Using fractional order method to generalize strengthening generating operator buffer operator and weakening buffer operator

Traditional integer order buffer operator is extended to fractional order buffer operator, the corresponding relationship between the weakening buffer operator and the strengthening buffer operator is revealed. Fractional order buffer operator not only can generalize the weakening buffer operator and the strengthening buffer operator, but also realize tiny adjustment of buffer effect. The effectiveness of GM(1,1) with the fractional order buffer operator is validated by six cases

Multi-variable weakening buffer operator and its application

To weaken the disturbances of multi-variable and reveal the real situation, it is proved that the essence of the weakening buffer operator (abbreviated as WBO) can weaken the disturbance of one variable. According to this, the multi-variable weakening buffer operator is put forward. The multi-variable weakening buffer operator can satisfy the desire to use the freshest data and its buffer effect is obvious when the sample size is small. Four real cases show that the proposed multi-variable weakening buffer operator has higher forecasting performances

Prediction of air quality indicators for the Beijing-Tianjin-Hebei region

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Beijing-Tianjin-Hebei region is facing a very serious air pollution problem. To obtain the future trend of air quality, the GM(1,1) model with the fractional order accumulation (FGM(1,1)) is used to predict the average annual concentrations of PM2.5, PM10, SO2, NO2, 8-h O3, and 24-h O3 in the Beijing-Tianjin-Hebei region from 2017 to 2020. The concentrations of PM2.5 and SO2 will decrease and the 8-h O3 and 24-h O3 will increase in this region. The concentrations of PM10 and NO2 will decrease in the Taihang-Mountain-adjacent region (Baoding, Shijiazhuang, Xingtai, Handan and Hengshui) and increase in the Northern region (Zhangjiakou, Chengde and Qinhuangdao). The concentration of PM10 will decrease and NO2 will increase in the Bohai Sea region (Tangshan, Tianjin, Cangzhou, Beijing and Langfang). Our results can be directly exploited in the decision-making processes for air quality management

Grey double exponential smoothing model and its application on pig price forecasting

The file attached to this record is the authors final peer reviewed version. The version of record can be found by following the DOI link.To resolve the conflict between our desire for a good smoothing effect and desire to give additional weight to the recent change, a grey accumulating generation operator that can smooth the random interference of data is introduced into the double exponential smoothing method. The results of practical numerical examples have demonstrated that the proposed grey double exponential smoothing method outperforms the traditional double exponential smoothing method in forecasting problems

Grey Self-memory Combined Model for Complex Equipment Cost Estimation

The file attached to this record is the author's final peer reviewed version.To improve the using rationality of complex equipment cost, this paper presents a novel grey self-memory combined model for predicting the equipment cost. The proposed model can improve the modeling accuracy by means of the self-memory prediction technique. The combined model combines the advantages of the self-memory principle and traditional grey model through coupling of the above two prediction methods. The weakness of the traditional grey prediction model, i.e., being sensitive to initial value, can be overcome by using multi-time-point initial field instead of only single-time-point initial field in the system's self-memorization equation. As shown in the two case studies of complex equipment cost estimation, the novel grey self-memory combined model can take full advantage of the system's multi-time historical monitoring data and accurately predict the system's evolutionary trend. Three popular accuracy test criteria are adopted to test and verify the reliability and robustness of the combined model, and its superior predictive performance over other traditional grey prediction models. The results show that the proposed combined model enriches equipment cost estimation methods, and can be applied to other similar complex equipment cost estimation problems

Machine Learning in Oil and Gas Exploration: A Review

A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning’s involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field’s dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into less-charted territories in this evolving field, offering valuable insights and guidance for future research

Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The electricity consumption forecasting problem is especially important for policy making in developing region. To properly formulate policies, it is necessary to have reliable forecasts. Electricity consumption forecasting is influenced by some factors, such as economic, population and so on. Considering all factors is a difficult task since it requires much detailed study in which many factors significantly influence on electricity forecasting whereas too many data are unavailable. Grey convex relational analysis is used to describe the relationship between the electricity consumption and its related factors. A novel multi-variable grey forecasting model which considered the total population is developed to forecast the electricity consumption in Shandong Province. The GMC(1,N) model with fractional order accumulation is optimized by changing the order number and the effectiveness of the first pair of original data by the model is proven. The results of practical numerical examples demonstrate that the model provides remarkable prediction performances compared with the traditional grey forecasting model. The forecasted results showed that the increase of electricity consumption will speed up in Shandong Province

Using grey Holt-Winters model to predict the air quality index for cties in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe randomness, non-stationarity and irregularity of air quality index series bring the di fficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt-Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt-Winters method is employed to deal with the seasonal e ects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt-Winters method for its higher forecast accuracy

A novel phosphatidylinositol(3,4,5)P3 pathway in fission yeast

The mammalian tumor suppressor, phosphatase and tensin homologue deleted on chromosome 10 (PTEN), inhibits cell growth and survival by dephosphorylating phosphatidylinositol-(3,4,5)-trisphosphate (PI[3,4,5]P3). We have found a homologue of PTEN in the fission yeast, Schizosaccharomyces pombe (ptn1). This was an unexpected finding because yeast (S. pombe and Saccharomyces cerevisiae) lack the class I phosphoinositide 3-kinases that generate PI(3,4,5)P3 in higher eukaryotes. Indeed, PI(3,4,5)P3 has not been detected in yeast. Surprisingly, upon deletion of ptn1 in S. pombe, PI(3,4,5)P3 became detectable at levels comparable to those in mammalian cells, indicating that a pathway exists for synthesis of this lipid and that the S. pombe ptn1, like mammalian PTEN, suppresses PI(3,4,5)P3 levels. By examining various mutants, we show that synthesis of PI(3,4,5)P3 in S. pombe requires the class III phosphoinositide 3-kinase, vps34p, and the phosphatidylinositol-4-phosphate 5-kinase, its3p, but does not require the phosphatidylinositol-3-phosphate 5-kinase, fab1p. These studies suggest that a pathway for PI(3,4,5)P3 synthesis downstream of a class III phosphoinositide 3-kinase evolved before the appearance of class I phosphoinositide 3-kinases