Primitive processing of e-waste potentially releases abundant organic contaminants to the environment, but the magnitudes and mechanisms remain to be adequately addressed. We conducted thermal treatment and open burning of typical e-wastes, that is, plastics and printed circuit boards. Emission factors of the sum of 39 polybrominated diphenyl ethers (∑PBDE) were 817-1.60 × 10 ng g in thermal treatment and nondetected-9.14 × 10 ng g, in open burning. Airborne particles (87%) were the main carriers of PBDEs, followed by residual ashes (13%) and gaseous constituents (0.3%), in thermal treatment, while they were 30%, 43% and 27% in open burning. The output-input mass ratios of ∑PBDE were 0.12-3.76 in thermal treatment and 0-0.16 in open burning. All PBDEs were largely affiliated with fine particles, with geometric mean diameters at 0.61-0.83 μm in thermal degradation and 0.57-1.16 μm in open burning from plastic casings, and 0.44-0.56 and nondetected- 0.55 μm, from printed circuit boards. Evaporation and reabsorption may be the main emission mechanisms for lightly brominated BDEs, but heavily brominated BDEs tend to affiliate with particles from heating or combustion. The different size distributions of particulate PBDEs in emission sources and adjacent air implicated a noteworthy redisposition process during atmospheric dispersal.

Plant defence mechanisms are suppressed in the absence of pathogen attack to prevent wasted energy and growth inhibition. However, how defence responses are repressed is not well understood. Histone deacetylase 6 (HDA6) is a negative regulator of gene expression, and its role in pathogen defence response in plants is not known. In this study, a novel allele of hda6 (designated as shi5) with spontaneous defence response was isolated from a forward genetics screening in Arabidopsis. The shi5 mutant exhibited increased resistance to hemibiotrophic bacterial pathogen Pst DC3000, constitutively activated expression of pathogen-responsive genes including PR1, PR2, etc. and increased histone acetylation levels at the promoters of most tested genes that were upregulated in shi5. In both wild type and shi5 plants, the expression and histone acetylation of these genes were upregulated by pathogen infection. HDA6 was found to bind to the promoters of these genes under both normal growth conditions and pathogen infection. Our research suggests that HDA6 is a general repressor of pathogen defence response and plays important roles in inhibiting and modulating the expression of pathogen-responsive genes in Arabidopsis.

SUMMARYIn this paper, an improved substructure-based damage detection approach is proposed to locate and quantify damages in a shear structure, which extends from a previously established substructure approach. This method requires only three sensors to locate and quantify the damage in any story of a shear structure building. Similarly, as in the previous approach, a substructure approach is adopted in the improved procedure to divide a complete structure into several substructures. To improve the noise immunity and damage detection robustness under different types of excitations and realistic conditions, this paper proposes an autoregressive-moving average with exogenous inputs (ARMAX) model residual-based technique to correct the former damage indicator. The correction coefficient is defined as the normalized Kolmogorov-Smirnov (KS) test statistical distance between the two distinguished data sets of ARMAX model residual generalized from input-output data process for undamaged and damaged states. To better assess the performance of the improved procedure, simulation and experimental verifications of the proposed approach have been conducted, and the results are compared with the previous method. It shows that the improved procedure works much better and more stable than the previous method especially when it is applied to realistic problems.

Uncertain data clustering has been recognized as an essential task in the research of data mining. Many centralized clustering algorithms are extended by defining new distance or similarity measurements to tackle this issue. With the fast development of network applications, these centralized methods show their limitations in conducting data clustering in a large dynamic distributed peer-to-peer network due to the privacy and security concerns or the technical constraints brought by distributive environments. In this paper, we propose a novel distributed uncertain data clustering algorithm, in which the centralized global clustering solution is approximated by performing distributed clustering. To shorten the execution time, the reduction technique is then applied to transform the proposed method into its deterministic form by replacing each uncertain data object with its expected centroid. Finally, the attribute-weight-entropy regularization technique enhances the proposed distributed clustering method to achieve better results in data clustering and extract the essential features for cluster identification. The experiments on both synthetic and real-world data have shown the efficiency and superiority of the presented algorithm.

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