Using the Modified FPE Criteria Forecasting the Realized Variance: a Statistical Analysis

Numéro de référence interne originel : a1.1 g 112

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data

Coupling mechanism of dual-excitation fatigue loading system of wind turbine blades

A new dual-excitation fatigue loading system of wind turbine blades was designed in this paper. However, the two excitations and blade constituted a complicated non-liner energy transferring system in which the vibration coupling effect would influence the sequent accurate control of fatigue test. To study the mechanism of the coupling system mentioned above, the electromechanical coupling mathematical model was established by simplifying the loading system rationally and the factors affecting the vibration coupling were obtained accordingly. Then the simulation model of the system was built in Matlab/Simulink environment to mainly analyze the basic influence laws of the motor speed and the initial phase difference of two excitations. Finally, a small dual-excitation fatigue loading system was established to verify the correctness of the mathematical and simulation model. It could be concluded that the results of on-site test were consistent with the results of simulation

APETALA2 antagonizes the transcriptional activity of AGAMOUS in regulating floral stem cells in Arabidopsis thaliana.

APETALA2 (AP2) is best known for its function in the outer two floral whorls, where it specifies the identities of sepals and petals by restricting the expression of AGAMOUS (AG) to the inner two whorls in Arabidopsis thaliana. Here, we describe a role of AP2 in promoting the maintenance of floral stem cell fate, not by repressing AG transcription, but by antagonizing AG activity in the center of the flower. We performed a genetic screen with ag-10 plants, which exhibit a weak floral determinacy defect, and isolated a mutant with a strong floral determinacy defect. This mutant was found to harbor another mutation in AG and was named ag-11. We performed a genetic screen in the ag-11 background to isolate mutations that suppress the floral determinacy defect. Two suppressor mutants were found to harbor mutations in AP2. While AG is known to shut down the expression of the stem cell maintenance gene WUSCHEL (WUS) to terminate floral stem cell fate, AP2 promotes the expression of WUS. AP2 does not repress the transcription of AG in the inner two whorls, but instead counteracts AG activity

ADRC Method for Noncascaded Integral System Based on the Total Derivative of Composite Functions of Several Variables

The standard ADRC controller usually selects the canonical plant in the form of cascaded integrators. However, the condition variables of practical system do not necessarily have the cascaded integral relationship. Therefore, this paper proposes a method of total derivative of composite functions of several variables and a structure, which can convert the state space system of noncascaded integral form into the cascaded integral form. In this way, the converted system can be directly controlled with ADRC. Meanwhile, the control of Chen chaotic system is discussed in detail to show the conversion and the controller design. The control performances under different levels of complication and different strengths of disturbance are comparably researched. The converted system achieves significantly better control effects under ADRC than that of the PID. This converting method solves the control problem of some noncascaded integral systems in both theory and application and greatly expands the application scope of the standard ADRC method

An Exploratory Study on CLU, CR1 and PICALM and Parkinson Disease

Recent GWAS and subsequent confirmation studies reported several single-nucleotide polymorphisms (SNPs) at the CLU, CR1 and PICALM loci in association with late-onset Alzheimer's disease (AD). Parkinson disease (PD) shares several clinical and pathologic characteristics with AD; we therefore explored whether these SNPs were also associated with PD risk.791 non-Hispanic Whites cases and 1,580 matched controls were included in the study. Odds ratios (OR) and 95% confidence intervals (CI) were obtained from logistic regression models. rs11136000 at the CLU locus was associated with PD risk under the recessive model (comparing TT versus CC+CT: OR = 0.71, 95% CI: 0.55-0.92, p = 0.008) after adjusting for year of birth, gender, smoking, and caffeine intake. Further adjustment for family history of PD and ApoE ε4 status did not change the result. In addition, we did not find evidence for effect modification by ApoE or known PD risk factors. The association, however, appeared to be stronger for PD with dementia (OR = 0.49, 95% CI: 0.27-0.91) than for PD without dementia (OR = 0.81, 95% CI: 0.61-1.06). The two other SNPs, rs6656401 from CR1, and rs3851179 from PICALM region were not associated with PD (p>0.05).Our exploratory analysis suggests an association of CLU with PD. This exploratory finding and the role of dementia in explaining this finding needs further investigation