The roles of radiative, structural and physiological information of sun-induced chlorophyll fluorescence in predicting gross primary production of a corn crop at various temporal scales

Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2&gt;0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 % to -27.5 %. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 % at a half-hour resolution to 92.4 % at a one-month resolution, while the radiative contribution decreased from 118.0 % at a half-hour resolution to 11.7 % at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.</p

Investigation of atmospheric effects on retrieval of sun-induced fluorescence using hyperspectral imagery

Significant research progress has recently been made in estimating fluorescence in the oxygen absorption bands, however, quantitative retrieval of fluorescence data is still affected by factors such as atmospheric effects. In this paper, top-of-atmosphere (TOA) radiance is generated by the MODTRAN 4 and SCOPE models. Based on simulated data, sensitivity analysis is conducted to assess the sensitivities of four indicators—depth_absorption_band, depth_nofs-depth_withfs, radiance and Fs/radiance—to atmospheric parameters (sun zenith angle (SZA), sensor height, elevation, visibility (VIS) and water content) in the oxygen absorption bands. The results indicate that the SZA and sensor height are the most sensitive parameters and that variations in these two parameters result in large variations calculated as the variation value/the base value in the oxygen absorption depth in the O2-A and O2-B bands (111.4% and 77.1% in the O2-A band; and 27.5% and 32.6% in the O2-B band, respectively). A comparison of fluorescence retrieval using three methods (Damm method, Braun method and DOAS) and SCOPE Fs indicates that the Damm method yields good results and that atmospheric correction can improve the accuracy of fluorescence retrieval. Damm method is the improved 3FLD method but considering atmospheric effects. Finally, hyperspectral airborne images combined with other parameters (SZA, VIS and water content) are exploited to estimate fluorescence using the Damm method and 3FLD method. The retrieval fluorescence is compared with the field measured fluorescence, yielding good results (R2 = 0.91 for Damm vs. SCOPE SIF; R2 = 0.65 for 3FLD vs. SCOPE SIF). Five types of vegetation, including ailanthus, elm, mountain peach, willow and Chinese ash, exhibit consistent associations between the retrieved fluorescence and field measured fluorescence

Thermal buckling analysis of cylindrical shell with functionally graded material coating

As an excellent heat-resistant material with broad potential application, the mechanical behavior of functionally graded material (FGM) is of research focus in many fields. However, the thermal buckling behavior of FGM thin-walled shell has been widely investigated, but little work was done for the cylindrical shell with FGM coating due to more complex material distribution and mathematical expressions. Therefore, the present work mainly carried out the theoretical derivation and buckling behavior analysis for a cylindrical shell with FGM coating subjected to a thermal load. The results show that the theoretical solution of the critical buckling temperature rise is in good agreement with the developed numerical approach. In addition, an empirical engineering formula of the critical buckling temperature rise with a more concise mathematical expression is proposed for solving this practical complex engineering application based on the significant amount of numerical calculation and theoretical analysis

Sample-Specific Debiasing for Better Image-Text Models

Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One common approach involves contrasting semantically similar (positive) and dissimilar (negative) pairs of data points. Drawing negative samples uniformly from the training data set introduces false negatives, i.e., samples that are treated as dissimilar but belong to the same class. In healthcare data, the underlying class distribution is nonuniform, implying that false negatives occur at a highly variable rate. To improve the quality of learned representations, we develop a novel approach that corrects for false negatives. Our method can be viewed as a variant of debiased constrastive learning that uses estimated sample-specific class probabilities. We provide theoretical analysis of the objective function and demonstrate the proposed approach on both image and paired image-text data sets. Our experiments demonstrate empirical advantages of sample-specific debiasing

Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption

Fractionation of Asphaltenes in Understanding Their Role in Petroleum Emulsion Stability and Fouling

SARA fractionation separates crude oil into fractions of saturates (S), aromatics (A), resins (R), and asphaltenes (A) based on the differences in their polarizability and polarity. Defined as a solubility class, asphaltenes are normally considered as a nuisance in the petroleum industry mainly as a result of their problematic precipitation and adsorption at oil–water and oil–solid interfaces. Because a broad range of molecules fall within the group of asphaltenes with distinct sizes and structures, considering the asphaltenes as a whole was noted to limit the deep understanding of governing mechanisms in asphaltene-induced problems. Extended-SARA (E-SARA) is proposed as a concept of asphaltene fractionation according to their interfacial activities and adsorption characteristics, providing critical information to correlate specific functional groups with certain characteristics of asphaltene aggregation, precipitation, and adsorption. Such knowledge is essential to addressing asphaltene-related problems by targeting specific subfractions of asphaltenes

Patient and Physician Predictors of FFR/iFR Utilization in ACS and SIHD

Background Despite guidelines supporting FFR/iFR to guide PCI, these modalities remain underutilized. We sought to characterize factors associated with FFR/iFR use in patients undergoing index PCI for an acute coronary syndrome (ACS) or stable ischemic heart disease (SIHD). Methods ICD-9/10 codes were used to identify patients undergoing PCI and receiving FFR/iFR for an ACS (n=1,042,896) or SIHD (n=255,213) in a Medicare claims database from Jan. 1, 2013-June 30, 2018. Patients with functional/anatomical testing were excluded (5d prior in ACS; 60d prior in SIHD). Individuals with FFR/iFR performed 1-60 days prior to PCI were also excluded to limit analysis to non-staged procedures. Results FFR/iFR was performed the same day as PCI in 5.9% and 11.5% of patients with an ACS and SIHD, respectively. FFR/iFR was less likely to be utilized in patients that were \u3e65 years, male, and in those with diabetes, chronic kidney disease or peripheral arterial disease. Of note, a substantial proportion of physicians were non-utilizers of FFR/iFR in ACS (23.9%) and SIHD (18.6%). Use of FFR/iFR was inversely correlated with years since medical school graduation, with the lowest rate observed in physicians \u3e=31 years since graduation (Table). Conclusion This analysis highlights the underutilization of FFR/iFR. Identification of patient- and physician-factors associated with lower rates of FFR/iFR can be helpful to target areas for improvement to increase implementation of this guideline-recommended intervention