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In this paper, a series of novel trans-1, 4-polyisoprene/isotactic polybutene (TPI/iPB) in-reactor blends were synthesized by isoprene and butene sequential two-stage polymerization technology with spherical Ti[Cl.sub.4]/Mg[Cl.sub.2] type Ziegler-Natta catalyst. The components, structures, and properties of the asobtained TPI/iPB reactor blends were characterized by gel permeation chromatography, Fourier transform Infrared spectroscopy, and differential scanning calorimetry. The active trans-1, 4-polyisoprene (TPI) particles obtained in the initial isoprene polymerization by the Z-N catalyst can be acted as microreactors to initiate butene polymerization subsequently. The TPI/iPB reactor blends with varied components were in situ synthesized within the reactor. The preparative-temperature rising elution fractionation (p-TREF) technique was used to fractionate the TPI/iPB reactor blends based on the elution temperature ranged from -40[degrees]C to 90[degrees]C. The weight distribution and microstructure of each fraction were investigated. The reactor blends are composed of crystallizable high trans-1, 4-uint polyisoprene obtained from the first-stage isoprene polymerization, high isotactic polybutene obtained from the secondstage butene polymerization and TPI-b-iPB block copolymer with different sequence structure obtained from the initial time of the second stage. This work is expected to propose the possible polymerizations of a-olefins and conjugated dienes by using heterogeneous Ziegler-Natta catalyst and provide a kind of novel rubber/plastic reactor blend materials. Highlights * The first synthesis of the trans-1,4-polyisoprene/isotactic polybutene reactor blends. * p-TREF technique was used to analyze the composition of TPI/iPB reactor blends. * Detailed structure characterization of the fractions including GPC and NMR * TPI-b-iPB block copolymer can be obtained through the sequential polymerization. * Rubber/plastic reactor blend materials were fabricated by the Z-N catalyst. KEYWORDS 4-polyisoprene, in-reactor blends, polybutene, temperature rising elution fractionation, trans-1, Ziegler-Natta catalyst, 1 | INTRODUCTION The discovery of Ziegler-Natta catalysts has been one of the greatest developments in the synthesis of stereospecific polymers in both academy and modern industry. (1-4) The first [...]

In order to understand the different performances of robots under different control systems, the author has carried out dynamic optimization research on the control system of robots combined with differential algebraic equations. In this study, the general form of the discrete differential-algebraic equation (DAE) optimization problem using the Orthogonal Configuration of Finite Element (OCFE) method is deeply analyzed, and the equivalent conditions of the direct discrete scheme and the indirect discrete scheme are obtained through rigorous proof. On this basis, a variety of common configuration methods are simulated and analyzed, and it is found that indirect Lobatto configuration can achieve better results in many aspects. The results show that the discrete algorithm using differential algebraic equations can effectively achieve dynamic optimization of the control system, thus achieving the author’s research purpose.

Phylogenetic association analysis plays a crucial role in investigating the correlation between microbial compositions and specific outcomes of interest in microbiome studies. However, existing methods for testing such associations have limitations related to the assumption of a linear association in high-dimensional settings and the handling of confounding effects. Hence, there is a need for methods capable of characterizing complex associations, including nonmonotonic relationships. This article introduces a novel phylogenetic association analysis framework and associated tests to address these challenges by employing conditional rank correlation as a measure of association. The proposed tests account for confounders in a fully nonparametric manner, ensuring robustness against outliers and the ability to detect diverse dependencies. The proposed framework aggregates conditional rank correlations for subtrees using weighted sum and maximum approaches to capture both dense and sparse signals. The significance level of the test statistics is determined by calibration through a nearest-neighbour bootstrapping method, which is straightforward to implement and can accommodate additional datasets when these are available. The practical advantages of the proposed framework are demonstrated through numerical experiments using both simulated and real microbiome datasets. [ABSTRACT FROM AUTHOR]

Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is used in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction's weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, k-means clustering, and k-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation's accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the article. for this article are available online. [ABSTRACT FROM AUTHOR]

Cadmium zinc telluride (CZT) is an ideal material for room temperature nuclear radiation detection, but CZT crystals of high quality and low defects concentration are difficult to obtain. Therefore, in order to improve the performance of the CZT detector, the working conditions of the CZT detector could be appropriately changed to make the internal electric field of the CZT detector close to uniform distribution so as to improve the electron transport performance. In this paper, alpha induced transient charge analysis has been used to study the internal electric field of the CZT detector, and deep level defects in CZT were linked with internal electric field distribution. Based on the process, a variety of deep level defects on electron trapping and detrapping by changing the temperature, the output waveform change of charge sensitive preamplifier (the pulse height spectra for alpha radiation at different temperatures) was observed, and then the effects of deep level defects on electron mobility (μ e), electron transport time (T R), the internal electric field, and the electron collection efficiency of the CZT detector were analyzed. The experimental results indicated that the influence of deep level defects was a main factor to the internal electric field in the range of −140 to 40 °C. As the temperature rises, the influence of these defects weakens, μ e and electron collection efficiency both increase, and internal electric field distribution tends to be uniform. Moreover, with the further increasing temperature (−40 to 20 °C), μ e decreased and internal electric field distribution became fluctuating, but electron collection efficiency was basically unchanged, which suggested that the influence of lattice vibration in the range of −40 to 20 °C turned to be the main factor. The above conclusions demonstrated that although the CZT detector has excellent room temperature detection ability, room temperature was not its optimal working temperature due to the influence of high concentration deep level defects. At −20 °C, the CZT detector presented the highest electron collection efficiency and maximum which limited the influence of deep level defects on electron transport, performing the optimal properties. [ABSTRACT FROM AUTHOR]

The value of a novel soybean male-sterile mutation msLC01 in breeding practice was determined by its outcrossing properties. Then, the effects of different planting arrangements on the pod set characteristics of male-sterile plants were assessed by using orthogonal experiments at two sites. At the same time, the effects of msLC01 male sterility on other traits were assessed in two C2F2 populations. In addition, the nectar secretion and natural outcross of male-sterile plants from four msLC01 lines were compared with one ms1 line and one ms6 line. The results of the orthogonal experiment showed that the pod numbers and pod set rates of male-sterile plants were decisively different between the two experimental sites but not between the two levels of the other factors. Both increasing the ratio of paternal parent to maternal parent and planting the parental seeds in a mixed way, the proportion of seeds pollinated by the target parent pollen could be increased. Except for the pod number per plant trait, there was no significant difference between male-sterile plants and their fertile siblings. The amount of nectar significantly differed among the lines. Compared with ms1 and ms6 male-sterile plants, the four msLC01 lines possessed significantly more or similar numbers of pod sets. The results of this study lay a foundation for the future use of this mutant in soybean breeding. [ABSTRACT FROM AUTHOR]

Motivation: Differential abundance analysis is an essential and commonly used tool to characterize the difference between microbial communities. However, identifying differentially abundant microbes remains a challenging problem because the observed microbiome data are inherently compositional, excessive sparse, and distorted by experimental bias. Besides these major challenges, the results of differential abundance analysis also depend largely on the choice of analysis unit, adding another practical complexity to this already complicated problem. Results: In this work, we introduce a new differential abundance test called the MsRDB test, which embeds the sequences into a metric space and integrates a multiscale adaptive strategy for utilizing spatial structure to identify differentially abundant microbes. Compared with existing methods, the MsRDB test can detect differentially abundant microbes at the finest resolution offered by data and provide adequate detection power while being robust to zero counts, compositional effect, and experimental bias in the microbial compositional dataset. Applications to both simulated and real microbial compositional datasets demonstrate the usefulness of the MsRDB test. Availability and implementation: All analyses can be found under https://github.com/lakerwsl/MsRDB-Manuscript-Code. [ABSTRACT FROM AUTHOR]

Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address these challenges. Compared with existing methods, the robust differential abundance test is simple and computationally efficient, is robust to prevalent zero counts in compositional datasets, can take the data's compositional nature into account, and has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the robust differential abundance test can work with covariate-balancing techniques to remove potential confounding effects and draw reliable conclusions. The proposed test is applied to several numerical examples, and its merits are demonstrated using both simulated and real datasets. [ABSTRACT FROM AUTHOR]