerful, and that with assessed result of 0.644 is average.

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.

Predicting Model for Complex Production Process Based on Dynamic Neural Network

Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.

Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

Advances and outlook of integrated reservoir-wellbore-pipe network simulation technology

The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications

Set stabilization is one of the essential problems in engineering systems, and self-triggered control(STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed,respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.

The Budget Constrained Multi-product Newsboy Problem with Reactive Production:A Problem from Entrepreneurial Network Construction

This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solu-tion. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.

China reshapes the East Asian production network

From the perspective of intra-product specialization and with in-depth analysis of trade statistics,this paper investigates the influence of China's rise on the East Asian production network.Our conclusions suggest that in integrating into the East Asian production network,China has gradually emerged as the manufacturing center of East Asia,weakening the regional influence of the Four Asian Tigers.Meanwhile,the competitive effect of China's rise has helped promote the specialization levels of the network's members and even the network as a whole.With cooperation in various processes of intra-product specialization,internal connections of the East Asian production network were further strengthened.In addition,China became an export platform of East Asia,transforming the export pattern of the East Asian production network to world markets from "bilateral trade" into "triangular trade," trade via China.

Tree Network Formation in Poisson Equation Models and the Implications for the Maximum Entropy Production Principle

This paper presents not only practical but also instructive mathematical models to simulate tree network formation using the Poisson equation and the Finite Difference Method (FDM). Then, the implications for entropic theories are discussed from the viewpoint of Maximum Entropy Production (MEP). According to the MEP principle, open systems existing in the state far from equilibrium are stabilized when entropy production is maximized, creating dissipative structures with low entropy such as the tree-shaped network. We prepare two simulation models: one is the Poisson equation model that simulates the state far from equilibrium, and the other is the Laplace equation model that simulates the isolated state or the state near thermodynamic equilibrium. The output of these equations is considered to be positively correlated to entropy production of the system. Setting the Poisson equation model so that entropy production is maximized, tree network formation is advanced. We suppose that this is due to the invocation of the MEP principle, that is, entropy of the system is lowered by emitting maximal entropy out of the system. On the other hand, tree network formation is not observed in the Laplace equation model. Our simulation results will offer the persuasive evidence that certifies the effect of the MEP principle.

Decent Work and Industrial Upgrading in Global Production Network: a Case of China Apparel Industry

China apparel industry, which is deeply embedded in the global production network (GPN), faces two urgent issues, social upgrading and economic upgrading. The study of GPN places great emphasis on the two issues. Based on the survey of Ningbo apparel industry, four key components of decent work in China apparel industry are discussed. The role of buyers in promoting decent work in suppliers can't be neglected. There are significant correlations between business type and some indicators of decent work. Though the majority of the apparel firms are engaging in processing, more and more firms are involved in marketing and branding. The upgrading trajectory of China apparel industry leads to the economic and social performances.