Search Results (126)

Accurate oil debris analysis and wear monitoring of a gearbox are essential to ensure its stable and reliable operation. Element types of wear debris and their changes in the lubrication oil of the gearbox can be monitored by spectral analysis. However, it is still difficult to identify wear parts of the gearbox due to the complex composition of elements of wear debris. An improved multi-variable grey prediction model by incorporating a multi-objective genetic algorithm (MOGA-GM(1, N)) is proposed to evaluate weight coefficients of element concentrations of wear debris in the lubrication oil of the gearbox. Moreover, a wear growth rate of each element in the lubrication oil is proposed as an index for oil debris analysis to analyze the multi-variable correlation between the common element of iron (Fe) and other related elements of wear parts of the gearbox. Oil debris analysis of the gearbox is conducted on optimal weight coefficients of related elements to the common element Fe using the MOGA-GM(1, N) model. Wear experiment results verify feasibility of the proposed oil debris analysis method. Full article

Earthquakes are sudden and highly destructive events that severely disrupt infrastructure and logistics systems, making accurate and timely emergency material demand forecasting a critical challenge in disaster response. However, the scarcity of reliable data during the early stages of an earthquake limits the effectiveness of traditional forecasting methods. To address this issue, this study proposes a hybrid forecasting framework that integrates the Grey Model (GM(1,1)) with Bayesian Dynamic Linear Models (BDLMs), aiming to improve both the accuracy and adaptability of demand predictions. The approach operates in two phases: first, GM(1,1) generates preliminary forecasts using limited initial observations; second, BDLMs dynamically update these forecasts in real time as new data become available. The model is validated through a case study of the 2010 M7.1 Yushu earthquake in Qinghai Province, China. The results indicate that the hybrid method produces reliable forecasts even at the earliest stages of the disaster, with increasing accuracy as more observational data are incorporated. Our case study demonstrates that the integrated GM(1,1)-BDLM framework substantially reduces prediction errors compared to standalone GM(1,1). Using the first five days’ data to forecast fatalities and emergency material demand for days 6–10, the hybrid model achieves a 4.01% error rate—a 19.62 percentage point improvement over GM(1,1)’s 23.63% error rate. This adaptive forecasting mechanism offers robust support for evidence-based decision-making in emergency material allocation, enhancing the efficiency and responsiveness of post-disaster relief operations. Full article

Background: The ability of Graph Neural Networks (GNNs) to analyse brain structural patterns in various kinds of neurodegenerative diseases, including Parkinson’s disease (PD), has drawn a lot of interest recently. One emerging technique in this field is brain age prediction, which estimates biological age to identify ageing patterns that may serve as biomarkers for such disorders. However, a significant problem with most of the GNNs is their depth, which can lead to issues like oversmoothing and diminishing gradients. Methods: In this study, we propose SAGEFusionNet, a GNN architecture specifically designed to enhance brain age prediction and assess PD-related brain ageing patterns using T1-weighted structural MRI (sMRI). SAGEFusionNet learns important ROIs for brain age prediction by incorporating ROI-aware pooling at every layer to overcome the above challenges. Additionally, it incorporates multi-layer feature fusion to capture multi-scale structural information across the network hierarchy and auxiliary supervision to enhance gradient flow and feature learning at multiple depths. The dataset utilised in this study was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. It included a total of 580 T1-weighted sMRI scans from healthy individuals. The brain sMRI scans were parcellated into 56 regions of interest (ROIs) using the LPBA40 brain atlas in CAT12. The anatomical graph was constructed based on grey matter (GM) volume features. This graph served as input to the GNN models, along with GM and white matter (WM) volume as node features. All models were trained using 5-fold cross-validation to predict brain age and subsequently tested for performance evaluation. Results: The proposed framework achieved a mean absolute error (MAE) of $4.24\pm 0.38$ years and a mean Pearson’s Correlation Coefficient (PCC) of $0.72\pm 0.03$ during cross-validation. We also used 215 PD patient scans from the Parkinson’s Progression Markers Initiative (PPMI) database to assess the model’s performance and validate it. The initial findings revealed that out of 215 individuals with Parkinson’s disease, 213 showed higher and 2 showed lower predicted brain ages than their actual ages, with a mean MAE of 13.36 years (95% confidence interval: 12.51–14.28). Conclusions: These results suggest that brain age prediction using the proposed method may provide important insights into neurodegenerative diseases. Full article

Green technology innovation (GTI) is pivotal for driving energy transition and low-carbon development in manufacturing. This study evaluates the spatiotemporal efficiency and predicts trends of GTI in China’s Yangtze River Economic Belt (YREB, 2010–2022) using a combined “input-desirable output-undesirable output” framework. Combining the SBM and super-efficiency SBM models, we evaluate regional GTI efficiency (2010–2022) and reveal its spatiotemporal patterns. An improved GM(1,N|λ,γ) model with a new information adjustment parameter (λ) and nonlinear parameter (γ) is applied for prediction. Key findings include: (1) The GTI efficiency remains generally low during the study period (provincial average: 0.7049–1.4526), showing an “east-high, west-low” spatial heterogeneity. Temporally, provincial efficiency peaked in 2016, with intensified fluctuations around 2020 due to policy iterations and external shocks. (2) Regional efficiency displays a stepwise decline pattern from downstream to middle-upstream areas. Middle-upstream regions face efficiency constraints from insufficient inputs and undesirable output redundancy, yet exhibit significant optimization potential. (3) Parameter analysis highlights that downstream provinces (γ ≈ 1) exhibit mature green adoption, while mid-upstream regions (e.g., Hubei) face severe technological lock-in and reliance on traditional energy. Additionally, middle and downstream provinces (e.g., Sichuan, Anhui) with low λ values show rapid policy responsiveness, but face efficiency volatility from frequent shifts. (4) The improved GM(1,N|λ,γ) model shows markedly enhanced prediction accuracy compared to traditional grey models, effectively addressing the “poor-information, grey-characteristic” data trend extraction challenges in GTI research. Based on these findings, targeted policy recommendations are proposed to advance GTI development. Full article

To address the issue of insufficient prediction accuracy in traditional GM(1,1) models caused by significant nonlinear fluctuations in time-series data for ancient building structural health monitoring, this study proposes a wavelet decomposition-based GM(1,1)-BP neural network coupled prediction model. By constructing a multi-scale fusion framework, we systematically resolve the collaborative optimization between trend prediction and detail modeling. The methodology comprises four main phases: First, wavelet transform is employed to decompose original monitoring sequences into time-frequency components, obtaining low-frequency trends characterizing long-term deformation patterns and high-frequency details reflecting dynamic fluctuations. Second, GM(1,1) models are established for the trend extrapolation of low-frequency components, capitalizing on their advantages in limited-data modeling. Subsequently, BP neural networks are designed for the nonlinear mapping of high-frequency components, leveraging adaptive learning mechanisms to capture detail features induced by environmental disturbances and complex factors. Finally, a wavelet reconstruction fusion algorithm is developed to achieve the collaborative optimization of dual-channel prediction results. The model innovatively introduces a detail information correction mechanism that simultaneously overcomes the limitations of single grey models in modeling nonlinear fluctuations and enhances neural networks’ capability in capturing long-term trend features. Experimental validation demonstrates that the fused model reduces the Root Mean Square Error (RMSE) by 76.5% and 82.6% compared to traditional GM(1,1) and BP models, respectively, with the accuracy grade improving from level IV to level I. This achievement provides a multi-scale analytical approach for the quantitative interpretation of settlement deformation patterns in ancient architecture. The established “decomposition-prediction-fusion” technical framework holds significant application value for the preventive conservation of historical buildings. Full article

We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive integrated moving average (ARIMA($p,\mathrm{d},\mathrm{q}$)) model. The stationarity of China’s energy consumption data from 2000 to 2018 is assessed, with an augmented Dickey–Fuller (ADF) test conducted on the $\mathrm{d}$-order difference series. Based on the auto correlation function (ACF) and partial auto correlation function (PACF) plots of the difference time series, the optimal parameters $\mathrm{p}$ and $\mathrm{q}$ are selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), thereby determining the specific ARIMA configuration. By simulating real values using the ARIMA model and calculating relative errors, the estimated values are categorized into states. These states are then combined with a Markov transition probability matrix to determine the final predicted values. The ARIMAMKM model is validated using China’s energy consumption data, achieving high prediction accuracy as evidenced by metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), $\mathrm{S}\mathrm{T}\mathrm{D}$, and ${\mathrm{R}}^2$. Comparative analysis demonstrates that the ARIMAMKM model outperforms five other competitive models: the grey model (GM(1,1)), ARIMA(0,4,2), quadratic function model (QFM), nonlinear auto regressive neural network (NAR), and fractional grey model (FGM(1,1)) in terms of fitting performance. Additionally, the model is applied to Guangdong province’s resident population data to further verify its validity and practicality. Full article

The grey prediction model is designed to characterize systems comprising both partially known information (referred to as white) and partially unknown dynamics (referred to as black). However, traditional GM(1,1) models are based on linear differential equations, which limits their capacity to capture nonlinear and non-stationary behaviors. To address this issue, this paper develops a generalized grey differential prediction approach based on the Bernoulli equation framework. We incorporate the Bernoulli mechanism with a nonlinear exponent n and a dynamic polynomial-driven term. In this work, we propose a new model designated as BPGM(1,1). Another key innovation of this work is the adoption of a nonlinear least squares direct parameter identification strategy to calculate the exponent and polynomial parameters in the Bernoulli equation, which achieves a higher degree of freedom in parameter selection and effectively circumvents the model distortion issues caused by traditional background value estimation. Furthermore, the Euler discretization method is utilized for numerical solving, reducing the reliance on traditional analytical solutions for linear structures. Numerical experiments indicate that BPGM(1,1) surpasses GM(1,1), NFBM(1,1), and their improved versions. By leveraging the synergistic mechanism between Bernoulli-type nonlinear regulation and polynomial-driven external excitation, this framework significantly enhances prediction accuracy for systems characterized by non-stationary behaviors and multi-scale trends. Full article

Grey theory is applied to forecasting, decision-making, and control as this theory is appropriate for predictive analysis. Incomplete information is a primary characteristic of the grey system, necessitating the supplementation of information to transform the relationships between various information elements from grey to white and improve the accuracy of predictive models. However, for the first-order grey prediction model with n variables, specifically the traditional GM(1,n) model, modelling values are derived using a rough approximation method. It is assumed in this method that the elements of the one-order accumulated generating series of each associated series are constant, leading to an unreasonable relationship between the forecast series and the associated series, which is fundamentally an incorrect model. The elements of a non-negative series’s one-order accumulated generating series cannot be constants; even if they are constant series, this is not true. Consequently, the traditional GM(1,n) model yields significant errors. There have been few papers addressing the errors of this model. To improve the GM(1,n) model, correct algorithms must be used by incorporating convolution algorithms or fitting system action quantities with basic functions to derive particular solutions. The modelling procedure of the grey convolution prediction model GMC(1,n) demonstrates that the traditional grey prediction model GM(1,n) is incorrect. Full article

In this study, grey system theory is applied through the implementation of GM(1,1) modelling and Grey Relational Analysis (GRA) to forecast and evaluate sturgeon aquaculture production dynamics in Guizhou Province. The results demonstrate a marked temporal dependency in predictive efficacy, with GM(1,1) exhibiting a superior short-term forecasting performance that progressively diminishes with temporal extension. Utilizing 2018–2022 observational data, the GM(1,1) framework achieved Grade 2 precision (mean absolute percentage error, MAPE = 4.172%; 1% <$\overline{ ∆k}$ ≤ 5%), projecting sustained annual production growth. The decade-long forecast (2023–2032) yielded the following production estimates (×10³ tons): 32.3, 39.1, 47.3, 57.2, 69.2, 83.7, 101.2, 122.4, 148.1, and 179.2. GRA identified three principal determinants: the aquatic seed production value (X₉, r = 0.8336), freshwater fishery output (X₂, r = 0.8019), and per capita fisher income (X₅, r = 0.8003). Furthermore, technological promotion funding (X₆) and fishery workforce parameters (X₄), while demonstrating weaker correlations (r < 0.75), maintain critical roles in technological advancement and labour competency enhancement. This methodological framework provides empirical support for sustainable development strategies in Guizhou’s sturgeon aquaculture sector, emphasizing the necessity of temporal-scale considerations and multifactorial optimization in production management. Full article

The pursuit of high-quality higher education in ethnic minority regions is of paramount importance in the contemporary era. As China advances towards its distinctive model of higher education modernization, the imperative for a robust evaluation framework for higher education modernization becomes increasingly evident. Achieving synchronized educational modernization across all ethnic groups is not only a persistent and evolving facet of the historical national strategy of “Four Modernizations in Synchronization” but also a critical milestone and salient indicator of concurrent progress in higher education modernization across ethnic minority regions in the present context. From 2012 to 2020, the nation executed a nine-year strategic plan aimed at revitalizing higher education in the central and western regions. This study, employing literature analysis and grounded in the five core functions of universities, utilizes a comprehensive dataset spanning a decade (2012 to 2021) to construct a bespoke evaluation index system for the modernization of higher education in ethnic minority regions. Through quantitative assessments, this study evaluates the trajectory of modernization in higher education development in these regions over the past decade, thereby offering indirect insights into the efficacy of the national initiative to rejuvenate higher education in the central and western regions. The findings indicate a fluctuating yet upward trend in the overall level of higher education development across the eight ethnic minority regions. Nevertheless, substantial disparities persist when compared with other provinces. Notably, Tibet and Guizhou have demonstrated significant growth, with respective growth rates of 5.533 and 4.341. Moreover, an in-depth analysis leveraging the grey GM(1,1) prediction model projects the future developmental trajectories of higher education in ethnic minority regions. To ensure the sustainable advancement of higher education in ethnic minority regions, supplementary policy interventions are indispensable. This entails fostering a conducive environment of resonance and coordinated development between ethnic economies and higher education, thereby nurturing a harmonious relationship that propels progress. Full article

This study investigates the level of sustainable development, evolution patterns, and obstacles in Qinghai Province. Considering the province’s unique characteristics and ecological significance, we have established an evaluation indicator system based on the DPSIR model. The entropy weight–TOPSIS model is used to assess the overall sustainability of Qinghai from 2008 to 2022. The grey GM(1,1) model is used to predict future sustainability trends, while the coupling coordination model quantifies the degree of coordination among subsystems. Furthermore, the barrier degree model is used to explore the factors hindering the improvement of Qinghai’s sustainable development. (1) The study finds that Qinghai’s overall sustainable development has shown a fluctuating upward trend, increasing from a weaker phase in 2008 to a stronger phase in 2022. All five subsystems in the sustainability evaluation system have shown gradual improvements in their index scores. This suggests that Qinghai’s sustainability level is expected to continue improving in the future. (2) From 2008 to 2022, the highest barrier degrees were observed in the pressure and state systems, with the barrier degrees of other systems gradually decreasing. Nine main factors, including the number of students in higher education, urban unemployment rate at year-end, and input–output ratio, have been identified as the obstacles to improving the province’s sustainable development level. (3) The coupling coordination degree of the five subsystems has shown a positive development trend, progressing through three stages: mild imbalance, basic coordination, and good coordination. The coordination type has shifted from deterioration to improvement. To achieve high-level sustainable development in Qinghai, leveraging the province’s advantageous environmental resources is crucial. Strengthening ecological protection, optimizing the industrial structure, accelerating urbanization, and emphasizing science and education are key pathways for Qinghai’s future development. Full article

With the intensification of global climate change, the discerning identification of carbon emission drivers and the accurate prediction of carbon emissions have emerged as critical components in addressing this urgent issue. This paper collected carbon emission data from Chinese provinces from 1997 to 2021. Machine learning algorithms were applied to identify province characteristics and determine the influence of provincial development types and their drivers. Analysis indicated that technology and energy consumption had the greatest impact on low-carbon potential provinces (LCPPs), economic growth hub provinces (EGHPs), sustainable growth provinces (SGPs), low-carbon technology-driven provinces (LCTDPs), and high-carbon-dependent provinces (HCDPs). Furthermore, a predictive framework incorporating a grey model (GM) alongside a tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) model was employed to forecast carbon emissions for the forthcoming decade. Findings demonstrated that this approach provided substantial improvements in prediction accuracy. Based on these studies, this paper utilized a combination of SHapley Additive exPlanation (SHAP) and political, economic, social, and technological analysis—strengths, weaknesses, opportunities, and threats (PEST-SWOTs) analysis methods to propose customized carbon emission reduction suggestions for the five types of provincial development, such as promoting low-carbon technology, promoting the transformation of the energy structure, and optimizing the industrial structure. Full article

As urbanization and construction activities in China continue to accelerate, the management of construction waste has become crucial. This study comprehensively investigated the current status and challenges in construction waste management in China. Through the application of building area estimation methodology combined with the Grey Prediction GM (1,1) model, we analyzed historical waste generation patterns from 2000 to 2022 and projected future trends for the next 10 years. The results revealed significant regional disparities in waste generation, with the East China region contributing over 50% of the national total, while maintaining continuous growth. National construction waste generation is projected to reach 3.084 billion tons in 2032, highlighting escalating management challenges. This study identified several critical issues in China’s current management system, including incomplete statistical data, weak implementation of source reduction measures, underdeveloped classification systems, and a notably low resource utilization rate (below 10% as of 2022). Drawing on successful international practices and domestic pilot experiences, we proposed a comprehensive management framework emphasizing full-process supervision, enhanced data collection systems, improved classification management, advanced resource utilization technologies, and strengthened policy mechanisms. These proposals will foster the development of sustainable construction waste management in China’s transition, in parallel with the realization of circular economy principles within the construction sector. Full article

An interchange is a pivotal traffic facility that connects highways and controls access. It is necessary to study their dynamic response characteristics to analyze the operational safety of ramp bridges on interchanges. Based on the numerical simulation results of the finite element model of the Fuxing Interchange Bridge, non-destructive measurement techniques were used to conduct field dynamic load tests on the bridge, including ramp strain testing and acceleration testing. These tests aimed to study the dynamic response characteristics of the ramp bridge under moving loads. Due to the design speed limitation of the ramp bridge, the grey prediction GM(1, 1) model was used to predict the maximum dynamic deflection, maximum dynamic strain, and vibration acceleration when the vehicle speed was 60 km/h. Subsequently, finite element software was used to simulate the dynamic deflection under vehicle speeds ranging from 30 to 60 km/h. The simulated value was compared with the predicted value, and the difference between the simulated value and the predicted value was slight. This model can evaluate the operational safety performance of off-ramps at different speeds. Full article

Taxation serves as a vital lifeline for government revenue, directly contributing to national development and the welfare of its citizens. Ensuring the efficiency and effectiveness of the tax collection process is essential for maintaining a sustainable economic framework. This study investigates (a) trends and patterns of direct tax collection, (b) the cost of tax collection, (c) the proportion of direct tax in total tax collection, and (d) the tax-to-GDP ratio in India. By utilizing a novel grey forecasting model (GM (1,1)), this study attempted to predict the future trends of India’s direct tax collections, through which it aims to provide a concurrent and accurate future outlook on tax revenue, ensuring resources are optimally allocated for the country’s growth. Results revealed that direct tax collection has consistently increased in the past two decades, and the proportion of direct tax in total tax has also improved significantly. On the contrary, the cost of tax collection has decreased regularly, indicating the efficiency of tax collection. Forecasting shows that the collection from direct tax is expected to reach INR 30.67 trillion in 2029–30, constituting around 54.41% of the total tax, leaving behind collections from indirect tax at a total of INR 25.70 trillion. Such findings offer insights that could enhance revenue management strategies with policy decisions relevant to economists, government, and other stakeholders to understand trends and the efficiency of direct tax collection in India. Full article