Transgenerational Effects of Di(2-Ethylhexyl) Phthalate on Anogenital Distance,Sperm Functions and DNA Methylation in Rat Offspring

Di(2-ethylhexyl) phthalate (DEHP) is widely used as a plasticizer in the manufacture of polyvinylchloride plastics and has been associated with concerns regarding male reproductive toxicity. In this study, we hypothesized that maternal exposure to DEHP induces transgenerational inheritance of adult-onset adverse reproductive outcomes through the male germline in the F1, F2, and F3 generations of male offspring. Pregnant rats were treated with 5 or 500 mg of DEHP/kg/day through gavage from gestation day 0 to birth. The offspring body weight, anogenital distance (AGD), anogenital index (AGI), sperm count, motility, and DNA fragmentation index (DFI) were measured for all generations. Methyl-CpG binding domain sequencing was performed to analyze sperm DNA methylation status in the F3. DEHP exposure at 500 mg/kg affected AGD, AGI, sperm count, mean DFI, and %DFI in the F1; AGD, sperm count, and mean DFI in the F2; and AGD, AGI, mean DFI, and %DFI in the F3. DEHP exposure at 5 mg/kg affected AGD, AGI, sperm count, and %DFI in the F1; sperm count in the F2; and AGD and AGI in F3. Compared with the control group, 15 and 45 differentially hypermethylated genes were identified in the groups administered 5 mg/kg and 500 mg/kg DEHP, respectively. Moreover, 130 and 6 differentially hypomethylated genes were observed in the groups administered 5 mg/kg and 500 mg/kg DEHP. Overall, these results demonstrated that prenatal exposure to DEHP caused transgenerational epigenetic effects, which may explain the observed phenotypic changes in the male reproductive system.     

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.     

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd–Bullough–Mikhailov and Tzitzéica–Dodd–Bullough equations, are obtained using the exp_a function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.     

Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc.     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

A model- and simulation-based approach for tolerancing and verifying the functional capability of micro/nano-structured workpieces

The evaluation of functional features of manufactured workpieces is based on GO- and NO-GO-test results, which are obtained by comparing measured geometric characteristics with nominal dimensions and tolerances specified by the designer. These geometrical specifications are based on a tolerancing system, which was originally defined for the function mating capability. Against the background of upcoming lots of other new functions (like reduction of flow resistance, light absorption, reduction of friction, diffraction of light, self-cleaning or mass transmission) are to be realized with our products – particularly by micro- and nano scaled features. If the verification process will deliver the prediction of the achievable degree of functionality, the usability of a part can be assessed more accurately and in consequence quality and economics can be improved. So, a new principle for tolerancing and verifying turns out to be necessary. In this paper the fundamental deficit of the actual tolerancing and specification systems GPS and ASME Y14.5 is derived and the path for enlarging the system by preposing a functional model is shown. To verify the functional capability of the workpieces an approach based on simulations done with the parameterized mathematical–physical model of the function is suggested. Advantages of this approach will be discussed and demonstrated by examples with microstructured inking rolls, crankshafts and injection valves.     

Simultaneous enhancement of the efficiency and stability of organic solar cells using PEDOT:PSS grafted with a PEGME buffer layer

We report a simple processing method to simultaneously improve the efficiency and stability of organic solar cells (OSCs). Poly(4-styrene sulfonate)-doped poly(3,4-ethylenedioxy-thiophene (PEDOT:PSS), widely used as hole transport layer (HTL) in OSCs, tends to accelerate the degradation of devices because of its hygroscopic and acidic properties. In this regard, we have modified PEDOT:PSS to reduce its hygroscopic and acidic properties through a condensation reaction between PEDOT:PSS and poly(ethylene glycol) methyl ether (PEGME) in order to improve the efficiency and stability of OSCs. As a result, the power conversion efficiency (PCE) increased by 21%, from 2.57% up to 3.11%. A better energy level alignment by the reduced work function of the modified PEDOT:PSS with a highest occupied molecular orbital (HOMO) level of poly(3-hexylthiophene-2,5-diyl) (P3HT) is considered the origin of the improved the efficiency. The half-life of OSCs with PEDOT:PSS modified with PEGME buffer layer also increased up to 3.5 times compared to that of devices with pristine PEDOT:PSS buffer layer.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

基于分形插值的改进趋势面拟合法在地面沉降中的应用

针对区域地面沉降监测点数量有限、分布不均的情况,一般采用空间插值的手段建立表面拟合模型来解决。基于分形插值,采用随机选择迭代函数的思想对传统的趋势面拟合法作出改进,改进后拟合优度系数提高0.03,达到0.995,且改进前后的拟合结果符合显著性检验的要求。实验结果表明:改进前后趋势面拟合法拟合结果满足地面沉降监测的精度要求,改进后方法的拟合优度更高,对现实地面沉降量变化的描绘更加真实,可为沉降灾害评价工作提供更准确的依据。