Comprehensive Sensitivity Analysis Research Articles

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.

Association between gut microbiota and acute pancreatitis: a bidirectional Mendelian randomization study.

The dysbiosis of gut microbiota has been reported in acute pancreatitis. However, the direction and magnitude between host microbiota and pancreas remains to be established. This study investigated the association between gut microbiota and acute pancreatitis using Mendelian randomization (MR) methods. Summary statistics of gut microbiota abundance and acute pancreatitis were extracted from genome-wide association studies (GWAS). The two-sample bidirectional MR design was employed to assess genetic association between the microbiota and pancreatitis, followed by a comprehensive sensitivity analysis to verify the robustness of the results. Seven microbiota taxa have been identified as significantly associated with the development of pancreatitis. Host genetic-driven order Bacteroidales and class Bacteroidia are associated with an increased risk of pancreatitis. The genera Coprococcus and Eubacterium fissicatena group also exhibit a positive effect on the development of pancreatitis, while the genera Prevotella, Ruminiclostridium, and Ruminococcaceae act as protective factors against pancreatitis. In contrast, acute pancreatitis was positively correlated with phylum Proteobacteria and genus Lachnospiraceae and negatively correlated with genus Holdemania. The bidirectional relationship between gut microbiota and acute pancreatitis suggests a critical role for host-microbiota crosstalk in the development of the disease. Targeted modulation of specific gut microbiota enables the prevention and treatment of acute pancreatitis.

Utilizing MicroGenetic Algorithm for Optimal Design of Permanent-Magnet-Assisted WFSM for Traction Machines

With increasing worries about the environment, there is a rising focus on saving energy in various industries. In the e-mobility industry of electric motors, permanent magnet synchronous motors (PMSMs) are widely utilized for saving energy due to their high-efficiency motor technologies. However, challenges like environmental degradation from rare earth development and difficulties in controlling magnetic field fluctuations persist. To address these issues, active research focuses on the wound field synchronous motor (WFSM), known for its ability to regulate field current efficiently across various speeds and operating conditions. Nevertheless, compared with other synchronous motors, the WFSM tends to exhibit relatively lower efficiency and torque density. Because the WFSM involves winding both the rotor and the stator, it results in increased copper and iron losses. In this article, a model that enhances torque density by inserting permanent magnets (PMs) into the rotor of the basic WFSM is proposed. This proposed model bolsters the d axis magnetic flux, thereby enhancing the motor’s overall performance while addressing environmental concerns related to rare-earth materials and potentially reducing manufacturing costs when compared with those of the PMSM. The research methodology involves a comprehensive sensitivity analysis to identify key design variables, followed by sampling using optimal Latin hypercube design (OLHD). A surrogate model is then constructed using the kriging interpolation technique, and the optimization process employs a micro-genetic algorithm (MGA) to derive the optimal model configuration. The algorithm was performed to minimize the use of PMs when the same torque as that of the basic WFSM is present, and to reduce torque ripple. Error assessment is conducted through comparisons with finite element method (FEM) simulations. The optimized permanent-magnet-assisted WFSM (PMa-WFSM) model improved efficiency by 1.08% when it was the same size as the basic WFSM, and the torque ripple decreased by 5.43%. The proposed PMa-WFSM derived from this article is expected to be suitable for use in the e-mobility industry as a replacement for PMSM.

Comprehensive sensitivity analysis of repeated eigenvalues and eigenvectors for structures with viscoelastic elements

The paper discusses systems with viscoelastic elements that exhibit repeated eigenvalues in the eigenvalue problem. The mechanical behavior of viscoelastic elements can be described using classical rheological models as well as models that involve fractional derivatives. Formulas have been derived to calculate first- and second-order sensitivities of repeated eigenvalues and their corresponding eigenvectors. A specific case was also examined, where the first derivatives of eigenvalues are repeated. Calculating derivatives of eigenvectors associated with repeated eigenvalues is complex because they are not unique. To compute their derivatives, it is necessary to identify appropriate adjacent eigenvectors to ensure stable control of eigenvector changes. The derivatives of eigenvectors are obtained by dividing them into particular and homogeneous solutions. Additionally, in the paper, a special factor in the coefficient matrix has been introduced to reduce its condition number. The provided examples validate the correctness of the derived formulas and offer a more detailed analysis of structural behavior for structures with viscoelastic elements when altering a single design parameter or simultaneously changing multiple parameters.

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management

As electric vehicles (EVs) continue to rise in popularity, there has been an intensified focus on the distribution of power within hybrid renewable energy systems. In this context, the significance of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) models for mitigating grid pressures has been increasingly recognized. These models help mitigate grid pressures by using EVs as reliable loads. This research delves into the technical and economic aspects of a hybrid microgrid integrated with various components such as photovoltaic panels (PVs), wind turbines (WTs), battery energy storage systems (BESSs), and EV grid connections, situated at a specific latitude of 40°39.2′N and longitude of 29°13.2′E. The methodology employed is grounded in advanced stochastic metaheuristic approaches. The infrastructure accommodates two distinct loads: a primary load and another dedicated to EV charging. The cornerstone of the energy framework is renewables, particularly PV panels and wind turbines. A dynamic rule-based energy management scheme is implemented to ensure consistent fulfillment of energy demands with an emphasis on cost efficiency. In defining the dimensions of the microgrid, various algorithms including the Coati Optimization Algorithm (COA), Driving Training-Based Optimization (DTBO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Turbulent Flow of Water-based Optimization (TFWO), White Shark Optimizer (WSO), Zebra Optimization Algorithm (ZOA), and Flying Foxes Optimization (FFO) were used. This configuration was evaluated in terms of the annual system cost, present-day value, loss of power supply probability (LPSP), and leveled cost of energy (LCOE). Thanks to the strategic deployment of the TFWO algorithm, optimal results were achieved for the system, including a PV capacity of 411.0560 kW, a WT capacity of 327.0229 kW, and a BESS capacity of 561.1750 kW. With this configuration, the annual cost of the system was determined to be $4.0499 × 105, the present value was set at $4.6452 × 106, the nominal LPSP was calculated at 0.0487 %, and the LCOE was established at $0.1597/kWh. Notably, a significant portion of the energy demand, approximately 69.87 %, was met through renewables, with the remaining 30.13 % covered by traditional sources. Comprehensive sensitivity analysis of key parameters enabled forecasting of future trends. All research activities underwent thorough review, confirming the TFWO algorithm's superior efficiency and faster convergence compared to other methods. The optimization algorithms were implemented in MATLAB 2022b, and statistical evaluations performed using the R programming language.

Metabolomic insights into pulmonary fibrosis: a mendelian randomization study

BackgroundThis study leverages a two-sample Mendelian Randomization (MR) approach to explore the causal relationships between 1,400 metabolites and pulmonary fibrosis, using genetic variation as instrumental variables. By adhering to stringent criteria for instrumental variable selection, the research aims to uncover metabolic pathways that may influence the risk and progression of pulmonary fibrosis, providing insights into potential therapeutic targets.MethodsUtilizing data from the OpenGWAS project, which includes a significant European cohort, and metabolite GWAS data from the Canadian Longitudinal Aging Study (CLSA), the study employs advanced statistical methods. These include inverse variance weighting (IVW), weighted median estimations, and comprehensive sensitivity analyses conducted using the R software environment to ensure the robustness of the causal inferences.ResultsThe study identified 62 metabolites with significant causal relationships with pulmonary fibrosis, highlighting both risk-enhancing and protective metabolic factors. This extensive list of metabolites presents a broad spectrum of potential therapeutic targets and biomarkers for early detection, underscoring the metabolic complexity underlying pulmonary fibrosis.ConclusionsThe findings from this MR study significantly advance our understanding of the metabolic underpinnings of pulmonary fibrosis, suggesting that alterations in specific metabolites could influence the risk and progression of the disease. These insights pave the way for the development of novel diagnostic and therapeutic strategies, emphasizing the potential of metabolic modulation in managing pulmonary fibrosis.

Relationship between human serum metabolites and angina pectoris: a Mendelian randomization study.

We aimed to explore the causal relationship between human serum metabolites and angina pectoris. This study used two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and angina pectoris. The analytical methods employed to reduce study bias included inverse variance weighted, MR-Egger, and weighted median method. A comprehensive sensitivity analysis was performed using the leave-one-out method, while instrumental variable pleiotropy was tested with MR-Pleiotropy RESidual Sum and Outlier. Metabolic pathways of angina-associated metabolites were analysed on the MetaboAnalyst metabolomics analysis tool platform. In this study, 42 serum metabolites were found to be strongly associated with angina pectoris. They mainly belonged to seven groups: amino acids, carbohydrates, cofactors and vitamins, lipids, nucleotides, unknown metabolites, and exogenous substances. Pipecolate posed the highest risk for the development of angina pectoris among the 42 serum metabolites. The main metabolic pathways associated with angina pectoris were glycine, serine, threonine metabolism, primary bile acid biosynthesis, and caffeine metabolism. We identified 25 high-risk and 17 protective human serum metabolites associated with angina pectoris. Their associated major metabolic pathways were also determined. The serum metabolite pipecolate was significantly and positively correlated with the risk of angina pectoris. This finding may serve as a valuable reference for testing serum markers associated with angina pectoris.

The causal relationship between the gut microbiota and acute pancreatitis: A 2-sample Mendelian randomization study.

Several observational studies have reported a correlation between the gut microbiota (GM) and the risk of acute pancreatitis (AP). However, the causal relationship between them remains uncertain. We conducted a 2-sample Mendelian randomization (MR) study using pooled data from genome-wide association studies of 211 taxa (131 genera, 35 families, 20 orders, 16 classes, and 9 phyla) and AP patients. We evaluated the causal relationship between the GM and AP using methods such as inverse-variance weighting, MR-Egger, weighted medians, simple mode, and weighted mode. Cochran Q test, MR-Egger regression intercept analysis, and MR-PRESSO were used to examine the heterogeneity, multipotency, and outlier values of the variables, respectively. The reverse causal relationship between AP and the GM was assessed with reverse MR. In total, 5 gut microbial taxa were significantly associated with AP. The inverse-variance weighting results indicated that Acidaminococcaceae (odds ratio [OR]: 0.81, 95% confidence interval [CI]: 0.66-1.00, P = .045) and Ruminococcaceae UCG004 (OR: 0.85, 95% CI: 0.72-0.99, P = .040) were protective factors against the occurrence of AP. Coprococcus 3 (OR: 1.32, 95% CI: 1.03-1.70, P = .030), Eisenbergiella (OR: 1.13, 95% CI: 1.00-1.28, P = .043), and the Eubacterium fissicatena group (OR: 1.18, 95% CI: 1.05-1.33, P = .006) were risk factors for the development of AP. A comprehensive sensitivity analysis proved our results to be reliable. Reverse MR analysis did not indicate any causal relationship between AP and the GM. This study revealed a complex causal relationship between 5 GM taxa and AP, providing new insights into the diagnostic and therapeutic potential of the GM in AP patients.

Causal role of immune cells in hypertension: a bidirectional Mendelian randomization study.

Although Hypertension (HTN) is considered to be a cardiovascular disease caused by multiple factors, the cause of it is still unknown. In this study, we aim to find out whether circulating immune cell characteristics have an impact on susceptibility to HTN. This study employed a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the causal association between immune cell characteristics and HTN. Utilizing publicly accessible genetic data, we examined the causal relationship between HTN and the susceptibility to 731 immune cell signatures. To ensure the reliability and validity of the findings, a comprehensive sensitivity analysis was conducted to assess heterogeneity, confirm the robustness of the results and evaluate the presence of horizontal pleiotropy. After FDR correction, immune phenotype had an effect on HTN. In our study, one immunophenotype was identified as being positively associated with HTN risk significance: HLA DR on CD33- HLA DR+. In addition, we examined 8 immune phenotype with no statistically significant effect of HTN, but it is worth mentioning that they had an unadjusted low P-value phenotype. Our MR study by genetic means demonstrated the close relationship between HTN and immune cells, thus providing guidance for future clinical prediction and subsequent treatment of HTN.