The Excitation of Guided-waves by Underground Point Source: an Investigation with Theoretical Seismograms

AbstractNear-Source scattering of Rg into S appears to be the primary contributor to the low-frequency Lg. The authors further suggest that the prominent low-frequency spectral null in Lg is due to Rg from a compensated linear vector dipole (CLVD) source, and the low-frequency null in Rg excitation is due to a zero-crossing of the horizontal displacement eigenfunctions. In this study, the mechanism of the excitation of Lg from explosions in layered earth structures are analyzed with theoretical seismograms. Our result shows that the CLVD source generates prominent Lg waves,and the null in the Lg spectra showing remarkably good agreement with those expected from Rg due to a CLVD source. We conclude that the derivative of displacement eigenfunction also takes a key role in the excitation of the null, only zero-crossing of the horizantall displacement eigenfunction can not fully explain it

Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation

Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labeled source data and unlabeled target data. These solutions have gained significant popularity; however, they require the model to be retrained when the test environment changes. This can result in unbearable costs in certain applications due to the time-consuming training process and concerns regarding data privacy. One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data. Despite this, the referring style transfer module still faces issues with computation cost and over-fitting problems. To address this problem, we propose a novel framework called Informative Data Mining (IDM) that enables efficient one-shot domain adaptation for semantic segmentation. Specifically, IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. We then perform a model adaptation method using these selected samples, which includes patch-wise mixing and prototype-based information maximization to update the model. This approach effectively enhances adaptation and mitigates the overfitting problem. In general, we provide empirical evidence of the effectiveness and efficiency of IDM. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively. The code will be released at \url{https://github.com/yxiwang/IDM}.Comment: Accepted by ICCV 202

Granular risk assessment of earthquake induced landslide via latent representations of stacked autoencoder

Earthquake-induced landslides are ubiquitous on slopes in terrestrial environments, which can pose a serious threat to local communities and infrastructures. Data-driven landslide assessments play a crucial role in preventing future landslide occurrences and recurrences. We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an end-to-end learning strategy to obtain a central latent vector with reduced dimensionality. The multivariate landslide dataset was used as both the input and output to train the stacked autoencoder algorithm. Subsequently, in the central latent vector of the stacked autoencoder, the Fuzzy C-means clustering algorithm was applied to cluster the landslides into various groups with different risk levels, and the intervals for each group were computed using the granular computing approach. An empirical case study in Wenchuan County, Sichuan, China, was conducted. A comparative analysis with other state-of-the-art approaches including Density-based spatial clustering of applications with noise (DBSCAN), K-means clustering, and Principal Component Analysis (PCA), is provided and discussed. The experimental results demonstrate that the proposed approach using a stacked autoencoder integrated with fuzzy information granulation provides superior performance compared to those by other state-of-the-art approaches, and is capable of studying deep patterns in earthquake-induced landslide datasets and provides sufficient interpretation for field engineers

Exogenous Fe2+ alleviated the toxicity of CuO nanoparticles on Pseudomonas tolaasii Y-11 under different nitrogen sources

Extensive use of CuO nanoparticles (CuO-NPs ) inevitably leads to their accumulation in wastewater and toxicity to microorganisms that effectively treat nitrogen pollution. Due to the effects of different mediums, the sources of CuO-NPs-induced toxicity to microorganisms and methods to mitigating the toxicity are still unclear. In this study, CuO-NPs were found to impact the nitrate reduction of Pseudomonas tolaasii Y-11 mainly through the action of NPs themselves while inhibiting the ammonium transformation of strain Y-11 through releasing Cu2+. As the content of CuO-NPs increased from 0 to 20 mg/L, the removal efficiency of NO3− and NH4+ decreased from 42.29% and 29.83% to 2.05% and 2.33%, respectively. Exogenous Fe2+ significantly promoted the aggregation of CuO-NPs, reduced the possibility of contact with bacteria, and slowed down the damage of CuO-NPs to strain Y-11. When 0.01 mol/L Fe2+ was added to 0, 1, 5, 10 and 20 mg/L CuO-NPs treatment, the removal efficiencies of NO3- were 69.77%, 88.93%, 80.51%, 36.17% and 2.47%, respectively; the removal efficiencies of NH4+ were 55.95%, 96.71%, 38.11%, 20.71% and 7.43%, respectively. This study provides a method for mitigating the toxicity of CuO-NPs on functional microorganisms

A self-assembling fluorescent dipeptide conjugate for cell labelling

Derivatives of fluorophore FITC (fluorescein isothiocyanate) are widely used in bioassays to label proteins and cells. An N-terminal leucine dipeptide is attached to FITC, and we show that this simple conjugate molecule is cytocompatible and is uptaken by cells (human dermal and corneal fibroblasts) in contrast to FITC itself. Co-localisation shows that FITC-LL segregates in peri-nuclear and intracellular vesicle regions. Above a critical aggregation concentration, the conjugate is shown to self-assemble into beta-sheet nanostructures comprising molecular bilayers

Reliability Analysis of 6-Component Star Markov Repairable System with Spatial Dependence

Star repairable systems with spatial dependence consist of a center component and several peripheral components. The peripheral components are arranged around the center component, and the performance of each component depends on its spatial “neighbors.” Vector-Markov process is adapted to describe the performance of the system. The state space and transition rate matrix corresponding to the 6-component star Markov repairable system with spatial dependence are presented via probability analysis method. Several reliability indices, such as the availability, the probabilities of visiting the safety, the degradation, the alert, and the failed state sets, are obtained by Laplace transform method and a numerical example is provided to illustrate the results

Domain Adaptive Semantic Segmentation without Source Data: Align, Teach and Propagate

Benefiting from considerable pixel-level annotations collected from a specific situation (source), the trained semantic segmentation model performs quite well but fails in a new situation (target) due to the large domain shift. To mitigate the domain gap, previous cross-domain semantic segmentation methods always assume the co-existence of source data and target data during domain alignment. However, the accessing to source data in the real scenario may raise privacy concerns and violate intellectual property. To tackle this problem, we focus on an interesting and challenging cross-domain semantic segmentation task where only the trained source model is provided to the target domain. Specifically, we propose a unified framework called ATP, which consists of three schemes, ie, feature Alignment, bidirectional Teaching, and information Propagation. First, we devise a curriculum-style entropy minimization objective to implicitly align the target features with unseen source features via the provided source model. Second, besides positive pseudo labels in vanilla self-training, we are among the first ones to introduce negative pseudo labels to this field and develop a bidirectional self-training strategy to enhance the representation learning in the target domain. Finally, the information propagation scheme is employed to further reduce the intra-domain discrepancy within the target domain via pseudo semi-supervised learning. Extensive results on synthesis-to-real and cross-city driving datasets validate \textbf{ATP} yields state-of-the-art performance, even on par with methods that need access to source data

Evaluation of sulfur trioxide detection with online isopropanol absorption method

Measurement of the SO3 concentration in flue gas is important to estimate the acid dew point and to control corrosion of downstream equipment. SO3 measurement is a difficult question since SO3 is a highly reactive gas, and its concentration is generally two orders of magnitude lower than the SO2 concentration. The SO3 concentration can be measured online by the isopropanol absorption method; however, the reliability of the test results is relatively low. This work aims to find the error sources and to evaluate the extent of influence of each factor on the measurement results. The test results from a SO3 analyzer showed that the measuring errors are mainly caused by the gas-liquid flow ratio, SO2 oxidation, and the side reactions of SO3. The error in the gas sampling rate is generally less than 13%. The isopropanol solution flow rate decreases 3% to 30% due to the volatilization of isopropanol, and accordingly, this will increase the apparent SO3 concentration. The amount of SO2 oxidation is linearly related to the SO2 concentration. The side reactions of SO3 reduce the selectivity of SO42- to nearly 73%. As sampling temperature increases from 180 to 300 degrees C, the selectivity of SO42- decreases from 73% to 50%. The presence of H2O in the sample gas helps to reduce the measurement error by inhibiting the volatilization of the isopropanol and weakening side reactions. A formula was established to modify the displayed value, and the measurement error was reduced from 25%-54% to less than 15%. (C) 2017 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V

Fe²⁺ Alleviated the Toxicity of ZnO Nanoparticles to <i>Pseudomonas tolaasii</i> Y-11 by Changing Nanoparticles Behavior in Solution

The negative effect of ZnO nanoparticles (ZnO-NPs) on the biological removal of nitrate (NO3−) has received extensive attention, but the underlying mechanism is controversial. Additionally, there is no research on Fe2+ used to alleviate the cytotoxicity of NPs. In this paper, the effects of different doses of ZnO-NPs on the growth and NO3− removal of Pseudomonas tolaasii Y-11 were studied with or without Fe2+. The results showed that ZnO-NPs had a dose-dependent inhibition on the growth and NO3− removal of Pseudomonas tolaasii Y-11 and achieved cytotoxic effects through both the NPs themselves and the released Zn2+. The addition of Fe2+ changed the behavior of ZnO-NPs in an aqueous solution (inhibiting the release of toxic Zn2+ and promoting the aggregation of ZnO-NPs), thereby alleviating the poisonous effect of ZnO-NPs on the growth and nitrogen removal of P. tolaasii Y-11. This study provides a theoretical method for exploring the mitigation of the acute toxicity of ZnO-NPs to denitrifying microorganisms

Fe2+ Alleviated the Toxicity of ZnO Nanoparticles to Pseudomonas tolaasii Y-11 by Changing Nanoparticles Behavior in Solution

The negative effect of ZnO nanoparticles (ZnO-NPs) on the biological removal of nitrate (NO3−) has received extensive attention, but the underlying mechanism is controversial. Additionally, there is no research on Fe2+ used to alleviate the cytotoxicity of NPs. In this paper, the effects of different doses of ZnO-NPs on the growth and NO3− removal of Pseudomonas tolaasii Y-11 were studied with or without Fe2+. The results showed that ZnO-NPs had a dose-dependent inhibition on the growth and NO3− removal of Pseudomonas tolaasii Y-11 and achieved cytotoxic effects through both the NPs themselves and the released Zn2+. The addition of Fe2+ changed the behavior of ZnO-NPs in an aqueous solution (inhibiting the release of toxic Zn2+ and promoting the aggregation of ZnO-NPs), thereby alleviating the poisonous effect of ZnO-NPs on the growth and nitrogen removal of P. tolaasii Y-11. This study provides a theoretical method for exploring the mitigation of the acute toxicity of ZnO-NPs to denitrifying microorganisms