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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]

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]

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]

Copyright of Chemical Engineering of Oil & Gas / Shi You Yu Tian Ran Qi Hua Gong is the property of PetroChina Southwest Oil & Gas Field Company 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 Gold (1001-1277) is the property of Gold Magazine 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.)

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 Journal of Liaoning Technical University (Natural Science Edition) / Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of Liaoning Technical University (Natural Science) Editorial Department 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 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.)

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.)

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 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.)

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]

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]

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.)

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]

Copyright of Journal of Capital Normal University (Natural Science Edition) is the property of Journal of Capital Normal University (Natural Science Edition) 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.)

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]

Copyright of Journal of Nanjing University of Information Science & Technology (Natural Science Edition) / Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban) is the property of Editorial Office of Journal of Nanjing University of Information Science & 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.)

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]

Copyright of Electric Power is the property of Electric Power 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.)

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]

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]

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]

Changes of customer requirements (CRs) are inevitable disturbances for products which will lead to product obsolescence or frequent redesign. The property of product defending and recovering from the shock of ever-changing CRs is resilience. It is beneficial for designers to explore and evaluate product resilience towards changing CRs. Fortunately, various resilience assessment methods in engineering realm have offered some significant insights. Motivated by the deficiencies of the existing studies and the advantages of engineering resilience assessment methods, product resilience towards changing CRs is measured. Firstly, joint customer satisfaction is defined by integrating competition analysis and importance analysis. Secondly, the evolution trends of CRs are depicted and captured based on a modified grey model and polynomial fitting, respectively. Then, a three-stage model considering absorption, adaptation, and recovery is established to evaluate product resilience. Lastly, a case study of an electric vehicle is implemented to elucidate the utility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) 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.)

This study proposes a generalized grey Lotka–Volterra model with a finite number of variables. The model is obtained by applying the grey modelling method to estimate the parameters of a finite dimensional quadratic Lotka–Volterra system. Subsequently, the model is used to analyze the competition and cooperation relationship between four macroeconomic indicators, namely Gross Domestic Product, Export, Import and Investment, and to obtain short-time forecasting for them. The data used in the empirical investigation cover the time periods 2005–2022 and 2011–2022, for the European Union. The empirical results are compared to the ones obtained by using the grey model G M (1,1) and the two-dimensional grey Lotka–Volterra model. Finally, economic interpretations of the empirical findings are formulated. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

Missing data presents a significant challenge when forecasting unknown data in lime series. To address this issue. this paper proposes an Altenuated Long and Short-Term Memory model based on a dec·aying filliction, which is abbreviated as A D-LSTM, for forecasting unknown data in time series. As strong correlations exist in adjacent data, the Grey model is level·aged to predict and impute missing values in small-sample datasets. To improve the accuracy offorecasting in time series with missing data, we introduce subspace decomposition, which can correct errors in the memory state cell caused by missing elements, and a time decay function, which utilizes the number of adjacent continuous missing data as a weight to modify the memory cell and enables the model to detect the existence of missing elements, into the LSTM unit. These enhancements reduce the impact of imptited data on model training in time series analysis. Finally, to verify the effectiveness of our proposed method, we conduct experimental validation and comparative analysis using the Beijing house price dataset. Our results indicate that the AD-LSTM model perfbrnis better in reducing the impact of missing data and achieving lower fbrecast ert·ors compared to the baseline models. [ABSTRACT FROM AUTHOR]

Through the rapid carbonation test of SFRRC with different fiber volume fractions at ultra-low temperature, the influence of ultra-low temperature damage on the carbonation resistance of SFRRC was analyzed, which provides a theoretical basis for the application of SFRRC in ultra-low temperature engineering. The experimental results show that ultra-low temperatures can significantly weaken the carbonization resistance of SFRRC. When the temperature reaches 160 °C, the carbonization depth increases by 67.66 % compared with the normal state. The proper amount of steel fiber has an evident influence on the carbonation resistance of the material. However, when the addition amount exceeds the optimum content, the carbonation resistance of the material decreases. The grey prediction model established by constructing the original sequence can reasonably predict the carbonation resistance of SFRRC after ultra-low temperatures. [ABSTRACT FROM AUTHOR]

To accurately predict the time series of energy data, an optimized Hausdorff fractional grey seasonal model was proposed based on the complex characteristics of seasonal fluctuations and local random oscillations of seasonal energy data. This paper used a new seasonal index to eliminate the seasonal variation of the data and weaken the local random fluctuations. Furthermore, the Hausdorff fractional accumulation operator was introduced into the traditional grey prediction model to improve the weight of new information, and the particle swarm optimization algorithm was used to find the nonlinear parameters of the model. In order to verify the reliability of the new model in energy forecasting, the new model was applied to two different energy types, hydropower and wind power. The experimental results indicated that the model can effectively predict quarterly time series of energy data. Based on this, we used China's quarterly natural gas production data from 2015 to 2021 as samples to forecast those for 2022–2024. In addition, we also compared the proposed model with the traditional statistical models and the grey seasonal models. The comparison results showed that the new model had obvious advantages in predicting quarterly data of natural gas production, and the accurate prediction results can provide a reference for natural gas resource allocation. [ABSTRACT FROM AUTHOR]

Kolhapur city has witnessed consistent growth in motor vehicles (MV), and an accurate forecast is essential. To this end, a combined grey model was developed by combining the grey model (GM(1,1)) and the simple linear regression (SLR) model. The new model, named the grey simple linear regression model (abbreviated as GSLRM), is newly utilised for MV prediction. A total of five years (2008–2012) of MV data were employed. The accuracy of the proposed GSLRM was compared with the GM(1,1) and SLR models in terms of the mean absolute percentage error (MAPE). The results revealed that all models meet high accuracy (MAPE < 10%). However, the GSLRM was slightly more accurate (MAPE = 3.85%) than the competing models. Moreover, with a reasonable development coefficient value (a ≤ 0.3), the GSLRM could be used for mid-long-term forecasts. Subsequently, the GSLRM was used to forecast MV for the next seven years (2013–2019). The forecast results showed that the total MV would increase continuously. In summary, the GSLRM proved its reliability and validity in forecasting the total MV in Kolhapur city, and it can assist the government in drafting relevant policies. Moreover, this study also attempted to investigate the relationship between the population and RMV growth and found that population could be one of the responsible factors. [ABSTRACT FROM AUTHOR]

According to the principle of minimum information, new information priority, and difference information, most existing grey forecast models and their improvement are inconsistent with the grey theory. Therefore, a novel discrete multivariable grey model with unequal fractional-order accumulation is proposed. To improve the accuracy and stability of the model, an optimization algorithm for unequal fractional-order is proposed. The proposed model and algorithm are evaluated with four actual cases. The results show that the novel model has better performance and the proposed unequal fractional-order accumulation operator is better than other existing accumulation operators. Considering the energy consumption, the carbon dioxide emissions in the USA have been forecasted to decrease but remain at a high level by using the novel discrete multivariable grey model. Reducing energy consumption is conducive to reducing carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

The overall performance of composites will be seriously affected when their interfacial properties degrade after thermo-oxidative aging (TOA). In this paper, the interfacial properties of carbon fiber (CF)/bismaleimide (BMI) resin aged for 10, 30, 90, 120 and 180 days at 200°C and 250°C were quantitatively studied by transverse fiber bundle tensile (TFBT) testing. In addition, a prediction model of interfacial strength based on the grey model theory was established. The results show that the interfacial strength for CF/BMI resin decreases exponentially with the extension of aging time, and the strength of the TFBT specimens aged at 200°C for 180 days and 250°C for 90 days decreases by 82.01% and 84.65%, respectively. This is caused by the oxidation decomposition of BMI resin and the mismatch of the thermal expansion coefficient between the CF and BMI resin. In addition, the grey model is used to predict the interfacial properties of CF-reinforced BMI resin under TOA. The prediction accuracy is higher than that of the traditional exponential model, and the prediction error is less than 10%. To sum up, this work can provide a reference for studying the evolution of interfacial properties of other kinds of fiber-reinforced polymer composites under extreme environments. [ABSTRACT FROM AUTHOR]

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The demanding forecast of coal logistics is the premise of integrating coal logistics resources and improving its efficiency. First, we considered the principles of index system construction and many other factors and chose GDP, average consumption, value added of the secondary industry, coal import and export volume, urban population quantity, coal production, total energy consumption, average annual rain PH, and coal consumption as a measure of indicators. Then, based on the idea of a combination model, the combination of grey model and BP neural network is selected, and FOA-GNNM is used to predict the coal consumption in China from 2018 to 2022, and the coal transportation volume in railway, highway and waterway is further calculated. The findings can help for better understanding of coal logistics, and then the following suggestions are proposed to improve its demanding forecast for China's future coal logistics operation arrangements, route planning and reserve system construction. [ABSTRACT FROM AUTHOR]

Public opinion is an aggregate of people's views, attitudes, and emotions about events that can spread through the Internet to generate online public opinion. Studying the evolution of online public opinion during emergencies can help relevant departments to take targeted measures to respond in advance. Tweets and Weibo texts with negative emotions are essential factors affecting the evolution of online public opinion. To this end, this paper proposes a novel improved grey model, SISGM(1,1), that optimizes initial conditions and background values for predicting the number of negative Weibo texts generated during emergencies. The model is improved as follows: First, the background value is reconstructed by the Simpson rule to achieve the effect of smoothing the data sequence. Second, the ISRU activation function is used to modify the initial condition, which can better reveal the characteristics of data growth and improve the model's adaptability. Then, the modified background value is combined with the optimized initial condition to realize the double optimization. Finally, the PSO algorithm is used to calculate the introduced parameters to improve the prediction accuracy further. Additionally, the model is compared with five competing models to predict the evolution of online public opinion during emergencies. The experimental results demonstrate that the proposed model has apparent advantages compared to the other five competing models. [ABSTRACT FROM AUTHOR]

Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO 2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy. [ABSTRACT FROM AUTHOR]

The incredible increase in carbon emissions is a major global concern. Thus, academicians and policymakers at COP26 are continuously urging to devise strategies to reduce carbon and other greenhouse gas emissions. The transportation sector is a major contributor to greenhouse gas emissions in developing countries. Therefore, this study projected an increase in fossil fuel demand for transportation and corresponding carbon dioxide emission in Pakistan from 2018 to 2030 by employing the Grey model and using annual data from 2010 to 2018. Furthermore, the determinant of fossil fuel demand is modeled using an environmental sustainability model such as stochastic regression IPAT that links environmental impact as a product of population, affluence, and technology on annual time series data spanning from 1990 to 2019. The projected values of oil demand and carbon emissions reveal an increasing trend, with average annual growth rates of 12.68% and 11.45%, respectively. The fully modified ordinary least squares (FM-OLS) findings confirmed the environmental Kuznets hypothesis. The increase in population growth emerged as the major driver for oil demand and carbon dioxide emissions, while technological advancement can reduce oil demand and corresponding carbon emissions. This study urges Pakistan to switch from oil to gas and other green energies by encouraging hybrid vehicles, as the number of vehicles on the road positively impacts the transport sector's oil demand. Moreover, increasing economic growth and controlling the population growth rate by discouraging more children can be a valid policy for reducing oil demand and corresponding carbon emissions. [ABSTRACT FROM AUTHOR]

Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings inaccuracy due to the intrinsic merits and demerits of the models. In this work, oil monitoring and fault pre-warning of wind turbine gearboxes were studied based on oil inspection data of three wind turbines that have been working continuously for 3.5 years. The Grey Model (GM) and the Double Exponential Smoothing (DES) were combined by a modified inverse-variance weighting method proposed in this work, which used relative errors to calculate weight coefficients, reducing the errors and improving the accuracy as a whole. The predicted data were compared with the measured data to verify the predicting accuracy. Subsequently, a statistical method and linear regression method were adopted to jointly develop a pre-warning threshold for the oil inspection data. Comparing the predicted data with the threshold, the results showed that one of the wind turbines was in a warning state. The prediction was validated by an endoscope inspection of the gearbox, which found that some parts were slightly worn. [ABSTRACT FROM AUTHOR]

As part of China's compulsory nine-year education system, junior secondary education (JSSE) plays a vital role in supporting students' physical and mental development. The accurate prediction of the development scale trend of JSSE is helpful for the government to estimate the scale of educational development within a chosen time frame so as to aid decision making.Nevertheless, China's education system is complex, highly dimensional, and largely influenced by policy and other factors, which results in difficulty in modeling the education sample. Based on gray system theory, this paper proposes an improved fractional-order grey prediction model, OCFNGBM(1,1), to predict the development scale of JSSE. We describe the basic expressions of the model, the parameter estimation method, and the optimization method for hyperparameters and construct a scheme for optimizing the background value coefficients. Data collected from official websites from 2011 to 2021 are used to build the forecasting model, and data from 2011 to 2017 are used to evaluate the model's accuracy. Our experimental results indicate that the OCFNGBM(1,1) model has higher accuracy than the classical nonlinear gray prediction model. The OCFNGBM(1,1) model was employed to forecast the development scale of JSSE in China from 2022 to 2024, which provided useful information. This research provides a resource to help the national education department to develop a comprehensive and long-term plan for the development goals, scale, speed, steps, and measures of relevant education. [ABSTRACT FROM AUTHOR]

The fractional grey model is an effective tool for modeling small samples of data. Due to its essential characteristics of mathematical modeling, it has attracted considerable interest from scholars. A number of compelling methods have been proposed by many scholars in order to improve the accuracy and extend the scope of the application of the model. Examples include initial value optimization, order optimization, etc. The weighted least squares approach is used in this paper in order to enhance the model's accuracy. The first step in this study is to develop a novel fractional prediction model based on weighted least squares operators. Thereafter, the accumulative order of the proposed model is determined, and the stability of the optimization algorithm is assessed. Lastly, three actual cases are presented to verify the validity of the model, and the error variance of the model is further explored. Based on the results, the proposed model is more accurate than the comparison models, and it can be applied to real-world situations. [ABSTRACT FROM AUTHOR]

Based on particle swarm optimization (PSO), a new exponential time delay fraction order grey prediction model is proposed in this paper. Firstly, the original data is preprocessed by fractional-order accumulation; on the basis of fractional-order accumulation, it is proved that the initial value of the original sequence satisfies the fixed point theorem. On the basis of GM(1,1) model, a new model is established by adding exponential time delay term. The model is discretized by integral, the least square estimation of the linear parameters and the approximate time response equation are obtained. Finally, PSO is used to search the optimal parameters of the model and the experimental results are verified by Wilcoxon rank sum test. In order to test the good adaptability and strong prediction ability of the new model, two groups of data are verified for the production and consumption of oil and electricity in China. The results show that the new model has better prediction accuracy and adaptability than the other existing six grey models. [ABSTRACT FROM AUTHOR]

The prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information should be given more weight. The disadvantage of first-order accumulative grey models is that with the first-order accumulative method, equal weight is given to the original data. In this paper, a fractional-order cumulative grey model (FAGM) is used to establish the prediction model, and an intelligent optimization algorithm known as particle swarm optimization (PSO) combined with a genetic algorithm (GA) is used to determine the optimal order. The model discussed in this paper is used for the prediction of Internet cyber security situations. The results of a comparison with the traditional grey model GM(1,1), the grey model GM(1,n), and the fractional discrete grey seasonal model FDGSM(1,1) show that our model is suitable for cases with insufficient data and irregular sample sizes, and the prediction accuracy and stability of the model are better than those of the other three models. [ABSTRACT FROM AUTHOR]

Featured Application: Based on a novel multi-sensor system for monitoring the deep displacement of slopes, the feasibility of previous work was verified. A new multivariate gray model was developed to predict displacement values with more accurate results than some previous methods. A new discriminative method for slope stability is proposed which can be used to determine hazards in advance, avoiding the vulnerability of previous methods to environmental influences. Landslides are frequent and catastrophic geological hazards, and forecasting their movement is an important aspect of risk assessment and engineering prevention. Based on the integrated deep displacement three-dimensional measuring sensor with sensing unit array structure, an improved multivariable grey model based on dynamic background value and multivariable feedback is proposed to build predictive models for the evolutionary condition of landslides. In the modeling process, the traditional grey model was replaced by extracting the trend information of each variable, instead of summing up each independent variable after assigning weights to it, besides, the Whale Optimization Algorithm (WOA) is used to modify the default value in the model's background variables. By predicting more than 1000 sets of deep displacement monitoring data collected in the landslide simulation test conducted at the landslide simulation test device, the displacement prediction accuracy of our purposed model is 26%, 47%, and 87% respectively higher than the optimizing grey model (OGM) for three sensing units at different depths. Moreover, a new landslide risk assessment approach based on the orientation vector angle is proposed to make stability discriminations which is less susceptible to volatile data than the TOPSIS-Entropy weight theory and avoids the problem of lack of uniform standards due to the complexity of environmental factors. [ABSTRACT FROM AUTHOR]