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The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we explore the effect of adding isovector pairing terms, which are responsible for different proton and neutron pairing fields, by comparing a 13D model without the isovector pairing term against the extended 14D model. At the heart of the calibration is a carefully selected heterogeneous dataset of experimental observables representing ground-state properties of spherical even-even nuclei. To quantify the impact of the calibration dataset on model parameters and the importance of the new terms, we carry out advanced sensitivity and correlation analysis on both models. The extension to 14D improves the overall quality of the model by about 30%. The enhanced degrees of freedom of the 14D model reduce correlations between model parameters and enhance sensitivity.

Methane (CH4) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

Pulmonary hypertension is a cardiovascular disorder manifested by elevated mean arterial blood pressure (>20 mmHg) together with vessel wall stiffening and thickening due to alterations in collagen, elastin, and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study introduces a novel approach to formulating a reduced order nonlinear elastic structural wall model for a large pulmonary artery. The model relating blood pressure and area is calibrated using ex vivo measurements of vessel diameter and wall thickness changes, under controlled pressure conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, and anisotropic model incorporating residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation, subset selection, and physical plausibility arguments are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced one layer model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive animals. The pressure-area relation developed in this novel manner has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.

This study explores the dynamics of crime and substance abuse within a population by developing a novel mathematical model that integrates social interactions, rehabilitation efforts, and relapse probabilities. The model introduces a critical metric, the control reproduction number , to quantify the invasion threshold for these behaviors. The findings reveal that the crime/substance-free equilibrium is globally asymptotically stable when . At the same time, entrenched equilibria become stable where . Additionally, the model predicts the potential for a co-existent equilibrium where crime/substance abuse and a free state can coexist if all reproduction numbers exceed unity. Sensitivity analysis identifies key factors influencing , including behavioral transmission, internal progression rates, intervention efficacy, and recovery/relapse probabilities. Numerical simulations validate theoretical predictions regarding the stability of different equilibria, highlighting the critical importance of interventions targeting transmission reduction and rehabilitation efficiency. The research underscores the significance of understanding invasion dynamics for the coexistence of behaviors. It demonstrates the utility of mathematical modeling in elucidating the spread of social phenomena and informing effective control strategies. [ABSTRACT FROM AUTHOR]

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments. [ABSTRACT FROM AUTHOR]

Aim: Climate extremes are becoming more frequent under global warming, with substantial repercussions for vegetation growth. The degree to which climate extremes increase the risk of high‐impact events on vegetation growth is of high concern. Location: Northern Hemisphere (north of 30°N). Time Period: From 2001 to 2022. Major Taxa Studied: Plants. Methods: We utilised solar‐induced chlorophyll fluorescence (SIF) and the normalised difference vegetation index (NDVI) as proxies for vegetation growth. We performed event coincidence and sensitivity analyses to attribute satellite‐derived vegetation growth extremes to diverse climate extremes (extreme heat, cold, wet and drought) in the Northern Hemisphere. Results: Our results showed that extreme heat and cold were the main climatic extremes that induced positive and negative vegetation growth extremes north of 30°N, respectively, mainly in cold and humid ecosystems (boreal and temperate forests). Water‐related extreme events accounted for less than one‐third of vegetation extremes. The contribution of drought to positive vegetation growth extreme events (approximately 17%), mainly in cold and humid ecosystems, was even slightly higher than that of extreme wet (approximately 12%), which predominantly impacted relatively warm and arid ecosystems (croplands and temperate grasslands). We further identified potential climatic thresholds that could reverse vegetation growth responses to climate extremes (temperature is 12.5°C and climatic water deficit is ‐60 mm, approximately). We also showed that the past two decades of warming and precipitation changes did not induce a shift in the main climatic drivers of vegetation extremes across northern ecosystems. Main Conclusions: Our results emphasise the crucial role of background hydrothermal conditions in the attribution of vegetation growth extremes to diverse climate extremes across northern ecosystems and have substantial implications for predicting how Northern Hemisphere vegetation will respond to increasing climate extremes in the future. [ABSTRACT FROM AUTHOR]

A new passive RC analog of a coupled double tuned circuit, as is commonly used in broad-band RF amplifiers, is presented in this paper. It consists of a cascade of bandstop Wien network and a bandpass parallel-T network. A by-product of this study is that by inverting this circuit, we get a wide-band rejection circuit. By interchanging the order of the cascade, we get different frequency responses. All of these circuits are amenable for IC fabrication, and have several advantages over the existing active RC simulation circuits for the purpose. [ABSTRACT FROM AUTHOR]

Power characteristics are important indicators of fuel cell performance. In the actual operation of fuel cells, changes in operating conditions lead to variations in their power characteristics. Therefore, it is imperative to explore the impact of operating conditions on power characteristics. This paper analyzes the factors influencing fuel cell power and uses sensitivity analysis to investigate how different factors affect fuel cell performance. The operating parameters are optimized using a Bayesian-optimized Gaussian process regression model. The research results indicate that temperature has the greatest impact on fuel cell power, followed by stoichiometry and backpressure. The Bayesian-optimized Gaussian process regression model performs the best, reducing its RSME from 0.1 to 0.0556. Residual analysis and regression characteristic analysis verify the optimized model's improved fitting and regression characteristics. Based on the Bayesian–Gaussian process regression model, the optimized operating parameters are obtained for maximum power: a temperature of 80 °C, stoichiometry of 4, and backpressure of 1.7 bar. This paper provides theoretical support for improving fuel cell performance. [ABSTRACT FROM AUTHOR]

Failure in cancer treatment often stems from drug resistance, which can manifest as either intrinsic (pre-existing) or acquired (induced by drugs). Despite extensive efforts, overcoming this resistance remains a challenging task due to the intricate and highly individualized biological mechanisms involved. This paper introduces an innovative extension of an already well-established mathematical model to account for tumour resistance development against chemotherapy. This study examines the existence and local stability of model solutions, as well as exploring the model asymptotic dynamics. Additionally, a numerical analysis of the optimal control problem is conducted using an objective functional. The numerical simulations demonstrate that a constant anti-angiogenic treatment leads to a concatenation of bang-bang and singular intervals in chemotherapy control, resembling a combined protocol comprising maximal tolerated dose and metronomic protocols. This observation lends support to the hypothesis that mean-dose chemotherapy protocols may help circumvent acquired drug resistance. Lastly, a sensitivity analysis is undertaken to scrutinize the dependence of model parameters on the outcomes of the previously examined therapeutic protocols. • We consider a heterogeneous tumour consisting of two cellular subpopulations: sensitive and resistant to chemotherapy. • We change the therapy paradigm from total tumour eradication to controlling tumour growth and preventing drug resistance. • An optimal chemotherapy scheduling directly penalizes the subpopulation of resistant cells. • Penalizing chemoresistance gives rise to singular controls, supporting the metronomic chemotherapy administration scheme. • Sensitivity analysis shows how the optimal control structure depends on model parameters and initial conditions. [ABSTRACT FROM AUTHOR]

The multi drug-resistant Neisseria gonorrhoeae has been classified by the World Health Organisation (WHO) as a high-priority global public health problem. This underlines the need for better understanding of the transmission dynamics and proposing an optimal intervention strategy to control the disease. In this article, a deterministic mathematical model for the transmission dynamics of gonorrhoea as an antibiotic resistant disease in a population with an imperfect vaccination is proposed and analysed. The model incorporates the classes of vaccinated individuals and individuals equipped with self protection interventions to reduce antibiotic resistant cases. The threshold parameter R 0 , the basic reproduction number, for the analysis of the model is calculated. In the given setting, the model exhibits a backward bifurcation for R 0 < 1. However, if the efficacy of the vaccine is 100% without a waning effect, the model is shown to be without a backward bifurcation and the disease-free equilibrium is globally asymptotically stable whenever R 0 < 1. The global sensitivity analysis of the model to variations in parameter values is also performed to determine the most influential parameters on the disease transmission. Moreover, the optimal control analysis of the full model is presented and the optimal intervention strategies are proposed. The proposed intervention strategies are shown to be able to control the disease within a relatively shorter period of time. Finally, numerical experiments are presented to support the theoretical analysis of the model. • Imperfect vaccination is not helpful to eradicate the disease from the population. • Awareness campaign about the antibiotic resistant strain can reduce the transmission of the disease. • Sensitive parameters for the transmission dynamics of the disease are identified. • With the use of the optimal control analysis, cost effective intervention measures are proposed. [ABSTRACT FROM AUTHOR]

Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model's performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non‐separation yields the best approximation to the full order model. [ABSTRACT FROM AUTHOR]

Signal decomposition is crucial in several domains, particularly in the dissection of complex signals present in electrical power systems. Understanding the oscillations and patterns within these signals can significantly influence energy resource management, grid stability, and efficient system operation. This paper presents an advanced enhanced decomposition method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to mitigate the inherent drawbacks of the conventional CEEMDAN and its improved version. Unlike CEEMDAN's generalized noise approach, the proposed method introduces adaptive noise, enhancing target signal noise handling by incorporating a tailored filtering and updating process after each iteration. This leads to more accurate signal decomposition compared to traditional methods. Comprehensive tests were conducted using artificially generated signals characterized by mode mixing, varying frequency oscillations, complex real‐world electrical demand signals, generator axis vibrations and partial discharge signals. The results demonstrate that the proposed method outperforms traditional techniques in two significant aspects. First, it provides superior spectral separation of the intrinsic modes (IMF) of the signal, thereby enhancing decomposition accuracy. Second, it significantly reduced the number of shifting iterations, thereby alleviating the computational load. These advancements have led to a more accurate and efficient framework that is essential for analyzing nonlinear and nonstationary signals. [ABSTRACT FROM AUTHOR]

The focus of this study is to optimize the exploration of biomass-driven multi-energy systems, which include combined heat, power, and gas generation. The objective is to enhance the thermal, environmental, and economic performance indicators of the system. The optimization objectives encompass the quantities of internal combustion engines and air source heat pumps, as well as the dimensions of tanks utilized for anaerobic fermentation. A mathematical model was developed to optimize multiple objectives for combined heat, power, and gas generation systems by employing multi-objective intelligent optimization algorithms. The validation and analysis were conducted using rural residences in Lanzhou, Gansu Province, China, as a case study. The sensitivity analysis of biomass gasification combined heat and power systems was conducted from both technical and cost perspectives, examining the dynamic impact characteristics on the outcomes of multi-objective optimization. The findings indicate that the annual energy-saving rate of the optimized combined generation system decreased from 3.62% to -6.78%, while the growth in carbon emissions reduction rate increased from 76.05 to 81.38%, and the annual cost-saving rate grew from 0.97 to 14.96%. The power generation efficiency of the cogeneration station and hydraulic retention time were found to have a significant impact on the multi-objective optimization results of the combined generation system among the technical parameters. The unit cost of anaerobic fermentation tanks had a more significant impact on the multi-objective optimization results in terms of cost parameters, compared to the price of biogas residue. [ABSTRACT FROM AUTHOR]

Objective: To estimate the costs and cost-effectiveness of introducing highly active antiretroviral therapy (HAART) in Denmark based on real-world evidence for the three treatment eras pre-HAART (1985–1995), early HAART (1996–2005), and late HAART (2006–2017). Methods: We performed a cohort study using Danish clinical and administrative registries to estimate costs, quality-adjusted life-years (QALYs), and life-years (LY) gained per person living with human immunodeficiency virus (PLHIV) in three treatment eras. The study utilized Markov modeling for a health economic evaluation, which summarized inputs from real-world evidence and estimated the cost-effectiveness in 2017 prices of the introduction of HAART in Denmark. We performed deterministic and probabilistic sensitivity analyses to assess the robustness of the results. Results: The total annual costs per PLHIV increased with the introduction of HAART for the index year but decreased in the incremental years and the last year of life. The total lifetime discounted (and undiscounted) cost for an average PLHIV was €91,010 (€128,981) in pre-HAART, €103,130 (€199,062) in early HAART, and €126,317 (€254,964) in late HAART. The estimated incremental cost-effectiveness ratios showed that early HAART was cost-effective compared with pre-HAART with an incremental cost-effectiveness ratio (ICER) of €1378 per QALY, and that late HAART was cost-effective compared with early HAART with an ICER of €7385 per QALY. Sensitivity analyses confirmed cost-effectiveness in all scenarios. Conclusions: The introduction and implementation of HAART in Danish healthcare was cost-effective, and in some scenarios, even disruptive, i.e., led to both cheaper and more effective care of PLHIV. [ABSTRACT FROM AUTHOR]

In this article, I propose a tractable approach to Bayesian inference in a simple linear regression model for which the standard exogeneity assumption does not hold. By specifying a beta prior for the squared correlation between an error term and regressor, I demonstrate that the implied prior for a bias parameter is t-distributed. If the posterior distribution for the identified regression coefficient is normal, this implies that the posterior distribution for the unidentified treatment effect is the convolution of a normal distribution and a t-distribution. This result is closely related to the literatures on unidentified regression models, imperfect instrumental variables, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

An extensive methodology for analyzing the impact of catchment and sewer network retention on drainage system operating conditions during hydraulic overloading is presented. To evaluate the performance of the sewer system and identify the need for repair actions, logistic regression models were developed to predict the unit flooding volume and manhole overflowing. An advanced sensitivity analysis was performed to determine the key parameters (retention and roughness of impervious and pervious areas as well as sewer channel retention) conditioning the reduction of uncertainty in the simulation results and ensuring the assumed hydraulic effect. A coefficient expressing the quotient of the duration of rainfall conditioning the exceedance of the limits of the unit flooding volume (13 m³·ha−1) as well as the degree of overflowed manholes (0.32) was determined, allowing the determination of the key performance criterion of the sewer network to take corrective action depending on field and channel retention. It was shown that the catchment area retention had the key influence on the conditions of sewer operation and the probability of remedial work. Increasing the rainfall duration led to a decrease in sensitivity coefficients with respect to the identified parameters of the SWMM model, which is important when selecting rainfall events for the calibration and validation sets. The usefulness of the developed methodology was demonstrated at the stage of building mechanistic models, which is of significance when planning field studies. [ABSTRACT FROM AUTHOR]

Background: Patients with osteoporosis (OP) are often associated with decreased hand grip strength and increased risk of falling. It remains unclear whether there is a genetic causal between hand grip strength and OP, falling risk. Methods: The Mendelian randomization study was used to investigate the genetic causal effect of low hand grip strength on total body bone mineral density (BMD) at different ages, OP, and falling risk. Genes for low hand grip strength, total body BMD at different ages, OP, and falling risk were obtained from published genome-wide association studies. Inverse variance weighted, MREgger, and weighted median were applied to perform the MR analysis. The Cochran's Q test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis were used to detect the pleiotropy or heterogeneity. Results: The results showed strong evidence that low hand grip strength was positively associated with OP (OR: 1.006, 95% CI: 1.003-1.010; P= 0.0001) and falling risk (OR: 1.069, 95% CI: 1.013-1.129; P= 0.0160), and could not directly affect the different ages of total body BMD (P> 0.05). There was no heterogeneity or horizontal pleiotropy in the sensitivity analysis (all P> 0.05). Conclusion: The study found a positive causal relationship between low hand grip strength and higher risk of OP and falling, which should be taken into account in the development of future prevention and screening strategies for OP and falling. [ABSTRACT FROM AUTHOR]

Background: Our goal was to examine the causal link between blood metabolites, their ratios, and the risk of developing proliferative diabetic retinopathy (PDR) from a genetic insight. Methods: Summary‐level data about 1400 blood metabolites and their ratios, as well as PDR, were sourced from prior genome‐wide association studies (GWAS). A two‐sample univariate and multivariate Mendelian randomization (MR) approach was utilized. Additionally, metabolic pathway analysis and sensitivity analysis were also conducted. Results: After adjusting for multiple tests, four blood metabolites significantly correlated with PDR risk. Two ceramides, including glycosyl‐N‐palmitoyl‐sphingosine (d18:1/16:0) (odds ratio [OR] = 1.12, 95% confidence interval (CI): 1.06–1.17, p < 0.001, false discovery rate (FDR) = 0.005) and glycosyl‐N‐behenoyl‐sphingadienine (d18:2/22:0) (OR = 1.11, 95% CI: 1.06–1.16, p < 0.001, FDR = 0.017), were linked to increased risk. Additionally, 3‐methylcytidine (OR = 1.05, 95% CI: 1.03–1.08, p < 0.001, FDR = 0.021) also posed a risk, whereas (N(1)+N(8))‐acetylspermidine (OR = 0.91, 95% CI: 0.87–0.94, p < 0.001, FDR = 0.002) appeared protective. Multivariable MR analysis further confirmed a direct, protective effect of (N(1)+N(8))‐acetylspermidine on PDR risk (OR = 0.94, 95% CI: 0.89–1.00, p = 0.040). The sensitivity analysis results indicated that evidence for heterogeneity and pleiotropy was absent. Conclusion: These metabolites have the potential to be used as biomarkers and are promising for future research into the mechanisms and drug targets for PDR. [ABSTRACT FROM AUTHOR]

Background: The relationship between serum uric acid (SUA) and osteoporosis (OP) has yielded conflicting results in observational studies. This Mendelian randomization (MR) study aims to elucidate the causal association between SUA and OP. Methods: A two-sample MR analysis was conducted using summary-level data from genome-wide association studies (GWAS). Two sets of polygenic instruments strongly associated (p < 5 × 10-8) with SUA were extracted from the CKDGen consortium and UK biobank. Polygenic instruments associated with OP (p < 5 × 10-8) were derived from FinnGen biobank and UK biobank. Inverse variance weighting (IVW) was employed as the primary analysis method. Additionally, we utilized MR-Egger, weighted median, the simple mode method, and the weighted mode as complementary analyses. Cochran's Q statistics were used to assess heterogeneity, with MR-Egger intercept testing and MR pleiotropy residual sum and outlier (MR-PRESSO) to examine horizontal pleiotropy. Sensitivity analysis was performed using the leave-one-out method. Results: The IVW analysis conducted across four groups confirms no significant causal relationship between SUA concentration and OP: UKB-UKB (OR: 1.001, 95% CI: 0.999-1.003, p=0.464), CKD-UKB (OR: 1.001, 95% CI: 0.999-1.003, p=0.349), UKB-Fin (OR: 0.934, 95% CI: 0.747-1.168, p=0.549), CKD-Fin (OR: 1.041, 95%CI: 0.934-1.161, p=0.470). Furthermore, additional four MR analyses corroborated these findings. Upon excluding all outliers identified by the MRPRESSO test, no significant directional pleiotropy was observed, except for some data heterogeneity noted in the UKB-UKB group (Q=50.65, P=0.002). Additionally, a leave-one-out analysis indicated that no single SNP exerted undue influence on the results. Conclusion: This MR analysis provides convincing genetic evidence that there is no causal association between SUA and OP, SUA is unlikely to increase or reduce the risk of OP. [ABSTRACT FROM AUTHOR]

Tree plantations have become a common economic activity in the Ethiopian Highlands, particularly in our study areas. This is mainly due to the degradation and restrictions of natural forests, the introduction and expansion of fast‐growing tree species, and the potential short‐term economic benefits of woodlot plantations. Our research was conducted in Dangila and the nearby Adiskedam Woreda in North‐western Ethiopia to evaluate the profitability of monoculture and intercropping land‐use systems. We used the land expectation value (LEV) model developed by Faustmann to assess the profitability of the different land‐use systems in the study area. We obtained production costs and benefits of the land‐use system from the farmers through questionnaires and key informant interviews. Data analysis was carried out using Excel (2016) and SPSS statistical software. The findings revealed that the Acacia decurrens (Ad) farming practice showed the highest LEV at all interest levels, followed by intercropping land use. At a 15% interest rate, the eucalyptus with maize (E + M) land‐use system was the second most profitable with a value of 3677.5 USD. However, at 5% and 2% interest rates, the Ad with maize (Ad + M) land‐use system demonstrated the second highest LEV of 14,585.6 and 39,695.1 USD, respectively. The M land‐use system had the lowest LEV at all interest rates except at a 15% interest rate. The sensitivity analysis showed that fluctuations in product prices significantly impacted all land use systems, except M. In addition, changes in interest rates were the secondary factor affecting the Ad, E, and Ad + M land‐use systems. The LEV of M monoculture was notably influenced by the wage rate; however, it had the least impact on the other land‐use systems. Taking into account the interests of farmers, the profitability of land‐use systems, and the perception of farmers concerning adjacent land productivity, Ad and Ad intercropping with the M farming systems are recommended, and identifying the optimal mix of intercropping and optimum tree rotation age is fundamental in the future, which could increase productivity and make the farmer more profitable. [ABSTRACT FROM AUTHOR]

Human biomonitoring (HBM) data indicate that exposure to pyrethroids is widespread in Europe, with significantly higher exposure observed in children compared to adults. Epidemiological, toxicological, and mechanistic studies raise concerns for potential human health effects, particularly, behavioral effects such as attention deficit hyperactivity disorder (ADHD) in children at low levels of exposure. Based on an exposure-response function from a single European study and on available quality-assured and harmonized HBM data collected in France, Germany, Iceland, Switzerland, and Israel, a preliminary estimate of the environmental burden of disease for ADHD associated with pyrethroid exposure was made for individuals aged 0–19 years. The estimated annual number of prevalence-based disability-adjusted life years (DALYs) per million inhabitants were 27 DALYs for Israel, 21 DALYs for France, 12 DALYs for both Switzerland and Iceland, and 3 DALYs for Germany; while the annual ADHD cases per million inhabitants attributable to pyrethroids were 2189 for Israel, 1710 for France, 969 for Iceland, 944 for Switzerland, and 209 for Germany. Direct health costs related to ADHD ranged between 0.3 and 2.5 million EUR yearly per million inhabitants for the five countries. Additionally, a substantial number of ADHD cases, on average 18%, were associated with pyrethroid exposure. Yet, these figures should be interpreted with caution given the uncertainty of the estimation. A sensitivity analysis showed that by applying a different exposure-response function from outside the EU, the population attributable fraction decreased from an average of 18 to 7%. To ensure more robust disease burden estimates and adequate follow-up of policy measures, more HBM studies are needed, along with increased efforts to harmonize the design of epidemiological studies upfront to guarantee meta-analysis of exposure-response functions. This is particularly important for pyrethroids as evidence of potential adverse health effects is continuously emerging. [ABSTRACT FROM AUTHOR]

Predicting impacts of potential carbon dioxide (CO2) leakage into shallow aquifers that overlie geologic CO2 storage formations is an important part of developing reliable carbon storage technology. To quantifying the effect of permeability anisotropy, a three-dimensional hypothetical reservoir model was formulated to analyze the migration behavior of CO2 under diverse permeability anisotropy scenarios. Sensitivity analyses for parameters corresponding to the permeability anisotropy and the leakage rate are conducted, and the results suggest that permeability anisotropy significantly affect the CO2 migration characteristics. Increasing the parameter of vertical/horizontal permeability ratio results in longer CO2 migration distances, which enhances the aqueous phase ratio and safety through more interaction with the aquifer, but also raises the potential of the leakage reaching the ground surface due to higher gas ratio. A comprehensive understanding of these dynamics is crucial for implementing effective monitoring and management strategies. [ABSTRACT FROM AUTHOR]

Malaria remains a substantial public health challenge and economic burden globally. Currently, malaria has been declared as endemic in 85 countries. In this study, we developed and analyzed a fractional-order mathematical model for malaria transmission dynamics that incorporates variability of temperature and rainfall using Caputo-type AB operators. The existence and uniqueness of the model's solutions were established using the Banach fixed-point theorem. The model system's equilibria (both disease-free and endemic) were identified, and lemmas and theorems were developed to prove their stability. Furthermore, we used different temperature ranges and rainfall data, validating them against existing literature. Numerical simulations using the Toufik-Atangana schemes with various fractional-order alpha values revealed that as the value of alpha approaches 1, the behavior of the fractional-order model converges to that of the classical model. The numerical results are promising and are expected to be valuable for future research related to fractional-order models. [ABSTRACT FROM AUTHOR]

The Strategic Research Agenda (SRA; https://www.ejp-eurad.eu/publications/eurad-sra) of the European Joint Programme on Radioactive Waste Management (EURAD; https://www.ejp-eurad.eu/) describes the scientific and technical domains and sub-domains and knowledge management needs of common interest between EURAD participant organizations. Theme number 7 is entitled "Performance assessment, safety case development and safety analyses." A list of research and development priorities and activities of common interest to be addressed within EURAD for theme 7 have been established. Amongst others, the Understanding and modelling of multi-physical Thermo-Hydro-Mechanical-Chemical coupled processes (THMC) occurring in radioactive waste disposal is a major and permanent issue to support optimization of design and safety case abstraction. To tackle this challenge a research work package entitled "DONUT: Development and improvement of numerical methods and tools for modelling coupled processes" has been conducted within the EURAD join programming initiative. The purpose of this work package is to improve/develop methods or numerical tools in order to go a step further in development of (i) relevant, performant and cutting-edge numerical methods that can easily be implemented in existing or new tools, in order to carry out high-performance computing to facilitate the study of highly coupled processes in large systems, (ii) numerical scale transition schemes for coupled processes, (iii) innovative numerical methods to carry out uncertainty and sensitivity analyses. In this paper the work carried out within the DONUT work package is put in perspective regarding the existing concept and literature on the field. It does not pretend to be exhaustive but rather to put emphasis on particular issues tackled during the project. [ABSTRACT FROM AUTHOR]

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm. [ABSTRACT FROM AUTHOR]

The establishment of a natural gas production model under multi factor control provides support for the formulation of planning schemes and exploration deployment decisions, and is of great significance for the rapid development of natural gas. Especially the growth rate and decline rate of production can be regulated in the planning process to increase natural gas production. The exploration and development of conventional gas in the Sichuan Basin has a long history. Firstly, based on the development of conventional gas production, the influencing factors of production are determined and a production model under multi factor control is established. Then, single factor analysis and sensitivity analysis are conducted, and multi factor analysis is conducted based on Bayesian networks. Finally, combining the multivariate Gaussian mixture model and production sensitivity analysis, a production planning model is established to predict production uncertainty under the influence of multiple factors. The results show that: 1) the production is positively correlated with the five influencing factors, and the degree of influence is in descending order: recovery rate, proven rate, growth rate, decline rate, and recovery degree. After being influenced by multiple factors, the fluctuation range of production increases and the probability of realization decreases. 2) The growth rate controls the amplitude of the growth stage, the exploration rate and recovery rate control the amplitude of the stable production stage, the recovery degree controls the amplitude of the transition from the stable production stage to the decreasing stage, and the decreasing rate controls the amplitude of the decreasing stage. 3)The article innovatively combines multiple research methods to further obtain the probability of achieving production under the influence of multiple factors, providing a reference for the formulation of production planning goals. [ABSTRACT FROM AUTHOR]

Background: The ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study. Methods: We adopt the estimand framework in the context of a study using "trial emulation" to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A "while on treatment" strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis. Results: The primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p < 0.001). For the sensitivity analysis, when we apply the "jump to off prophylaxis" approach, the hazard ratio is almost the same compared to that from the primary analysis (HR 0.80 [0.69, 0.95], p = 0.009). The sensitivity analysis confirmed that the new regime exhibits a clear improvement over the existing guidelines for PcP prophylaxis when those lost to follow-up "jump to off prophylaxis". Conclusions: Our application using reference based multiple imputation demonstrates the method's flexibility and simplicity for sensitivity analyses in the context of the estimand framework for (emulated) trials. [ABSTRACT FROM AUTHOR]

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation. [ABSTRACT FROM AUTHOR]

For a long time, the decline in lung function has been regarded as a potential factor associated with the risk of osteoporosis (OP). Although several observational studies have investigated the relationship between lung function and OP, their conclusions have been inconsistent. Given that Mendelian randomization (MR) studies can help reduce the interference of confounding factors on outcomes, we adopted this approach to explore the causal relationship between lung function and OP at the genetic level. To investigate the potential causality between lung function (FVC, FEV1, FEV1/FVC, PEF) and OP, we conducted a MR analysis employing three approaches: inverse variance weighted (IVW), MR-Egger, and weighted median. We used Cochran's Q test to detect potential heterogeneity, MR-Egger regression to evaluate directional pleiotropy, and the MR-PRESSO method to evaluate horizontal pleiotropy. In addition, we used MR-PRESSO and MR radial methods to exclude SNPs exhibiting pleiotropic outliers. Upon identification of potential outliers, we removed them and subsequently ran MR analysis again to assess the reliability of our findings. The MR analysis suggested that there was no causal effect of lung function (FVC, PEF, FEV1/FVC, FEV1) on OP, which is consistent with the. results after excluding potential outliers using MR-PRESSO and MR radial. methods. Sensitivity analysis confirmed the reliability and consistency of these. results. The study concluded that there is no causal link between lung function and OP. The association found in observational studies might be attributable to shared risk factors. [ABSTRACT FROM AUTHOR]

Central serous chorioretinopathy (CSC) is a prevalent exudative maculopathy. Understanding risk factors for CSC is important for disease prevention and to provide evidence‐based advice to patients. In this study, we systematically reviewed the literature and performed meta‐analysis on the association between tobacco consumption and CSC. We searched 12 literature databases on May 5, 2024, and identified 11 eligible studies of 27 595 patients with CSC and 105 354 control individuals. Studies were predominantly clinic‐based case–control studies. We calculated a summary estimate of tobacco consumption as a risk factor for CSC at an odds ratio of 2.99 (95% CI: 1.82–4.93, p = 0.000017), which remained statistically significant in the sensitivity analyses. The exact mechanism by which tobacco consumption contribute to the pathophysiology of CSC remains unclear, although several potential hypotheses exist. However, tobacco consumption is a modifiable behaviour and tobacco cessation is an actionable advice with which patients with CSC themselves can play a large role in disease management. Further studies are warranted to understand the impact of tobacco cessation for risk modification and for the prognosis of patients who already have CSC. [ABSTRACT FROM AUTHOR]

An accurate finite element model (FEM) plays a critical role in the structural damage identification. However, due to the existence of the uncertainties, such as material properties and modeling errors, it always exists some gaps between the analytical FEM and experimental structure. While an artificial neural network (ANN)-based model updating methods have been widely adopted to narrow the gap and obtain a baseline FEM, it still faces inaccurate results and fails to meet the physical law. In this regard, the study proposes a novel physics-based loss function inspired by modal sensitivity analysis and incorporates it into the residual neural network, thereby forming a novel physics-guided neural network (PGNN) method. The mapping relationship between the input of structural responses and output of model updating variables is constrained to retain its physical meaning by guiding the training process instead of pure data association, which aims to improve the accuracy of the ANN-based method and achieve accurate and high-efficiency model updating. An experimental example of a continuous rigid frame bridge is adopted to verify the feasibility of the proposed method. Additionally, other common model updating methods, including moth-flame optimization and regularization method, are used to make a comparison. The noise-robustness of the proposed method is investigated as well. Compared to the existing method, the results illustrate that the proposed PGNN method can achieve better model updating and good noise-robustness under high uncertainties, which means the introduction of the physics-based loss function significantly enhances the parameters updating ability of the neural network. The proposed method exhibits high efficiency and promising potential for large-scale bridge structure model updating. [ABSTRACT FROM AUTHOR]

Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete. [ABSTRACT FROM AUTHOR]

Objective To explore the causal relationship between testosterone and endometriosis, andto identify and quantify the role of gut microbiota as a potential mediator through Mendelian randomization analysis. Methods Utilizing the summary data from the IEU GWAS database for testosterone (n=199 569) and endometriosis (8 288 cases and 68 969 controls) as the exposure and outcome variables, and the aggregated gut microbiota data from the MiBioGen Consortium (n=18 340) as the mediator, a bidirectional, two-step Mendelian randomization analysis was conducted. Causal relationships were assessed using inverse varianceweighted, MR-Egger, weighted median, weighted model, and simple modelto assess causality, while sensitivity analysis was conducted to ensure the robustness of the findings. Results Impact of testosterone on endometriosis:Inverse variance weighted estimates suggested that testosterone had negative causal effects on endometriosis (OR=0.804, 95%CI:0.670-0.964, P=0.018). Impact of endometriosis on testosterone:Reverse Mendelian randomization showed that endometriosis did not present a causal effect on testosterone (OR=0.991, 95%CI:0.958-1.026, P=0.616). The class Melainabacteria, one of the gut microbiotas, accounted for a 17.21% reduction in the risk of endometriosis associated with testosterone (mediation proportion: 17.21%, 95%CI:0.090-0.264, P=0.035). Conclusion Testosterone is one of the protective factors of endometriosis. Low testosterone levels increase the risk of endometriosis. Gut microbiota may be a mediator of the causal effect of testosterone on endometriosis. [ABSTRACT FROM AUTHOR]

Simple Summary: In this article, the authors introduce a new metric called the modified Fragility Index (mFI) to assess the accuracy of determining the maximum tolerated dose (MTD) in early oncology clinical trials. The mFI measures how sensitive the MTD decision is to the inclusion of a few more participants in the trial. The authors analyzed three published cancer trials and found that two trials were robust to adding more participants, indicating that the MTD estimate remained stable. However, in the other trial, the MTD estimate was more fragile and could have changed with just one or two more participants. The mFI metric helps researchers make more reliable decisions about the appropriate MTD. By considering the potential impact of additional participants, researchers can improve accuracy and confidence in dose determination, leading to better treatment outcomes for patients. Objectives: The sample sizes of phase I trials are typically small; some designs may lead to inaccurate estimation of the maximum tolerated dose (MTD). The objective of this study was to propose a metric assessing whether the MTD decision is sensitive to enrolling a few additional subjects in a phase I dose-finding trial. Methods: Numerous model-based and model-assisted designs have been proposed to improve the efficiency and accuracy of finding the MTD. The Fragility Index (FI) is a widely used metric quantifying the statistical robustness of randomized controlled trials by estimating the number of events needed to change a statistically significant result to non-significant (or vice versa). We propose a modified Fragility Index (mFI), defined as the minimum number of additional participants required to potentially change the estimated MTD, to supplement existing designs identifying fragile phase I trial results. Findings: Three oncology trials were used to illustrate how to evaluate the fragility of phase I trials using mFI. The results showed that two of the trials were not sensitive to additional subjects' participation while the third trial was quite fragile to one or two additional subjects. Conclusions: The mFI can be a useful metric assessing the fragility of phase I trials and facilitating robust identification of MTD. [ABSTRACT FROM AUTHOR]

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we develop a multi-item inventory model with stochastic demand, and the demand is considered as a random variable following a power demand pattern. A cumulative cycle demand is first identified for each product, and then the demand is gradually released to the inventory system by the power patterns within a cycle. The replenishment schedule is predetermined and well-known. Shortages are permitted but partially backlogged, and the rest are taken as lost sales. An effective inventory policy is utilized to reduce the total expected cost per unit of time by acquiring the optimal amount of stock to be stored at the start of the inventory cycle, which may protect against excessive lost sales and also avoid overstocking. This model is better suited for use in online purchases and the offline market where the buyer is assured that the goods will be shipped within a few days rather than at the time of purchase. We evaluated the system with items deteriorating instantaneously, which makes it appropriate to have a larger initial demand with a power pattern index greater than one. Numerical illustrations and sensitivity analysis provide an efficient tool for the insights of the theoretical solutions over the managerial foundation. [ABSTRACT FROM AUTHOR]

The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the perturbations. Eliminating small scales from the perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates. Plain Language Summary: This study examines a deep‐learning approach to understanding how small changes to initial conditions impact weather forecasts. Traditionally, a linear approach known as the adjoint method has been used to determine the sensitivity of forecasts to initial conditions. We leverage recent advancements in machine learning to find optimal initial conditions using the backpropagation method within deep‐learning frameworks. This approach iteratively searches for initial conditions that produce the best forecasts. We apply this method to GraphCast forecasts of the June 2021 Pacific Northwest extreme heatwave. We find that small changes to the initial conditions yield nearly perfect 10‐day weather forecasts of the heatwave in both the GraphCast and the Pangu‐Weather models. This research suggests that a significant increase in forecast skill may be possible from existing observations through better estimates of initial conditions. Key Points: We use nonlinear gradient descent to optimize initial conditions for weather forecasting with machine learning modelsApplication to the Pacific Northwest June 2021 heatwave reduces 10‐day forecast error by over 90 percentForecast improvements are not sensitive to the forecast model, and derive mainly from analysis errors on synoptic and larger scales [ABSTRACT FROM AUTHOR]

The pipe-soil interaction force is a crucial load that ensures the on-bottom stability of submarine pipelines and acts as a vital input parameter for the design of controlled pipeline buckling. It is necessary to precise prediction of soil lateral resistance and sensitivity to environmental parameters. This study developed a numerical simulation technique based on the coupled Euler-Lagrange (CEL) method to systematically analyse the deformation of the pipeline and the soil-pipeline forces on the seabed foundation during service. To validate the numerical method for large lateral displacement of pipe-soil interaction, an experimental approach employing parameter variation was utilized to conduct sensitivity analysis on the environmental parameters. The key parameters affecting soil-pipeline interaction were determined through an analysis of relative variation factors. The research findings demonstrate a satisfactory agreement, with an amplitude difference range of within 15 %, between the numerical simulation technique using the CEL method and the experimental results. This method resolves the issue of non-convergence due to grid distortion under conditions of large displacement, thereby enhancing computational accuracy. Sensitivity analysis shows that the relative variation factors for soil friction angle, initial burial depth, and soil density exceed 50 %, which is notably higher than the elastic modulus, friction coefficient, and Poisson's ratio of the soil. The results of this study enable the prediction of lateral forces in pipe-soil interaction and identification of environmentally sensitive parameters. This study establishes a foundation for calculating pipeline buckling strength in complex environments and holds paramount importance for the design and construction of pipelines to ensure on-bottom stability. [ABSTRACT FROM AUTHOR]

There is increasing evidence of a significant association between the gut microbiome and juvenile idiopathic arthritis (JIA). However, whether this association is causal remains to be determined. This study was a two‐sample Mendelian randomization (MR) study using publicly available genome‐wide association study (GWAS) summary data to investigate the causal relationship between the gut microbiome and JIA. We used summary data on gut flora and JIA obtained from genome‐wide association studies (GWAS) from MiBioGen and NHGRI‐EBI, using inverse variance weighting as the main method to analyse causality in the TSMR causality analysis. To check the stability of the TSMR results, we performed several sensitivity analyses and assessed the presence of reverse causality through a reverse TSMR analysis. We calculated the degree of sample overlap where applicable. The current TSMR analyses identified four bacterial taxa associated with JIA. Specifically, two bacteria, Catenibacterium (p = 2 × 10–2) and Holdemania (p = 4 × 10–2), were negatively associated with the risk of developing JIA, suggesting a protective effect, while Olsenella (p = 1 × 10–2) and Rikenellaceae (RC9gutgroup) (p = 1 × 10–2) were positively associated with the risk of JIA, suggesting that these two bacteria may be risk factors for JIA. However, the results for Catenibacterium and Holdemania should be interpreted with caution due to instability observed in 'leave‐one‐out' sensitivity analyses. Reverse TSMR analyses found no evidence of reverse causality between JIA and gut flora. Our confirmation of a causal relationship between gut flora and JIA provides an innovative perspective for the study of JIA: targeting and modulating dysregulation of specific bacterial taxa to prevent and treat JIA. [ABSTRACT FROM AUTHOR]

Groundwater salinization is a crucial socio-economic and environmental issue that is significant for a variety of reasons, including water quality and availability, agricultural productivity, health implications, socio-political stability and environmental sustainability. Salinization degrades the quality of water, rendering it unfit for human consumption and increasing the demand for costly desalination treatments. Consequently, there is a need to find simple, sustainable, green and cost-effective methods that can be used in understanding and minimizing groundwater salinization. Therefore, this work employed the implementation of cost-effective neurocomputing approaches for modeling groundwater salinization. Before starting the modeling approach, correlation and sensitivity analyses of the independent and dependent variables were conducted. Hence, three different modeling schema groups (G1–G3) were subsequently developed based on the sensitivity analysis results. The obtained quantitative results illustrate that the G2 input grouping depicts a substantial performance compared to G1 and G3. Overall, the evidential neural network (EVNN), as a novel neurocomputing technique, demonstrates the highest performance accuracy, and has the capability of boosting the performance as against the classical robust linear regression (RLR) up to 46% and 46.4% in the calibration and validation stages, respectively. Both EVNN-G1 and EVNN-G2 present excellent performance metrics (RMSE ≈ 0, MAPE = 0, PCC = 1, R2 = 1), indicating a perfect prediction accuracy, while EVNN-G3 demonstrates a slightly lower performance than EVNN-G1 and EVNN-G2, but is still highly accurate (RMSE = 10.5351, MAPE = 0.1129, PCC = 0.9999, R2 = 0.9999). Lastly, various state-of-the-art visualizations, including a contour plot embedded with a response plot, a bump plot and a Taylor diagram, were used in illustrating the performance results of the models. [ABSTRACT FROM AUTHOR]

(1) Background: In most cases, passive isolation control methods are commonly used for the seismic isolation design in bridge engineering. However, passive seismic isolation devices, due to their non-adjustable performance parameters, struggle to achieve effective seismic isolation across a wide frequency range of 0 Hz to 20 Hz in response to random and varying seismic loads. (2) Methods: The sensitivity of the design parameters of the seismic isolation bearing was analyzed using the optimization center gradient method, and an improved genetic algorithm was employed to quickly optimize and obtain the optimal design parameters. (3) Results: The effectiveness of the three-dimensional seismic isolation bearing was validated through experiments. (4) Conclusions: The multi-factor sensitivity analysis approach used in this study for designing novel isolation bearings is applicable not only to seismic design in bridges but also serves as a reference for parameter design in isolation bearings requiring medium to high precision in seismic performance. [ABSTRACT FROM AUTHOR]

The objective of this study is to comprehensively evaluate the environmental and economic impacts of remanufactured constant velocity (CV) joints using a newly proposed framework for automotive parts replacement. This framework utilizes actual vehicle sales data to accurately estimate the demand for CV joint replacements. Specific parameters are established to calculate this demand, enabling a quantitative assessment of both environmental and economic impacts associated with remanufacturing CV joints and other automotive parts. Additionally, sensitivity analyses are conducted to explore the relationship between environmental benefits and economic outcomes, particularly focusing on the preferences for remanufactured parts and their profitability. The results indicate that increasing the proportion of remanufactured CV joints significantly benefits the environment by reducing CO2 emissions, raw material usage, and energy consumption. However, this shift also leads to a decrease in total profits within the CV joint replacement market. To address this trade-off, the study suggests that financial incentives, such as subsidies or tax benefits, are necessary to support the remanufacturing industry and facilitate market expansion. These findings provide valuable insights for policymakers aiming to promote sustainable manufacturing practices through effective subsidy policies. [ABSTRACT FROM AUTHOR]

The transition towards sustainable construction is crucial, and demountable building elements are frequently advocated for achieving this goal. While these elements offer relocatability during refurbishments, their adoption may increase initial environmental impact due to higher material use and steel connections. To address this, a quantitative assessment of demountable building elements in refurbishment scenarios at the building level is needed, filling a gap in the existing literature. This study bridges the gap by comparing the total environmental impact of demountable and traditional space-dividing walls in refurbishment scenarios for a semi-detached dwelling. Using a life cycle assessment, seven space-dividing wall types, including metal studs, wood structures, and masonry walls, are evaluated under four refurbishment scenarios spanning a 60-year building lifespan. The results reveal that traditional metal stud walls have a lower environmental impact in scenarios with limited refurbishments. In contrast, demountable walls become more environmentally beneficial only when refurbishing at least 60% of the wall area with three or more refurbishments. This conclusion was further validated through sensitivity analysis on the refurbishment rate, refurbished area, and impact assessment method. In this study, the assumed environmental benefits of demountable walls are challenged, providing a robust evaluation in a specific building typology and offering insights for policymakers and industry professionals on the environmental implications of incorporating demountable building elements. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the methodological, reporting and evidence quality of systematic reviews or meta-analyses of Janus kinases (JAK) inhibitors for the treatment of rheumatoid arthritis (RA). Methods: Our study systematically retrieved reviews from various databases, spanning from inception to June 2024. Two evaluators independently assessed the methodological, reporting, and evidence quality of each review using the AMSTAR-2 and PRIAMA2020 tools. The evidence quality was evaluated according to GRADE criteria. Six aspects were evaluated: publication year, study type, homogeneity, risk of publication bias, AMSTAR-2 methodology, and PRIAMA2020 reporting quality. Excel 2016 facilitated conversion of scores into radar plots. Results: Following stringent selection criteria, a total of 18 relevant studies were identified. The AMSTAR-2 scores ranged from 4 to 13 points, with five studies rated as low quality and the remaining 13 as critically low quality. All studies encompassed populations, interventions, controls, and outcome measures, demonstrating commendable integrity. However, there is room for improvement in study protocol development and registration, comprehensive search strategies, inclusion and exclusion criteria, conflict of interest disclosure, and discussion of heterogeneity. PRIAMA2020 assessments ranged from 14.5 to 21 points, with two studies scoring below 15 points due to increased bias risk from data transformation and sensitivity analysis. Notably, all reviews (100%) adhered to PRIAMA2020 guidelines for certain items but none met all criteria. GRADE evaluation included 446 outcome measures, with 158 of moderate, 156 of low, and 132 of very low quality, indicating JAK inhibitors is effective in improving RA. According to radar chart, the average rank score was 13.13. One study achieved a balanced score across all dimensions, while 11 exceeded the average, five showed significant differences in PRIAMA2020 scores, and four in AMSTAR two scores. Conclusion: Despite summarizing the efficacy and safety of JAK inhibitors in treating RA, the included studies exhibited poor methodological and reporting quality, along with low-quality evidence overall. Therefore, caution is warranted among decision-makers regarding the use of JAK inhibitors in RA treatment. Urgent requirements include high-quality, multicenter studies investigating JAK inhibitors for RA. [ABSTRACT FROM AUTHOR]

Ropeginterferon alfa-2b (Ropeg) is approved for the treatment of adults with polycythemia vera (PV). This report aims to analyze the ethnic sensitivity of Ropeg for the treatment of PV, comparing the pharmacokinetics (PK), efficacy, and safety profiles across diverse ethnic groups. We conducted a relevant review of PV and analysis of data obtained from clinical studies involving Ropeg. The PK behavior of ropeg showed no significant differences between Chinese and overseas populations. Their efficacy and safety profiles were similar across the ethnic groups. The analyses indicated that the dose-exposure-response profile of Ropeg was consistent irrespective of ethnic variations. The results suggest that Ropeg exhibits a consistent PK and pharmacodynamics profile and a similar therapeutic effect across different ethnic groups, confirming its efficacy and safety in the global treatment of PV. More generally, these findings support the broader application of Ropeg in diverse patient populations and emphasize the need for an inclusive clinical practice. [ABSTRACT FROM AUTHOR]

Background: The relationship between gut microbiota and osteoarthritis (OA) occurrence remains unclear. Existing research needs to clearly understand how basal metabolic rate (BMR) regulates this relationship. Therefore, using a two-step bidirectional Mendelian Randomization approach, our study aims to investigate whether BMR levels mediate the causal relationship between gut microbiota and OA. Methods: In this study, we examined publicly available summary statistics from Genome-Wide Association Studies (GWAS) to determine the correlation between gut microbiota and OA. The analysis included one primary dataset and two secondary datasets. Initially, a two-step, two-sample, and reverse MR analysis was performed to identify the causal relationship between gut microbiota and OA. Subsequently, a two-step MR analysis revealed that the relationship between microbiota and OA is mediated by BMR. Sensitivity analyses confirmed the robustness of the study results. Results: In our analysis of the primary dataset, we discovered a positive correlation between three taxa and the outcome of OA, and eight taxa exhibited a negative correlation with the OA outcome. Through comparisons with the secondary dataset and multiple testing corrections, we found a negative association between the class Actinobacteria (OR=0.992886277, pvalue = 0.003) and the likelihood of OA occurrence. Notably, knee osteoarthritis (KOA) and hip osteoarthritis (HOA) had a strong negative correlation (OR = 0.927237553/0.892581219). Our analysis suggests that BMR significantly mediates the causal pathway from Actinobacteria to OA, with a mediated effect of 2.59%. Additionally, BMR mediates 3.98% of the impact in the path from the order Bifidobacteriales and the family Bifidobacteriaceae to OA. Besides these findings, our reverse analysis did not indicate any significant effect of OA on gut microbiota or BMR. Conclusion: Our research results indicate that an increase in the abundance of specific gut microbial taxa is associated with a reduced incidence of OA, and BMR levels mediate this causal relationship. Further large-scale randomized controlled trials are necessary to validate the causal impact of gut microbiota on the risk of OA. This study provides new insights into the potential prevention of OA by modulating the gut microbiota. [ABSTRACT FROM AUTHOR]

The decline in regeneration efficiency after birth in mammals is a significant roadblock for regenerative medicine in tissue repair. We previously developed a computational agent based-model (ABM) that recapitulates mechanical interactions between cells and the extracellular-matrix (ECM), to investigate key drivers of tissue repair in adults. Time calibration alongside a parameter sensitivity analysis of the model suggested that an early and transient decrease in ECM cross-linking guides tissue repair toward regeneration. Consistent with the computational model, transient inhibition or stimulation of fiber cross-linking for the first six days after subcutaneous adipose tissue (AT) resection in adult mice led to regenerative or scar healing, respectively. Therefore, this work positions the computational model as a predictive tool for tissue regeneration that with further development will behave as a digital twin of our in vivo model. In addition, it opens new therapeutic approaches targeting ECM cross-linking to induce tissue regeneration in adult mammals. [ABSTRACT FROM AUTHOR]

Validating large-scale water quality models is challenging because of the variety of water quality constituents, and scales for which observations are limited. Here, in this perspective, we propose 13 alternative strategies to build trust in large-scale water quality models beyond validation and discuss their strengths and weaknesses regarding their validity, reliability, and applicability. Our alternative strategies aim to evaluate separately model inputs (Strategies 1–4), outputs (Strategies 5–6) and structures (Strategy 7) as well as these aspects together (Strategies 8–13). This is done via methods such as comparisons (Strategies 1–3, 6–8, 12–13), sensitivity analysis (Strategy 5), use of innovations (Strategy 9), expert knowledge (Strategy 11) and local models (Strategy 13). The proposed strategies vary in their validity, reliability, and applicability. Validation is an important starting point but should be used in combination with other strategies. Our proposed list opens the discussion to improve methods to evaluate global water quality models. [ABSTRACT FROM AUTHOR]