Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

The grey forecasting model has been successfully applied in numerous fields since it was proposed. The nonhomogeneous discrete grey model (NDGM) was approximately constructed based on the nonhomogeneous index trend; it increased the applicability of discrete grey model. However, the NDGM required accurate data and better effect when the original data did not meet the conditions and fitting and prediction errors were larger. For this, the NDGM with the fractional order accumulating operator (abbreviated as NDGMp/q) has higher performance. In this paper, the matrix perturbation bound of the parameters was used to analyze the stability of NDGMp/q and the NDGMp/q can decrease the disturbance bound. Subsequently, the parameter estimation method of NDGMp/q was studied and the Particle Swarm Optimization algorithm was employed to optimize the order number of NDGMp/q and some steps were provided. In addition, the results of two practical examples demonstrated that the perturbation of NDGMp/q was smaller than that of NDGM and provided remarkable predication performance compared with the traditional NDGM model and DGM model

Strain prioritization and genome mining for enediyne natural products

The enediyne family of natural products has had a profound impact on modern chemistry, biology, and medicine, and yet only 11 enediynes have been structurally characterized to date. Here we report a genome survey of 3,400 actinomycetes, identifying 81 strains that harbor genes encoding the enediyne polyketide synthase cassettes that could be grouped into 28 distinct clades based on phylogenetic analysis. Genome sequencing of 31 representative strains confirmed that each clade harbors a distinct enediyne biosynthetic gene cluster. A genome neighborhood network allows prediction of new structural features and biosynthetic insights that could be exploited for enediyne discovery. We confirmed one clade as new C-1027 producers, with a significantly higher C-1027 titer than the original producer, and discovered a new family of enediyne natural products, the tiancimycins (TNMs), that exhibit potent cytotoxicity against a broad spectrum of cancer cell lines. Our results demonstrate the feasibility of rapid discovery of new enediynes from a large strain collection. IMPORTANCE Recent advances in microbial genomics clearly revealed that the biosynthetic potential of soil actinomycetes to produce enediynes is underappreciated. A great challenge is to develop innovative methods to discover new enediynes and produce them in sufficient quantities for chemical, biological, and clinical investigations. This work demonstrated the feasibility of rapid discovery of new enediynes from a large strain collection. The new C-1027 producers, with a significantly higher C-1027 titer than the original producer, will impact the practical supply of this important drug lead. The TNMs, with their extremely potent cytotoxicity against various cancer cells and their rapid and complete cancer cell killing characteristics, in comparison with the payloads used in FDA-approved antibody-drug conjugates (ADCs), are poised to be exploited as payload candidates for the next generation of anticancer ADCs. Follow-up studies on the other identified hits promise the discovery of new enediynes, radically expanding the chemical space for the enediyne family

The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression

This study investigates the impact of foreign direct investment (FDI), economic growth and energy consumption on carbon emissions in five selected member countries in the Association of South East Asian Nations (ASEAN-5), including Indonesia, Malaysia, the Philippines, Singapore and Thailand. This paper employs a panel quantile regression model that takes unobserved individual heterogeneity and distributional heterogeneity into consideration. Moreover, to avoid an omitted variable bias, certain related control variables are included in our model. Our empirical results show that the effect of the independent variables on carbon emissions is heterogeneous across quantiles. Specifically, the effect of FDI on carbon emissions is negative, except at the 5th quantile, and becomes significant at higher quantiles. Energy consumption increases carbon emissions, with the strongest effects occurring at higher quantiles. Among the high-emissions countries, greater economic growth and population size appear to reduce emissions. The results of the study also support the validity of the halo effect hypothesis in higher-emissions countries. However, we find little evidence in support of an inverted U-shaped curve in the ASEAN-5 countries. In addition, a higher level of trade openness can mitigate the increase in carbon emissions, especially in low- and high-emissions nations. Finally, the results of the study also provide policymakers with important policy recommendations

On Connected m-HPK(n1,n2,n3,n4)[Kt]-Residual Graphs

We define m-HPK(n1,n2,n3,n4)[Kt]-residual graphs in which HPK is a hyperplane complete graph. We extend P. Erdös, F. Harary, and M. Klawe's definition of plane complete residual graph to hyperplane and obtain the hyperplane complete residual graph. Further, we obtain the minimum order of HPK(n1,n2,n3,n4)[Kt]-residual graphs and m-HPK(n1,n2,n3,n4)[Kt]-residual graphs. In addition, we obtain a unique minimal HPK(n1,n2,n3,n4)[Kt]-residual graphs and a unique minimal m-HPK(n1,n2,n3,n4)[Kt]-residual graphs

An Optimization Grey Bernoulli Model and Its Application in Forecasting Oil Consumption

Energy consumption in the world is mainly dependent on fossil energy, and oil is one of the main energy sources. Accurate prediction of oil consumption can provide an important basis for national energy security, which can provide reference and early warning for the implementation of the environmental strategy developed by the government. According to the nonlinearity of the energy system, this paper uses the principle of the grey nonlinear prediction model NGBM(1,1) to improve the background value of the model, and by the simulated annealing algorithm, we put forward the optimized grey nonlinear model ONGBM(1,1). At the same time, the model is applied to the oil consumption of China, Chile, Mexico, and Japan. Based on the validity analysis of the existing data of the four countries, the model ONGBM(1,1) is basically superior to the other six grey forecast models. Finally, ONGBM(1,1) is used to predict the oil consumption of the four countries in the next five years, which can provide effective information for energy economic policy

A novel optimized grey model and its application in forecasting CO2 emissions

Carbon dioxide emissions are the main cause of global warming. At present, how to reduce carbon dioxide emissions while promoting energy savings and emission reduction is a hot research topic. Hence, China’s carbon dioxide emissions must be reasonably and accurately predicted because it is very important for the Chinese government to formulate energy and environmental policies. In this study, the classical optimization theory of the Fibonacci sequence and golden ratio were applied to the grey prediction model of an approximately inhomogeneous exponential series. Then, a new optimization model was established, and the properties of the optimization model were studied. The purpose is to reduce the parameter estimation errors of the model and improve the simulation and prediction accuracy of the model. Next, the novel model was applied to the simulation and prediction of CO2 emissions in China. The experimental results show that the effectiveness of the novel model was much better than that of the other models, which confirms the effectiveness of the new model. Based on this, China’s carbon dioxide emissions were predicted and analysed. The results show that China’s carbon dioxide emissions will still be on the rise over the next five years, and carbon dioxide emissions remain a serious problem

Forecasting Crude Oil Consumption in China Using a Grey Prediction Model with an Optimal Fractional-Order Accumulating Operator

Crude oil, which is an important part of energy consumption, can drive or hinder economic development based on its production and consumption. Reasonable predictions of crude oil consumption in China are meaningful. In this paper, we study the grey-extended SIGM model, which is directly estimated with differential equations. This model has high simulation and prediction accuracies and is one of the important models in grey theory. However, to achieve the desired modeling effect, the raw data must conform to a class ratio check. Unfortunately, the characteristics of the Chinese crude oil consumption data are not suitable for SIGM modeling. Therefore, in this paper, we use a least squares estimation to study the parametric operation properties of the SIGM model, and the gamma function is used to extend the integer order accumulation sequence to the fractional-order accumulation generation sequence. The first-order SIGM model is extended to the fractional-order FSIGM model. According to the particle swarm optimization (PSO) mechanism and the properties of the gamma function of the fractional-order cumulative generation operator, the optimal fractional-order particle swarm optimization algorithm of the FSIGM model is obtained. Finally, the data concerning China’s crude oil consumption from 2002 to 2014 are used as experimental data. The results are better than those of the classical grey GM, DGM, and NDGM models as well as those of the grey-extended SIGM model. At the same time, according to the FSIGM model, this paper predicts China’s crude oil consumption for 2015–2020

Chinese Word Segmentation at Peking University

Word segmentation is the first step in Chinese information processing, and the performance of the segmenter, therefore, has a direct and great influence on the processing steps that follow. Different segmenters will give different results when handling issues like word boundary. And we will present in this paper that there is no need for an absolute definition of word boundary for all segmenters, and that different results of segmentation shall be acceptable if they can help to reach a correct syntactic analysis in the end

Test Study on Vortex-Induced Vibration of Deep-Sea Riser under Bidirectional Shear Flow

A model test was carried out to reveal the vortex-induced vibration characteristics of a deep-sea riser under bidirectional shear flow. Bandpass filtering and modal analysis were used to process the test strain data, and the amplitude and frequency response characteristics of the vortex-induced vibration of the riser in the bidirectional shear flow field were obtained. The results of the test data analysis show that the dominant frequency of the vortex-induced vibration of the riser model under bidirectional shear flow is locked in the natural frequency of the riser and does not increase with the increase in flow velocity, that the average resistance coefficient of the riser model has little change under different flow velocities because of the distribution characteristics of the “bidirectional shear” flow field, that there is an extreme value of the shear force in the middle of the riser model, and that the Strouhal number in the transverse direction of the vortex-induced vibration under bidirectional shear flow is less than the recommended value of the current vortex-induced vibration prediction software. The obtained results provide basic data for the prediction of vortex-induced vibration and research into the fatigue analysis method of a riser under an internal wave flow field