Forecasting the multifactorial interval grey number sequences using grey relational model and GM (1, N) model based on effective information transformation

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.In the context of data eruption, the data often shows a short-term pattern and changes rapidly which makes it difficult to use a single real value to express. For this kind of small-sample and interval data, how to analyze and predict muti-factor sequences efficiently becomes a problem. By this means, grey system theory (GST) is developed in which the interval grey numbers, as a typical object of GST, characterize the range of data and the grey relational and prediction models analyze the relations of multiple grey numbers and forecast the future. However, traditional grey relative relational model has some limitations: the results obtained always show low resolution and there are no extractions for the interval feature information from the interval grey number sequence. In this paper, the grey relational analysis model (GRA) based on effective information transformation of interval grey numbers is established, which contains comprehensive information of area differences and slope variances and optimizes the resolution of traditional grey degree. Then, according to the relational results, the multivariable GM model (GM(1,N)) is proposed to forecast the interval grey number sequence. To verify the effectiveness of this novel model, it is established to analyze the relationship between the degree of traffic congestion and its relevant factors in the Yangtze River Delta of China and predict the development of urban traffic congestion degrees in this area over the next five years. In addition, some traditional statistical methods (principal component analysis, multiple linear regression models and curve regression models) are established for comparisons. The results show high performances of the novel GRA model and GM(1,N) model, which means the models proposed in this paper are suitable for interval grey numbers from regional data. The strengths which recommend the use of this novel method lie in its high recognition mechanism and muti-angle information transformation for interval grey numbers as well as its characteristic of timeliness in information processing

Effects of Different Shading Rates on the Photosynthesis and Corm Weight of Konjac Plant

To study the effects of shading level on the photosynthesis and corm weight of konjac plant, the chlorophyll fluorescence parameters, daily variation of relative electron transport rate (rETR), net photosynthetic rate (Pn), and corm weight of konjac plants under different treatments were measured and comparatively analyzed through covered cultivation of biennial seed corms with shade nets at different shading rates (0%, 50%, 70%, and 90%). The results showed that with the increase in shading rate, the maximum photochemical efficiency, potential activity, and non-photochemical quenching of photosystem Ⅱ (PSⅡ) of konjac leaves constantly increased, whereas the actual photosynthetic efficiency, rETR, and photochemical quenching of PSⅡ initially increased and then decreased. This result indicated that moderate shading could enhance the photosynthetic efficiency of konjac leaves. The daily variation of rETR in konjac plants under unshaded treatment showed a bimodal curve, whereas that under shaded treatment displayed a unimodal curve. The rETR of plants with 50% treatment and 70% treatment was gradually higher than that under unshaded treatment around noon. The moderate shading could increase the Pn of konjac leaves. The stomatal conductance and transpiration rate of the leaves under shaded treatment were significantly higher than those of the leaves under unshaded treatment. Shading could promote the growth of plants and increase corm weight. The comprehensive comparison shows that the konjac plants had strong photosynthetic capacity and high yield when the shading rate was 50%-70% for the area

Network meta-analysis of balloon angioplasty, nondrug metal stent, drug-eluting balloon, and drug-eluting stent for treatment of infrapopliteal artery occlusive disease

PURPOSE:We aimed to conduct a network meta-analysis of mixed treatments for the infrapopliteal artery occlusive disease.METHODS:We searched randomized controlled trials (RCTs) regarding balloon angioplasty (BA), nondrug metal stent (NDMS), drug-eluting balloon (DEB), or drug-eluting stent (DES) in PubMed, Embase, CENTRAL, Ovid, Sinomed, and other relevant websites. We selected and assessed the trials that met the inclusion criteria and conducted a network meta-analysis using the ADDIS software.RESULTS:We included 11 relevant trials. We analyzed data of 1322 patients with infrapopliteal artery occlusive disease, of which 351 were in the NDMS vs. DES trials, 231 in the NDMS vs. BA trials, 490 in the BA vs. DEB trials, 50 in the DEB vs. DES trials, and 200 in the BA vs. DES trials. The network meta-analysis indicated that with NDMS as the reference, DES had a better result with respect to restenosis (odds ratio [OR], 5.16; 95% credible interval [CI], 1.58–18.41; probability of the best treatment, 84%) and amputation (OR, 2.50; 95% CI, 0.81–7.11; probability of the best treatment, 61%) and DEB had a better result with respect to target lesion revascularization (TLR; OR, 3.74; 95% CI, 0.78–17.05; probability of the best treatment, 57%). Moreover, with BA as the reference, NDMS had a better result with respect to technical success (OR, 0.10; 95% CI, 0.00–1.15; probability of the best treatment, 86%).CONCLUSION:Our meta-analysis revealed that DES is a better treatment with respect to short-term patency and limb salvage rate, NMDS may provide a better technical success, and DEB and DES are good choices for reducing revascularization

Morphological and Physiological Changes in Sedum spectabile during Flower Formation Induced by Photoperiod

Sedum spectabile is an ornamental herbaceous perennial considered as a long-day plant. Varying levels of hormones and sugars possibly affect flower bud formation. This study aimed to determine the changes in endogenous hormones, sugars, and respiration levels in leaves and in apical buds. In addition, the current research was also conducted to observe the morphological changes during the induction, initiation and development of flower buds. Results showed that the periods of floral induction, initiation and development of S. spectabile were the period from 0 d to 1 d, 2 d to 10 d and after 11 d respectively under long day of 20 hours. High zeatin level in apical buds was conducive to floral induction; the increasing levels of gibberrelin and indole acetic acid favor floral initiation; floral development was regulated by mutually synergistic and antagonistic relationships of hormones. The total starch content in leaves remarkably decreased during floral induction. Moreover, soluble sugar content increased and reached the maximum level at 20 d of the treatment period. Afterward, soluble sugar content declined rapidly and was probably transported to the apical buds for rapid floral development. Furthermore, the total respiration of leaves maintained an upward trend; the cytochrome pathway also maintained an increasing trend after the plants were treated for 20 d. Such changes may favour the morphological differentiation of apical buds in floral development

Effects of Dual/Threefold Rootstock Grafting on the Plant Growth, Yield and Quality of Watermelon

To test the feasibility of multi-rootstock grafting, bottle gourd and pumpkin were used as rootstocks in a comparative analysis of the effects of single, dual, and threefold rootstock grafting on the plant growth, fruit yield, and quality of watermelon. Results showed that different grafts have significant effects on the abovementioned properties. The appropriate dual/threefold rootstock grafting allowed for higher survival rates. The combined rootstock of bottle gourd and pumpkin can enhance the plant growth potential and lower the incidence of wilt. The single fruit weight of the grafted plants with a combined rootstock from bottle gourd and pumpkin was the median of the weights obtained with the pumpkin rootstock and the bottle gourd rootstock. The plot yield of grafted plants with a pumpkin rootstock was higher than that of the plants with a bottle gourd rootstock. The low soluble solids content of the fruit grafted with a pumpkin rootstock had relatively high acidity, which could be improved by adding bottle gourd to the rootstock. The vitamin C content of the grafted fruit from the combined bottle gourd and pumpkin rootstock was higher than that of plants grafted with either bottle gourd or pumpkin alone. The subsequent analysis showed that the combined rootstock of bottle gourd and pumpkin has significant or extremely significant interaction effects on the stem diameter, number of leaves, single fruit weight, plot yield, and fruit vitamin C content of the grafted watermelon plants, which probably led to the higher related index values of some of grafting combinations

An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles

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 linkAn accurate prediction of electric vehicles stock and sales is a prerequisite for planning industrial policies for renewable sources to be used by a transportation system. We propose a novel time-varying grey Bernoulli model to investigate the nonlinear, complexity, and time-varying characteristics associated with electric vehicles stock and sales. We first design the time-varying parameters and a power exponent to explore the nonlinear developing trends of sequences. Subsequently, the cuckoo search algorithm determines optimum solutions because of its competence in dealing with complex optimization problems. Furthermore, its relationship with existing grey prediction models is presented, which demonstrates the flexibility and practicality of the newly-designed model. In order to validate this new model, the global electric vehicles stock and electric vehicles sales in France are predicted in comparison with six benchmark models. As demonstrated by the empirical findings, the proposed model is superior in terms of its capacity for forecasting, confirming its significant potential as a promising tool for electric vehicles stock and sales prediction

A novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbers

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.Since energy consumption (EC) is becoming an important issue for sustainable development in the world, it has a practical significance to predict EC effectively. However, there are two main uncertainty factors affecting the accuracy of a region's EC prediction. Firstly, with the ongoing rapid changes in society, the consumption amounts can be non-smooth or even fluctuating during a long time period, which makes it difficult to investigate the sequence's trend in order to forecast. Secondly, in a given region, it is difficult to express the consumption amount as a real number, as there are different development levels in the region, which would be more suitably described as interval numbers. Most traditional prediction models for energy consumption forecasting deal with long-term real numbers. It is seldom found to discover research that focuses specifically on uncertain EC data. To this end, a novel energy consumption forecasting model has been established by expressing ECs in a region as interval grey numbers combining with the optimized discrete grey model (DGM(1,1)) in Grey System Theory (GST). To prove the effectiveness of the method, per capita annual electricity consumption in southern Jiangsu of China is selected as an example. The results show that the proposed model reveals the best accuracy for the short data sequences (the average fitting error is only 2.19% and the average three-step forecasting error is less than 4%) compared with three GM models and four classical statistical models. By extension, any fields of EC, such as petroleum consumption, natural gas consumption, can also be predicted using this novel model. As the sustained growth in EC of China's, it is of great significance to predict EC accurately to manage serious energy security and environmental pollution problems, as well as formulating relevant energy policies by the government

Grey prediction model of interval grey numbers based on a novel compound function transformation

The file attached to this record is the author's final peer reviewed version.Focusing on the prediction accuracy of interval grey numbers, the primary goal of this paper is to investigate novel grey prediction models to predict four typical kinds of interval grey numbers sequences respectively. The models used in this study can be briefly described as a combination of function transformation and Grey Model (GM) of interval grey numbers. According to different interval grey numbers sequences, the approaches can be applied to the upper bound and lower bound sequences of interval grey numbers or the kernel and measurement sequences of interval grey numbers. Finally, these new improved models have been verified by numerical examples and cases to demonstrate their validity and practicability. It proves these new models are not only applicable for increasingly high growth sequences where traditional grey models are not effective, but also control the enlargement of grey degrees of interval grey numbers which is very important for interval grey numbers’ forecasting. In summary, the paper effectively extends function transformation technology to the field of interval grey numbers for grey prediction

An improved grey multivariable time-delay prediction model with application to the value of high-tech industry. Expert Systems with Applications

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 linkTo analyse the time lag effects between independent variables and dependent variables, we propose a discrete time-delay grey multivariable model . There are three improvements in this new model compared to the existing models. First, the time lag parameters are assigned different values for each independent variable. A linear correction term expands the new model. Second, with the given time lag, the least square method can be used to calculate the parameter vector. The time response function of is generated, which has the advantage of eliminating the jumping errors between discrete and continuous functions over the existing grey forecasting models. Third, all of the feasible combinations of the time lag parameters are compared by using a traversal algorithm to identify the best values with the minimized mean absolute percentage error (MAPE). In three different case studies, the performance of the new model is evaluated and compared to that of a number of mainstream grey models as well as non-grey models. According to the findings, the newly designed model performs significantly better than the compared models