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The water ecological security pattern is a core factor. A scientific, accurate, and practical evaluation of water ecological security provides a theoretical basis for regional water ecological management. Using water resource data from five cities in the Hexi Corridor of Gansu Province (Jiuquan (JQ), Jiayuguan (JYG), Zhangye (ZY), Jinchang (JC), and Wuwei (WW)) from 2006 to 2021, a water ecological security evaluation index system based on the PSR (pressure-state-response) framework was constructed, covering 27 factors related to water resources, socio-economics, and the ecological environment. The main obstacle factors of water ecological security were identified using the obstacle degree model, and the grey GM(1,1) model was employed to predict water ecological security. Results indicated that the comprehensive assessment index of water ecology in the Hexi Corridor increased from 2006 to 2021, showing a transition from relatively unsafe (0.319) to basic safety and then to relatively safe (0.672). The pressure and response systems were the main limiting factors affecting water ecological security in the Hexi Corridor. After a slight decline in 2008, the overall spatial distribution continued to rise, with WW City and ZY City leading since 2016. ZY had a higher safety grade proportion (25%) compared to other areas in the Hexi region. The pressure system was the most significant obstacle to water ecological security after 2006. Prediction results indicated that the comprehensive evaluation index of water ecological security would continue to rise annually from 2022 to 2031, reaching a very safe level by 2025. The evaluation results provide a scientific basis for ecological security and risk decision-making in the study area. [ABSTRACT FROM AUTHOR]

Accurate prediction of industrial electricity consumption is not only beneficial to maintaining the steady development of the economy but also to conserving energy. To improve the prediction accuracy of industrial electricity consumption, a grey cluster weighted Markov model is proposed. It is applied to predict the industrial electricity consumption in four different regions in China. The prediction results are compared with the traditional discrete grey prediction model, which shows that the present model is more effective in these aspects of prediction accuracy, stability and extensibility. The research can provide theoretical references for the "West-East electricity transmission project" in China. [ABSTRACT FROM AUTHOR]

The challenge of predicting time series with limited data has evolved over time due to nonlinearity, complexity, and limited information. It can be perceived as a mapping of dynamical systems in one-dimensional space. This article proposes a neural grey system to tackle this challenge. The system enhances its ability to fit nonlinearity by employing polynomials, captures complexity through a fractional-order cumulant operator, and resolves information-poor uncertainty by utilizing grey system modeling techniques. The model effectively integrates research findings from neural computing, uncertainty theory, and complexity theory at a theoretical level. It accurately describes dynamic processes of complex systems. Additionally, we have reduced the complexity of calculations in the algorithm design. We selected a dataset of total retail sales of consumer goods to test the model's validity and applicability. Our experiments demonstrate that the newly proposed grey forecasting model can effectively forecast time series with small samples, offering good forecasting outcomes and generalization ability. [ABSTRACT FROM AUTHOR]

This study aims to predict the carbon sequestration capacity of Chinese grasslands to address climate change and achieve carbon neutrality goals. Grassland carbon sequestration is a crucial part of the global carbon cycle. However, its capacity is significantly impacted by climate change and human activities, making its dynamic changes complex and challenging to predict. This study adopts a fractional-order accumulation grey model, using 11 provinces in China as samples, to analyze and forecast grassland carbon sequestration. The study finds significant differences in grassland carbon sequestration trends across the sample regions. The carbon sequestration capacity of the grasslands in Xizang (Tibet) and Heilongjiang province is increasing, while it is decreasing in other provinces. The varying prediction results are influenced not only by regional climatic and natural conditions, but also by human interventions such as overgrazing, irrational reclamation, excessive mineral resource exploitation, and increased tourism development. Therefore, more region-specific grassland management and protection strategies should be formulated to enhance the carbon sequestration capacity of grasslands and promote the sustainable development of ecosystems. The significance of this study lies not only in providing scientific guidance for the protection and sustainable management of Chinese grasslands, but also in contributing theoretical and practical insights into global carbon sequestration strategies. [ABSTRACT FROM AUTHOR]

The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain 'hotspots' with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality. [ABSTRACT FROM AUTHOR]

Abstract The water ecological security pattern is a core factor. A scientific, accurate, and practical evaluation of water ecological security provides a theoretical basis for regional water ecological management. Using water resource data from five cities in the Hexi Corridor of Gansu Province (Jiuquan (JQ), Jiayuguan (JYG), Zhangye (ZY), Jinchang (JC), and Wuwei (WW)) from 2006 to 2021, a water ecological security evaluation index system based on the PSR (pressure-state-response) framework was constructed, covering 27 factors related to water resources, socio-economics, and the ecological environment. The main obstacle factors of water ecological security were identified using the obstacle degree model, and the grey GM(1,1) model was employed to predict water ecological security. Results indicated that the comprehensive assessment index of water ecology in the Hexi Corridor increased from 2006 to 2021, showing a transition from relatively unsafe (0.319) to basic safety and then to relatively safe (0.672). The pressure and response systems were the main limiting factors affecting water ecological security in the Hexi Corridor. After a slight decline in 2008, the overall spatial distribution continued to rise, with WW City and ZY City leading since 2016. ZY had a higher safety grade proportion (25%) compared to other areas in the Hexi region. The pressure system was the most significant obstacle to water ecological security after 2006. Prediction results indicated that the comprehensive evaluation index of water ecological security would continue to rise annually from 2022 to 2031, reaching a very safe level by 2025. The evaluation results provide a scientific basis for ecological security and risk decision-making in the study area.

Abstract Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting process. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards, a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy’s prediction accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP.

Abstract The challenge of predicting time series with limited data has evolved over time due to nonlinearity, complexity, and limited information. It can be perceived as a mapping of dynamical systems in one-dimensional space. This article proposes a neural grey system to tackle this challenge. The system enhances its ability to fit nonlinearity by employing polynomials, captures complexity through a fractional-order cumulant operator, and resolves information-poor uncertainty by utilizing grey system modeling techniques. The model effectively integrates research findings from neural computing, uncertainty theory, and complexity theory at a theoretical level. It accurately describes dynamic processes of complex systems. Additionally, we have reduced the complexity of calculations in the algorithm design. We selected a dataset of total retail sales of consumer goods to test the model’s validity and applicability. Our experiments demonstrate that the newly proposed grey forecasting model can effectively forecast time series with small samples, offering good forecasting outcomes and generalization ability.

Objective This study addresses challenges associated with controlling the thickening time of cement slurries at elevated temperatures (exceeding 120 ℃), including prolonged experimental periods and difficulties in predicting thickening times. Methods This article explores the factors influencing the thickening time of cement slurries at high temperatures through fitting methods and constructs a predictive model develops a predictive model utilizing grey theory. Results The findings indicate that the thickening time of cement slurries is primarily influenced by the dosage of retarders, curing conditions, and specific additives, with varying degrees of impact under different conditions. The grey model used to predict the thickening time for the first time exhibits high accuracy and is suitable for small samples. Conclusion The sensitivity analysis of the factors affecting the thickening time at high temperatures and the establishment of a predictive model significantly reduce the cost of cement slurry thickening experiments and provide a theoretical basis for the formulation of thickening times in high-temperature cement slurries. [ABSTRACT FROM AUTHOR]

Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting process. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards, a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy's prediction accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP. [ABSTRACT FROM AUTHOR]

AbstractPrimary education plays an important role in the whole educational structure. The prediction of the development scale of primary schools is of great significance to the optimal allocation of educational resources by education departments. The paper introduces a novel multi-scale fractional-order Bernoulli grey model by combining a local cumulative operator with a global cumulative operator, abbreviated as MFGM. First, a nonlinear grey Bernoulli model based on conformable fractional derivative is proposed, where a hybrid accumulation is presented. Then, the MFGM parameters are obtained by an PSO optimizer, which are adaptive to data sets. Finally, the proposed model is validated and used to predict the scale of primary school scale. Experimental results show that the MFGM is superior to the other grey models and machine learning models in the fitting and testing primary school scale. The prediction results contribute to the decision-making of education sectors. [ABSTRACT FROM AUTHOR]

Cost control is a critical aspect of project construction management, particularly in the roadway con- struction of non-ferrous metal mines where the influencing factors are complex and variable, making cost prediction challenging. This paper proposes a cost prediction method for support engineering in non-ferrous metal mine roadways based on the grey model. A brief introduction to the grey model is provided, and a support model based on the grey model is established. Simulation of the support process is conducted, and the compilation and pricing of roadway support costs are completed. This method effectively predicts the cost of support projects, providing a scientific basis and reference for cost control in mine roadway support construction. [ABSTRACT FROM AUTHOR]

In order to study the variation of the absolute grey degree of reverse accumulation generates sequences, the general expression of the integer order reverse accumulation generates sequences absolute grey degree and its related properties are derived based on the class ratio relationship between the original sequence and its reverse accumulation generates sequences. The application effect of the backward-accumulative grey model incorporating absolute grey degree to the prediction of sudden-onset disasters is discussed in the context of practical problems. The results show that when the absolute grey degree of the original sequence is greater than 0.6, the reverse cumulative generation can effectively reduce the absolute grey degree of the sequence. When the absolute grey degree of the original sequence is less than 0.25, the reverse cumulative generation increases the absolute grey degree of the sequence. The findings of the study provide a reference for the model selection and data pre-processing of grey modeling in practical problems. [ABSTRACT FROM AUTHOR]

Soft tissue is an important operation object in robot-assisted surgery and its mechanical response is of great significance to the precision of surgical operation. Accurate prediction of soft tissue mechanical properties can effectively avoid the potential damage of biological tissue caused by excessive operating force. In this paper, three typical mechanical responses of soft tissue were obtained by no-slip compression experiments of liver tissue. Second, the prediction model of soft tissue mechanical response based on gray prediction was established, and the influence of key parameters of the model on the precision of mechanical response prediction was analyzed. The results show that the gray prediction model can accurately predict the mechanical response of soft tissue, and the prediction accuracy is the highest when the number of historical data is 7. The prediction method of soft tissue mechanical response proposed in this paper will provide important data reference for accurate operation of surgery. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. 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.)

Accurate parameter estimation of extreme wind speed distribution is of great importance for the safe utilization and assessment of wind resources. This paper emphatically establishes a novel grey generalized extreme value method for parameter estimation of annual wind speed extremum distribution (AWSED). Considering the uncertainty and frequency characteristics of the parent wind speed, the generalized extreme value distribution (GEVD) is selected as the probability distribution, and the Weibull distribution is utilized as the first-order accumulation generating operator. Then, the GEVD differential equation is derived, and it is transformed into the grey GEVD model using the differential information principle. The least squares method is used to estimate the grey GEVD model parameters, and then a novel estimation method is proposed through grey parameters. A hybrid particle swarm optimization algorithm is used to optimize distribution parameters. The novel method is stable under different sample sizes according to Monte Carlo comparison simulation results, and the suitability for the novel method is confirmed by instance analysis in Wujiaba, Yunnan Province. The new method performs with high accuracy in various indicators, the hypothesis test results are above 95%, and the statistical errors such as MAPE and Wasserstein distance yield the lowest, which are 3.33% and 0.2556, respectively.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy's 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator's health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator's health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model's prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability. [ABSTRACT FROM AUTHOR]

International students play a crucial role in China's talent development strategy. Thus, predicting overseas talent mobility is essential for formulating scientifically reasonable talent introduction policies, optimizing talent cultivation systems, and fostering international talent cooperation. In this study, we proposed a novel fractional-order grey model based on the Multi-Layer Perceptron (MLP) and Grey Wolf Optimizer (GWO) algorithm to forecast the movement of overseas talent, namely MGDFGM(1,1). Compared to the traditional grey model FGM(1,1), which utilizes the same fractional order at all time points, the proposed MGDFGM(1,1) model dynamically adjusts the fractional-order values based on the time point. This dynamic adjustment enables our model to better capture the changing trends in the data, thereby enhancing the model's fitting capability. To validate the effectiveness of the MGDFGM(1,1) model, we primarily utilize Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the evaluation criteria for the prediction accuracy, as well as standard deviation (STD) as an indicator of the model stability. Furthermore, we perform experimental analysis to evaluate the predictive performance of the MGDFGM(1,1) model in comparison to NAÏVE, ARIMA, GM(1,1), FGM(1,1), LSSVR, MLP, and LSTM. The research findings demonstrate that the MGDFGM(1,1) model achieves a remarkably high level of prediction accuracy and stability for forecasting overseas talent mobility in China. The implications of this study offer valuable insights and assistance to government departments involved in overseas talent management. [ABSTRACT FROM AUTHOR]

Accurate parameter estimation of extreme wind speed distribution is of great importance for the safe utilization and assessment of wind resources. This paper emphatically establishes a novel grey generalized extreme value method for parameter estimation of annual wind speed extremum distribution (AWSED). Considering the uncertainty and frequency characteristics of the parent wind speed, the generalized extreme value distribution (GEVD) is selected as the probability distribution, and the Weibull distribution is utilized as the first-order accumulation generating operator. Then, the GEVD differential equation is derived, and it is transformed into the grey GEVD model using the differential information principle. The least squares method is used to estimate the grey GEVD model parameters, and then a novel estimation method is proposed through grey parameters. A hybrid particle swarm optimization algorithm is used to optimize distribution parameters. The novel method is stable under different sample sizes according to Monte Carlo comparison simulation results, and the suitability for the novel method is confirmed by instance analysis in Wujiaba, Yunnan Province. The new method performs with high accuracy in various indicators, the hypothesis test results are above 95%, and the statistical errors such as MAPE and Wasserstein distance yield the lowest, which are 3.33% and 0.2556, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University 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.)

In traditional Wireless Sensor Network routing protocols, data collected through timed interval sensing tends to have high temporal redundancy, which leads to unnecessary energy drain. To alleviate this problem and enable sensor networks to save energy to some extent, a practical solution is to utilize prediction-based data fusion methods. To this end, this paper first proposes a Low Energy Adaptive Clustering Hierarchy-Energy-Kopt-N algorithm, an optimization algorithm explicitly designed to address the cluster-head election phase of the Low Energy Adaptive Clustering Hierarchy protocol. Then, a data collection model using data prediction techniques – the Grey Data Prediction Model is formatted. Combining these improvements, a new data fusion algorithm that relies on data prediction, Grey-Clusters-Leach (GCL), is proposed. Simulation experiments demonstrate that the network energy drain of the GCL algorithm is reduced by 18%, 35%, 21.5% and 20%, and the network operation critical period life is extended by 3%, 35%, 22%, and 5% compared to the EQDC LEACH, LEACH-E, and SEP algorithms, respectively. GCL can effectively manage the size and number of clusters and reduce the number of packet transmissions by 20%.

The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain ‘hotspots’ with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality.

This study aims to predict the carbon sequestration capacity of Chinese grasslands to address climate change and achieve carbon neutrality goals. Grassland carbon sequestration is a crucial part of the global carbon cycle. However, its capacity is significantly impacted by climate change and human activities, making its dynamic changes complex and challenging to predict. This study adopts a fractional-order accumulation grey model, using 11 provinces in China as samples, to analyze and forecast grassland carbon sequestration. The study finds significant differences in grassland carbon sequestration trends across the sample regions. The carbon sequestration capacity of the grasslands in Xizang (Tibet) and Heilongjiang province is increasing, while it is decreasing in other provinces. The varying prediction results are influenced not only by regional climatic and natural conditions, but also by human interventions such as overgrazing, irrational reclamation, excessive mineral resource exploitation, and increased tourism development. Therefore, more region-specific grassland management and protection strategies should be formulated to enhance the carbon sequestration capacity of grasslands and promote the sustainable development of ecosystems. The significance of this study lies not only in providing scientific guidance for the protection and sustainable management of Chinese grasslands, but also in contributing theoretical and practical insights into global carbon sequestration strategies.

The United Nations has promoted and supported the UNCTAD Creative Economy Programme since 2004 to help countries around the world understand how to promote economic development through creativity in industries. This research article aims to determine whether the creative economy will be the major engine to accelerate Thailand's economic development in the coming decade or not, and what the major creative economy sectors are that must be prioritized or initiated and focused on. The data implemented in this research cover 2011–2018, which consist of creative economy sector income, the IO table, and the SAM table. The methodology utilized in this research was the ML model, the GREY model for predicting the growth rate of income from the major creative economy sectors contribute to Thailand's economy between 2019–2025, and the CGE model. The study's empirical findings show that the significant sectoral creative economy consists of fashion, advertising, Thai food, and cultural tourism, which need to be given more stimulus. Furthermore, the economies of Chiang Mai, and Thailand as a whole, would eventually be high-income economies if creative economy sectors were to be promoted and continuously supported by efficient policies. the economic growth of Thailand and Chiang Mai would eventually become high income whenever these economies allow creative economy sectors to be promoted or supported by efficient policies continuously. [ABSTRACT FROM AUTHOR]

Based on the population data of Xi'an from 2014 to 2020, the predicted value of population in different ages were given by building the grey model and calculating with Matlab. The predicted results were compared and tested. According to the tested results, the predicted value of total population in different ages in 2021 to 2026 is obtained, the changing trend of the number of the elderly population, which indicates that the population aging in Xi'an is a serious problem. The situation about how to support the aged in the future will be severe. The local government should establish a sound social elderly security system as soon as possible. [ABSTRACT FROM AUTHOR]

The structural behavior of the Xiaowan ultrahigh arch dam is primarily influenced by external loads and time-varying characteristics of dam concrete and foundation rock mass during long-term operation. According to overload testing with a geological model and the measured time series of installed perpendicular lines, the space and time evolution characteristics of the arch dam structure were analyzed, and its mechanical performance was evaluated. Subsequently, the deformation centroid of the deflective curve was suggested to indicate the magnitude and unique distribution rules for a typical dam section using the measured deformation values at multi-monitoring points. The ellipse equations of the critical ellipsoid for the centroid were derived from the historical measured time series. Hydrostatic and seasonal components were extracted from the measured deformation values with a traditional statistical model, and residuals were adopted as a grey component. A time-varying grey model was developed to accurately predict the evolution of the deformation behavior of the ultrahigh arch dam during future operation. In the developed model, constant coefficients were modified so as to be time-dependent functions, and the prediction accuracy was significantly improved through introduction of a forgetting factor. Finally, the critical threshold was estimated, and predicted ellipsoids were derived for the Xiaowan arch dam. The findings of this study can provide technical support for safety evaluation of the actual operation of ultrahigh arch dams and help to provide early warning of abnormal changes.

Under the deep integration of emerging technologies and industries, big data + cultural and creative industry is not only a simple industrial superposition but also the cooperation and complementarity of natural sciences, humanities, and social sciences. In this paper, the development of intelligent cultural and creative industries is predicted by the gray model, then the residuals are corrected, and finally, the residuals are corrected again by the Markov chain to improve the prediction accuracy. The modified GM(1,1) model and the improved Markov chain are used to predict and analyze the development trend of the data-driven cultural and creative industry, and the actual total output is not much different from the predicted output, which indicates that the method constructed in this paper can reasonably predict the results. According to the prediction method designed in this paper, the development of intelligent cultural and creative industries from 2024 to 2033 is predicted, and from 2029 to 2033, the maximum total output is predicted to be 28.02×107 yuan, 32.77×107 yuan, 38.33×107 yuan, 44.83×107 yuan, and 52.43×107 yuan, respectively, and China’s intelligent cultural and creative industry will enter a stage of rapid development.

To promote the sustainable development of the karst region, investigating the spatial–temporal evolution of ecological quality and elucidating key influencing factors is significant. The study quantified the ecological quality of Central Yunnan Urban Agglomeration (CYUA) from 2002 to 2021 using Google Earth Engine (GEE) and the Modified Remote Sensing Ecological Index (MRSEI) with soil erosion indicators. Additionally, the study analyzed MRSEI's spatial distribution, spatial–temporal transfer, and driving factors by integrating the Sen + Mann-Kendall trend test and the grey correlation degree. The grey prediction model GM (1,1) and CA-Markov model were employed to predict CYUA's future ecological condition in terms of MRSEI's mean value and spatial distribution of MRSEI rank. Results indicate that: (1) Greenness and humidity significantly enhanced the ecological environment, while dryness, heat, and soil erosion indicators had the opposite effect. The first principal component, with a mean contribution of 70.20 % across 2002, 2007, 2012, 2017, and 2021, indicated that MRSEI based on PC1 was suitable for evaluating CYUA's ecological quality. (2) Spatially, the western CYUA generally outperformed the eastern part in terms of ecological quality, with the moderate class dominating the ecological quantity. Temporally, CYUA's holistic ecological condition consistently improved between 2002 and 2021. (3) Regional height, climatic conditions, and human construction projects were key factors altering ecological quality, as evidenced by the top five MRSEI influencing factors: greenness, humidity, normalized difference impervious surface index, elevation, and average annual rainfall. (4) Forecast results suggest that areas classified as good or excellent are expected to exceed 30 % by 2029, indicating ongoing and future improvements in CYUA's ecological quality. This study serves as a reference for ecological protection and regional sustainability in karst region.

Accurate forecasting of the enrollment scale of higher education and education expenditure is the key to scientific decision-making in education. Nevertheless, education data sets are usually small and affected by uncertainties including economy, education system policies, and so on, which result in difficulty in modeling. To address the tissues, we presented a novel time-delayed grey model based on a fractional-order accumulation time term, abbreviated as FHTDGM. The hyperparameters of the model are optimized using the particle swarm optimization. The experiment's results show that the FHTDGM model can fit and forecast enrollment scale and educational spending data with greater accuracy than a group of other grey models, and the forecast MAPEs are 2.396 and 1.244, respectively. The accurate prediction contributes to constructive suggestions for the decision-making of education. [ABSTRACT FROM AUTHOR]

In view of the complicated factors influencing the stock price, we revised the Long Short-Term Memory (LSTM) network, which is commonly used in time series, to predict stock prices under the condition of multivariable. First, the Variance Inflation Factor (VIF) was used to screen variables, and then the adaptive promotion (Adaboost) model was combined to check the importance of characteristic variables. Second, the crawler was used to conduct text analysis of investor sentiment, calculate indicators including sentiment index, and reveal the relationship between them and stock price. Then, prices of three stocks including Gree Electric Appliances, Flyco Electric Appliances and Midea Group were predicted by Multilayer Perceptron (MLP) and LSTM, and the appropriate model was selected as the benchmark model. Finally, indicators of sentiment index and investor concern were added to the benchmark model to construct the LSTM-EM model, and the GM (1,1) model was used to correct the residual term after considering investor sentiment. The empirical results show that the proposed model can predict the stock price accurately. [ABSTRACT FROM AUTHOR]

The accurate prediction of renewable energy consumption (REC) is of great significance to ensure energy security, reduce dependence on fossil energy, and promote sustainable economic and social development. In this paper, a novel grey model with conformable fractional opposite-direction accumulation (CFOA), abbreviated as the CFOGM (1,1) model, is proposed to forecast REC in Australia. The new model is discussed in detail with a new CFOA operation and the GM (1,1) model and can take full advantage of the information carried by the original data. The CFOGM (1,1) model has lower modeling error and better fitting and forecasting accuracy than other grey, Holt, and ARM models and can better capture the change trend of REC and achieve accurate prediction. The forecasting results present that the REC in Australia is 497–581 petajoules in 2021, 596–728 petajoules in 2022, and 715–912 petajoules in 2023, indicating that the REC in Australia is still accelerating. [ABSTRACT FROM AUTHOR]

Some grey prediction models suffer from outliers and overfitting, and their prediction performance can be improved. Based on the fractional grey model (FGM) and the fractional time delayed grey model (FTDGM), an improvedfractional single optimization parameter grey model (IFSGM) is proposed in this paper. A time-translation power term is used to reduce model overfitting. The data is pre-processed to reduce the influence of outliers based on data transformation in cumulants and summations. The convolutional computational formulation is used to perform the prediction and improve the prediction effect. The genetic optimization algorithm is used to optimize the order of the fractional cumulants, find the optimal value of the order, and improve the model fit and prediction effect. In 6 data sets, the IFSGM and the 7 grey models are compared and tested. The experimental results show that IFSGM achieves excellent prediction performance. [ABSTRACT FROM AUTHOR]

In the grey models, the GM(1,1) model is an important type of prediction model. The traditional grey GM(1,1) model has good prediction results in the case the original data show exponential variations at a slow rate. However, in practical problems, although showing exponential variations or approximately exponential variations, original data vary very fast sometimes. In these cases, the traditional grey GM(1,1) model tends to have poor prediction accuracy, mainly because the data fails to meet the laws presented by the traditional model. Therefore, the paper makes improvements in the following two aspects: first, the paper transforms the traditional accumulated generating sequence of original data; second, the paper extends the traditional grey model's structure, i.e., building a grey EGM(1,1,Σe^(ck)) model. The paper offers the parameter optimization method of the grey EGM(1,1,Σe^(ck)) model. Using the novel modeling method proposed, the paper builds the grey EGM(1,1,Σe^(ck)) models for China's total electricity consumption and China's GDP per capita, respectively, in the final section. Results show that the models built with the proposed modeling method have high simulation precision and prediction precision. [ABSTRACT FROM AUTHOR]

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Prefabricated building construction is an important method of enhancing construction productivity and promoting sustainable development in the construction industry. Evaluating and predicting the development performance of prefabricated buildings will contribute to identifying and implementing the most effective responses to promote prefabricated building technologies. Based on the Drives–Pressures–States–Impacts–Responses (DPSIR) framework, 14 evaluation indexes are determined to evaluate the development level of prefabricated buildings. The entropy weight method was used to determine the weight of the evaluation index, and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method with improved grey correlation was applied to comprehensively evaluate the trend of the index. The grey model (GM(1,1)) was developed to predict the development trend of prefabricated buildings. The development of prefabricated buildings in Shandong province, China, is employed as a case to apply the developed method and investigate development experiences. The results demonstrate that the case has achieved significant progress and has great potential in promoting the use of prefabricated buildings. The development recommendations include developing a policy and regulation system, strengthening a prefabricated building talent pool, and enhancing the investment in technological innovation. This study innovatively formulated the evaluation and prediction system based on the DPSIR, TOPSIS and GM(1,1) models, which could be used for evaluating development performance between social and environmental factors among various cause-effect relationships. [ABSTRACT FROM AUTHOR]

The dramatic increase of the aging population is a current and primary livelihood issue in China. It is very important to accurately predict the aging population for policy-makers such as governments and insurance companies. To explore the future development trend of China's aging population, a novel grey prediction FPTGM(1,1,α) model is established in this paper. The data from 2003 to 2012 are used to build the model, and the data from 2013 to 2018 are used to assess the modeling accuracy. The results show that the fitting and forecasting accuracy of the proposed FPTGM(1,1,α) model are higher than those of other models. This indicates that the novel grey model is more suitable for predicting the aging population in China. Combined with the idea of metabolism, the FPTGM(1,1,α) model is applied to predict China's aging population from 2019 to 2042. Finally, some reasonable suggestions for dealing with the aging population are put forward to the government and other decision-makers. [ABSTRACT FROM AUTHOR]