Analysis of dynamic characteristic for misalignment-spline gear shaft based on whole transfer matrix method

Spline-gear-shaft (SGF), which is mainly composed of gear and spline shaft, is an important transmission system of RAT (driving the generator to output power with ram air). And its dynamic characteristics affect performance of RAT greatly. In this situation, gear coupling and spline coupling, must be taken into account when analyzing dynamics characteristics of SGF. The misalignment of axis for internal and external spline is inevitable owing to mass eccentricity and unconstrained in axial direction in a high-speed operation. Consequently, the dynamic model of SGF is established considering gear coupling and spline coupling based on whole transfer matrix method (WTMM) in this paper. In addition, numerical calculation method of meshing force for spline teeth under the condition of misalignment is presented considering the distribution force on contact surface of spline teeth in this paper, based on this, the coupled transfer matrix of misaligned spline coupling is established and the dynamic characteristics of SGF is analyzed using whole transfer matrix method with Riccati (WTMMR). The test experiments of dynamic characteristics for SGF are carried out by using portable vibration monitoring analyzer in this paper, furthermore, the time-frequency characteristics of SGF are analyzed through the analysis and processing of acceleration signal. The orders of magnitude for the first four natural frequencies with WTMMR are respectively consistent with experiment results, and the relative error is in the accepted range. Therefore, the correctness of solving meshing force and WTMMR for SGF are all verified. Moreover, it also shows that WTMMR is suitable for the analysis of complex coupled systems SGF and provides a new thought for dynamic analysis of complex shafting and promotes dynamic analysis to efficient and streamlined development

Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization

Water wave optimization (WWO) is a novel metaheuristic method that is based on shallow water wave theory, which has simple structure, easy realization, and good performance even with a small population. To improve the convergence speed and calculation precision even further, this paper on elite opposition-based strategy water wave optimization (EOBWWO) is proposed, and it has been applied for function optimization and structure engineering design problems. There are three major optimization strategies in the improvement: elite opposition-based (EOB) learning strategy enhances the diversity of population, local neighborhood search strategy is introduced to enhance local search in breaking operation, and improved propagation operator provides the improved algorithm with a better balance between exploration and exploitation. EOBWWO algorithm is verified by using 20 benchmark functions and two structure engineering design problems and the performance of EOBWWO is compared against those of the state-of-the-art algorithms. Experimental results show that the proposed algorithm has faster convergence speed, higher calculation precision, with the exact solution being even obtained on some benchmark functions, and a higher degree of stability than other comparative algorithms

Deriving divide-and-conquer dynamic programming algorithms using solver-aided transformations

We introduce a framework allowing domain experts to manipulate computational terms in the interest of deriving better, more efficient implementations.It employs deductive reasoning to generate provably correct efficient implementations from a very high-level specification of an algorithm, and inductive constraint-based synthesis to improve automation. Semantic information is encoded into program terms through the use of refinement types. In this paper, we develop the technique in the context of a system called Bellmania that uses solver-aided tactics to derive parallel divide-and-conquer implementations of dynamic programming algorithms that have better locality and are significantly more efficient than traditional loop-based implementations. Bellmania includes a high-level language for specifying dynamic programming algorithms and a calculus that facilitates gradual transformation of these specifications into efficient implementations. These transformations formalize the divide-and conquer technique; a visualization interface helps users to interactively guide the process, while an SMT-based back-end verifies each step and takes care of low-level reasoning required for parallelism. We have used the system to generate provably correct implementations of several algorithms, including some important algorithms from computational biology, and show that the performance is comparable to that of the best manually optimized code.National Science Foundation (U.S.) (CCF-1139056)National Science Foundation (U.S.) (CCF- 1439084)National Science Foundation (U.S.) (CNS-1553510

A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine

The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.</p

Numerical analysis of evaluation methods and influencing factors for dynamic stability of bedding rock slope

As the inclination of a bedding surface is consistent with the inclination of a slope, the stability of a bedding rock slope is relatively poor, especially under dynamic loads such as earthquake and blasting. In the dynamic stability analysis of slope, the evaluation methods and influence factors of slope stability are two important concerns. Therefore, two typical bedding rock slopes are respectively established by FLAC3D to study the above concerns. The pseudo-static method, dynamic time-history method and dynamic strength reduction method is used to evaluate the dynamic stability of the model slope, and the applicability of the three methods is compared. The influence of five parameters including dynamic load frequency, slope angle, slope height, strata inclination and strata thickness on the dynamic stability is considered in the model slope with a set of bedding planes. The results show that the dynamic strength reduction method has good suitability for the stability evaluation of a bedding rock slope due to its good solution in the instability judgment and evaluation index. The dynamic stability of a slope becomes worse when the load frequency is close to the natural frequency of the slope. Due to the “elevation effect” and “bedding surface effect” in the dynamic slope response, the slope stability decreases with the increase of slope height and the reduction of strata thickness. The slope stability decreases with the increase of strata inclination and slope angle, and the strata inclination is the most sensitive parameter influencing the slope stability. When the slope angle and height increase to a certain value, the downward trend of slope stability gradually become gentle. For the model slope in this paper, when the slope angle reaches 55° and the slope height reaches 200 m, the reduction of slope stability will be no longer obvious with the increase of a slope angle and slope height

Leucine Carboxyl Methyltransferase Downregulation and Protein Phosphatase Methylesterase Upregulation Contribute Toward the Inhibition of Protein Phosphatase 2A by α-Synuclein

The pathology of Parkinson’s disease (PD) is characterized by intracellular neurofibrillary tangles of phosphorylated α-synuclein (α-syn). Protein phosphatase 2A (PP2A) is responsible for α-syn dephosphorylation. Previous work has demonstrated that α-syn can regulate PP2A activity. However, the mechanisms underlying α-syn regulation of PP2A activity are not well understood. In this study, we found that α-syn overexpression induced increased α-syn phosphorylation at serine 129 (Ser129), and PP2A inhibition, in vitro and in vivo. α-syn overexpression resulted in PP2A demethylation. This demethylation was mediated via downregulated leucine carboxyl methyltransferase (LCMT-1) expression, and upregulated protein phosphatase methylesterase (PME-1) expression. Furthermore, LCMT-1 overexpression, or PME-1 inhibition, reversed α-syn-induced increases in α-syn phosphorylation and apoptosis. In addition to post-translational modifications of the catalytic subunit, regulatory subunits are involved in the regulation of PP2A activity. We found that the levels of regulatory subunits which belong to the PPP2R2 subfamily, not the PPP2R5 subfamily, were downregulated in the examined brain regions of transgenic mice. Our work identifies a novel mechanism to explain how α-syn regulates PP2A activity, and provides the optimization of PP2A methylation as a new target for PD treatment

The endogenous exposome

The concept of the Exposome, is a compilation of diseases and one’s lifetime exposure to chemicals, whether the exposure comes from environmental, dietary, or occupational exposures; or endogenous chemicals that are formed from normal metabolism, inflammation, oxidative stress, lipid peroxidation, infections, and other natural metabolic processes such as alteration of the gut microbiome. In this review, we have focused on the Endogenous Exposome, the DNA damage that arises from the production of endogenous electrophilic molecules in our cells. It provides quantitative data on endogenous DNA damage and its relationship to mutagenesis, with emphasis on when exogenous chemical exposures that produce identical DNA adducts to those arising from normal metabolism cause significant increases in total identical DNA adducts. We have utilized stable isotope labeled chemical exposures of animals and cells, so that accurate relationships between endogenous and exogenous exposures can be determined. Advances in mass spectrometry have vastly increased both the sensitivity and accuracy of such studies. Furthermore, we have clear evidence of which sources of exposure drive low dose biology that results in mutations and disease. These data provide much needed information to impact quantitative risk assessments, in the hope of moving towards the use of science, rather than default assumptions