•Propose a novel reliability analysis approach to handle both the input variations and surrogate model uncertainty.•Propose a smooth sensitivity analysis approach to facilitate the reliability-based ...design optimization (RBDO) process.•Propose a surrogate-based optimization framework to enable the quantification of surrogate model uncertainty in reliability assessment.
Surrogate models have been widely employed as approximations of expensive physics-based simulations to alleviate the computational burden in reliability-based design optimization. Ignoring the surrogate model uncertainty due to the lack of training samples will lead to untrustworthy designs in product development. This paper addresses the surrogate model uncertainty in reliability analysis using the equivalent reliability index (ERI) and further develops a new smooth sensitivity analysis approach to facilitate the surrogate model-based product design process. By using the Gaussian process (GP) modeling technique, a Gaussian mixture model (GMM) is constructed for reliability analysis using Monte Carlo simulations. To propagate both input variations and surrogate model uncertainty, the probability of failure is approximated by calculating the equivalent reliability index using the first and second statistical moments of the GMM. The sensitivity of ERI with respect to design variables is analytically derived based on the GP predictions. Three case studies are used to demonstrate the effectiveness and robustness of the proposed approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle and climate change in the global terrestrial ecosystem. AGB estimation based on remote sensing is an ...effective method for regional scale. In this study, Landsat 8 Operational Land Imager and Sentinel-1A data and China's National Forest Continuous Inventory data in combination with three algorithms, either the linear regression (LR), random forest (RF), or the extreme gradient boosting (XGBoost), were used to estimate biomass of the subtropical forests in Hunan Province, China. XGBoost is a scalable tree boosting system that is widely used by data scientists and provides state-of-the-art results for many problems. It can process an entire dataset with billions of examples using a minimal amount of computational resources through the particular way of cache access patterns, data compression, and data fragmentation. The results include: (1) The combination of Landsat 8 and Sentinel-1A images as predictor variables in the XGBoost model provided the best AGB estimation. (2) In contrast to the LR method, the F-test results indicated that a significant improvement in AGB estimation was achieved with the RF and XGBoost algorithms. (3) The effect of parameter optimization was found to be more significant on XGBoost than on RF. (4) The XGBoost model is an effective method for AGB estimation and can reduce the problems of overestimation and underestimation. This research provides a new way of estimating AGB for the subtropical forest based on remote sensing through the synergy of different sensors datasets and modeling algorithms.
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•2D/2D Z-scheme heterojunctions of CuInS2/g-C3N4 were synthesized through a facile solvothermal method.•The heterojunctions exhibit superior photocatalytic activity in comparison of ...pure CuInS2/g-C3N4.•The photocatalytic mechanism of CuInS2/g-C3N4 heterojunctions was discussed in detail.
Photocatalysis is one of the most promising technologies owing to its great potential to relieve energy and environmental issues. Constructing Z-scheme heterojunction photocatalyst with strong redox ability to make for enhanced light absorption and efficient charge separation is extremely attractive but still underdeveloped. Herein, Z-scheme heterojunction of CuInS2/g-C3N4 with a “sheet-on-sheet” hierarchical structure showing enhanced photocatalytic performance for the tetracycline (TC) degradation under visible-light irradiation has been developed. This 2D/2D hierarchical structure of CuInS2/g-C3N4 composite not only enlarges the contact region in the heterojunction interface but also provides more active reaction sites, as demonstrated by the TEM and BET analyses. Particularly, 50 wt% CuInS2/g-C3N4 heterojunction shows the highest photocatalytic activity (20 mg/L; 83.7% degradation within 60 min), which the apparent rate constant is 15 and 11 times higher than that of pure g-C3N4 and CuInS2 nanosheets, respectively. The remarkably enhanced photocatalytic activity mainly derives from the synergistic effect between CuInS2 and g-C3N4, thereby promoting the charge separation during the photocatalytic reaction. This work is expected to provide a design idea to construct multifunctional 2D/2D nanocomposites for photocatalytic applications.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•The 2D/2D CPO-CN heterostructure was prepared by a facile precipitation method.•The introduction of CPO nanosheets broadens the light absorption range of pure CN.•2D/2D ...heterostructure can provide more contact areas to promote the interfacial charge transfer.
The photocatalytic overall water splitting is an ideal pathway to generate hydrogen gas for sustainable energy production. Herein, the 2D/2D Co3(PO4)2/g-C3N4 heterojunctions were successfully synthesized for overall water splitting from pure water by a simple direct precipitation route under the effect of coulomb electrostatic interaction. The introduction of Co3(PO4)2 nanosheets broadens the light absorption range of Co3(PO4)2/g-C3N4 heterojunction, and in addition, the unusual 2D/2D heterostructure can provide more contact areas to promote the interfacial charge transfer between g-C3N4 nanosheets and Co3(PO4)2 nanosheets, which considerably enhances the photogenerated charge separation. Among the Co3(PO4)2/g-C3N4 heterojunctions, 35% Co3(PO4)2/g-C3N4 exhibits the optimal H2 and O2 evolution rates which are 375.6 and 177.4 μmol g−1 h−1, respectively. Moreover, the apparent quantum efficiency of the 35% Co3(PO4)2/g-C3N4 reaches up to 1.32% at 420 nm. Furthermore, the 2D/2D Co3(PO4)2/g-C3N4 composite possesses the prominent stability and recyclability, testifying a potential application for the conversion of the sustainable energy.
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•RM-CN composite was directly prepared via one-step thermal polymerization method.•RM-CN has good catalytic activity under the synergistic effect of adsorption and photocatalysis.•The specific ...surface area of CN has been significantly enhanced with the introduction of RM.•The degradation mechanism of RM-CN was proposed based on capturing experiments and ESR technique.
The red mud/g-C3N4 (RM-CN) composite was directly prepared via one-step thermal polymerization method using melamine and industrial waste residue (red mud) as raw materials. Under the synergistic effect of adsorption and photocatalysis, RM-CN composites have a significant effect for the removal of organic pollutants from wastewater. Compared with pure CN, the specific surface area of RM-CN has been significantly enhanced with the introduction of RM. Additionality, the optical absorption and photocurrent response exhibit obvious enhancement with respect to RM-CN as compared with that of pristine CN photocatalyst. The optimized 0.8% RM-CN product (with the RM mass content of 0.8 wt% in precursor) displayed referable photocatalytic degradation efficiency for antibiotics and dyes under visible light, as well as excellent recycle performance. This work poses a great potential in the actual wastewater treatment, and it is of great significance to the resource utilization and the cost saving of the raw material.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•An FFT-based deep feature learning method has been developed for EEG classification.•In this study, The FFT is combined with the deep PCANet in a novel way to learn the distinctive information of ...EEG signals.•We have studied the effects of all the PCANet parameters to find an optimal solution for EEG analysis.•The proposed framework is fully automated and can be easily implemented as an intelligent application.
This study introduces a new method for electroencephalogram (EEG) signal classification based on deep learning model, by which relevant features are automatically learned in a supervised learning framework. The fast Fourier transform (FFT) has been applied in a novel way to generate the EEG matrix. And a PCA neural network (PCANet) is designed to learn the hidden information from the frequency matrix of EEG signals. And these deep features are then given as inputs to train a support vector machine (SVM) for recognition of epileptic seizures. The experiments are carried out with two authoritative databases provided by the Bonn University (Database A) and Children’s Hospital in Boston (Database B), relatively. Additionally, we have evaluated the influence of all parameters for the proposed scheme to obtain the optimal model with better generalization and expansibility. The proposed feature learning method concerned in this work is proved very useful to distinguish seizure events from both short and long EEG recordings. Experimental results obtained by analyzing Database A are not less than 99% accuracy in seven problems. The effectiveness is also verified on Database B with an average accuracy of 98.47% across 23 patients. Our FFT-based PCANet not only achieves the satisfied results, but also exhibits better stability across different classification cases or patients, which indicates the worth in practical applications for diagnostic reference in clinics.
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Climate change significantly influences changes in ecological phenomena and processes, such as species distribution and phenology, thus accelerating the rate of species extinction or prosperity. ...Climate change is considered to be one of the most important threats to global biodiversity in the 21st century and will pose significant challenges to biodiversity conservation in the future. The use of niche modelling to predict changes in the suitable distribution of species under climate change scenarios is becoming a hot topic of biological conservation. In this study, we use data from China’s National Forest Continuous Inventory as well as specimen collection data of Cunninghamia lanceolata (Lamb.) Hook to run optimized Maxent models to predict potential suitable distribution of the species in the present day, 2050s, and 2070s under different climate change scenarios in China. In the modeling process, the most important uncorrelated variables were chosen, and the sample-size-adjusted Akaike information criterion (AICc) was used to select the optimal combination of feature type and regularization multiplier. Variable selection reduced the number of variables used and the complexity of the model, and the use of the AICc reduced overfitting. Variables relating to precipitation were more important than temperature variables in predicting C. lanceolata distribution in the optimal model. The predicted suitable distribution areas of C. lanceolata were different for the different periods under different climate change scenarios, with the centroids showing a degree of northward movement. The suitable distribution area is predicted to become more fragmented in the future. Our results reveal the climate conditions required for the suitable distribution of C. lanceolata in China and the likely changes to its distribution pattern in the future, providing a scientific basis for the sustainable management, protection, and restoration of the suitable habitat of this economically important tree species in the context of climate change.
•This work provides a facile bottom-up strategy for improving the performance of CN.•The doping of Cl element is helpful to promote the rapid separation of CN excited electrons from holes.•The ...prepared CN-Cl samples have larger specific surface area than pure CN.
Utilizing highly efficient and stable photocatalysts to treat residual antibiotic pollutants in water is of great significance. In this work, Cl-doped porous g-C3N4 (CN-Cl) was prepared by a facile bottom-up synthetic route for photocatalytic degradation of tetracycline (TC). The synthesized samples were analyzed by a series of characterization methods. The optimum photocatalytic activity under visible-light irradiation for CN-Cl with the precursor mass ratio of ammonium chloride to melamine is 1:1 (92% degradation within 120 min), which is up to 2.4 times as high as that of pure CN. The remarkable improvement of the photocatalytic activity of CN-Cl is mainly due to the following three reasons: (i) Cl-doped element can help to regulate the electronic structure of CN; (ii) the prepared CN-Cl samples have larger specific surface area than pure CN, thus providing more reactive sites; (iii) Cl-doped element can inhibit the recombination of photo-induced electron and holes of CN. This work may provide a facile bottom-up strategy to improve the photocatalytic performance of CN for the mitigation of environmental problems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We herein demonstrate the unusual effectiveness of two strategies in combination to enhance photoelectrochemical water splitting. First, the work function adjustment via molybdenum (Mo) doping ...significantly reduces the interfacial energy loss and increases the open-circuit photovoltage of bismuth vanadate (BiVO
) photoelectrochemical cells. Second, the creation and optimization of the heterojunction of boron (B) doping carbon nitride (C
N
) and Mo doping BiVO
to enforce directional charge transfer, accomplished by work function adjustment via B doping for C
N
, substantially boost the charge separation of photo-generated electron-hole pairs at the B-C
N
and Mo-BiVO
interface. The synergy between the above efforts have significantly reduced the onset potential, and enhanced charge separation and optical properties of the BiVO
-based photoanode, culminating in achieving a record applied bias photon-to-current efficiency of 2.67% at 0.54 V vs. the reversible hydrogen electrode. This work sheds light on designing and fabricating the semiconductor structures for the next-generation photoelectrodes.
Esophageal squamous cell carcinoma (ESCC) is the sixth most common cause of cancer-related mortality worldwide, with more than half of them occurred in China. Radiotherapy (RT) has been widely used ...for treating ESCC. However, radiation-induced DNA damage response (DDR) can promote the release of cytokines and chemokines, and triggers inflammatory reactions and changes in the tumor microenvironment (TME), thereby inhibiting the immune function and causing the invasion and metastasis of ESCC. Radioresistance is the major cause of disease progression and mortality in cancer, and it is associated with heterogeneity. Therefore, a better understanding of the radioresistance mechanisms may generate more reversal strategies to improve the cure rates and survival periods of ESCC patients. We mainly summarized the possible mechanisms of radioresistance in order to reveal new targets for ESCC therapy. Then we summarized and compared the current strategies to reverse radioresistance.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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