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Jining City of Shandong Province is located in the coal mining area of the lower reaches of the Yellow River Basin, facing serious problems such as the destruction of arable land resources. Based on the land use data and socio-economic data of Jining City from 1980 to 2020, this paper explores the spatial and temporal characteristics and the driving mechanism of arable land by land use transfer matrix and principal component analysis, and predicts the future arable land area by using the gray prediction model. The results show that: （1）arable land in Jining City exhibits a typical characteristic of "more in the west, less in the east" and a continual decrease, land areas for industrial, coal mining and transportation construction are increasing.（2）From 1980 to 2020, the predominant changes to arable land in Jining City involved its conversion into construction land, grassland water bodies. Notably, from 1980 to 1990, there were significantly higher dynamics in arable land changes across counties and districts, whereas from 2000 to 2010, there were relatively minor fluctuations in arable land area.（3） Economic factors emerge as the primary driving forces behind changes in arable land, coal mining's proportion of the total industry output value and the secondary industry's share of the total output value are the principal determinants influencing arable land area.（4）Through forecasting, it is projected that the arable land area will continue to exhibit a decreasing trend from 2020 to 2035. In response, crafting arable land conservation policies should comprehensively account for economic factors, adopt tailored strategies based on local conditions, and propel the development of resource-efficient urban construction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background General practitioners (GPs) are gatekeepers of residents' health, the rational allocation of resources is the basic guarantee for achieving fairness in health services. At present, research on the allocation of GPs in China mainly focuses on the whole country and individual provinces, lack of research on the allocation of human resources for general practitioners in the western region as the research object. Objective To comprehensively evaluate the current situation of GPs resource allocation in western China from 2015 to 2020, provide a reference for optimizing the resources distribution of GPs in the western region of China. Methods The data for this study were derived from the China Health and Family Planning Statistical Yearbook (2016-2017), the China Health Statistics Yearbook (2018-2022), and the China Statistical Yearbook (2016-2022). Using Atkinson index and location entropy to calculate and decompose the overall allocation of GPs resources in the western region. Using grey prediction model to predict the demand for GPs in the western region from 2022 to 2025. Results From 2015 to 2020, the number of GPs in the western region increased from 39 290 to 94 652, with an average annual growth rate of 19.23%. The Atkinson Index by population, economy, and geography in 2015 and 2020 respectively were 0.1265 and 0.049 3, 0.124 4 and 0.038 2, 0.786 5 and 0.694 4. The location entropy of population, economy, and geographical indicators in the western provinces in 2020 respectively ranged from 0.794 3 to 1.219 0, 0.697 0 to 1.337 3, and 0.043 2 to 7.7270. The location entropy of geographical allocation in the southwest region is greater than 1, while the location entropy of population allocation in the northwest region is better than geographical allocation.The location entropy of economic indicators in Gansu, Qinghai and Guangxi is greater than 1 during the research period.The grey model predicts that the resources of GPs in the western region will show an increasing trend from 2022 to 2025. Conciusion The allocation level of GPs in the western region is gradually improving, and the structure is gradually optimizing. However, there are still problems such as insufficient total quantity, uneven distribution, low registration rate, and poor fairness. Suggestions should be based on geographical classification as the standard, with the principle of "Adjust increment and optimize inventory", the goal of occupational attractiveness, and the guidance of resource integration and regional collaboration to further optimize the resource allocation of GPs in the western region.

The 10,000-hour rate of civil aviation incidents is an important index parameter to measure flight safety. Predicting the development trend of the 10,000-hour rate of civil aviation incidents plays an important role in aviation accident prevention and safety decision-making. Many complex factors influence the occurrence of civil aviation incidents, so the 10,000-hour rate of civil aviation incidents changes randomly and volatilely. This study proposed the idea of prediction by combining the grey GM (1, 1) model and the Markov model. Specifically, the grey GM (1, 1) prediction model was constructed using the statistical data on the 10,000-hour rate of civil aviation incidents in China during 2005–2020. On this basis, a grey Markov prediction model was established. The prediction of the 10,000-hour rate of incidents in 2021 based on the two models showed that the grey Markov model displayed higher prediction accuracy than the grey GM (1, 1) model and conformed to the change laws of the 10,000-hour rate data of civil aviation incidents better. Moreover, the grey Markov model could effectively improve the accuracy of the grey prediction model, compensate for its deficiencies, and facilitate the mastery of the change laws of civil aviation incidents, providing a reliable basis for aviation safety management and incident prevention. [ABSTRACT FROM AUTHOR]

New urbanization in counties and the logistics industry are closely related and are essential in promoting regional economic and social development. There are specific challenges and obstacles to revealing the interaction mechanism and system state measurement between the two. This paper explains the two coupling mechanisms and constructs the evaluation index system. It proposes a new analysis method based on the coupling coordination degree model, the spatio-temporal evolution analysis, and the grey prediction model. The goal is to learn more about and fully realize the coordinated development mechanisms of the two. It then uses the Hebei province of China as an example to empirically analyze its systematic cross-sectional data from 2013 to 2022. Research findings: (1) In Hebei province, the new urbanization in counties and the logistics industry have a systematic coupling relationship. However, the logistics industry's comprehensive development level is relatively lagging. The two systems have been at a high-level coupling stage for the last decade and maintain a high coupling status. The coupling coordination has shown significant improvement. (2) Although the geographical distribution of the coupling and coordination degree of the new urbanization in counties and logistics industry system has short-term volatility, it is still stable in the long term and presents economic-related spatial characteristics. (3) Over the next five years, the coupling coordination of 11 cities in Hebei province will steadily grow. There will be greater harmonization between the two systems. (4) From the analysis results, the evaluation index system of the coupled system constructed is scientific and reasonable. The analysis method can not only measure the system's coupling degree, but it can also predict the development trend and analyze the spatial evolution. The technique has novelty and validity, which can be used as a reference for analyzing and making decisions about similar systems. [ABSTRACT FROM AUTHOR]

Purpose: The author aims to study and predict the sustainability governance performances of firms using an advanced grey prediction model. The case implication of the prediction model is also studied considering select firms in the Indian context. Design/methodology/approach: The author has proposed an advanced grey prediction model, the first-entry grey prediction model (FGM (1, 1)) for forecasting the sustainability governance performances of firms. The proposed model is tested using the periodic data of sustainability governance performances of 10 Indian firms. Findings: The author observes that the majority of firms (6 out of 10) show dipping performances for sustainability governance for the future predicted period. This throws insights into the direction of improving good governance practices for Indian firms. Practical implications: The idea and motivation for sustainability-focussed governance need a bi-directional focus from the side of managers that act as the agents and from the side of shareholders that act as the principals, as seen from an agency theory perspective for sustainability governance. Social implications: Sustainability governance culture can be inculcated to a firm at the strategic level by having a bi-directional focus from managers and shareholders, so as to enhance the social and environmental sustainability performances. Originality/value: The governance performance evaluations for firms particularly in developing countries were not dated back more than a decade or two. Hence, the author implements a prediction model that can be best suited, when there are small periodic data sets available for prediction. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unbalanced grid voltage will affect the normal grid connection operation of the wind energy conversion system WECS), resulting in fluctuations in system output power and distortion of output current. In addition, in the actual permanent magnet synchronous wind power system, there may be uncertainties in the network side circuit parameters due to changes in ambient temperature and detection errors. If the parameter changes, the performance of traditional control methods will decline. Based on this, a model-free adaptive control MFAC) scheme based on grey prediction was proposed to alleviate the performance degradation problem caused by parameter uncertainty and make the control system have better anti-interference ability. Furthermore, three independent control methods were adopted to solve the problem of grid connected output power fluctuation and current distortion under unbalanced grid voltage. The simulation results show that the proposed control strategy not only has good static and dynamic performance under nominal parameters, but also improves the robustness of the system when the grid side inductance changes, which verifies the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Unbalanced grid voltage will affect the normal grid connection operation of the wind energy conversion system WECS), resulting in fluctuations in system output power and distortion of output current. In addition, in the actual permanent magnet synchronous wind power system, there may be uncertainties in the network side circuit parameters due to changes in ambient temperature and detection errors. If the parameter changes, the performance of traditional control methods will decline. Based on this, a model-free adaptive control MFAC) scheme based on grey prediction was proposed to alleviate the performance degradation problem caused by parameter uncertainty and make the control system have better anti-interference ability. Furthermore, three independent control methods were adopted to solve the problem of grid connected output power fluctuation and current distortion under unbalanced grid voltage. The simulation results show that the proposed control strategy not only has good static and dynamic performance under nominal parameters, but also improves the robustness of the system when the grid side inductance changes, which verifies the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

To achieve accurate deformation prediction and assess the temporal stability of surrounding rock in underground mines, this study utilizes the G-P algorithm to determine the optimal embedding dimension and time delay for the reconstructed phase space of the deformation time series. Analyzing the dynamic evolution of the nearest phase point and employing grey prediction theory in the phase space reconstruction, we predict the time series of surrounding rock deformation. Results indicate that the optimal embedding dimension and time delay for the deformation time series are 3. The larger the average distance of the nearest phase point, the more pronounced the chaotic characteristics of perimeter rock deformation, signifying a complex intrinsic dynamical system. The grey theory proves effective in short-term, accurate deformation prediction for perimeter rock. Additionally, the identified stability of perimeter rock based on neighboring phase points and deformation prediction results offers a novel approach for evaluating surrounding rock stability in underground mining processes. [ABSTRACT FROM AUTHOR]

Objective To analyze the change of disease burden of injury in China from 1990 to 2019, and to predict the change trend from 2020 to 2034, so as to provide basis for the formulation of injury prevention and control strategies. Methods Using the global disease burden research database in 2019, the change trends of age - standardized incidence, prevalence, mortality and disability 一 adjusted life expectancy rate of injuries in China were analyzed, and the average annual change percentage was calculated. The grey prediction model GM (1) was used to predict the annual DALY change trend from 2020 to 2034. Results From 1990 to 2019, the incidence, prevalence, mortality and Daly rate of self 一 injury and interpersonal violence showed a downward trend, with the largest decrease among people aged 0-9 (AAPC = 一 2. 27%, -- 0. 26%, -- 4. 72%, - 6. 00%). The incidence and prevalence of traffic injuries showed an upward trend, while mortality and DALY rates generally showed a downward trend, but those over 70 years old showed an upward trend (AAPC 二 0. 26%, 0. 88%). The incidence of accidental injuries showed an overall downward trend (AAPC = 一 0. 08%), but showed an upward trend in 2006一2019 (AAPC = 2.69%), and the mortality and DALY rates showed an overall downward trend, among which the morbidity, prevalence, mortality and Daly rates of people M 70 years old showed an upward trend (AAPC = 1. 84%, 1. 15%, 0. 66%, 0. 51%). The grey prediction model showed that the DALY of the three kinds of injuries will show a downward trend from 2020 to 2034. It is estimated that by 2035, the Daly rate will decrease to 166. 304 / 100 000, 862. 338 / 100 000 and 715, 27 / 100 000 respectively. Conclusion The mortality rate and DALY rate of three kinds of injuries in China show a general downward trend from 1990 to 2019, but the incidence rate of traffic injuries and accidental injuries are still rising, and the disease burden of the elderly is still increasing. [ABSTRACT FROM AUTHOR]

Given that PM2.5 concentration is not only related to local pollutants, but also affected by long-distance transmission of PM2.5 in adjacent areas, the key to improving the prediction accuracy of PM2.5 concentration is to comprehensively consider the effect of local influencing factors and the transmission effect of PM2.5 in adjacent areas. For this purpose, a novel seasonal discrete multivariable grey prediction model, encompassing spatial influencing factors, has been established. Firstly, we analyze the mechanism of spatial influencing factors and the reasonableness of using PM2.5 concentration in adjacent areas as the spatial influencing factors. Secondly, based on the spatial agglomeration characteristics of PM2.5 concentration, the K - means clustering algorithm is used to cluster adjacent cities with similar PM2.5 concentration, then the comprehensive value of PM2.5 concentration in each city cluster is calculated by weighted average combination. On this basis, a driving term of spatial influencing factors and a cosine trigonometric function term are introduced into the novel model to characterize the effect of spatial influencing factors on PM2.5 concentration and the seasonal fluctuation of itself, respectively. More importantly, the Genetic Algorithm Toolbox is employed to optimally determine the emerging parameters of this model, and the time response function of the novel model is calculated by mathematical induction method. Lastly, the new model is deemed valid through testing its PM2.5 concentration predictions for the cities of Beijing, Tianjin, and Baoding in the Beijing-TianjinHebei region. Based on the original observations from 2018Q1 to 2023Q2, the novel model is built for PM2.5 concentration prediction in 2023Q3 to 2024Q2 for the three cities. The findings imply that the newly developed model outperforms its competitors significantly and has the potential to serve as a robust tool for predicting PM2.5 concentration. [ABSTRACT FROM AUTHOR]

【Objective】By analyzing the total amount of carbon emissions from agriculture in Gansu Province during 2000 to 2020 and its development and evolution characteristics,and the decoupling effect as a reference for the development of green and low-carbon agriculture in Gansu Province.【Methods】The agricultural carbon emissions in Gansu Province between 2000 to 2020 were measured based on the IPCC carbon emission coefficient method using agricultural inputs as the carbon source,and using the grey prediction model GM（1,1）,the agricultural carbon emissions of different years were selected as samples to predict the agricultural carbon emissions in Gansu Province from 2025 to 2030,and the prediction results were compared and analyzed. The Tapio decoupling model was used to study and analyse the relationship between agricultural carbon emissions and economic development.【Results】Agricultural carbon emissions in Gansu Province from 2000 to 2020 show a trend of increasing and then decreasing.The source of carbon emissions were mainly chemical fertilizers and agricultural films,chemical fertilizer carbon emissions accounted for 31.27% to 43.22% of agricultural carbon emissions,always at the highest level during the 20-year period;agricultural carbon emissions reached a maximum of 2.825 138 million tons in 2015. After 2015,it began to decline gradually,and the agricultural carbon emissions in 2020 were 2.288 601 million tons. The decoupling relationship of agricultural carbon emissions from 2000 to 2020 mostly showed strong decoupling or weak decoupling. Using the data of the last 10 years and the data of the last 5 years as the samples to predict the agricultural carbon emissions as a whole all showed a clear downward trend.The model′s prediction accuracy was the highest when using the data of the last 5 years as the sample for prediction. The agricultural carbon emissions in Gansu Province have reached the peak before 2025,agricultural carbon emissions projected for a sample of the last 5 years of data will be 17.08% lower in 2025 compared to 2020,the rate of reduction will reach 30.36% in 2030,and the average annual reduction rate will be 0.139 million tons.【Conclusion】Gansu Province′s agricultural carbon emissions continue to decline,the agricultural economy shows stable growth,and agricultural carbon emissions will peak before 2025,indicating that Gansu Province has made some progress in green low-carbon agriculture in recent years. Measures should be taken according to the actual local situation in order to achieve the goal of continuously promoting the development of green agriculture,reducing high-carbon emission activities,increasing agricultural carbon sinks,and realizing agricultural carbon emission reduction in Gansu Province. [ABSTRACT FROM AUTHOR]

The prediction of shock disturbed systems is always a major challenge in the field of grey prediction. Considering the characteristics of grey buffer operator, this paper proposes a new grey buffer operator based on inverse accumulation, new information priority and logarithmic function to cope with the prediction challenge. In addition, some relevant properties of the new grey buffer operator are discussed in this paper, including adjustment intensity and smoothness. The new grey buffer operator is used to process monotonically increasing sequences, monotonically decreasing sequences and oscillating sequences, respectively. Experimental results show that the proposed buffer operator can effectively improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to effectively assess the level of water supply and demand and the intensity of water consumption in Henan Province, and to quantitatively evaluate the regional water resources use, a three-dimensional energy-value water ecological footprint model was built by combining the energy-value theory with the three-dimensional ecological footprint model, evaluating the spatial and temporal changes of water resources use in Henan Province from 2008 to 2020 in terms of the size and depth of the energy-value water ecological footprint, and predicting the per capita energy-value water ecological footprint in Henan Province in the next 10 years. The results show that: on the temporal scale, the per capita water ecological footprint size and depth are 0.07 hm² and 1.76 respectively, with an overall trend of “M” shape, peaking at 2.42 and 2.68 in 2013 and 2019 respectively; on the spatial scale, both indicators show a significant positive correlation, with the distribution of the size of the per capita energy-value water ecological footprint being high in the south and low in the north, and the distribution of the depth of the energy-value water ecological footprint being low in the south and high in the north; it is predicted that the per capita energy-value water ecological footprint in Henan Province will still show a slow increase in the next decade, growing to 0.117 hm² in 2030. [ABSTRACT FROM AUTHOR]

Due to the large-scale development of rooftop photovoltaics and the continuous improvement of the national carbon trading market, the carbon credits generated by rooftop photovoltaics power generation can obtain additional income through carbon allocation offset or participation in carbon trading market. Firstly, the path and methods of participating in the carbon trading market for rooftop photovoltaic projects are studied, focusing on how large-scale rooftop photovoltaic projects participate in the carbon trading market and the degree of impact on the project's economy. Secondly, the life-cycle economic model of rooftop photovoltaic with carbon trading income is established based on the power generation model. Thirdly, in order to quantitatively analyze the income of carbon trading, an improved grey prediction GM(1, 1) model based on the differential threshold filtering method is proposed to predict the future carbon price. Finally, an example of large-scale rooftop photovoltaic is analyzed. The results show that large-scale rooftop photovoltaic considering participation in the carbon trading market has a relatively significant economic improvement.

Abstract In many related studies, educational data mining technology has been proven to play an important role in predicting the development direction of entrepreneurship education for college students. To further improve the accuracy of the prediction, we chose the grey prediction model as the basic prediction model and automatically optimized the weighting method to improve the model. To solve the problem of predicting the development direction of students’ employment in the guidance of entrepreneurship and employment in colleges and universities, the study selects the grey prediction model as the basic prediction model and chooses the automatic optimization and weighting method to improve the model. Meanwhile, the study establishes a variable system containing six dimensions: academic achievement; physical and mental development; cultural, physical, and artistic quantified status; ideological and political quantified status; scientific and technological innovation quantified status; social work quantified status. The final study used the actual prediction test to analyze the prediction effect. We have selected a variable system consisting of six dimensions, which are the results of extensive research. These dimensions include academic achievement, physical and mental development, cultural/sports/art quantitative status, ideological and political quantitative status, technological innovation quantitative status, and social work quantitative status. Each dimension provides us with important predictions about student entrepreneurship and employment. The results show that the model designed by the survey has only two cases of error in the prediction of 20 actual samples. At the same time, there is no prediction error in the two prediction directions of entrepreneurship and social employment. This shows that the model designed by the study is stable and accurate, and the prediction results are more reliable in the prediction directions of entrepreneurship and social employment. Compared with other relevant research results, our model performs well in predicting accuracy, especially in predicting entrepreneurial and social employment directions, without any prediction errors, indicating that our model has superior performance in predicting stability and accuracy compared to other studies.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are many and complex big data in digital power grid regulation, which leads to the difficulty of big data asset management. Therefore, a model of big data asset management system in digital power grid regulation is constructed. The model consists of three parts: data acquisition, data safe storage and data index. The data acquisition architecture is designed, and the data acquisition results are filled with missing values and corrected with grey prediction method. Using AR-Tree index organization to realize the digital power grid regulation big data index, and achieve the goal of high-quality management of digital power grid regulation big data assets. Store the filled and corrected data in the blockchain to ensure data security. The experimental results show that the average recall and precision of this method are 96.9% and 97.9%, and the data acquisition quality is high. After the application of this method, there is almost no unsafe data, and the proportion of safe data is higher, which shows that this method can ensure the security of big data storage. The response time of digital power grid regulation big data index is below 0.21s, and the index efficiency is higher. [ABSTRACT FROM AUTHOR]

On the decision of whether to continue to implement the industrial support policy, two scenarios are set to predict the market penetration rate of China's electric vehicles (EVs) (In this paper, the term Electric Vehicles (EVs) refers to both full-battery EVs and plug-in hybrids). In order to weaken the disturbance caused by international oil prices and industrial policies, the grey buffer operator was firstly applied, to preprocess the original data series. The sales data for EVs and fuel vehicles were buffered for second order and first order, respectively. Based on the obtained buffer data sequence, the GM (1, 1) model was used to predict the sales of EVs and fuel vehicles between 2022 and 2025 in China. The results demonstrate a significantly improved fit compared to directly modeling the raw data. This method is suitable for studying the market penetration rate prediction of China's EVs. If the industry support policies continue (Scenario I), an EV market penetration rate of 22.45% can be achieved in 2024, and the expected target can be achieved one year ahead of schedule. Even if the corresponding industrial support policies are no longer implemented (Scenario II), the EV market penetration rate will reach 20.58% in 2025, and the set target of 20% will be achieved on schedule. [ABSTRACT FROM AUTHOR]

Simulation of indoor temperature provides important references for thermal environment not only for buildings at design stage but also for existing buildings. The current thermal environment simulation software tools suit for buildings at design stage, however not for an existing building. A model is proposed to simulate indoor temperature combining Optimization multivariable grey prediction model (OGM(1,N)) and Elman neural network. The proposed model is trained by short-term field measured data. A unit is assembled to measure and record thermal parameters in a case natural ventilated building at half-hourly intervals during 7:00 May 29 and 6:30 June 2010. Programming in Matlab implements the proposed model and referenced models. The maximum mean deviation is 0.46°C, the maximum standard mean square deviation is 0.65°C. Three referenced indoor temperature simulation models, OGM(1,N), Elman neural network, and Designer’s Simulation Toolkit are executed, respectively, in case building to provide comparison. Compared with referenced models, the proposed model has higher accuracy and stronger robustness. It is expected that this study provides important references for thermal environment assessment in existing buildings using short-term field measured data.

New urbanization in counties and the logistics industry are closely related and are essential in promoting regional economic and social development. There are specific challenges and obstacles to revealing the interaction mechanism and system state measurement between the two. This paper explains the two coupling mechanisms and constructs the evaluation index system. It proposes a new analysis method based on the coupling coordination degree model, the spatio-temporal evolution analysis, and the grey prediction model. The goal is to learn more about and fully realize the coordinated development mechanisms of the two. It then uses the Hebei province of China as an example to empirically analyze its systematic cross-sectional data from 2013 to 2022. Research findings: (1) In Hebei province, the new urbanization in counties and the logistics industry have a systematic coupling relationship. However, the logistics industry’s comprehensive development level is relatively lagging. The two systems have been at a high-level coupling stage for the last decade and maintain a high coupling status. The coupling coordination has shown significant improvement. (2) Although the geographical distribution of the coupling and coordination degree of the new urbanization in counties and logistics industry system has short-term volatility, it is still stable in the long term and presents economic-related spatial characteristics. (3) Over the next five years, the coupling coordination of 11 cities in Hebei province will steadily grow. There will be greater harmonization between the two systems. (4) From the analysis results, the evaluation index system of the coupled system constructed is scientific and reasonable. The analysis method can not only measure the system’s coupling degree, but it can also predict the development trend and analyze the spatial evolution. The technique has novelty and validity, which can be used as a reference for analyzing and making decisions about similar systems.

This paper addresses the issue of the conventional DGM(1, N) model's prediction results not taking into account the grey system theory pri1nciple of the "non-uniqueness of solutions". Firstly, before presenting the interval grey action quantity, the practical significance of grey action quantity is examined. In the DGM(1, N) model, the grey action quantity is transformed into an interval grey action quantity. Then, the calculation of the parameters uses the least squares method. A DGM(1, N, ⊗ c ) model containing interval grey action is then built, and meanwhile, the program code for DGM(1, N, ⊗ c ) is provided. Lastly, the aforementioned model is used to forecast the hydroelectricity consumption of China. The findings indicate that it produces more rational outcomes than the traditional DGM(1, N) model. Overall, the research carries significant pragmatic implications for broadening the conceptual underpinnings of multivariate grey forecasting models and enhancing their structural arrangement. [ABSTRACT FROM AUTHOR]

A new grey power model is proposed for modeling periodic small sample systenis. In the modeling process, a trigonometric tutiction is introduced to identifj, the periodicity characteristics of the data, and the specific time response formula and modeling steps are given. At the same time, in order to minimize the average relative error, the least square method and genetic algorithm are used to optimize and solve each parameter. Finally, the model is applied to forecast the traffic flow of the Herigcla Expressway iii Hebei Province and the PM2. 5 coment of air pollutants in Tianjin. The results are compared with those of other models. The results show that the proposed model has higher prediction accuracy, which further verifies the validity of the model. [ABSTRACT FROM AUTHOR]

Featured Application: Only when deep in situ rock samples are tested in the same environments as their occurrences can their true physical and mechanical properties be obtained. Therefore, a fidelity test system that can simulate the deep in situ geological conditions has become an urgent need to promote the deep in situ rock mechanics. This work is the key component of the fidelity test system, being used to accurately reconstruct a deep in situ high-temperature and high-pressure environment in the system. The reconstruction of deep in situ environment up to 95 °C and 70 MPa using water is critical for the fidelity testing of deep Earth rocks. The temperature and pressure of the water have strong coupling in such an environment, which makes the control of temperature and pressure very difficult. The paper firstly presents the design of the system of deep in situ environment reconstruction and core transfer (SERCT); secondly, for the problem of high temperature and pressure control, a pressure-temperature (P-T) interpolation control algorithm based on the iso–density P-T curves of water is proposed. A P-T coupling control path is decomposed into two independent interpolation paths: an iso–thermal pressure control and an iso–mass temperature control, which realizes the decoupling control of temperature and pressure. Then, a fuzzy-PID dual mode method is adopted for the pressure control after decoupling, which reduces the overshoot and the dynamic response time of the system. For temperature control, a segmented and grouped electric heating mode is designed to improve the uniformity of the temperature field. A fuzzy PID temperature control algorithm based on grey prediction is proposed to achieve high precision temperature control with small overshoot. Finally, the effectiveness of the proposed methods is verified by experiments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] The ecological and environmental effects of land use transition and their development trends were analyzed in order to provide a basis for promoting land use transition, low-carbon cycle development, and realizing the goal of "double carbon" . [Methods] We used land use data for Zunyi City of Guizhou Province in 2005, 2010, 2015, and 2020 and spatial analysis technology for this study. [Results] ① Land transformation from 2005 to 2020 was mainly manifested by the interconversion between carbon sink sites and the transformation of carbon sink sites to carbon source sites. ② Net carbon emission increased from 5.99×105 tons in 2005 to 9.87×106 tons in 2020, an increase of 9.27×106 tons (1 548.15%). However, the incremental amount gradually decreased, and the rate of increase gradually decreased. ③ The value of ecosystem services in Zunyi City increased from 7.64×1010 yuan in 2005 to 7.73×1010 yuan in 2020, showing a fluctuating upward trend, with a total increase of 9.14×108 yuan and a growth rate of 1.2%. ④ The gray prediction model showed that the net carbon emissions and ecosystem service value for Zunyi City will continue to increase in the future, with both showing a consistent upward trend. [Conclusion] Zunyi City continues to facing considerable pressure in terms of the ecological environment, and needs to increase efforts to enhance the value of ecosystem services while striving to achieve the goal of carbon peaking.

[Objective] The spatial-temporal evolution characteristics and driving mechanism of agricultural carbon emissions in Henan Province were determined to predict the change trend of agricultural carbon emissions during the next ten years in order to formulate an agricultural carbon sequestration and emission reduction scheme, and to promote the transformation to low-carbon and green agricultural production. [Methods] We used a carbon emission equity evaluation model, GeoDetector, and the GM (1,1) model to accomplish the study objectives. [Results] ① Agricultural net carbon emissions in Henan Province declined over time during the study period, with a distribution pattern of "high in the southwest and low in the northeast" . Carbon emissions from animal husbandry accounted for a large proportion of total emissions, and carbon emissions were mainly from cattle, sheep, and pigs. Wheat, corn and vegetables contributed more to carbon absorption than other sinks. ② The ecological carrying coefficient of agricultural carbon emissions was higher in the north and south, and lower in the west. The coefficient of economic contribution was high in the southeast and low in the southwest. ③ The agricultural employee population, per capita disposable income of rural residents, agricultural machinery gross power, and fiscal expenditures on education were the key factors affecting spatial differences in agricultural carbon emissions, and interactions among these factors were strong. ④ Agricultural net carbon emissions in Henan Province will continue to decrease from 2021 to 2030. It is estimated that Henan Province will achieve the goal of agricultural carbon neutrality by 2029. [Conclusion] In the future, Henan Province should strengthen science popularization, actively promote low-carbon agricultural production technology, and increase the efficiency of comprehensive utilization of agricultural resources. Additionally, all localities should give increased attention to system integration, strengthening regional cooperation, and achieving regional integration of agricultural carbon emission reduction.

ObjectiveThe propulsion shafting system is an important part of a ship, and the bearing load directly affects its operating state and service life. In this paper, bearing load under hull deformation is studied using grey system theory. Method First, according to the empirical formula of the relative displacement of each bearing caused by the deformation of the hull of a 57 000 DWT oil tanker, the relative displacement of each bearing is calculated and input into a finite element model, and the load value of each bearing is output. On this basis, grey relationship analysis of grey system theory is introduced to study the influence degree of stern bearing displacement on the load of each bearing, and the relative change of the load of each bearing caused by the displacement of the stern bearings is analyzed. A GM (1,1) prediction model is then established for the bearing load considering the bearing displacement conditions, and the hull deformation-fitting and prediction of each bearing load are made. ResultsThe results show that grey relationship analysis can effectively reflect the influence of hull deformation on bearing load. The GM (1,1) prediction model has high accuracy and prediction errors less than 6.0%, and the model test indexes can represent the accuracy of the prediction. ConclusionGrey system theory is effective and practical in research on propulsion shaft load. It can accurately predict bearing load under bearing displacement, giving it certain reference value for research on bearing load under actual sailing conditions.

In order to meet the development needs of economic and efficient in oilfield distribution network,and to realize the goal of low carbon more quickly,the investment decision-making method of energy-saving measures optimization is proposed. Firstly,the loading behaviors of pumps is analyzed,an calculation model for special line loss of oilfield distribution network based on load superposition characteristics is proposed,which is corrected according to the actual load of oilfield. Next, according to different energy-saving models and the GM(1,1)gray prediction model,the optimization investment decision model of oilfield distribution network is established. Then,based on the characteristics of oilfield energy-saving measures,the direct comparison objective method and the taboo search method are respectively selected to solve the solution of the different oilfield scale. Finally,a 30-node 6 kV distribution network line in an oilfield is taken as an example for simulation verification. The results show that the energy-saving and loss reduction scheme of oilfield distribution network obtained by the investment decision-making method in this paper has high cost performance and can effectively improve the voltage at the end of the line.

• Effective trend characterization method is necessary because of uncertain numerical distribution of interval grey numbers. • Interval grey numbers with center points are taken as research objects. • A grey interval prediction model combined with Markov model based on interval features is proposed for data modeling. • The proposed model corrects the interval deviations or irregularities making the uncertainty information clearer. • Examples of private car ownership and milk production in representative regions verify the efficacy of the proposed model. Data sequences represent complex changes in external environments or internal systems, and patterns of some index sequences have become more difficult to characterize using real number sequences. Considering the imbalances between regional development levels in terms of socioeconomic indicators, a discrete grey-Markov model based on interval distribution characteristics is proposed in this paper. Based on the classic discrete grey model, a difference coefficient sequence and median sequences of the upper and lower information domains, respectively, are introduced to establish differential equations for the representation and prediction of different development level trends. Furthermore, a combination of discrete grey models with the Markov model is used to modify the nonstationary data sequences and further improve the new model prediction accuracy. The parameter estimation and solution of the proposed model are organized using analytical methods, and the accuracy of the model is investigated. By fitting and predicting examples of private car ownership and milk production in representative unbalanced development regions, the results of the proposed model are compared with those of four other prediction models to verify its effectiveness. The results demonstrate that the prediction accuracy of the proposed model is better than those of other grey models in nonstationary data sequences. By capturing the interval features, this model achieves the goal of characterization of interval distribution information and further modification of the nonstationary series. [ABSTRACT FROM AUTHOR]