Grey Model Research Articles

Damped accumulation-based volatility multivariable grey prediction power model and its application

PurposeIn this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.Design/methodology/approachFirstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.FindingsUnder the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.Originality/valueThe main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

Hospitalization for SARS-CoV-2 and the risk of self-harm readmission: a French nationwide retrospective cohort study.

The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the risk of self-harming behaviours warrants further investigation. Here, we hypothesized that people with a history of hospitalization for self-harm may be particularly at risk of readmission in case of SARS-CoV-2 hospitalization. We conducted a retrospective analysis based on the French national hospitalization database. We identified all patients hospitalized for deliberate self-harm (10th edition of the International Classification of Diseases codes X60-X84) between March 2020 and March 2021. To study the effect of SARS-CoV-2 hospitalization on the risk of readmission for self-harm at 1-year of the inclusion, we performed a multivariable Fine and Gray model considering hospital death as a competing event. A total of 61,782 individuals were hospitalized for self-harm. During the 1-year follow-up, 9,403 (15.22%) were readmitted for self-harm. Between inclusion and self-harm readmission or the end of follow-up, 1,214 (1.96% of the study cohort) were hospitalized with SARS-CoV-2 (mean age 60years, 52.9% women) while 60,568 were not (mean age 45years, 57% women). Multivariate models revealed that the factors independently associated with self-harm readmission were: hospitalization with SARS-CoV-2 (adjusted hazard ratio (aHR)=3.04 [2.73-3.37]), psychiatric disorders (aHR=1.61 [1.53-1.69]), self-harm history (aHR=2.00 [1.88-2.04]), intensive care and age above 80. In hospitalized people with a personal history of self-harm, infection with SARS-CoV-2 increased the risk of readmission of self-harm, with an effect that seemed to add to the effect of a history of mental disorders, with an equally significant magnitude. Infection may be a significantly stressful condition that precipitates self-harming acts in vulnerable individuals. Clinicians should pay particular attention to the emergence of suicidal ideation in these patients in the aftermath of SARS-CoV-2 infection.

Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.

Study of Global Seawater Corrosivity Classification Based on Marine Environmental Factors Data

ABSTRACTTo evaluate the global seawater corrosivity and ensure the service safety of marine equipment, the methods of seawater corrosivity classification, which contained the standard metal corrosion rates method and the environmental factors evaluation method, are proposed and then applied to the seawater corrosivity classification in typical China seas. By comparing the results from both methods, the feasibility of the environmental factors evaluation method based on the grey relational model is verified. Furthermore, with the collection and processing of typical seawater factors data of global ocean, the corrosivity of global seawater is classified into six grades with the corrosion test data of carbon steel as baseline, and its distribution in different months is visualized by means of ArcGIS technology. The results show that the sea areas with high corrosivity are mainly located at equatorial and tropical sea areas, and for carbon steel, seawater temperature was the main influence on seawater corrosivity.

Predicting Natural Gas Production in Various Nations Using a Fractional Grey Bernoulli Approach

Accurate forecasting of natural gas production is crucial for economic stability, environmental sustainability, and market investment. This study presents an advanced forecasting method using the fractional grey Bernoulli model, which combines fractional accumulation and Bernoulli processes to enhance the predictive accuracy for nonlinear datasets. The model’s versatility and flexibility allow it to adapt to various data characteristics and complexities, thereby outperforming traditional grey models in forecasting performance. To optimize the model parameters, this study employs the Particle Swarm Optimization (PSO) algorithm, further improving the model’s effectiveness. Empirical analysis of natural gas production data from Brazil, Italy, and Qatar demonstrates that the model exhibits significant advantages in both fitting and forecasting capabilities. The findings indicate that the fractional grey Bernoulli model achieves high accuracy and reliability in predicting natural gas production in these countries, providing a robust framework for strategic energy planning and investment decision-making. With average forecast errors of 1.9113%, 4.0353%, and 1.8902% for natural gas production in Brazil, Italy, and Qatar respectively, this study underscores the model’s effectiveness in enhancing forecast reliability and minimizing risk, providing valuable insights for sustainable energy development.

Cancer Risk Among Patients With Multiple Sclerosis: A 10-Year Nationwide Retrospective Cohort Study.

Previous literature has been diverging on cancer risk in people with multiple sclerosis (PwMS). Therefore, this study compared the risk of cancer in PwMS and a matched sample from the French general population. This 10-year nationwide retrospective matched cohort study (2012-2021) used data from the national French administrative health care database (99% coverage of the French population) to determine the time to the first incident cancer. PwMS were identified using their long-term disease (LTD) status, hospitalizations, and multiple sclerosis (MS)-specific drug reimbursements. The control population was matched 4:1 on age, sex, residence, insurance scheme, and cohort entry date. Participants were included if they had no history of cancer in the 3 years before inclusion. Patients with cancer were identified through LTD status, hospitalizations, chemotherapy, radiotherapy, or prostate cancer-specific drug reimbursements. Overall and cancer location-specific hazard ratios (HRs) for the first incident cancer were obtained from Fine and Gray models, and age- and sex-stratified estimates were reported. Participation in cancer screening through the 3 national programs (breast, colorectal, and cervical) were compared between groups. Cancer incidence was 799 per 100,000 person-years (PYs) (n = 8,368) among the 140,649 PwMS and 736 per 100,000 PYs (n = 31,796) among the 562,596 matched controls (70.8% of women; follow-up: 7.6 ± 3.2 years). A small overall risk increase was observed for PwMS (HR 1.06, 95% CI 1.03-1.08), mostly in women (HR 1.08, 95% CI 1.05-1.11). Risk varied by cancer types and was lower for prostate (HR 0.80, 95% CI 0.73-0.88), breast (HR 0.91, 95% CI 0.86-0.95), and colorectal (HR 0.90, 95% CI 0.84-0.97) cancer and higher for bladder (HR 1.71, 95% CI 1.54-1.89), brain (HR 1.68, 95% CI 1.42-1.98), and cervical (HR 1.24, 95% CI 1.12-1.38) cancer in PwMS. Cancer risk was higher in PwMS younger than 55 years (HR 1.20, 95% CI 1.15-1.24) but decreased in PwMS aged 65 years and older (HR 0.89, 95% CI 0.85-0.94). This trend was found in all cancer locations. There were fewer PwMS getting screened than controls (all programs), with a particularly pronounced difference among those aged 65 years and older. Cancer risk was slightly increased in PwMS, particularly for urogenital cancers, possibly due to surveillance bias. Risk fluctuated depending on age, perhaps due to varying generational screening practices (i.e., diagnosis neglect in the older PwMS) and risk factors.

Abstract 4136930: Plasminogen Activator Urokinase Receptor (PLAUR) Levels are a Predictor of Adverse Cardiovascular Outcomes in the General Population, Independent of High-Sensitive CRP (hs-CRP)

Background: The gene Plasminogen Activator Urokinase Receptor (PLAUR) encodes the urokinase plasminogen activator surface receptor (uPAR), which upon cleavage releases Soluble Urokinase Plasminogen Activator Receptor (suPAR). SuPAR has emerged as a novel biomarker of inflammation and immunity and is associated with adverse cardiovascular events, particularly coronary artery disease (CAD), regardless of hs-CRP levels, a well-established inflammatory biomarker. However, whether the PLAUR gene predicts adverse cardiovascular events remains uncertain. Methods: We investigated the relationship between PLAUR levels, measured via Olink Immunoassay, in 40,790 individuals from the UK Biobank, and the occurrence of adverse cardiovascular events such as myocardial infarction (MI), as well as all-cause and cardiovascular mortality. Our analysis involved the use of Cox and Fine and Gray proportional hazards models, adjusting for age, sex, race, HDL, LDL, triglyceride levels, BMI, prevalence of hypertension (HTN), prevalence of type 2 diabetes (T2D), smoking history, and use of blood pressure and cholesterol medications. Main Outcomes: The primary outcome was a composite outcome of cardiovascular mortality and MI and the secondary outcome was all-cause mortality. Results: The mean (SD) age was 58 (8) years, 45% men and 94% white. During a median follow-up of 14 (interquartile range 12.3 – 15) years, 7.9% of the population had an MI, 2.7% suffered cardiovascular death, 9.5% all-cause mortality. After adjusting for aforementioned demographic and clinical factors, as well as medication usage, each standard deviation increase in PLAUR levels was associated with a heightened risk of incident non-fatal MI and cardiovascular (CV) death, with a hazard ratio (HR) of 2.31 (95% CI 2.00-2.45), and all-cause mortality, with an HR of 3.81 (95% CI 3.48-4.18). Even after further adjustment for hs-CRP levels, PLAUR levels remained predictive of non-fatal MI and CV death (HR 2.03, 95% CI 1.83-2.26) and all-cause mortality (HR 3.43, 95% CI 3.12-3.77). Furthermore, in a fully adjusted Cox regression model, a significant interaction between PLAUR levels and sex (p = 0.0003) was observed in predicting the primary outcome. The interaction was found to have a stronger predictive value in females compared to males. Conclusion: PLAUR levels are a strong predictor of adverse cardiovascular outcomes in the general population, independent of traditional risk factors and inflammation.

Small-Sample Short-Term Photovoltaic Output Prediction Model Based on GRA-SSA-GNNM Method

The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined with the sparrow search algorithm (SSA) and the grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, the GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimized via SSA. A limited dataset was used to compare several models in different seasons and weather conditions. The prediction results agree well with the data from the PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field.

Prediction of provincial Digital Economy Development Index based on grey combination forecasting model

PurposeIn many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.Design/methodology/approachThe proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.FindingsUsing the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.Research limitations/implicationsFirst of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.Originality/valueThis article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Carbon Emission Optimization for Assembled Buildings Using Interval Grey GERT Modelling and Modify NSGA-III Algorithm in China

Carbon Emission Optimization for Assembled Buildings Using Interval Grey GERT Modelling and Modify NSGA-III Algorithm in China

Study on the Development of Higher Education in China Based on Economic Growth and Technological Advancement

This paper investigates the dynamic relationship between higher education, economic growth, and technological advancement in China. As the country has rapidly expanded its higher education system, challenges such as educational disparities, quality inconsistencies, and the limitations of an exam-oriented framework have become increasingly evident. Utilizing a grey prediction model, the study examines how economic and technological developments have both enhanced and complicated the landscape of higher education, exacerbating urban-rural divides and limiting opportunities for critical thinking and creativity. To address these challenges, the paper proposes strategic interventions, including curriculum reform, increased access to higher education, and the adoption of active learning methodologies. These strategies aim to foster a more equitable, innovative, and responsive educational system that aligns with China's long-term developmental objectives. The findings provide valuable insights for policymakers seeking to navigate the complexities of modernizing China's higher education while ensuring broad-based, sustainable growth.

A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation

Accurate prognostication of degradation plays an essential role in effectively enhancing the operational lifespan of proton exchange membrane fuel cells (PEMFCs). This paper proposes a novel enhanced correctional grey Verhulst model (IHS-CRGVM-RE), designed to prognosticate the degradation process of PEMFCs using the voltage of PEMFCs stack as a health indicator. First, the inverse hyperbolic sine function transformation is employed to attain optimal smoothness in data treatment. Then, the background value within the grey Verhulst model framework is modified based on cellular automata with rectangle techniques. Finally, a residual correction mechanism is applied to delineate the influences of error outcomes concerning PEMFCs degradation. Rigorous validation is provided via a comprehensive analysis based on two distinct PEMFCs datasets. The results demonstrate that the proposed model outperforms other data-driven models in prognostication accuracy, highlighting its significant importance for prognosticating the lifespan of PEMFCs.

Distinct comorbidity phenotypes among post-9/11 Veterans with epilepsy are linked to diverging outcomes and mortality risks.

To investigate phenotypes of comorbidity before and after an epilepsy diagnosis in a national cohort of post-9/11 Service Members and Veterans and explore phenotypic associations with mortality. Among a longitudinal cohort of Service Members and Veterans receiving care in the Veterans Health Administration (VHA) from 2002 to 2018, annual diagnoses for 26 conditions associated with epilepsy were collected over 5 years, ranging from 2 years prior to 2 years after the year of first epilepsy diagnosis. Latent class analysis (LCA) was used to identify probabilistic comorbidity phenotypes with distinct health trajectories. Descriptive statistics were used to describe the characteristics of each phenotype. Fine and Gray cause-specific survival models were used to measure mortality outcomes for each phenotype up to 2021. Six distinct phenotypes were identified: (1) relatively healthy, (2) post-traumatic stress disorder, (3) anxiety and depression, (4) chronic disease, (5) bipolar/substance use disorder, and (6) polytrauma. Accidents were the most common cause of death overall, followed by suicide/mental health and cancer, respectively. Each phenotype exhibited unique associations with mortality and cause of death, highlighting the differential impact of comorbidity patterns on patient outcomes. By delineating clinically meaningful epilepsy comorbidity phenotypes, this study offers a framework for clinicians to tailor interventions. Moreover, these data support systems of care that facilitate treatment of epilepsy and comorbidities within an interdisciplinary health team that allows continuity of care. Targeting treatment toward patients with epilepsy who present with specific heightened risks could help mitigate adverse outcomes and enhance overall patient care.

An innovative fractional grey system model and its application

In order to expand the applicability of the conventional fractional grey model, an innovative fractional grey model is proposed by the introduction of the innovative fractional accumulation. Three efforts are made in this study. First, the innovative fractional accumulation and its inverse operation are designed. Based on the innovative form, the parameter estimation and discrete time response of the novel model are given. In addition, the moth flame optimization algorithm is used to determine optimal hyper parameters for the novel model, and the rolling mechanism is used to enhance the prediction performance. To comprehensively confirm the forecasting ability of the proposed model, it is applied in three kinds of data with inverted U-shaped, W-shaped and oscillating features. The experimental results show the novel model is superior to all competitors in terms of level accuracy. Therefore, the novel model is considered a promising method for enhancing the existing fractional grey model.

A dynamic multivariate partial grey model based on the traffic flow parameter equation and its application

A dynamic multivariate partial grey model based on the traffic flow parameter equation and its application

Analysis and forecasting of the importance of smart contracts and blockchain technology in SCM in Vietnam by using DEA and Grey model

In the context of a complex and demanding business environment, blockchain and smart contracts are becoming increasingly important in supply chain management in Vietnam. The application of these technologies can help to optimize processes, create a reliable platform for tracking, managing, and forecasting supply chains, and enhance transparency and integrity. In the study of supply chain management in Vietnam, the methods applied are DEA (Data Envelopment Analysis) and the Grey forecasting model. These two methods not only focus on optimizing current processes but also create opportunities for analyzing, evaluating, and forecasting the importance of blockchain technology and smart contracts. This research topic not only raises questions about the potential for optimizing current processes, but also proposes innovative solutions to enhance transparency, integrity, and responsiveness to volatile markets. Additionally, the research aims to propose appropriate strategies for applying these new technologies to business practices in Vietnam, creating a flexible, secure, and adaptable supply chain system that is responsive to the changing business environment of the present.

Analysis of tourist flow prediction model of rural tourism on the edge of big cities in China

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

Beyond Traditional Forecasting: Machine Learning and Adaptive Algorithms in EV Sales Predictions

The Electric Vehicle market is growing fast, creating new challenges for sales predictions and market dynamics. Traditional forecasting methods like linear regression and expert judgment struggle with the market's non-linear and complex nature. To improve accuracy, new models such as machine learning and Adaptive Optimized Grey Models are being applied to predict EV sales, which is discussed in this review paper. These models dynamically adjust parameters and account for external factors, offering better precision. AOGMs, in particular, utilize data preprocessing and buffering to minimize external disturbances and enhance prediction smoothness. Techniques like the Particle Swarm Optimization help these models more effectively capture the EV sales trends. Additionally, adaptive grey models are not only essential for forecasting sales but also for identifying market risks like overcapacity or supply shortages. These advanced methods provide a more robust framework for understanding and anticipating changes in the rapidly evolving the EV market.

A combined mechanistic and empirical models for tailored microwave-assisted extraction of rice bran phenolic

ABSTRACT Rice bran is a promising source of functional food due to its high phenolic content. Microwave-assisted extraction (MAE) has become a widely used method for optimizing phenolic extraction from rice bran. However, understanding the phenolic extraction process using the MAE method is challenging due to the system’s complexity. Modeling and simulation are powerful tools to provide a better understanding of how the system behaves and can be optimized. This study proposes a novel approach that combines mechanistic and empirical models (gray box) to predict the phenolic content in rice bran extraction using MAE. It involved phenolic extraction using different ethanol concentrations (40%, 60%, 80%), microwave application time (1 min, 2 min, 3 min), and microwave power (90 W, 180 W, 270 W). The results showed a good agreement between experimental and computed extraction yields, as indicated by the reported statistical parameters and gray box model. The optimum conditions were reached at 60% ethanol concentration, 2-min microwave application time, and 180 W microwave power with the phenolic content at 0.534 mg/mL (model) and 0.561 mg/mL or 25.06 mg/g (experiment). Following the study, this combined approach can potentially reduce the time and cost required for optimization studies of phenolic extraction.

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.