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  1. The continued development of the automotive industry has led to a rapid increase in the amount of waste rubber tires, the problem of “black pollution” has become more serious but is often ignored. In this study, the emission characteristics, health risks, and environmental effects of volatile organic compounds (VOCs) from a typical, recycled rubber plant were studied. A total of 15 samples were collected by summa canisters, and 100 VOC species were detected by the GC/MS-FID system. In this study, the total VOCs (TVOCs) concentration ranged from 1000 ± 99 to 19,700 ± 19,000 µg/m3, aromatics and alkanes were the predominant components, and m/p-xylene (14.63 ± 4.07%–48.87 ± 3.20%) could be possibly regarded as a VOCs emission marker. We also found that specific similarities and differences in VOCs emission characteristics in each process were affected by raw materials, production conditions, and process equipment. The assessment of health risks showed that devulcanizing and cooling had both non-carcinogenic and carcinogenic risks, yarding had carcinogenic risks, and open training and refining had potential carcinogenic risks. Moreover, m/p-xylene and benzene were the main non-carcinogenic species, while benzene, ethylbenzene, and carbon tetrachloride were the dominant risk compounds. In the evaluation results of LOH, m/p-xylene (25.26–67.87%)more »was identified as the most key individual species and should be prioritized for control. In conclusion, the research results will provide the necessary reference to standardize the measurement method of the VOCs source component spectrum and build a localized source component spectrum.« less
  2. Abstract Motivation The generalized linear mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes random effects into account. Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation–Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e. each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects. Results Our collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction.more »The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction. We implemented the algorithm for collaborative GLMM (cGLMM) construction in R. The data communication was implemented using the rsocket package. Availability and implementation The software is released in open source at Supplementary information Supplementary data are available at Bioinformatics online.« less
  3. Abstract

    Although the frustrated (zigzag) spin chain is the Drosophila of frustrated magnetism, our understanding of a pair of coupled zigzag chains (frustrated spin ladder) in a magnetic field is still lacking. We address this problem through nuclear magnetic resonance (NMR) experiments on BiCu$$_2$$2PO$$_6$$6in magnetic fields up to 45 T, revealing a field-induced spiral magnetic structure. Conjointly, we present advanced numerical calculations showing that even a moderate rung coupling dramatically simplifies the phase diagram below half-saturation magnetization by stabilizing a field-induced chiral phase. Surprisingly for a one-dimensional model, this phase and its response to Dzyaloshinskii-Moriya (DM) interactions adhere to classical expectations. While explaining the behavior at the highest accessible magnetic fields, our results imply a different origin for the solitonic phases occurring at lower fields in BiCu$$_2$$2PO$$_6$$6. An exciting possibility is that the known, DM-mediated coupling between chirality and crystal lattice may give rise to a new kind of spin-Peierls instability.

  4. In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods.