137 research outputs found
Solid waste mixtures combustion in a circulating fluidized Bed: emission properties of NOx, Dioxin, and Heavy Metals
To efficiently and environment friendly combust the domestic garbage, sludge, and swill waste fuels, five different fuels are prepared by mixing the waste fuels together with coal, and grass biomass at different mixing ratios, and finally those fuels were combusted in a circulating fluidized bed (CFB) reactor. The emission performances of NOx, dioxin, and heavy metal during the combustion tests are studied. The results showed that a stable furnace temperature can be reached at approximately 850 °C when combusting all studied mixed fuels, benefiting the thermal processes of sludge and domestic garbage and thus realizing the purpose of waste-to-fuel. In addition, the dioxin emissions are much lower than the emission standards, and NOx emissions could be reduced significantly by adjusting the ratio of waste fuels. However, the emissions of mercury, lead, and the combinations of chromium, tin, antimony, cupper and manganese components all exceeded the pollution control standard for hazardous wastes incineration, a further technology is required for heavy metal reductions to achieve the emission standards
Cationic chitosan derivatives as potential antifungals: A review of structural optimization and applications
The increasing resistance of pathogen fungi poses a global public concern. There are several limitations in current antifungals, including few available fungicides, severe toxicity of some fungicides, and drug resistance. Therefore, there is an urgent need to develop new antifungals with novel targets. Chitosan has been recognized as a potential antifungal substance due to its good biocompatibility, biodegradability, non-toxicity, and availability in abundance, but its applications are hampered by the low charge density results in low solubility at physiological pH. It is believed that enhancing the positive charge density of chitosan may be the most effective approach to improve both its solubility and antifungal activity. Hence, this review mainly focuses on the structural optimization strategy of cationic chitosan and the potential antifungal applications. This review also assesses and comments on the challenges, shortcomings, and prospect of cationic chitosan derivatives as antifungal therapy
Hypothesis test on a mixture forward-incubation-time epidemic model with application to COVID-19 outbreak
The distribution of the incubation period of the novel coronavirus disease
that emerged in 2019 (COVID-19) has crucial clinical implications for
understanding this disease and devising effective disease-control measures. Qin
et al. (2020) designed a cross-sectional and forward follow-up study to collect
the duration times between a specific observation time and the onset of
COVID-19 symptoms for a number of individuals. They further proposed a mixture
forward-incubation-time epidemic model, which is a mixture of an
incubation-period distribution and a forward time distribution, to model the
collected duration times and to estimate the incubation-period distribution of
COVID-19. In this paper, we provide sufficient conditions for the
identifiability of the unknown parameters in the mixture
forward-incubation-time epidemic model when the incubation period follows a
two-parameter distribution. Under the same setup, we propose a likelihood ratio
test (LRT) for testing the null hypothesis that the mixture
forward-incubation-time epidemic model is a homogeneous exponential
distribution. The testing problem is non-regular because a nuisance parameter
is present only under the alternative. We establish the limiting distribution
of the LRT and identify an explicit representation for it. The limiting
distribution of the LRT under a sequence of local alternatives is also
obtained. Our simulation results indicate that the LRT has desirable type I
errors and powers, and we analyze a COVID-19 outbreak dataset from China to
illustrate the usefulness of the LRT.Comment: 34 pages, 2 figures, 2 table
TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision
End-to-end text spotting is a vital computer vision task that aims to
integrate scene text detection and recognition into a unified framework.
Typical methods heavily rely on Region-of-Interest (RoI) operations to extract
local features and complex post-processing steps to produce final predictions.
To address these limitations, we propose TextFormer, a query-based end-to-end
text spotter with Transformer architecture. Specifically, using query embedding
per text instance, TextFormer builds upon an image encoder and a text decoder
to learn a joint semantic understanding for multi-task modeling. It allows for
mutual training and optimization of classification, segmentation, and
recognition branches, resulting in deeper feature sharing without sacrificing
flexibility or simplicity. Additionally, we design an Adaptive Global
aGgregation (AGG) module to transfer global features into sequential features
for reading arbitrarily-shaped texts, which overcomes the sub-optimization
problem of RoI operations. Furthermore, potential corpus information is
utilized from weak annotations to full labels through mixed supervision,
further improving text detection and end-to-end text spotting results.
Extensive experiments on various bilingual (i.e., English and Chinese)
benchmarks demonstrate the superiority of our method. Especially on TDA-ReCTS
dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by
13.2%.Comment: MIR 2023, 15 page
Efficient precision and recall metrics for assessing generative models using hubness-aware sampling
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