Weight Adjustment Research Articles

Mitigating subjectivity and bias in AI development indices: A robust approach to redefining country rankings

Artificial Intelligence (AI) indices have emerged to assess countries’ progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices are derived from a linear weighted sum of various criteria, employing deterministic weights. However, this approach fails to capture interactions among criteria, and the use of deterministic weights is susceptible to debate. To mitigate these issues, we conduct a methodological analysis based on Choquet integral (CI) and Stochastic Multicriteria Acceptability Analysis 2 (SMAA-2). We assess correlations between different AI dimensions and employ CI to model them. Additionally, we apply SMAA-2 to conduct a sensitivity analysis using both weighted sum and CI in order to evaluate the stability of the indices with regard to the weights. Finally, we introduce a ranking methodology based on SMAA-2, which considers several sets of weights to derive the ranking of countries. In the computational analysis, we evaluate our approach using the dataset employed in The Global AI Index, as proposed by the British news website Tortoise. The results reveal that our approach effectively mitigates bias. Furthermore, we scrutinize changes in the ranking resulting from weight adjustments and demonstrate that our proposed rankings closely align with those derived from variations in weights, indicating robustness.

Ammonia and urea metabolism in acute liver failure: A multicentre cohort study.

Ammonia is metabolized into urea in the liver. In acute liver failure (ALF), ammonia has been associated with survival. However, urea variation has been poorly studied. Observational cohort including ALF patients from Curry Cabral Hospital (Lisbon, Portugal) and Clinic Hospital (Barcelona, Spain) between 10/2010 and 01/2023. The United States ALF Study Group cohort was used for external validation. Primary exposures were serum ammonia and urea on ICU admission. Primary endpoint was 30-day transplant-free survival (TFS). Secondary endpoint was explanted liver weight. Among 191 ALF patients, median (IQR) age was 46 (32; 57) years and 85 (44.5%) were males. Overall, 86 (45.0%) patients were transplanted and 75 (39.3%) died. Among all ALF patients, following adjustment for age, sex, body weight, and aetiology, higher ammonia or lower urea was independently associated with higher INR on ICU admission (p < .009). Among all ALF patients, following adjustment for sex, aetiology, and lactate, higher ammonia was independently associated with lower TFS (adjusted odds ratio (95% confidence interval [CI]) = 0.991 (0.985; 0.997); p = .004). This model predicted TFS with good discrimination (area under receiver operating curve [95% CI] = 0.78 [0.75; 0.82]) and reasonable calibration (R2 of 0.43 and Brier score of 0.20) after external validation. Among transplanted patients, following adjustment for age, sex, actual body weight, and aetiology, higher ammonia (p = .024) or lower (p < .001) urea was independently associated with lower explanted liver weight. Among ALF patients, serum ammonia and urea were associated with ALF severity. A score incorporating serum ammonia predicted TFS reasonably well.

Amino‐containing α‐Diimine Nickel Complexes with Adjustable Electronic Properties and their Effective Use in Forming Highly Branched Polyethylene

AbstractA family of ten amino‐containing bis(arylimino)acenaphthene‐nickel (II) bromide complexes varied in the electronic properties of para‐substituents (R = Me, iPr, tBu, OMe, Cl, F, NO2) as well as two amino‐free comparators (R = Me, Cl) are synthesized in good yield from their corresponding unsymmetrical N,N’‐ligands. All organic compounds are well characterized by different analysis techniques. On activation with Me2AlCl, all nickel complexes exhibit moderate to high activities with a maximum activity up to 14.19 × 106 g (PE) mol−1 (Ni) h−1 and deliver narrow dispersed polyethylene (PDI: 1.85–2.65) with adjustable molecular weight values (Mw: 5.27–18.49 × 105 g mol−1). The presence of remote fluoro‐ group and (4‐amino‐3,5‐dimethylphenyl)(phenyl)methyl group has their own unique influence on activity due to their different electronic and steric properties. Nevertheless, the latter with higher steric hindrance results in better thermal stability of the current catalytic system. Moreover, all polyethylene is highly branched and exhibits typical characteristics of thermoplastic elastomers, especially good mechanical properties (εb up to 1175%) and elastic recovery rate (up to 86%).

Semi-Supervised One-Stage Object Detection for Maize Leaf Disease

Maize is one of the most important crops globally, and accurate diagnosis of leaf diseases is crucial for ensuring increased yields. Despite the continuous progress in computer vision technology, detecting maize leaf diseases based on deep learning still relies on a large amount of manually labeled data, and the labeling process is time-consuming and labor-intensive. Moreover, the detectors currently used for identifying maize leaf diseases have relatively low accuracy in complex experimental fields. Therefore, the proposed Agronomic Teacher, an object detection algorithm that utilizes limited labeled and abundant unlabeled data, is applied to maize leaf disease recognition. In this work, a semi-supervised object detection framework is built based on a single-stage detector, integrating the Weighted Average Pseudo-labeling Assignment (WAP) strategy and AgroYOLO detector combining Agro-Backbone network with Agro-Neck network. The WAP strategy uses weight adjustments to set objectness and classification scores as evaluation criteria for pseudo-labels reliability assignment. Agro-Backbone network accurately extracts features of maize leaf diseases and obtains richer semantic information. Agro-Neck network enhances feature fusion by utilizing multi-layer features for collaborative combinations. The effectiveness of the proposed method is validated on the MaizeData and PascalVOC datasets at different annotation ratios. Compared to the baseline model, Agronomic Teacher leverages abundant unlabeled data to achieve a 6.5% increase in mAP (0.5) on the 30% labeled MaizeData. On the 30% labeled PascalVOC dataset, the mAP (0.5) improved by 8.2%, demonstrating the method’s potential for generalization.

Improving computational efficiency in DSMC simulations of vacuum gas dynamics with a fixed number of particles per cell

The present study addresses the challenge of enhancing computational efficiency without compromising accuracy in numerical simulations of vacuum gas dynamics using the direct simulation Monte Carlo (DSMC) method. A technique termed ‘fixed particle per cell (FPPC)’ was employed, which enforces a fixed number of simulator particles across all computational cells. The proposed technique eliminates the need for real-time adjustment of particle weights during simulation, reducing calculation time. Using the SPARTA solver, simulations of rarefied gas flow in a micromixer and rarefied supersonic airflow around a cylinder were conducted to validate the proposed technique. Results demonstrate that applying the FPPC technique effectively reduces computational costs while yielding results comparable to conventional DSMC implementations. Additionally, the application of local grid refinement coupled with the FPPC technique was investigated. The results show that integrating local grid refinement with the FPPC technique enables accurate prediction of flow behaviour in regions with significant gradients. These findings highlight the efficacy of the proposed technique in improving the accuracy and efficiency of numerical simulations of complex vacuum gas dynamics at a reduced computational cost.

Adaptive analysis of the heteroscedastic multivariate regression modeling

Multivariate regression is a widely used technique for large-scale statistical applications. In this paper, we propose a novel method for the multivariate regression model, called multivariate weighted lasso (MWL). We consider the heteroscedasticity in the noise matrix and introduce an adjustable weight to calibrate the fitting residuals of different responses. The proposed method shows an advantage in complex multivariate regressions with heteroscedasticity. To implement the procedure, an efficient algorithm is provided based on the multivariate coordinate descent. We provide theoretical guarantees for the proposed method. Numerical simulations and financial applications are conducted using the proposed and other existing methods. The results indicate that the proposed method performs well in both variable selection and coefficient estimation.

Placental chronic inflammatory histopathology and fetal growth in a cohort with universal placental examination

IntroductionChronic placental inflammation is a routinely diagnosed group of placental lesions that reflect immunologic dysfunction of the mother, fetus, or both. MethodsComplete placental pathology examinations were performed for all term births at New York Presbyterian- Brooklyn Methodist Hospital from January 2010–August 2016. Diagnoses were blinded except to gestational age. CPI lesions were marked as chronic choriodeciduitis, decidual plasma cells, chronic inflammation of basal plate with anchoring villitis, and chronic villitis. ResultsIn this cohort of term pregnancies, 257 (11.6 %) males and 218 (9.8 %) females had ≥1 CPI lesions. Chronic villitis was the most common (319 or 14 %), with chronic choriodeciduitis, decidual plasma cells, and chronic inflammation of basal plate with anchoring villitis in 94 (4 %), 69 (3 %) and 170 (8 %), respectively. In males, chronic villitis was associated with lower gestational adjusted birthweight and had no association with placental weight. In females, chronic villitis was associated with lower gestational adjusted birthweight, but the effect became nonsignificant after adjustment for placental weight. DiscussionIn summary, CPI lesions’ incidence and association with birth weight vary by sex. Chronic villitis is associated with lower birthweight in females; this effect is completely mediated by placental weight. Chronic villitis showed a weak direct association of chronic villitis in males, but no association with lower placental weight in males. We suggest that differences between our results and previous publications reflect effects of sampling bias.

Stable training of probabilistic models using the leave-one-out maximum log-likelihood objective

Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. In this paper, an adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. The leave-one-out maximum log-likelihood (LOO-MLL) criterion is proposed to prevent the singular solutions that the regular MLL criterion gives rise to, and it is proven that LOO-MLL prevents these. Relying on this guaranteed robustness, the model is extended by adjustable weights for the kernels. In addition, a modified expectation–maximization algorithm is employed to accelerate the optimization speed reliably. The performance of the proposed method and models are exhibited on two power systems datasets using different statistical tests and by comparison with Gaussian mixture models. Results show that the proposed models have promising performance, in addition to their singularity prevention guarantees.

A lightweight underwater fish image semantic segmentation model based on U‐Net

AbstractSemantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real‐time performance, and imprecise location segmentation in current methods, a novel lightweight U‐Net model is proposed. The proposed model acquires more segmentation details by applying a multiple‐input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi‐scale residual structure (MRS) module is proposed to reduce parameters and compensate for the accuracy loss caused by the reduction of channels. To improve segmentation accuracy, a multi‐scale skip connection (MSC) structure is further proposed, and the convolution block attention mechanism (CBAM) is introduced at the end of each decoder level for weight adjustment. Experimental results demonstrate a notable reduction in model volume, parameters, and floating‐point operations by 94.20%, 94.39%, and 51.52% respectively, compared to the original model. The proposed model achieves a high mean intersection over union (mIOU) of 94.44%, mean pixel accuracy (mPA) of 97.03%, and a frame rate of 43.62 frames per second (FPS). With its high precision and minimal parameters, the model strikes a balance between accuracy and speed, making it particularly suitable for underwater image segmentation.

An improved weighted topology optimization lattice Boltzmann model for porous structures of advection–diffusion chemical reaction systems

An improved topology optimization lattice Boltzmann model for porous structures is proposed for advection–diffusion chemical reaction systems. Nonlinear porous medium drag models related to the mesoscale porosities and velocities are introduced for the momentum and mass transport coupled with the topology structure evolution. The evolution of the temporal-spatially varying porosity field is governed by a mesoscale porosity transfer equation. At each evolution step, the porous structure is sharpened by the porosity adjustment of a transient deviation of the objective function relative to porosity, and is slightly smoothed by the porosity diffusion simultaneously. Adjustable weights for chemical reaction, mass diffusion, kinetic energy, and viscous shears are applied for calculation of the transient deviation fields, which control the evolution speeds of the topology structures due to local chemical reaction, mass diffusion, flow convection, and interface drag, respectively. The effects of governing parameters and objective weights on structure patterns are illustrated. Three topology structure patterns with natural shapes are observed: (i) trunk shaped convective flow induced channels, (ii) fractal-branch shaped diffusion enhanced sub-channels, and (iii) leaf shaped chemical reaction fins. The trunk–branch–leaf topology structures result from the dynamic balance of the mesoscale convection, diffusion, and reaction.

Randomnet: clustering time series using untrained deep neural networks

Neural networks are widely used in machine learning and data mining. Typically, these networks need to be trained, implying the adjustment of weights (parameters) within the network based on the input data. In this work, we propose a novel approach, RandomNet, that employs untrained deep neural networks to cluster time series. RandomNet uses different sets of random weights to extract diverse representations of time series and then ensembles the clustering relationships derived from these different representations to build the final clustering results. By extracting diverse representations, our model can effectively handle time series with different characteristics. Since all parameters are randomly generated, no training is required during the process. We provide a theoretical analysis of the effectiveness of the method. To validate its performance, we conduct extensive experiments on all of the 128 datasets in the well-known UCR time series archive and perform statistical analysis of the results. These datasets have different sizes, sequence lengths, and they are from diverse fields. The experimental results show that the proposed method is competitive compared with existing state-of-the-art methods.

Sleep and aberrant eating behaviors in metabolic/bariatric presurgical candidates

BackgroundMetabolic/bariatric surgery (MBS) is the most effective treatment for obesity, yet many factors influence successful individual weight loss. Among those are a variety of health behaviors that are assessed in the process of presurgical psychological evaluations, including eating pathology and sleep disturbance (both of which are relatively common among surgical candidates). ObjectivesThis study aims to examine the relationship between sleep, binge eating, and night eating behaviors among individuals seeking MBS. SettingMedical center and private psychological practice in Mississippi. MethodsPatients (N = 311) seeking presurgical psychological evaluations for bariatric surgery completed self-report measures. Of these, 83.0% were females and 70.7% of those with ethnicity data identified as White. Average body mass index (BMI) in the sample was 46.5 (standard deviation [SD] = 8.02). Correlations between variables were calculated and examination of the indirect effect of sleep disturbance on night eating as mediated by binge eating was conducted. ResultsSleep disturbance, binge eating, and night eating were significantly associated (b = .22–.45). Mediation analysis yielded a significant indirect effect, indicating that binge eating propensity explains the relationship between impaired sleep and night eating symptoms (b = .09, standard error [SE] = .03, confidence interval [CI]: .04–.18). ConclusionsThe observed relationship between sleep disturbance, night eating, and binge eating among bariatric candidates provides implications for future research and treatment approaches. Specifically, additional attention to sleep disturbance in the presurgical assessment process and consideration of sleep hygiene as a potential target for intervention may facilitate improvements in overall health, adjustment, and sustained weight loss.

Vision transformer-based robust learning for cloth-changing person re-identification

The domain of Cloth-Changing Person Re-identification has garnered substantial attention recently owing to its pivotal role in security applications. The essence of Cloth-Changing Person Re-identification lies in acquiring information unaffected by clothing variations, such as gait and silhouette. Most studies focus on the Convolutional Neural Network models. However, main drawback of Convolutional Neural Network is the limitation of receptive field. Methods based on Convolutional Neural Network primarily focus on modeling local information, making it complex to extract the discriminative part of multiple spatial contexts. Therefore, we proposed a Vision Transformer-based Cloth-Changing Person Re-identification framework called TransRL-ReID. Specifically, we encoded an image as a sequence of patches and constructed a Vision Transformer-based framework. In addition, a module named DropAll was designed to reduce local bias by adaptive adjustment of attention weights for each patch. We conducted extensive experiments on the real-world benchmark PRCC dataset. TransRL-ReID achieves 66.7% on Rank-1 and 60.0% on mean Average Precision, outperforming previous Convolutional Neural Network-based methods. After visual analysis of the results, we found that TransReID could extract information unrelated to clothing, such as gait and silhouette, and paid less attention to clothing information. DropAll improves the model’s ability to fit pedestrian distributions.

FA-VTON: A Feature Alignment-Based Model for Virtual Try-On

The virtual try-on technology based on 2D images aims to seamlessly transfer provided garments onto target person images. Prior methods mainly concentrated on warping garments and generating images, overlooking the influence of feature alignment on the try-on results. In this study, we initially analyze the distortions present by existing methods and elucidate the critical role of feature alignment in the extraction stage. Building on this, we propose a novel feature alignment-based model (FA-VTON). Specifically, FA-VTON aligns the upsampled higher-level features from both person and garment images to acquire precise boundary information, which serves as guidance for subsequent garment warping. Concurrently, the Efficient Channel Attention mechanism (ECA) is introduced to generate the final result in the try-on generation module. This mechanism enables adaptive adjustment of channel feature weights to extract important features and reduce artifact generation. Furthermore, to make the student network focus on salient regions of each channel, we utilize channel-wise distillation (CWD) to minimize the Kullback–Leibler (KL) divergence between the channel probability maps of the two networks. The experiments show that our model achieves better results in both qualitative and quantitative analyses compared to current methods on the popular virtual try-on datasets.

A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data

Various missing values inevitably exist in the settlement monitoring data of prolonged operation of earth-rockfill dams, which delays or even interrupts the dam structure analytical procedure. This study aims to develop a reliable method for addressing the missing data problem and monitoring earth-rockfill dam settlement behavior. First, an imputation model, support vector regression based on the finite element method and time input (FEMTSVR), is proposed and offers superior performance when capturing complex nonlinear mappings from environmental variables to settlement on small sample data. Herein, an improved particle swarm optimization algorithm (IPSO) is developed, realizing the nonlinear adjustment of weights and parameter reduction during hyperparameter optimization. Then, this study establishes a sequential prediction model based on gate recurrent unit (GRU) networks to monitor dam behavior on the imputed and complete dataset. Eventually, the proposed method is evaluated using real-world earth-rockfill dam monitoring data with the help of statistical indicators, demonstrating its efficiency in monitoring settlement data subjected to large-scale missingness. This study provides a robust database for dam structural health monitoring, while also providing a promising framework for the safety assessment of other civil or hydraulic engineering based on raw monitoring data.

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

This article outlines the advancements made in broadening the application scope of the OpenMC neutron transport code to include thermohydraulic coupling and nuclear data uncertainty propagation. These developments primarily involve the incorporation of the correlated sampling (CS) technique, facilitating the propagation of thermal feedback or cross-section sampling on neutronic calculations through neutron weight adjustments. The CS technique is integrated with computer-aided-design (CAD)–based meshing and the Transient Fission Matrix (TFM) approach. Together, these components enable comprehensive handling of neutronics-thermohydraulic coupling: The TFM approach addresses neutron kinetics via precalculated neutron transport matrices, the CS technique accounts for thermal feedback impacts on the matrices, and CAD meshing defines volumes corresponding to each matrix bin to align results with computational fluid dynamics codes, specifically OpenFOAM. Implementation details and verification procedures are elaborated, alongside an analysis on existing limitations and possible perspectives.

Predicting sales and cross-border e-commerce supply chain management using artificial neural networks and the Capuchin search algorithm

E-commerce provides a large selection of goods for sale and purchase, which promotes regular transactions and commodity flows. Efficient distribution of goods and precise estimation of customer wants are essential for cost reduction. In order to improve supply chain efficiency in the context of cross-border e-commerce, this article combines machine learning approaches with the Internet of Things. The suggested approach consists of two main stages. Order prediction is done in the first step to determine how many orders each merchant is expected to get in the future. In the second phase, allocation operations are conducted and resources required for each retailer are supplied depending on their needs and inventory, taking into account each store’s inventory as well as the anticipated sales level. This suggested approach makes use of a weighted mixture of neural networks to anticipate sales orders. The Capuchin Search Algorithm (CapSA) is used in this weighted combination to concurrently enhance the learning and ensemble performance of models. This indicates that an effort is made to reduce the local error of the learning model at the model level via model weight adjustments and neural network configuration. To guarantee more accurate output from the ensemble model, the best weight for each individual component is found at the ensemble model level using the CapSA method. This method yields the ensemble model’s final output in the form of weighted averages by choosing suitable weight values. With a Root Mean Squared Error of 2.27, the suggested technique has successfully predicted sales based on the acquired findings, showing a minimum decrease of 2.4 in comparison to the comparing methodologies. Additionally, the suggested method’s strong performance is shown by the fact that it was able to minimize the Mean Absolute Percentage Error by 14.67 when compared to other comparison approaches.

Improved marine predators algorithm for engineering design optimization problems

The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.

A Retrospective Cohort Analysis of Analgosedation Requirements in COVID-19 Compared to Non-COVID-19 Extracorporeal Membrane Oxygenation Patients.

Reports have described increased sedation requirements in patients with acute respiratory distress syndrome (ARDS) while on extracorporeal membrane oxygenation (ECMO) and for intubated COVID-19 patients. Thus, the objective of this study was to assess the analgosedation requirements of COVID-19 patients receiving ECMO compared to non-COVID-19 ECMO patients. This retrospective, observational cohort study included adult patients with ARDS requiring venovenous or venopulmonary arterial ECMO admitted to a single intensive care unit from January 2017 to December 2021. Patients were categorized as COVID-19 ECMO or non-COVID-19 ECMO. The primary outcome was median daily dosing of parenteral analgosedative medications. Pertinent secondary outcomes included incidence of extubation or tracheostomy and change in sedation following tracheostomy or addition of oral agents. A total of 109 patients were evaluated; 63 COVID-19 ECMO patients and 46 non-COVID ECMO patients. The primary outcome was statistically higher in the COVID-19 compared to non-COVID-19 patients for propofol (4131.0 mg vs 2704.8 mg, P < .001), dexmedetomidine (1581.4 mcg vs 1081.3 mcg, P = .016), and parenteral morphine equivalents ([PME], 209.3 mg vs 154.1 mg, P = .027), but only propofol remained significant after adjustment for weight (31.1 mcg/kg/day vs 37.7 mcg/kg/day, P = .014). COVID-19 was significantly associated with increased propofol and PME requirements after adjustment for confounders on linear regression analysis. COVID-19 patients had more days with non-zero dose for propofol (8 days vs 7 days), dexmedetomidine (13 days vs 8.5 days), and PME (17 days vs 8.5 days). The only interventions that were associated with reductions in propofol dose were tracheostomy and antipsychotics. COVID-19 patients on ECMO had significantly longer durations and higher doses of propofol, dexmedetomidine, and parenteral opioids over the first 28 days of cannulation. The only interventions that were associated with statistical reductions in propofol were antipsychotics and tracheostomy.

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

BackgroundMediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation.MethodsIn this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing.ResultsSimulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene.ConclusionsThe proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.