Comprehensive Sensitivity Analysis Research Articles

Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions

The study of physically-based rainfall interception is crucial for comprehending the water balance within forest ecosystems and the contribution of vegetation to the hydrological cycle, particularly in arid/semi-arid ecosystems. Despite its importance, there is a lack of comprehensive sensitivity analysis and parameter optimization, resulting in uncertain or suboptimal predictive accuracy. To mitigate these shortcomings, this research involved the establishment and assessment of three quintessential forest canopy interception models namely, the power Návar model, the reformulated Gash model, and the Liu model, within semi-arid forest environments at two different elevations. A global sensitivity analysis conducted on the three physical models indicated that the canopy saturation point and the mean rainfall intensity required for canopy saturation were the parameters to which the reformulated Gash and Liu models were most sensitive when applied to high-altitude settings. Conversely, for the Návar model, the most sensitive parameters were the interception coefficient of the linear equation, and the parameters of the power equation k and c. The quantification indices of model sensitivity exert a certain influence on the ranking of parameter sensitivities. However, for models with a limited number of parameters, the impact of these results is constrained. Conversely, the identification and utilization of characteristics specific to the parameter tuning process can significantly enhance the efficiency of model calibration. The three models employed by the research institute have all demonstrated commendable performance in modeling the canopy interception process of subalpine P. crassifolia in arid, high-altitude regions, achieving a "good" rating with Nash-Sutcliffe Efficiency values exceeding 0.7. In practical applications, we recommend giving priority to the use of the Liu model. The findings of this study provide a reference for model selection, sensitivity analysis, parameter calibration, and model evaluation in the context of extensive canopy interception modeling in arid areas with significant altitudinal variation. This constitutes an important theoretical support for the refined modeling of hydrological processes in high-altitude forests within arid zones.

A Comparative Study of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling

Lithium-ion battery (LIB) with high specific energy density and long cycle life is one widely used energy storage technology. In order to ensure longevity and safety of the battery system, a battery management system (BMS) that relies on battery model is required. With the development of BMS technique, more attention has been given to electrochemical models that provide insights into the battery internal states. In general, battery electrochemical models consist of partial differential equations (PDEs) describing the thermodynamic and electrochemical process inside the cell, and those PDEs can only be solved numerically due to their complexity.Existing battery simulation software utilize numerical methods such as finite difference method (FDM), finite volume method (FVM), and finite element method (FEM) to solve PDEs in battery models. However, there are little discussions for the selection of a specific numerical methods. In our recent study [1], we compare two spatial discretization methods commonly used to numerically solve the governing PDEs of LIB electrochemical models, namely FDM and FVM, in terms of model accuracy and mass conservation guarantee. First, we provide the mathematical details of the spatial discretization process for both FDM and FVM to solve the battery single particle model (SPM), this result has not been shown in any publication before. Second, we propose a new Hermite extrapolation based FVM scheme leading to higher accuracy when compared to the normally used linear extrapolation based FVM scheme. Then, SPM parameters are identified using experimental data, and a comprehensive parameter sensitivity analysis is conducted under different current input profiles to study parameter identifiability. Finally, model accuracy and mass conservation analysis of the FDM and FVM schemes are presented. Our study shows that the FVM scheme with Hermite extrapolation leads to accurate and robust control-oriented battery model while guaranteeing mass conservation and high accuracy. Also, the proposed new FVM scheme can be extended to solve other battery models, such as SPM model with electrolyte and Doyle-Fuller-Newman model. Moreover, we provide findings of mass conservation analysis for FDM and FVM schemes, which we hope can facilitate BMS model selection.

The Circular Path Towards Decarbonising the Li-Ion Battery Value Chain

The rapid transition to clean energy and electric mobility has led to an unprecedented demand for Li-ion batteries (LIBs), necessitating a comprehensive decarbonisation of their entire value chain to meet climate goals [1]. This talk takes a holistic view at the LIB production and recycling system to explore cradle-to-cradle decarbonisation strategies. The Life Cycle Assessment (LCA) framework is used to quantify the comparative importance of developments in battery manufacturing, raw material sourcing and battery recycling in reducing greenhouse-gas emissions (GHG) arising from the globally-distributed LIB value chain.On the upstream side, sourcing low-carbon materials is found to reduce the GHG footprint of LIB manufacturing by up to a factor of 4, depending on raw material type and source, and battery chemistry [2]. The geographical links between battery Gigafactory location and GHG emissions are also explored, revealing substantial differences between US states, Chinese provinces and European countries, traced to the carbon intensity of electricity generation [2]. On the downstream side, hydrometallurgical and pyrometallurgical treatment options for end-of-life LIBs are shown to achieve a GHG benefit of up to 40%, when recovered materials are circulated back to LIB production [3].The battery production and recycling LCA models are integrated to present a comprehensive sensitivity analysis on the effectiveness of LIB recycling in reducing GHG emissions. This is shown to depend on battery chemistry, with lithium iron phosphate material recovery achieving lower benefits, but also on the geographical specificity of key operations, directly affecting the type of materials being displaced through secondary routes. The talk concludes with an outlook to 2035, presenting scenarios to reduce the GHG emissions of the battery value chain through technological developments, improvements in manufacturing and the establishment of circularity.

Impact of power outages depends on who loses it: Equity-informed grid resilience planning via stochastic optimization

This research presents a novel approach for enhancing power grid resilience with a focus on social equity in light of increasing natural disasters. Utilizing a two-stage stochastic optimization model for flood mitigation investments, we optimize substation hardening and power flow decisions to minimize the weighted combination of expected load shed and expected equity metric. Our method uniquely approximates community power outages stemming from transmission grid disruptions, sidestepping the complexities of the distribution grid, and it incorporates two equity metrics (affected population and duration of loss) to address the uneven postdisaster well-being loss across communities. Our approach’s novelty lies in integrating this equity-informed resilience model with realistic flood scenario generation and utilizing a large-scale synthetic but a realistic power grid of Texas. The findings highlight the importance of the composite objective function in altering power flow decisions to prioritize electricity provision and save communities in disadvantaged areas even without investing in substation hardening. The results also quantify the equity and load shed benefits of substation hardening as a function of the investment budget with a parameterized analysis. With an attention to equity, power outages increase in nonvulnerable communities — a trade-off made to mitigate well-being loss in the most vulnerable areas. We further explore a justice model inspired by the government’s Justice40 initiative but find it less effective than our equity-informed models at preventing well-being loss. Our research, enriched by a comprehensive sensitivity analysis, offers valuable insights for policymakers, grid operators, and utilities aiming for a more resilient and equitable power grid.

Quantitative evaluation of uncertainty and interpretability in machine learning-based landslide susceptibility mapping through feature selection and explainable AI

Landslide susceptibility mapping (LSM) is essential for determining risk regions and guiding mitigation strategies. Machine learning (ML) techniques have been broadly utilized, but the uncertainty and interpretability of these models have not been well-studied. This study conducted a comparative analysis and uncertainty assessment of five ML algorithms—Random Forest (RF), Light Gradient-Boosting Machine (LGB), Extreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM)—for LSM in Inje area, South Korea. We optimized these models using Bayesian optimization, a method that refines model performance through probabilistic model-based tuning of hyperparameters. The performance of these algorithms was evaluated using accuracy, Kappa score, and F1 score, with accuracy in detecting landslide-prone locations ranging from 0.916 to 0.947. Among them, the tree-based models (RF, LGB, XGB) showed competitive performance and outperformed the other models. Prediction uncertainty was quantified using bootstrapping and Monte Carlo simulation methods, with the latter providing a more consistent estimate across models. Further, the interpretability of ML predictions was analyzed through sensitivity analysis and SHAP values. We also expanded our investigation to include both the inclusion and exclusion of predictors, providing insights into each significant variable through a comprehensive sensitivity analysis. This paper provides insights into the predictive uncertainty and interpretability of ML algorithms for LSM, contributing to future research in South Korea and beyond.

Autothermal downdraft oxy-steam co-gasification of biomass and plastic wastes for hydrogen-rich syngas production: An Aspen plus modelling

The potential of H2-rich syngas generation using downdraft co-gasification of biomass and plastic wastes is investigated using an autothermal restricted equilibrium study using Aspen Plus. A comprehensive sensitivity analysis is carried out to impart the influence of important process parameters, viz. equivalence ratio(ER: 0.1–0.5), feed plastic content(FPC: 0–30 %), steam to feed ratio(SFR: 0–4) and steam temperature(150–1000 °C) on performance parameters, viz. syngas composition, gasification temperature(GT), lower heating value(LHV), cold gas efficiency(CGE), gasification energy efficiency(GEE) and hydrogen energy efficiency(HEE). The ER and SFR showed dominant effects on performance. The increase in ER from 0.1 to 0.5 causes an increase in GT from 565 °C to 1734 °C. The highest syngas H2 content and HEE of 52.1 % and 36.9 %, respectively, are obtained for 0.25 ER. The increase in SFR improves the syngas H2 content at the cost of LHV and GEE, with the highest HEE of 36.9 % obtained at 0.25 ER. The analysis of variance(ANOVA) and response surface methodology(RSM) has also been studied along with the multi-objective optimisation(MOO).

A multi-dimensional sensitivity analysis approach for evaluating the robustness of renewable energy sources in European countries

The necessity of making reliable decisions is exceptionally important in the field of sustainable development, particularly when evaluating the management of renewable energy sources. A thorough analysis becomes imperative in complex decision problems where various factors influence outcomes. Multi-Criteria Decision Analysis (MCDA) methods have emerged as valuable tools for addressing such challenges, enabling decision-makers to navigate through conflicting criteria and make informed choices. Combined with sensitivity analysis approaches, comprehensive assessments can be achieved, ensuring the robustness of decision-making processes. To increase the reliability of the results, different aspects of input data fluctuations should be examined to provide a broader view of the stability of the results.This paper proposes a comprehensive multi-dimensional sensitivity analysis approach to assess the robustness of renewable energy source (RES) development in selected European countries. By evaluating RES management in terms of electricity and energy consumption and generation, the study addresses key components of sustainable development. It offers a holistic perspective on result reliability and stability by analyzing five sensitivity dimensions: a comparative analysis of four MCDA methods, varying criteria weights scenarios, probabilistic modifications, criteria relevance identification, and ranking stability. This approach enhances decision-making in sustainable energy development, providing valuable insights for policymakers on prioritizing sustainable technologies. The provided open-source implementation promotes transparency and accessibility in decision support systems.

Appraising the effectiveness of immune cells on thyroid cancer: a Mendelian randomization study.

The intricate interplay between the immune system and tumor plays a pivotal role in thyroid cancer (TC) pathogenesis, potentially influencing both the causation and therapeutic outcomes. Despite extensive research, existing literature offers ambiguous insights regarding the association between immune cell traits and thyroid cancer progression. To elucidate the potential causal relationships, we conducted an integrated two-sample Mendelian randomization (MR) analysis. This study utilized publicly genetic datasets to explore the causalities between 731 immune cell traits (categorized into four trait types across seven panels) and thyroid cancer. We ensured the robustness of our findings through comprehensive sensitivity analyses, meticulously assessing potential sources of bias such as pleiotropy. After False Discovery Rate (FDR) correction, six immune cell traits were identified to be significantly associated with thyroid cancer risk (Inverse Variance Weighted, IVW): Absolute count of gamma delta T cells/ T-cell receptor gamma delta absolute count (TCRgd AC) 0.8464 (OR95% CI = 0.7477-0.9580, P = 0.0083, PFDR = 0.0103); CD8 on bright CD8 cells (CD8 on CD8br) 0.8867 (OR95% CI = 0.8159-0.9637, P = 0.0047, PFDR = 0.0093); CD127 on CD45RA negative CD4 T cells not regulatory T cells (CD127 on CD45RA- CD4 not Treg) 0.8969 (OR95% CI = 0.8192-0.9820, P = 0.0186, PFDR = 0.0186); CD80 on CD62L positive plasmacytoid dendritic cells (CD80 on CD62L+ plasmacytoid DC) 1.1091 (OR95% CI = 1.0267-1.1982, P = 0.0086, PFDR = 0.0103); CD80 on plasmacytoid DC 1.1283 (OR95% CI = 1.0462-1.2168, P = 0.0017, PFDR = 0.0093); Side scatter-area on bright CD8 cells (SSC - A on CD8br) 1.1622 (OR95% CI = 1.0507-1.2854, P = 0.0035, PFDR = 0.0093). Our study demonstrated the causalities between immune cell traits and thyroid cancers by Mendelian randomization study, thus guiding future mechanism studies.

Assessing the economic-statistical performance of an attribute SVSSI-np control chart based on genetic algorithms

The SVSSI scheme, featuring three variable sample sizes and two variable sampling intervals, considerably enhances the performance of control charts in detecting shifts. As a novel contribution, this study proposes an optimized design of the SVSSI-np control chart using genetic algorithms, illustrating its superior performance over other sampling schemes by considering economic and statistical perspectives in a unified model. The outcomes of implementing the SVSSI alongside other sampling schemes are assessed via a numerical example, demonstrating the influence of varying parameters such as process failure rate, shift value, and proportion of nonconforming items. Based on the obtained results, the SVSSI scheme outperforms other schemes by achieving over a 10% reduction in costs and over a 30% improvement in reducing time to signal and false alarm rates in most runs. The impact of inaccurately estimating the shift size is also investigated, revealing that incorrect estimates can lead to ineffective adjustments and improvements. Finally, optimal SVSSI designs are explored through comprehensive sensitivity analysis and robust optimization to address uncertainty, and the results of applying fractional factorial design are statistically analyzed. In summary, the SVSSI-np control chart demonstrates potential for enhanced cost reduction, process monitoring, and efficiency gains.

Ensemble-based sensitivity analysis of track forecasts of typhoon In-fa (2021) without and with model errors in the ECMWF, NCEP, and CMA ensemble prediction systems

Ensemble-based sensitivity analysis (ESA) has been widely applied to identifying and investigating the sources of forecast uncertainty in tropical cyclone (TC) track. The standard ESA used in most preceding studies involves calculating the time-lagged covariance of ensemble perturbations by removing the ensemble mean. This method primarily focuses on the influence of initial errors. However, such studies ignore two critical dimensions of ESA. One is how ensemble sensitivity is influenced by the varying forecast performance across different ensemble prediction systems (EPSs). Secondly, the impact of model errors on forecast uncertainties remains unaddressed. An in-depth examination of these two aspects provides a more comprehensive understanding of the factors contributing to TC track uncertainties.This study employed the standard ESA to analyze and compare the sources of uncertainty in the track forecast of Typhoon In-fa (2021) in three representative operational EPSs. Our findings reveal that the EPS's specific performance in ensemble spread markedly influence ensemble sensitivity. We identified that variations in the shape and location of key synoptic systems, such as the western Pacific subtropical high and monsoon trough, across ensemble members were notably distinct. These variations played a significant role in shaping the uncertainty for In-fa's track forecast within each system. Furthermore, we introduced a modified ESA to better account for the influence of model errors on TC track uncertainties. The modified ESA, when applied to ensembles with substantial systematic deviations, predominantly reflects the impact of model errors on track forecast inaccuracies, offering a notably different perspective compared to the standard ESA. Significance statementEnsemble-based sensitivity analysis (ESA) serves as an effective method for identifying the origins of forecast uncertainty in tropical cyclone (TC) tracks. Most preceding studies based on the standard ESA have predominantly concentrated on examining key physical processes within individual ensemble prediction system (EPS) and the impact of initial condition uncertainties. They often overlook the dependence of the ensemble sensitivity on the varying forecast performance of EPSs, as well as the impact of model errors on sensitivity assessment. This study revealed that the ensemble forecasts across different EPSs exhibit different performance in terms of ensemble spread in the shape and location of primary weather systems. This variability significantly contributes to the uncertainty in TC track forecasts through diverse processes. Furthermore, this research introduced a modified ESA, which effectively identifies the impact of model systematic deviations on TC track forecast errors. This approach along with the standard ESA provide a more comprehensive and nuanced analysis of the forecast error sensitivity associated with both initial condition and model related uncertainties.

Causal association between skin cancer and immune cells: mendelian randomization (MR) study

BackgroundNumerous meta-analyses and clinical studies have shown that subtypes of immune cells are associated with the development of skin cancer, but it is not clear whether this association is causal or biased. Mendelian randomization (MR) analysis reduces the effect of confounding factors and improves the accuracy of the results when compared to traditional studies. Thus, in order to examine the causal relationship between various immune cell and skin cancer, this study employs two-sample MR.MethodsThis study assesses the causal association between 731 immune cell characteristics and skin cancer using a two-sample Mendel randomization (MR) methodology. Multiple MR methods were used to bias and to derive reliable estimates of causality between instrumental variables and outcomes. Comprehensive sensitivity analyses were used to validate the stability, heterogeneity and horizontal multiplicity of the results.ResultsWe discovered that potential causal relationships between different types of immune cells and skin cancer disease. Specifically, one type of immune cell as potentially causal to malignant melanoma of skin (MM), eight different types of immune cells as potentially causal to basal cell carcinoma (BCC), four different types of immune cells as potentially causal to actinic keratosis (AK), and no different types of immune cells were found to have a potential causal association with squamous cell carcinoma(SCC), with stability in all of the results.ConclusionThis study demonstrates the close connection between immune cells and skin cancer disease by genetic means, which enriches the current knowledge about the role of immune cells in skin cancer and also contributes to the design of therapeutic strategies from an immunological perspective.

Causal effects of denture wearing on epigenetic age acceleration and the mediating pathways: a mendelian randomization study

BackgroundThe epigenetic-age acceleration (EAA) represents the difference between chronological age and epigenetic age, reflecting accelerated biological aging. Observational studies suggested that oral disorders may impact DNA methylation patterns and aging, but their causal relationship remains largely unexplored. This study aimed to investigate potential causal associations between dental traits and EAA, as well as to identify possible mediators.MethodsUsing summary statistics of genome-wide association studies of predominantly European ancestry, we conducted univariable and multivariable Mendelian randomization (MR) to estimate the overall and independent effects of ten dental traits (dentures, bleeding gums, painful gums, loose teeth, toothache, ulcers, periodontitis, number of teeth, and two measures of caries) on four EAA subtypes (GrimAge acceleration [GrimAA], PhenoAge acceleration [PhenoAA], HannumAge acceleration [HannumAA] and intrinsic EAA [IEAA]), and used two-step Mendelian randomization to evaluate twelve potential mediators of the associations. Comprehensive sensitivity analyses were used to verity the robustness, heterogeneity, and pleiotropy.ResultsUnivariable inverse variance weighted MR analyses revealed a causal effect of dentures on greater GrimAA (β: 2.47, 95% CI: 0.93–4.01, p = 0.002), PhenoAA (β: 3.00, 95% CI: 1.15–4.85, p = 0.001), and HannumAA (β: 1.96, 95% CI: 0.58–3.33, p = 0.005). In multivariable MR, the associations remained significant after adjusting for periodontitis, caries, number of teeth and bleeding gums. Three out of 12 aging risk factors were identified as mediators of the association between dentures and EAA, including body mass index, body fat percentage, and waist circumference. No evidence for reverse causality and pleiotropy were detected (p > 0.05).ConclusionsOur findings supported the causal effects of genetic liability for denture wearing on epigenetic aging, with partial mediation by obesity. More attention should be paid to the obesity-monitoring and management for slowing EAA among denture wearers.

Type 2 Diabetes, Circulating Metabolites, and Calcific Aortic Valve Stenosis: A Mendelian Randomization Study.

Epidemiological studies have shown an association between type 2 diabetes (T2D) and calcific aortic valve stenosis (CAVS), but the potential causal relationship and underlying mechanisms remain unclear. Therefore, we conducted a two-sample and two-step Mendelian randomization (MR) analysis to evaluate the association of T2D with CAVS and the mediating effects of circulating metabolites and blood pressure using genome-wide association study (GWAS) summary statistics. The inverse variance weighted (IVW) method was used for the primary MR analysis, and comprehensive sensitivity analyses were performed to validate the robustness of the results. Our results showed that genetically predicted T2D was associated with increased CAVS risk (OR 1.153, 95% CI 1.096-1.214, p < 0.001), and this association persisted even after adjusting for adiposity traits in multivariable MR analysis. Furthermore, the two-step MR analysis identified 69 of 251 candidate mediators that partially mediated the effect of T2D on CAVS, including total branched-chain amino acids (proportion mediated: 23.29%), valine (17.78%), tyrosine (9.68%), systolic blood pressure (8.72%), the triglyceride group (6.07-11.99%), the fatty acid group (4.78-12.82%), and the cholesterol group (3.64-11.56%). This MR study elucidated the causal impact of T2D on CAVS risk independently of adiposity and identified potential mediators in this association pathways. Our findings shed light on the pathogenesis of CAVS and suggest additional targets for the prevention and intervention of CAVS attributed to T2D.

Optimal operation for P2H system based on a power–temperature–hydrogen coupling model for alkaline water electrolyzer plant

For the effective operation of a Power-to-Hydrogen (P2H) system that integrates renewable energy sources with an Alkaline Water Electrolyzer (AWE), precise modeling of the AWE plant is paramount. This research delves into the electrothermal dynamics of the AWE and constructs an advanced Power–Temperature–Hydrogen (P–T–H) coupling model of the AWE plant. The developed model intricately captures the pivotal role of temperature in modulating hydrogen production rates, power constraints, and system flexibility. Furthermore, to enable the application of the model to system-level optimization, we undertake a rational simplification process to transform it into a linear model. Building upon this model, we devise an optimized power scheduling strategy for the P2H system that enhances the system’s economic efficiency and adaptability. A comprehensive sensitivity analysis of crucial variables such as hydrogen demand, transmission line capacity, and AWE capacity underscores the strategic insights this model offers for system development and implementation. In a word, the developed P–T–H model is well feasible for the improvement of the system’s operation with AWE.

A self-adaptive co-evolutionary algorithm for multi-objective flexible job-shop rescheduling problem with multi-phase processing speed selection, condition-based preventive maintenance and dynamic repairman assignment

Production scheduling and maintenance planning are two interactive factors in modern manufacturing system. However, at present, almost all studies ignore the impact of unpunctual maintenance activities on the integrated production and maintenance scheduling since the unavailabilities of repairmen are dynamically changed, e.g., repairmen increase, decrease and their unavailable intervals update. Under these contexts, this paper addresses a novel integrated optimization problem of condition-based preventive maintenance (CBPM) and production rescheduling with multi-phase processing speed selection and dynamic repairman assignment. More precisely, (1) a novel multi-phase-multi-threshold CBPM policy with remaining-useful-life-based inspection and multi-phase processing speed selection is proposed to obtain some selectable maintenance plans for each production machine; (2) a hybrid rescheduling strategy (HRS) that includes three rescheduling strategies is designed for responding to the dynamic changes of repairman; and (3) an adaptive clustering- and Meta-Lamarckian learning-based bi-population co-evolutionary algorithm (ACML-BCEA) is developed to deal with the concerned problem. In the numerical simulations, the effectiveness of designed operators and proposed ACML-BCEA algorithm is first verified. Next, the superiority and competitiveness of the proposed CBPM policy and HRS are separately demonstrated by comparing with other CBPM policies and rescheduling strategies. After that, a comprehensive sensitivity analysis is performed to analyze the effect of optional range of processing speed, skill level of selectable repairmen and total number of processing phases, and the analyzing results show that these factors all have a significant impact on the integrated optimization.

The causal effect of atopic dermatitis on lung cancer: A Mendelian randomization study.

Growing evidence has shown that atopic dermatitis (AD) may decrease lung cancer (LC) risk. However, the causality between the two diseases is inconsistent and controversial. Therefore, we explored the causal relationship between AD and different histological subtypes of LC by using the Mendelian randomization (MR) method. We conducted the MR study based on summary statistics from the genome-wide association studies (GWAS) of AD (10,788 cases and 30,047 controls) and LC (29,266 cases and 56,450 controls). Instrumental variables (IVs) were obtained after removing SNPs associated with potential confounders. We employed inverse-variance weighted (IVW), MR-Egger, and weighted median methods to pool estimates, and performed a comprehensive sensitivity analysis. The results of the IVW method suggested that AD may decrease the risk of developing lung adenocarcinoma (LUAD) (OR=0.91, 95% CI: 0.85-0.97, P=0.007). Moreover, no causality was identified between AD and overall LC (OR=0.96, 95% CI: 0.91-1.01, P=0.101), lung squamous cell carcinoma (LUSC) (OR=1.04, 95% CI: 0.96-1.036, P=0.324), and small cell lung carcinoma (SCLC) (OR=0.95, 95% CI: 0.82-1.10, P=0.512). A comprehensive sensitivity test showed the robustness of our results. The present study indicates that AD may decrease the risk of LUAD in the European population, which needs additional investigations to identify the potential molecular mechanisms.

Dissecting the causal role of immunophenotypes in primary sclerosing cholangitis risk: A Mendelian randomization study.

Primary sclerosing cholangitis (PSC), a chronic cholestatic liver condition, is frequently associated with inflammatory bowel disease. Specific immune cells have been implicated in PSC pathogenesis with the emergence of the "microbiota" and "gut lymphocyte homing" hypotheses, albeit their identities remain controversial. The first genome-wide association analysis leveraged nonoverlapping data from 3757 Europeans to evaluate 731 immunophenotypes. A genome-wide association analysis comprising 2871 cases and 12,019 controls yielded summary statistics for PSC. An inverse-variance weighted (IVW) analysis was performed to identify immunophenotypes causally related to PSC, and the results were validated using weighted mode, MR-Egger, and weighted median methods. Comprehensive sensitivity analyses were performed to verify the robustness, heterogeneity, and horizontal pleiotropy of the results. IVW analysis revealed 26 immune traits exhibiting causal associations with PSC. CD3 on HLA-DR+ CD4+ (IVW odds ratio [OR]: 0.904; 95% confidence interval [CI]: 0.828-0.986, P = .023) and CD3 on secreting Treg (IVW OR: 0.893; 95% CI: 0.823-0.969, P = .007) were negatively associated with PSC susceptibility and demonstrated high consistency across the 3 validation methods. Moreover, 7 other immune traits, including CD39+ resting Treg absolute cell (IVW OR = 1.083, 95% CI: 1.013-1.157, P = .019), CD39+ secreting Treg absolute cell (IVW OR = 1.063, 95% CI: 1.012-1.118, P = .015), CD3 on naive CD8br (IVW OR = 0.907, 95% CI: 0.835-0.986, P = .022), CD3 on CD39+ activated Treg (IVW OR = 0.927, 95% CI: 0.864-0.994, P = .034), CD28 on resting Treg (IVW OR = 0.724, 95% CI: 0.630-0.833, P = 5.95E-06), and CD39 on CD39+ CD4+ (IVW OR = 1.055, 95% CI: 1.001-1.112, P = .044) exhibited consistent results in the Weighted Median and Weighted Mode validation methods. Moreover, no significant heterogeneity or horizontal pleiotropy was observed across the single nucleotide polymorphisms. The leave-one-out results revealed that sequentially eliminating each single nucleotide polymorphism had no significant influence on model effect estimates or qualitative inference. This study evaluated potential causal links between 731 immune traits and PSC susceptibility. Twenty-six immune traits were identified using the IVW method. Verification across multiple methods revealed 9 immune traits with a plausible causal connection to PSC. These findings may uncover mechanistic pathways and novel therapeutic approaches.

Modeling COVID-19 Transmission in Closed Indoor Settings: An Agent-Based Approach with Comprehensive Sensitivity Analysis

Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and χ2(1,N=34)=(44.11,p=0.11)). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings.

Intensified reforming reactor for blue hydrogen and nitrogen production

The development of Chemical-looping Reforming (CLR) for blue hydrogen production requires a precise reactor design and control to achieve optimal efficiency and operability. This study proposes a 1D heterogeneous model for a CLR fixed-bed reactor, targeting the production of blue hydrogen and nitrogen. Validation against experimental data confirms the model’s accuracy and reliability. A comprehensive sensitivity analysis highlights reactor variables, including active metal content in the oxygen carrier, steam-to-methane ratio, and molar flow rates during reduction and reforming stages, as key variables in determining reactor performance. Optimizing these variables is crucial for process intensification and enhancing hydrogen production efficiency. Under optimal conditions, the reactor yields nitrogen with a concentration above 98% during the oxidation stage and hydrogen with a 66.7% concentration during the reforming stage. Moreover, the CLR reactor operates autothermally and reaches a hydrogen production efficiency of 63.6%, prior to any further purification. The study concludes with a discussion of the reactor’s challenges and opportunities, laying the groundwork for potential future research and advancements in the field.

Multi-parameter similarity model-based lightweight design of battery electric vehicle body-in-white

The bending stiffness, torsional stiffness, and low-order modal frequencies of the body-in-white (BIW) are crucial factors in structural lightweight design. This paper introduces a BIW optimization approach for electric vehicles (BEVs) that integrates comprehensive sensitivity analysis and multi-parameter similarity models. The aim is to efficiently balance the BIW’s stiffness performance with minimal weight, while also managing potential manufacturing cost increases from design modifications. Firstly, the validity of the computational model is demonstrated by the BIW bending and torsion stiffness test and modal test. On this basis, the integrated sensitivity evaluation indexes of the structural member thickness parameters to the stiffness, weight and low-order modal frequency response of the BIW were calculated. The sheet metal thickness parameters of 17 structural members were preferred as design variables, and a multi-parameter similarity prediction model for BIW weight reduction design was constructed. Minimum weight, maximum stiffness and higher modal frequency of the BIW are defined as the optimization objectives. The optimal solution was finally solved using a multi-objective optimization algorithm. The results indicate a 1.582% reduction in weight of the BIW compared to the pre-optimization BIW. This paper delves into the intricate interplay between the parameters of the BIW’s structural components using simulation optimization techniques alongside experimental validation. This study not only proposes a new idea for the problem of how to efficiently and accurately determine the best optimization object from a large number of structural components in the development of vehicle structural safety, but also provides a research basis for automobile manufacturers to implement further weight reduction and optimization work on the BIW at the later stage of BEV development.