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Background: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis. Methods: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation "cross-world" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH). Results: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions. Discussion: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods. [ABSTRACT FROM AUTHOR]

Machine learning models perform satisfactorily in landslide displacement prediction, but they are generally black-box models that are difficult to gain the trust of decision-makers. Therefore, a three-stage prediction framework based on the Hodrick–Prescott (HP) filter, double exponential smoothing (DES), natural gradient boosting (NGBoost), and Shapley additive explanations (SHAP) was proposed. The framework quantifies the uncertainty in the predictions and provides fully transparent outputs. In the first stage, the HP filter decomposes cumulative displacements into trend and period displacements. The second stage uses DES and NGBoost to predict them separately. In the third stage, we compute the SHAP values of the features to analyze the impact of the features on the model output. It is applied to the Bazimen and Baishuihe landslides in the Three Gorges Reservoir area and compared with other literature. The results show that the framework can achieve high accuracy in both point and interval prediction, and its performance is similar to or even better than other models. And it is easier to operate and applicable to a wider range of people. Most importantly, the framework can interpret the model, allows users to verify the consistency of the model prediction basis with the landslide evolution mechanism, and reduces random errors by optimizing the data. Overall, the prediction framework can capture the complex nonlinear relationships between features, solving the current challenge of machine learning models that cannot be applied in the final optimization scenario due to the lack of convincing, and providing a reliable tool for intelligent disaster prevention. [ABSTRACT FROM AUTHOR]

Phosphogypsum, which contains toxic components (e.g., heavy metal elements and fluoride), is one of the byproducts of phosphoric acid production, and filling subgrade is one of the recycling methods for it. In this study, phosphogypsum was stabilized by lime to improve the mechanical properties [California bearing ratio (CBR), resilience modulus, unconfined compressive strength, and shear strength], water stability, and harmful substances dissolubility. Combined with scanning electron microscopy, the strength formation and water stability enhancement mechanism of lime-stabilized phosphogypsum (LSP) were explored. The results demonstrated that the mechanical properties of LSP were better with the lime content of 6%–10%. The CBR, resilience modulus, unconfined compressive strength, and shear strength were 3.35 times, 2.46 times, 8.61 times, and 1.39 times that of plain phosphogypsum, respectively. An intensity prediction model with a correlation of 97% was constructed. The CBR and resilience modulus softening coefficient of LSP reached best values when lime content was 6%–8%. The leaching concentration of arsenic, chromium, and lead of LSP with 2% lime met the quality standards of groundwater levels I, II, and IV, respectively. Fluoride and phosphate were not detected in LSP when lime content was greater than 6.0%. The results show that LSP is feasible as subgrade filler. Considering the mechanical properties, water stability, and dissolution of hazardous substances of LSP, it is recommended to add 6%–8% lime content to LSP as highway subgrade filler. [ABSTRACT FROM AUTHOR]

Herein we synthesized biochar/iron oxide composite (C/α‐Fe2O3/γ‐Fe2O3) by two‐step calcination method. The prepared C/α‐Fe2O3/γ‐Fe2O3 had a highly pure and a concentration ratio of α‐Fe2O3 and γ‐Fe2O3 about 1 : 1. The optical properties of as‐synthesized C/α‐Fe2O3/γ‐Fe2O3 show a lower bandgap energy (1.90 eV) than that of pure C/α‐Fe2O3 (2.02 eV). The effects of different biochar composite catalysts on the removal of amino black were investigated. Upon illumination, the total removal rate of amino black dye by C/α‐Fe2O3/γ‐Fe2O3 catalyst reached 98 %, which was about 1.7 times that of C/α‐Fe2O3. In addition, the coercivity and the remanence intensity of C/α‐Fe2O3/γ‐Fe2O3 is respectively 1.81 times and 7.17 times that of C/α‐Fe2O3. The catalyst could be magnetically separated and reused for five times with a slight loss of its reactivity. The study of biochar composite provides a new direction for the removal of toxic organic dye in wastewater. [ABSTRACT FROM AUTHOR]

Liver fibrosis (LF) is a common reversible consequence of chronic liver damage with limited therapeutic options. Yinchen Gongying decoction (YGD) composed of two homologous plants: (Artemisia capillaris Thunb , Taraxacum monochlamydeum Hand.-Mazz.), has a traditionally application as a medicinal diet for acute icteric hepatitis. However, its impact on LF and underlying mechanisms remain unclear. This study aims to assess the impact of YGD on a carbon tetrachloride (CCl 4) induced liver fibrosis and elucidate its possible mechanisms. The study seeks to establish an experimental foundation for YGD as a candidate drug for hepatic fibrosis. LC-MS/MS identified 11 blood-entry components in YGD, and network pharmacology predicted their involvement in the FoxO signaling pathway, insulin resistance, and PI3K-AKT signaling pathway. Using a CCl 4 -induced LF mouse model, YGD's protective effects were evaluated in comparison to a positive control and a normal group. The underlying mechanisms were explored through the assessments of hepatic stellate cells (HSCs) activation, fibrotic signaling, and inflammation. YGD treatment significantly improved liver function, enhanced liver morphology, and reduced liver collagen deposition in CCl 4 -induced LF mice. Mechanistically, YGD inhibited HSC activation, elevated MMPs/TIMP1 ratios, suppressed the FoxO1/TGF-β1/Smad2/3 and YAP pathways, and exhibited anti-inflammatory and antioxidant effects. Notably, YGD improved the insulin signaling pathway. YGD mitigates LF in mice by modulating fibrotic and inflammatory pathways, enhancing antioxidant responses, and specifically inhibiting FoxO1/TGF-β1/Smad2/3 and YAP signal pathways. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Catalytic methane/air combustion in a micro-channel was parametrically studied. • Operating parameters correlate with the heterogeneous and heterogeneous reactions. • The heterogeneous reactions are insensitive to the inlet velocity. • The homogeneous reaction is enhanced at the increased inlet temperature. • Increasing the equivalence ratio moves the combustion zone upstream. Micro-combustion is a promising technology for addressing global energy consumption challenges; however, its practical implementation is hindered by a limited understanding of the underlying reaction characteristics. To address this, a comprehensive numerical investigation was conducted using a well-established two-dimensional (2D) steady model to examine the influence of key operating parameters (e.g., inlet velocity, equivalence ratio, and inlet temperature) on catalytic methane/air combustion in micro-channels. The results show that the heterogeneous reaction exhibits limited sensitivity to changes in inlet velocity, whereas the homogeneous reaction shows substantial enhancement with increasing inlet temperature. With the increment of inlet velocity, the peak value of stage III- heterogeneous ignition (conversion of CO to CO 2 and H 2 to H 2 O) decreases and levels off once u in ≥ 0.50 m/s. Simultaneously, the minimum value of S (the ratio of heat released by the heterogeneous reaction to that of the total reaction) decreases as well. As the equivalence ratio is increased, the combustion zone shifts towards the upstream direction. The value of S initially increases and then decreases, reaching a maximum of 17.61 % at Φ = 0.95. Raising the inlet temperature results in the upstream movement of the flame, accompanied by reduced combustion heat release, lower combustion temperature, and diminished intensity of homogeneous combustion. The influence of the heterogeneous reaction on combustion is more pronounced under operating conditions with lower inlet velocities, an equivalence ratio approaching 0.95, and higher inlet temperature. Additionally, under the condition of higher inlet velocities, an equivalence ratio close to 1.0, and elevated inlet temperature, the heterogeneous reaction aids in igniting the homogeneous reaction more effectively. [ABSTRACT FROM AUTHOR]