► Two new moment-independent importance measures of the input variable are proposed. ► The intrinsic relationship of two proposed importance measures with the variance-based importance measures is ...revealed. ► The high-efficient SDP solutions for two importance measures are established.
To analyze the effect of basic variable on output of the structure or system in reliability engineering, two moment-independent importance measures of the basic variable are proposed respectively on the failure probability and distribution function of the output. The importance measures proposed not only inherit the advantages of the traditional moment-independent importance measures, but also reflect the intrinsic relationship of the moment-independent measures and the corresponding variance-based importance measures. For the problem that the computational effort of the moment-independent importance measure is usually too high, the computation of the proposed moment-independent importance measures is transformed into that of the variance-based importance measures on their intrinsic relationship. And then combining the high efficient state dependent parameter (SDP) method for the calculation of the conditional moments of the model output, a SDP solution is established to solve two moment-independent importance measures. Several examples are used to demonstrate that the proposed importance measures can effectively describe the effect of the basic variable on the reliability of the structure system, and the established solution can obtain the two importance measures simultaneously with only a single set of model runs, which allows for a strong reduction of the computational cost.
•A new method for performing global reliability sensitivity analysis is proposed.•It requires as an input only samples that fall in the failure domain.•It can be carried out without extra model ...evaluations following reliability analysis.•It can be implemented with any sampling-based reliability analysis method.•It is much more efficient than currently existing methods.
Global reliability sensitivity analysis (RSA) can help to assess the effects of input random variables X on the probability of failure Pr(F) of an engineering system. Conventionally, this requires repeated evaluations of the conditional failure probability Pr(F|Xi = xi) for multiple values of the input random variable Xi and for all Xi of interest. Such a solution is straightforward but computationally expensive. In this paper, we propose a new method to perform global RSA, which requires as an input only samples of X that fall in the failure domain. Such samples are a by-product of many sampling-based reliability analysis methods. The proposed method constructs the Pr(F|Xi = xi) by application of Bayes’ rule, based on the probability density function (PDF) of X conditioned on system failure F. This conditional PDF is approximated with a kernel density estimation from the failure samples. In this way, the reliability sensitivities of all the input random variables can be computed following a sampling-based reliability analysis with no additional computation cost. The approach is investigated on numerical examples in conjunction with crude Monte Carlo simulation, importance sampling and subset simulation. The results demonstrate the computational advantages over existing single-loop sampling methods for global RSA.
•An elaborate algorithm of analyzing the moment-independent sensitivity is derived.•The proposed algorithm avoids the probability density function estimation.•The proposed algorithm obtains ...sensitivity indices of all inputs simultaneously.•The computational burden of the proposed algorithm does not depend on the dimensionality of model inputs.
Borgonovo moment-independent sensitivity index (BMSI) was proposed to measure the sensitivity of model inputs according to the whole distribution of model output not only a specific moment. The main computational difficulty of the BMSI is to estimate the unconditional probability density function (PDF) and the conditional PDF of the model output. Generally, the estimation of cumulative distribution function (CDF) is easier than that of the PDF, but CDF-based method needs to calculate the extreme points of the differences between the unconditional PDF and the conditional PDF of model output. In addition, the computational cost of the existing CDF-based method also depends on the dimensionality of model inputs. To avoid these accessional computations, this paper derives a new formula by innovatively combining the law of total expectation in the successive intervals without overlapping and the Bayes theorem. The proposed new formula can obtain every input's BMSI only by one group of unconditional model inputs-output samples and does not need to estimate the PDF and the extreme points, which greatly reduces the computational difficulty of the BMSI by replacing the PDF estimation with the probability estimation. Four case studies are analyzed, and the results demonstrate the effectiveness of the proposed algorithm for estimating the BMSI.
Pancreatic ductal adenocarcinoma (PDAC) is projected to rise to the second leading cause of U.S. cancer-related deaths by 2020. Novel therapeutic targets are desperately needed. MicroRNAs (miRs) are ...small noncoding RNAs that function by suppressing gene expression and are dysregulated in cancer. miR-21 is overexpressed in PDAC tumor cells (TC) and is associated with decreased survival, chemoresistance and invasion. Dysregulation of miR regulatory networks in PDAC tumor-associated fibroblasts (TAFs) have not been previously described. In this study, we show that miR-21 expression in TAFs promotes TC invasion.
In-situ hybridization for miR-21 was performed on the 153 PDAC patient UCLA tissue microarray and 23 patient-matched lymph node metastases. Stromal and TC histoscores were correlated with clinicopathologic parameters by univariate and multivariate Cox regression. miR-21 positive cells were further characterized by immunofluorescence for mesenchymal/epithelial markers. For in vitro studies, TAFs were isolated from freshly resected human PDAC tumors by the outgrowth method. miR-21 was overexpressed/inhibited in fibroblasts and then co-cultured with GFP-MiaPaCa TCs to assess TC invasion in modified Boyden chambers.
miR-21 was upregulated in TAFs of 78% of tumors, and high miR-21 significantly correlated with decreased overall survival (P = 0.04). Stromal miR-21 expression was also significantly associated with lymph node invasion (P = 0.004), suggesting that it is driving TC spread. Co-immunofluorescence revealed that miR-21 colocalized with peritumoral fibroblasts expressing α-smooth muscle actin. Moreover, expression of miR-21 in primary TAFs correlated with miR-21 in TAFs from patient-matched LN metastases; evidence that PDAC tumor cells induce TAFs to express miR-21. miR-21 expression in TAFs and TCs promotes invasion of TCs and is inhibited with anti-miR-21.
miR-21 expression in PDAC TAFs is associated with decreased overall survival and promotes TC invasion. Anti-miR-21 may represent a novel therapeutic strategy for dual targeting of both tumor and stroma in PDAC.
Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in ...vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes.
Pansharpening techniques fuse the complementary information from panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution MS image. However, the majority of existing pansharpening ...techniques suffer from spectral distortion owing to the low correlation between the MS and PAN images, and difficulties in obtaining appropriate injection gains. To address these issues, this article presents a novel pansharpening method based on the variational fractional-order geometry (VFOG) model and optimized injection gains. Specifically, to improve the correlation between the PAN and MS images, the VFOG model is constructed to generate a refined PAN image with a similar spatial structure to the MS image, while maintaining the gradient information of the original PAN image. Furthermore, to obtain accurate injection gains, and considering that the vegetated and nonvegetated regions should be dissimilar, an optimized adaptive injection gain based on the normalized differential vegetation index is designed. The final pansharpened image is obtained by an injection model using the refined PAN image and optimized injection gains. Extensive experiments on various satellite datasets demonstrate that the proposed method offers superior spectral and spatial fidelity compared to existing state-of-the-art algorithms.
Penthiopyrad (PO), a succinate dehydrogenase inhibitor (SDHI) fungicide, poses a potential risk to fish. Here, we investigated the adverse effects of PO on endocrine regulation and reproductive ...capacity in zebrafish during a 21-d sublethal exposure to PO concentrations ranging from 0.02 to 2.00 mg/L. Following exposure to PO (0.20 and 2.00 mg/L), female-specific effects including follicle necrosis, structural disturbance of the yolk follicle, fusion of cortical follicles appeared in ovarian tissue of adult females, which led to a significant reduction in fertility. Correspondingly, 0.20 and 2.00 mg/L PO led to a marked reduction in the GSI values of females, and 2.00 mg/L PO caused a 31% decline in the proportion of perinucleolar oocytes (PCO) in oocytes. In addition, testosterone (T) level was obviously suppressed and 17β-estradiol (E2) level was increased in females after exposure to 2.00 mg/L PO. Male zebrafish treated with 0.20 and 2.00 mg/L of PO exhibited significant interstitial enlargement, edema in the testes, and reduced diameter of seminiferous tubules, along with a thinner basement membrane. The effects of PO on males were associated with significant increase in E2 level, suggesting that PO has an estrogenic effect on male fish. Greater E2 levels in serum were further supported by increased transcription levels of genes linked to the hypothalamic-pituitary-gonad-liver (HPGL) axis. Notably, transcription levels of cyp19a, er2b, era, and cyp19b was remarkably increased, exhibiting a clear link with variations in E2 levels. Overall, the present study demonstrates that PO induces reproductive impairment in zebrafish by promoting steroidogenesis.
Abstract
Condensation heat transfer in tube is widely applied in industrial production, and the heat transfer process is often weakened by non-condensable gas (NCG) in actual production. Enhancing ...condensation heat transfer is beneficial to improve production efficiency, which has always been a hot topic in current research. Foam metal material with large specific surface area and good thermal conductivity is an ideal material to enhance heat transfer. In order to study enhancement heat transfer effect and optimize structure of foam metal, this paper investigated condensation heat transfer in tube strengthened by foam metal in presence of NCG experimentally. Section shape of foam metal is annular, and the pores per inch (PPI) of foam metals is 10, 15, 20 respectively. The effects of PPI value, steam/air mixture mass flow, and NCG mass fraction on heat transfer coefficient (HTC) and flow resistance are studied. The results reveal the following: (1) Compared with smooth tube, the foam metal enhances heat transfer significantly, and HTC increases by 1.5-2.3 times. (2) At same steam/air mixture mass flow, 10PPI foam metal tube has the highest HTC compared to others. (3) With increase of NCG mass fraction and PPI value, pressure drop increases and the HTC decreases. Based on experimental data, pressure drop and HTC correlations are developed. This paper provides an important technical basis for foam metal material application in enhancement heat transfer area.
After years of treatment, the water pollution situation in the Huaihe River Basin (HRB) is still grim, and agricultural nonpoint source pollution has become the leading cause of the problem. However, ...agricultural nonpoint source pollution in the HRB is complicated due to the compounding effects of multiple factors. In this study, we first applied the export coefficient model to estimate the total nitrogen (TN) and total phosphorus (TP) loads used as two pollution source indicators in HRB. Then we constructed an index evaluation system of nonpoint source pollution risk by coupling the two source indicators with five additional indicators: rainfall erosion, river network distribution, soil erodibility, slope length, and land use. The primary source of TN and TP loads is fertilizer application (81.96%), followed by livestock and poultry breeding (16.3%) and rural domestic wastes (1.74%). The risk assessment results indicate that 66.43% of the HRB is at medium to high risk of nonpoint source pollution, 12.37% is at high risk, and 11.20% is at low risk. Moreover, the medium-to-high-risk areas are mainly concentrated in the Henan and Anhui provinces. In contrast, the medium-risk regions are mainly distributed along the mainstream of the Huaihe River. Finally, the observed water quality categories were used to verify our findings. The controlling areas of nonpoint source pollution in HRB are identified. This study could provide a scientific basis for effectively preventing and treating water pollution in the HRB.
Yellow phosphorus slag (YPS) is a typical industrial solid waste, while it contains abundant silicon micronutrient required for the growth of rice. The key scientific problem to use the YPS as rice ...fertilizer is how to activate the slag efficiently during the phosphorite reduction smelting process. In this work, an alkaline rice fertilizer from the activated YPS was successfully prepared to use the micronutrients. Thermodynamic analyses of SiO
-CaO, SiO
-CaO-Al
O
and SiO
-CaO-Al
O
-MgO systems were discussed to optimize the acidity for reduction smelting. Results showed that the reduction smelting followed by the water quenching process can realize the reduction of phosphorite and activation of YPS synchronously. Ternary acidity m(SiO
)/(m(CaO) + m(MgO)) of 0.92 is suitable for the reduction smelting and activation of the slag. After smelting, the molten YPS can be effectively activated by water quenching, and 78.28% P, 90.03% Ca, and 77.12% Si in the YPS are activated, which can be readily absorbed by the rice roots. Finally, high-strength granular rice fertilizers with a particle size of Φ2-4 mm were successfully prepared from the powdery nitrogen-phosphorus-potassium (NPK) and activated YPS mixture.
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