Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions.

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and - 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Improving groundwater vulnerability assessment in structurally controlled hard rock aquifer: insight from lineament density and land use/land cover pattern.

A comprehensive seasonal assessment of groundwater vulnerability was conducted in the weathered hard rock aquifer of the upper Swarnrekha watershed in Ranchi district, India. Lineament density (Ld) and land use/land cover (LULC) were integrated into the conventional DRASTIC and Pesticide DRASTIC (P-DRASTIC) models and were extensively compared with six modified models, viz. DRASTIC-Ld, DRASTIC-Lu, DRASTIC-LdLu, P-DRASTIC-Ld, P-DRASTIC-Lu, and P-DRASTIC-LdLu, to identify the most optimal model for vulnerability mapping in hard rock terrain of the region. Findings were geochemically validated using NO3- concentrations of 68 wells during pre-monsoon (Pre-M) and post-monsoon (Post-M) 2022. Irrespective of the applied model, groundwater vulnerability shows significant seasonal variation, with > 45% of the region classified as high to very high vulnerability in the pre-M, increasing to Ì´67% in post-M season, highlighting the importance of seasonal vulnerability assessments. Agriculture and industries' dominant southern region showed higher vulnerability, followed by regions with high Ld and thin weathered zone. Incorporating Ld and LULC parameters into DRASTIC-LdLu and P-DRASTIC-LdLu models increases the 'Very High' vulnerability zones to 17.4% and 17.6% for pre-M and 29.4% and 27.9% for post-M, respectively. Similarly, 'High' vulnerable zones increase from 32.5% and 25% in pre-M to 33.8% and 35.3% in post-M for respective models. Model output comparisons suggest that modified DRASTIC-LdLu and P-DRASTIC-LdLu perform better, with accurate estimations of 83.8% and 89.7% for pre-M and post-M, respectively. However, results of geochemical validation suggest that among all the applied modified models, DRASTIC-LdLu performs best, with accurate estimations of 34.4% and 20.6% for pre-M and post-M, respectively.

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Evolutionary game analysis between regional governments and shipping companies: Considering the impact of government subsidy on shipping companies.

Maritime agencies are imposing stricter limits on fuel sulfur content, and regional governments are encouraging the reduction of various emissions through subsidies. In this study, an evolutionary game model is constructed to analyze the interaction between regional governments and shipping companies under the fixed and dynamic subsidies. The sensitivity analysis reveals the effect of parameters on stabilization strategies. The results show that the bilateral stakeholders can adopt stabilization strategies under dynamic subsidies. The fines, maximum subsidies and extra cost paid by regional governments have a significant impact on these strategies. To reduce the dependence of shipping companies on subsidy policies, it is recommended to improve the LSFO refining technology in the future. Expanding the implementation scope of LSFO subsidy policies will increase the utilization of LSFO by shipping companies. This study offers insights for governments to optimize the LSFO subsidy policy and shipping companies to choose sulfur oxides reduction approaches.

NITIRSOIL: A model that balances complexity with prediction uncertainty for improving nitrogen fertilization in agriculture.

Mismanagement of the nitrogen (N) fertilization in agriculture leads to low N use efficiency (NUE) and therefore pollution of waters and atmosphere due to NO3- leaching, and N2O and NH3 emissions. The use of N simulation models of the soil-plant system can help improve the N fertilizer management increasing NUE and decreasing N pollution issues. However, many N simulation models lack balance between complexity and uncertainty with the result that they are not applied in actual practice. The NITIRSOIL is a one-dimensional transient-state model with a monthly time step that aims at addressing this lack in the estimation of, mainly, dry matter yield (DMY), crop N uptake (Nupt), soil mineral N (Nmin), and NO3- leaching in agricultural fields. According to its global sensitivity analysis for horticulture, the NITIRSOIL simulations of the aforementioned outputs mostly depend on the critical N dilution curve, harvest index, dry matter fraction, potential fresh yield and nitrification coefficients. According to its validation for 35 nitrogen fertilization trials with 11 vegetables under semi-arid Mediterranean climate in Eastern Spain, the NITIRSOIL presents indices of agreement between 0.87 and 0.97 for the prediction of total dry matter, DMY, Nupt, NO3- leaching and soil Nmin at crop season end. Therefore, the NITIRSOIL model can be used in actual practice to improve the sustainability of the N management in, particularly horticulture, due to the balance it features between complexity and prediction uncertainty. For this aim, the NITRISOIL can be used either on its own, or in combination with "Nmin" on-site N fertilization recommendation methods, or even could be implemented as the calculation core of decision support systems.

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on hidden Markov model.

To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.

How many Monte Carlo samples are needed for probabilistic cost-effectiveness analyses?

OBJECTIVE: Probabilistic sensitivity analysis (PSA) is conducted to account for the uncertainty in cost and effect of decision options under consideration. PSA involves obtaining a large sample of input parameter values (N) to estimate the expected cost and effect of each alternative in the presence of parameter uncertainty. When the analysis involves using stochastic models (e.g., individual-level models), the model is further replicated P times for each sampled parameter set. We study how N and P should be determined. METHODS: We show that PSA could be structured such that P can be an arbitrary number (say, P=1). To determine N, we derive a formula based on Chebyshev's inequality such that the error in estimating the incremental cost-effectiveness ratio (ICER) of alternatives (or equivalently, the willingness-to-pay value at which the optimal decision option changes) is within a desired level of accuracy. We described two methods to confirmed, visually and quantitatively, that the N informed by this method results in ICER estimates within the specified level of accuracy. RESULTS: When N is arbitrarily selected, the estimated ICERs could be substantially different from the true ICER (even as P increases), which could lead misleading conclusions. Using a simple resource allocation model, we demonstrate that the proposed approach can minimize the potential for this error. CONCLUSIONS: The number of parameter samples in probabilistic CEAs should not be arbitrarily selected. We describe three methods to ensure that enough parameter samples are used in probabilistic CEAs.

Mathematical and sensitivity analysis for chemical species in multistep dynamical system: A computational study.

This study presents a significant contribution to the field of chemical kinetics by providing a detailed analysis of a multi-step chemical kinetic process using ordinary differential equations (ODEs). The aim is to describe complex chemical processes' kinetics and the steady-state behavior of chemical species. The research employs reduction techniques to simplify the model by separating fast and slow processes based on their time scales, with a focus on a two-step reversible reaction mechanism. Special consideration is given to the phase flow of solution trajectories near equilibrium points, providing a clear depiction of system behavior. MATLAB simulations demonstrate the physical properties of observed data, while sensitivity analysis reveals parameters' impact on species behavior. Overall, this study enhances our understanding of chemical kinetics and offers insights into modeling complex reaction processes, with implications for various applications in chemistry and related fields.

Impact of variable economic conditions on the cost of energy and the economic viability of floating photovoltaics.

This work evaluates the effects of economic conditions' variations on the costs and viability of floating photovoltaics, a novel solution where modules are installed on or above water. A sensitivity analysis of key economic criteria is conducted across multiple European countries, first generating country-specific baseline scenarios and then introducing systematic variations into the input parameters. The results show that capital expenditure and electricity prices, which have both experienced significant variations in recent years, have the largest influence on the net present value and the internal rate of return. Similarly, capital expenditure and discount rate are found to be the most influencing factors for the levelized cost of electricity. Overall, this study contributes to the literature by identifying the correlations between the economic variables and the viability of floating photovoltaics. The findings can be used to assess the effectiveness of potential government policies and support mechanisms and to evaluate the viability of this technology under varying national and international economic conditions.

Model simplification and sensitivity analysis of chemical species for oxidation behavior in multi route complex chemical mechanism.

This study employs a Model Reduction Technique (MRT) to simplify the four-step catalytic carbon monoxide (CO) oxidation reaction. The C-matrix method identifies key elements, key/non key components, and key reactions, while the Intrinsic Low-Dimensional Manifold (ILDM) pinpoints a Slow-Invariant Manifold (SIM) important for understanding key species behavior. Sensitivity analysis can be considered for measuring the efficiency of the chemical species in detailed mechanism. This systematic approach contributes to optimizing and controlling complex reactions offering broad application potential. In addition to the mathematical proof, the validation of the given chemical model is rectified. The comparison between the slow invariant manifold of both reaction routes is reported and the computational based results performed in this study are obtained through MATLAB.

Contrasting carbon cycle responses of semiarid abandoned farmland to simulated warmer-drier and warmer-wetter climates.

Rewilding abandoned farmlands provides a nature-based climate solution via carbon (C) offsetting; however, the C-cycle-climate feedback in such restored ecosystems is poorly understood. Therefore, we conducted a 2-year field experiment in Loess Plateau, China, to determine the impacts of warming (~1.4 °C) and altered precipitation (±25 %, ±50 %, and ambient), alone or in concert on soil C pools and associated C fluxes. Experimental warming significantly enhanced soil respiration without affecting the ecosystem net C uptake and soil C storage; these variables tended to increase along the manipulated precipitation gradient. Their interactions increased ecosystem net C uptake (synergism) but decreased soil respiration and soil C accumulation (antagonism) compared with a single warming or altered precipitation. Additionally, most variables related to the C cycle tended to be more responsive to increased precipitation, but the ecosystem net C uptake responded intensely to warming and decreased precipitation. Overall, ecosystem net C uptake and soil C storage increased by 94.4 % and 8.2 %, respectively, under the warmer-wetter scenario; however, phosphorus deficiency restricted soil C accumulation under these climatic conditions. By contrast, ecosystem net C uptake and soil C storage decreased by 56.6 % and 13.6 %, respectively, when exposed to the warmer-drier climate, intensifying its tendency toward a C source. Therefore, the C sink function of semiarid abandoned farmland was unsustainable. Our findings emphasize the need for management of post-abandonment regeneration to sustain ecosystem C sequestration in the context of climate change, aiding policymakers in the development of C-neutral routes in abandoned regions.

Spatial heterogeneity, sediment-water exchange, and diffusion mechanisms of organophosphate esters from river to coastal aquatic system in a typical economic circle of northern China.

Organophosphate esters (OPEs) have proven to be pervasive in aquatic environments globally. However, understanding their partitioning behavior and mechanisms at the sediment-water interface remains limited. This study elucidated the spatial heterogeneity, interfacial exchange, and diffusion mechanisms of 14 OPEs (∑14OPEs) from river to coastal aquatic system. The transport tendencies of OPEs at the sediment-water interface were quantitatively assessed using fugacity methods. The total ∑14OPEs concentrations in water and sediments ranged from 154 ng/L to 528 ng/L and 2.41 ng/g dry weight (dw) to 230 ng/g dw, respectively. This result indicated a descending spatial tendency with moderate variability. OPE distribution was primarily influenced by temperature, pH, and dissolved oxygen levels. As the carbon atom number increased, alkyl and chlorinated OPEs transitioned from diffusion towards the aqueous phase to equilibrium. In contrast, aryl OPEs and triphenylphosphine oxide, which had equivalent carbon atom counts, maintained equilibrium throughout. Diffusion trends of individual OPE congener at the sediment-water interface varied at the same total organic carbon contents (foc). As the foc increased, the fugacity fraction values for all OPE homologs showed a declining trend. The distinct molecular structure of each OPE monomer might lead to unique diffusive behaviors at the sediment-water interface. Higher soot carbon content had a more pronounced effect on the distribution patterns of OPEs. The sediment-water distribution of OPEs was primarily influenced by total organic carbon, sediment particle size, dry density, and moisture content. OPEs displayed the highest sensitivity to fluctuations in ammonium and dissolved organic carbon. This study holds significant scientific and theoretical implications for elucidating the interfacial transport and driving forces of OPEs and comprehending their fate and endogenous release in aquatic ecosystems.

Modeling and analysis of a human papilloma virus transmission model with impact of media.

The human papillomavirus (HPV) is threatening human health as it spreads globally in varying degrees. On the other hand, the speed and scope of information transmission continues to increase, as well as the significant increase in the number of HPV-related news reports, it has never been more important to explore the role of media news coverage in the spread and control of the virus. Using a decreasing factor that captures the impact of media on the actions of people, this paper develops a model that characterizes the dynamics of HPV transmission with media impact, vaccination and recovery. We obtain global stability of equilibrium points employing geometric method, and further yield effective methods to contain the HPV pandemic by sensitivity analysis. With the center manifold theory, we show that there is a forward bifurcation when R0=1. Our study suggested that, besides controlling contact between infected and susceptible populations and improving effective vaccine coverage, a better intervention would be to strengthen media coverage. In addition, we demonstrated that contact rate and the effect of media coverage result in multiple epidemics of infection when certain conditions are met, implying that interventions need to be tailored to specific situations.

A mathematical model for wound healing in the reef-building coral Pocillopora damicornis.

Coral reefs, among the most diverse ecosystems on Earth, currently face major threats from pollution, unsustainable fishing practices , and perturbations in environmental parameters brought on by climate change. Corals also sustain regular wounding from other sea life and human activity. Recent reef restoration practices have even involved intentional wounding by systematically breaking coral fragments and relocating them to revitalize damaged reefs, a practice known as microfragmentation. Despite its importance, very little research has explored the inner mechanisms of wound healing in corals. Some reef-building corals have been observed to initiate an immunological response to wounding similar to that observed in mammalian species. Utilizing prior models of wound healing in mammalian species as the mathematical basis, we formulated a mechanistic model of wound healing, including observations of the immune response and tissue repair in scleractinian corals for the species Pocillopora damicornis. The model consists of four differential equations which track changes in remaining wound debris, number of cells involved in inflammation, number of cells involved in proliferation, and amount of wound closure through re-epithelialization. The model is fit to experimental wound size data from linear and circular shaped wounds on a live coral fragment. Mathematical methods, including numerical simulations and local sensitivity analysis, were used to analyze the resulting model. The parameter space was also explored to investigate drivers of other possible wound outcomes. This model serves as a first step in generating mathematical models for wound healing in corals that will not only aid in the understanding of wound healing as a whole, but also help optimize reef restoration practices and predict recovery behavior after major wounding events.

Economic evaluation of submerged anaerobic hybrid membrane bioreactor operating at mesophilic temperature.

A laboratory-scale mesophilic submerged anaerobic hybrid membrane bioreactor (An-HMBR) was operated for 270 days for the treatment of high-strength synthetic wastewater at different hydraulic retention times (HRTs) (3 days, 2 days, 1 day, and 0.5 days). Chemical oxygen demand (COD) removal efficiency of 92% was obtained with methane yield rate of 0.18 LCH4/g CODremoval at 1-day HRT. The results of lab scale reactor at 1-day HRT were utilized for upscaling and cost analysis. Cost analysis revealed that the total capital cost comprised tank system (48%), membrane cost (32%), screen and PUF sponge (5% each), PLCs (4%), liquid pumps (3%), and others (2%). The operational cost comprised chemical cost (46%), pumping energy (42%), and sludge disposal (12%). The results revealed that the tank and heating costs accounted for the largest fraction of the total life cycle cost for full-scale An-HMBR. The heating cost can be compensated by gas recovery. Sensitivity analysis revealed that the interest rates, influent flow, and membrane flux were the most crucial parameters which affected the total cost of An-HMBR.

Comparative FEM study on intervertebral disc modeling: Holzapfel-Gasser-Ogden vs. structural rebars.

Introduction: Numerical modeling of the intervertebral disc (IVD) is challenging due to its complex and heterogeneous structure, requiring careful selection of constitutive models and material properties. A critical aspect of such modeling is the representation of annulus fibers, which significantly impact IVD biomechanics. This study presents a comparative analysis of different methods for fiber reinforcement in the annulus fibrosus of a finite element (FE) model of the human IVD. Methods: We utilized a reconstructed L4-L5 IVD geometry to compare three fiber modeling approaches: the anisotropic Holzapfel-Gasser-Ogden (HGO) model (HGO fiber model) and two sets of structural rebar elements with linear-elastic (linear rebar model) and hyperelastic (nonlinear rebar model) material definitions, respectively. Prior to calibration, we conducted a sensitivity analysis to identify the most important model parameters to be calibrated and improve the efficiency of the calibration. Calibration was performed using a genetic algorithm and in vitro range of motion (RoM) data from a published study with eight specimens tested under four loading scenarios. For validation, intradiscal pressure (IDP) measurements from the same study were used, along with additional RoM data from a separate publication involving five specimens subjected to four different loading conditions. Results: The sensitivity analysis revealed that most parameters, except for the Poisson ratio of the annulus fibers and C01 from the nucleus, significantly affected the RoM and IDP outcomes. Upon calibration, the HGO fiber model demonstrated the highest accuracy (R2 = 0.95), followed by the linear (R2 = 0.89) and nonlinear rebar models (R2 = 0.87). During the validation phase, the HGO fiber model maintained its high accuracy (RoM R2 = 0.85; IDP R2 = 0.87), while the linear and nonlinear rebar models had lower validation scores (RoM R2 = 0.71 and 0.69; IDP R2 = 0.86 and 0.8, respectively). Discussion: The results of the study demonstrate a successful calibration process that established good agreement with experimental data. Based on our findings, the HGO fiber model appears to be a more suitable option for accurate IVD FE modeling considering its higher fidelity in simulation results and computational efficiency.

Evaluating the impact of double dose vaccination on SARS-CoV-2 spread through optimal control analysis.

This paper presents a new nonlinear epidemic model for the spread of SARS-CoV-2 that incorporates the effect of double dose vaccination. The model is analyzed using qualitative, stability, and sensitivity analysis techniques to investigate the impact of vaccination on the spread of the virus. We derive the basic reproduction number and perform stability analysis of the disease-free and endemic equilibrium points. The model is also subjected to sensitivity analysis to identify the most influential model parameters affecting the disease dynamics. The values of the parameters are estimated with the help of the least square curve fitting tools. Finally, the model is simulated numerically to assess the effectiveness of various control strategies, including vaccination and quarantine, in reducing the spread of the virus. Optimal control techniques are employed to determine the optimal allocation of resources for implementing control measures. Our results suggest that increasing the vaccination coverage, adherence to quarantine measures, and resource allocation are effective strategies for controlling the epidemic. The study provides valuable insights into the dynamics of the pandemic and offers guidance for policymakers in formulating effective control measures.

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically - for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal - with a view to inform nonparametric inference.

Immune cell signatures and causal association with irritable bowel syndrome: A mendelian randomization study.

BACKGROUND: The mucosal barrier's immune-brain interactions, pivotal for neural development and function, are increasingly recognized for their potential causal and therapeutic relevance to irritable bowel syndrome (IBS). Prior studies linking immune inflammation with IBS have been inconsistent. To further elucidate this relationship, we conducted a Mendelian randomization (MR) analysis of 731 immune cell markers to dissect the influence of various immune phenotypes on IBS. Our goal was to deepen our understanding of the disrupted brain-gut axis in IBS and to identify novel therapeutic targets. AIM: To leverage publicly available data to perform MR analysis on 731 immune cell markers and explore their impact on IBS. We aimed to uncover immunophenotypic associations with IBS that could inform future drug development and therapeutic strategies. METHODS: We performed a comprehensive two-sample MR analysis to evaluate the causal relationship between immune cell markers and IBS. By utilizing genetic data from public databases, we examined the causal associations between 731 immune cell markers, encompassing median fluorescence intensity, relative cell abundance, absolute cell count, and morphological parameters, with IBS susceptibility. Sensitivity analyses were conducted to validate our findings and address potential heterogeneity and pleiotropy. RESULTS: Bidirectional false discovery rate correction indicated no significant influence of IBS on immunophenotypes. However, our analysis revealed a causal impact of IBS on 30 out of 731 immune phenotypes (P < 0.05). Nine immune phenotypes demonstrated a protective effect against IBS [inverse variance weighting (IVW) < 0.05, odd ratio (OR) < 1], while 21 others were associated with an increased risk of IBS onset (IVW ≥ 0.05, OR ≥ 1). CONCLUSION: Our findings underscore a substantial genetic correlation between immune cell phenotypes and IBS, providing valuable insights into the pathophysiology of the condition. These results pave the way for the development of more precise biomarkers and targeted therapies for IBS. Furthermore, this research enriches our comprehension of immune cell roles in IBS pathogenesis, offering a foundation for more effective, personalized treatment approaches. These advancements hold promise for improving IBS patient quality of life and reducing the disease burden on individuals and their families.

A mathematical fractal-fractional model to control tuberculosis prevalence with sensitivity, stability, and simulation under feasible circumstances.

BACKGROUND: Tuberculosis, a global health concern, was anticipated to grow to 10.6 million new cases by 2021, with an increase in multidrug-resistant tuberculosis. Despite 1.6 million deaths in 2021, present treatments save millions of lives, and tuberculosis may overtake COVID-19 as the greatest cause of mortality. This study provides a six-compartmental deterministic model that employs a fractal-fractional operator with a power law kernel to investigate the impact of vaccination on tuberculosis dynamics in a population. METHODS: Some important characteristics, such as vaccination and infection rate, are considered. We first show that the suggested model has positive bounded solutions and a positive invariant area. We evaluate the equation for the most important threshold parameter, the basic reproduction number, and investigate the model's equilibria. We perform sensitivity analysis to determine the elements that influence tuberculosis dynamics. Fixed-point concepts show the presence and uniqueness of a solution to the suggested model. We use the two-step Newton polynomial technique to investigate the effect of the fractional operator on the generalized form of the power law kernel. RESULTS: The stability analysis of the fractal-fractional model has been confirmed for both Ulam-Hyers and generalized Ulam-Hyers types. Numerical simulations show the effects of different fractional order values on tuberculosis infection dynamics in society. According to numerical simulations, limiting contact with infected patients and enhancing vaccine efficacy can help reduce the tuberculosis burden. The fractal-fractional operator produces better results than the ordinary integer order in the sense of memory effect at diffract fractal and fractional order values. CONCLUSION: According to our findings, fractional modeling offers important insights into the dynamic behavior of tuberculosis disease, facilitating a more thorough comprehension of their epidemiology and possible means of control.