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A mathematical model is designed to investigate Tuberculosis (TB) disease under the vaccination, treatment, and environmental impact with real cases. First, we introduce the model formulation in non-integer order derivative and then, extend the model into fractional order derivative. The fractional system's existence, uniqueness, and other relevant properties are shown. Then, we study the stability analysis of the equilibrium points. The disease-free equilibrium (DFE) 0 is locally asymptotically stable (LAS) when ℛ v < 1. Further, we show the global asymptotical stability (GAS) of the endemic equilibrium (EE) ∗ for ℛ v > 1 and 0 for ℛ v ≤ 1. The existence of bifurcation analysis in the model is investigated, and it is shown the system possesses the forward bifurcation phenomenon. Sensitivity analysis has been performed to determine the sensitive parameters that impact ℛ v. We consider the real TB statistics from Khyber Pakhtunkhwa in Pakistan and parameterized the model. The computed basic reproduction number obtained using the real cases is ℛ 0 ≈ 3.6615. Various numerical results regarding disease elimination of the sensitive parameters are shown graphically. [ABSTRACT FROM AUTHOR]

In recent years, artificial neural networks (ANNs) have gained significant popularity in a variety of engineering fields, with major breakthroughs in addressing geotechnical difficulties such as structure settlement, soil compaction, slope stability, and pile settlement. The goal of this research is to use MATLAB to create multiple ANN models based on different feedforward techniques, including Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient (SCG). In addition, the Adam Optimizer will be employed using Python and to compare it with backward progress ANN algorithm (BP-ANN) and prediction of the best suited ANN model for the present data set of settlement of ring footing. The input parameters for the development of present prediction model have been taken as inner diameter of footing, outer diameter of footing, load pressure, thickness, ring radii ratio (n). Experimental validation using FEM (ANSYS model) on a dataset of 200 ring footings demonstrates the correctness of the BP-ANN model, which has an R2 value of 0.98 when compared to alternative feed forward techniques. The results show that ANN model utilizing back propagation ANN algorithm (BP-ANN) predicted settlement with exceptional accuracy, generating R2 of 0.98 compared to other feed forward techniques. Notably, this study goes beyond typical assessments by performing SOBOL and SHAP sensitivity analyses, which give light on the impact of the input parameter on settlement prediction. Furthermore, parametric research improve our understanding of complicated systems, allowing for more informed decision-making. The entire methodology, which incorporates various ANN methodologies, validation methods, and sensitivity assessments, is innovative and original, increasing the accuracy and usefulness of settlement predictions in geotechnical engineering. [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]

Purpose: The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market. Design/methodology/approach: In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms' management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria' relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam. Findings: Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies. Practical implications: The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets. Originality/value: Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market. [ABSTRACT FROM AUTHOR]

The shortest path problem has emerged as one of the most significant problems in network optimization with broad applications in the real world. Modeling these real-world applications includes a variety of ambiguous phenomena that can be estimated using human beliefs. In dealing with such uncertainty, Liu's uncertainty theory is considered a legitimate tool. This paper examines the uncertain multi-objective multi-route shortest path problem (UMOMRSPP) for directed graphs. The zigzag uncertain variables are used to represent uncertain parameters. For UMOMRSPP, the expected and optimistic value models are developed, and their corresponding deterministic transformation is provided. The deterministic models are solved using three existing multi-objective genetic algorithms (MOGAs), namely the hybrid genetic algorithm, non-dominated sorting genetic algorithm (NSGA)-II & NSGA-III and two proposed aspiration level (AL)- based algorithms: AL-based NSGA-II and AL-based NSGA-III. An emergency medical services problem is studied as an application in the context of UMOMRSPP. A comparison between the proposed methods and the MOGAs mentioned above is presented. A sensitivity analysis of objective functions regarding the shape parameter and AL is performed. The performance measure coverage is computed to demonstrate the effectiveness of the proposed methods. This study suggests that AL-based NSGA-II & III provide solutions based on the decision-maker's AL and can handle real-world UMOMRSPP effectively with efficient outputs. [ABSTRACT FROM AUTHOR]

Marburg virus, like Ebola, causes haemorrhagic disease with high fatality rates. We developed a deterministic SEIRDVT model incorporating vaccination and treatment to study the disease dynamics. Qualitative analysis revealed a backward bifurcation when R 0 = 1 , meaning R 0 < 1 is insufficient to eradicate the virus. Sensitivity analysis using Latin Hypercube Sampling showed that applying four control measures—screening, prevention, continuous vaccination, and treatment—significantly reduced transmission. The most cost-effective strategy combines prevention, vaccination, and treatment. These findings provide a framework for designing efficient interventions to combat Marburg virus. [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]

AbstractThis paper introduces an innovative approach to soil parameter estimation using deep artificial neural networks (ANN) tailored to estimate a wide range of soil types. Previous research in this field has relied on limited datasets and oversimplified assumptions in semi-empirical models. In this work, the sensitivity of stress distribution is analyzed with respect to soil parameters, revealing significant errors resulting from the simplifications in semi-empirical models and emphasizing the need for more accurate estimation methods with minimal reliance on simplifying assumptions. Training an ANN requires a comprehensive dataset, which has been a challenge due to limited access to diverse soil samples. Addressing this issue, the paper conducts simulations of a single wheel’s movement across a wide range of soil parameter combinations to generate the required dataset. These simulations are done using a dynamic motion-compliant semi-empirical model detailed in the article. Subsequently, the network’s hyperparameters are fine-tuned through a grid search, followed by extensive training of the optimized ANN model. In the end, the ANN demonstrates promising performance in accurately estimating soil parameters, validated through simulation results of both single wheel and Mars rover motion. [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]

Summary Background Methods Results Conclusions Different sedation regimens have been used to facilitate awake tracheal intubation, but the evidence has not been synthesised robustly, particularly with respect to clinically important outcomes. We conducted a systematic review and network meta‐analysis to determine the sedation techniques most likely to be associated with successful tracheal intubation, a shorter time to successful intubation and a lower risk of arterial oxygen desaturation.We searched for randomised controlled trials of patients undergoing awake tracheal intubation for any indication and reporting: overall tracheal intubation success rate; tracheal intubation time; incidence of arterial oxygen desaturation; and other related outcomes. We performed a frequentist network meta‐analysis for these outcomes if two or more sedation regimens were compared between included trials. We also performed a sensitivity analysis excluding trials with a high risk of bias.In total, 48 studies with 2837 patients comparing 33 different regimens were included. Comparing overall awake tracheal intubation success rates (38 studies, 2139 patients), there was no evidence suggesting that any individual sedation regimen was superior. Comparing times to successful tracheal intubation (1745 patients, 24 studies), any sedation strategy was superior to placebo. When we excluded trials with a high risk of bias, we found no evidence of a difference between any interventions for time to successful tracheal intubation. Thirty‐one studies (1753 patients) suggested that dexmedetomidine and magnesium sulphate were associated with a reduced risk of arterial oxygen desaturation compared with other interventions, but excluding trials with a high risk of bias suggested no relevant differences between interventions. The quality of evidence for each of our outcomes was low.To maximise effective and safe awake tracheal intubation, optimising oxygenation, topical airway anaesthesia and procedural performance may have more impact than any given sedation regimen. [ABSTRACT FROM AUTHOR]

Tubular solid oxide fuel cells (TSOFCs) generate high-grade waste heat during operation, but the existing waste heat recovery technologies designed for flat solid oxide fuel cells cannot be directly applied to TSOFC due to the geometry mismatch. To efficient harvest the waste heat, a new geometry-matching hybrid system including TSOFC and annular thermoelectric generator (ATEG) is synergistically integrated to evaluate the performance upper limit. A mathematical model is formulated and verified to describe the hybrid system by considering various thermodynamic-electrochemical irreversible effects. Key performance indicators are established to assess the potential performance. Calculations show that the peak power density and corresponding efficiency of the proposed system are enhanced by 20.39 % and 13.89 %, respectively, compared to a standalone TSOFC. Furthermore, the exergy destruction rate is reduced by 7.04 %. Extensive sensitivity analyses indicate that higher operating temperatures enhance the system’s performance, while larger electrode tortuosity negatively affects it. Additionally, various optimization paths of ATEG are explored to improve the system performance, including considerations such as the number of thermocouples, leg radial width, leg thickness, or annular shape parameter. The three-objective optimization yields an efficient design solution for the entire system, offering valuable insights for its design and operation. [ABSTRACT FROM AUTHOR]

ABSTRACT Experimental studies on the cellular uptake of nanoparticles (NPs), useful for the investigation of NP‐based drug delivery systems, are often difficult to interpret due to the large number of parameters that can contribute to the phenomenon. It is therefore of great interest to identify insignificant parameters to reduce the number of variables used for the design of experiments. In this work, a model of the wrapping of elliptical NPs by the cell membrane is used to compare the influence of the aspect ratio of the NP, the membrane tension, the NP–membrane adhesion, and its variation during the interaction with the NP on the equilibrium state of the wrapping process. Several surrogate models, such as Kriging, Polynomial Chaos Expansion (PCE), and artificial neural networks (ANN) have been built and compared to emulate the computationally expensive model. Only the ANN‐based model outperformed the other approaches by providing much better predictivity metrics and could therefore be used to compute the sensitivity indices. Our results showed that the NP's aspect ratio, the initial NP–membrane adhesion, the membrane tension, and the delay for the increase of the NP–membrane adhesion after receptor dynamics are the main contributors to the cellular internalization of the NP, while the influence of other parameters is negligible. [ABSTRACT FROM AUTHOR]

In Canadian remote oil sites, natural gas extracted with the bitumen is flared or vented, impacting on the environment. Providing a pipeline network to recover the gas is unaffordable in most cases. Here we propose the economic framework of an innovative process to recover the gas, based on pyrolysis at T = 1223 K and P = 0.1 MPa, performed in situ. Part of produced hydrogen combusts to sustain the process, while solid carbon is sold. We conceptualised and simulated in Aspen Plus two process alternatives. In configuration 1, the remaining part of hydrogen is sent to a gas turbine, where it burns to produce electricity. In configuration 2, the unburnt hydrogen is purified and sold. Both configurations reduce CO 2 emissions by more than 92 % compared to flaring. Both configurations produce a profit over 10 years, being their IRR above 20 % and payback time below 3.5 y. • Flaring and venting natural gas contribute to 1.7 % of Canada's global emissions. • Methane pyrolysis is a promising technology to abate oil sector's emissions. • We present techno-economics and sensitivity analysis of two process alternatives. • Turquoise H 2 sustains energetically the process that produces carbon, to be sold. • Emissions compared to the flaring scenario are reduced by over 90 %. [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]

ABSTRACT Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose‐finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thus optimizing the risk–benefit trade‐off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose‐finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model‐assisted PKBOIN‐12 design, which enhances BOIN12 by integrating PK information into both the dose‐finding algorithm and the final OBD determination process. We further extend PKBOIN‐12 to TITE‐PKBOIN‐12 to address the challenges of late‐onset toxicity and efficacy outcomes. Simulation results demonstrate that PKBOIN‐12 more effectively identifies the OBD and allocates a greater number of patients to it than BOIN12. Additionally, PKBOIN‐12 decreases the probability of selecting inefficacious doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN‐12 and TITE‐PKBOIN‐12 in various scenarios. [ABSTRACT FROM AUTHOR]

ABSTRACT In the North China Plain (NCP), the assessment of water surplus or deficit (WSD), which is calculated as precipitation minus reference evapotranspiration (ET0), holds significant implications for water resource management and agricultural irrigation decision‐making, given the region's long‐standing severe shortage of water resources. However, the magnitude, trend and climatic drivers of WSD remain poorly understood in the NCP. This study analysed the spatial and temporal characteristics of WSD, and quantified the contribution of climatic factors to WSD based on the sensitivity and contribution rate analysis methods with climatic data from 75 meteorological stations. The result showed that: (1) Annual WSD decreased mainly in northeastern NCP and increased significantly in southern NCP during 1961–2022. Annual WSD increased slightly from 1961 to 2022 at a rate of 1.63 mm a−2 mainly due to the more significant decrease (−1.88 mm a−2) in ET0 compared to precipitation (−0.25 mm a−2). (2) In terms of the sensitivity of WSD to climatic factors, relative humidity had the highest sensitivity, followed by net radiation, wind speed, precipitation and average air temperature. (3) Significant declines of wind speed were the most dominant factor affecting WSD variation in most part of NCP during most of a year, and net radiation of four stations in the western high‐elevation regions played the most important role. This study enhances comprehension of the impact of climate change on WSD in the NCP and provides a reference for improving management of agricultural water resources under NCP's evolving climatic conditions. [ABSTRACT FROM AUTHOR]

AbstractPattern-mixture models have received increasing attention as they are commonly used to assess treatment effects in primary or sensitivity analyses for clinical trials with nonignorable missing data. Pattern-mixture models have traditionally been implemented using multiple imputation, where the variance estimation may be a challenge because the Rubin’s approach of combining between- and within-imputation variance may not provide consistent variance estimation while bootstrap methods may be time-consuming. Direct likelihood-based approaches have been proposed in the literature and implemented for some pattern-mixture models, but the assumptions are sometimes restrictive and the theoretical framework is fragile. In this article, we propose an analytical framework for an efficient direct likelihood estimation method for commonly used pattern-mixture models corresponding to return-to-baseline, jump-to-reference, placebo washout, and retrieved dropout imputations. A parsimonious tipping point analysis is also discussed and implemented. Results from simulation studies demonstrate that the proposed methods provide consistent estimators. We further illustrate the utility of the proposed methods using data from a clinical trial evaluating a treatment for type 2 diabetes. [ABSTRACT FROM AUTHOR]

A model of Escherichia coli concentrations in the runoff from grazed pastures was developed to increase our knowledge and ability to predict the relative impact of both cow and sheep grazing on water quality. Input parameters to the initial model were derived from published data and model outputs were compared with measured E. coli runoff concentration data from both sheep and cow grazed pasture. The model was designed to represent the complexities of year-round rotational grazing farm system. For cow grazing, the model estimated runoff E. coli concentrations in the same order of magnitude as the measured data. However, for sheep grazing, the model underestimated the measured runoff E. coli concentrations by 1.5 orders of magnitude. To understand this negative result, Monte Carlo simulation techniques were used to conduct a sensitivity analysis of the input parameters. This analysis identified that runoff concentrations were highly sensitive to inputs of the E. coli concentrations in the dung and the mobilisation rates of E. coli from the dung. This modelling study demonstrates that significant gaps and uncertainties remain in our understanding and prediction of the mobilisation and transport of E. coli from grazed pasture systems.HIGHLIGHTS Faecal microbial losses from agriculture are poorly understood.A runoff concentration model was developed based on the area of faecal pats.The area-based model appears to work for cow grazed pastures.The area-based model does not work for sheep grazed pastures.Faecal microbial losses from agriculture are poorly understood.A runoff concentration model was developed based on the area of faecal pats.The area-based model appears to work for cow grazed pastures.The area-based model does not work for sheep grazed pastures. [ABSTRACT FROM AUTHOR]

Detailed chemical kinetic mechanisms are necessary for resolving many important chemical processes. As the chemistry of smaller molecules has become better grounded and quantum chemistry calculations have become cheaper, kineticists have become interested in constructing progressively larger kinetic mechanisms to model increasingly complex chemical processes. These large kinetic mechanisms prove incredibly difficult to refine and time‐consuming to interpret. Traditional sensitivity analysis on a large mechanism can range from inconvenient to practically impossible without special techniques to reduce the computational cost. We first present a new time‐local sensitivity analysis we term transitory sensitivity analysis. Transitory sensitivity analysis is demonstrated in an example to accurately identify traditionally sensitive reactions at an 18,000x speed up over traditional sensitivities. By fusing transitory sensitivity analysis with more traditional time‐local branching, pathway, and cluster analyses, we develop an algorithm for efficient automatic mechanism analysis. This automatic mechanism analysis at a time point is able to identify the reactions a target is most sensitive to using transitory sensitivity analysis and then propose hypotheses why the reaction might be sensitive using branching, pathway, and cluster analyses. We implement these algorithms within the reaction mechanism simulator (RMS) package, which enables us to report the automatic mechanism analysis results in highly readable text formats and in molecular flux diagrams. [ABSTRACT FROM AUTHOR]

PurposeMaterials and methodsResultsDiscussionConclusionEvaluating students’ attitudes toward research is essential for instructors of any research methods class, as students’ general attitudes toward research may impact if and how they integrate research into practice decision making. However, few psychometrically sound, multidimensional instruments that can be used with Master of Social Work (MSW) students exist.This work used confirmatory factor analysis to evaluate the psychometric properties of the Revised Attitudes Toward Research Scale with a diverse sample of 396 master’s level social work students. Multiple indicators, multiple causes models and a series of Monte Carlo simulations were then used to assess the relationship between various dimensions of students’ attitudes toward research, their prior research exposure/training, and their sociodemographic characteristics.Results indicate that the overall performance of the measure with this sample of MSW students was strong, and the factor structure was consistent with that found when evaluated with different samples in previous research. Moreover, various sociodemographic characteristics predicted scores on the research usefulness, research anxiety, and positive research predisposition subscales.The use of this tool allows instructors to identify students with high levels of research-related anxiety and those who may not intuitively comprehend the need to understand empirical research findings and integrate them into practice decision making.The authors offer suggestions for integrating this validated tool into social work research methods classes to assist in developing students’ ability to engage in all steps of the evidence-based practice process once in the field. [ABSTRACT FROM AUTHOR]

During peak passenger flow periods, congestion propagation directly affects the operational safety and efficiency of multi-mode rail transit interconnections. By analyzing the key factors affecting congestion propagation, such as the train stop schedule, and considering parameters such as the basic reproduction number and propagation threshold, this study proposes a multi-mode rail transit susceptible-infected-recovered-susceptible (MRT-SIRS) epidemic model to analyze passenger flow congestion propagation. Simulation experiments and sensitivity analyses using data from the multi-mode rail transit in Beijing, China, were conducted to examine the influence mechanism of key factors on congestion propagation. The degree of influence of each factor was investigated using Gray correlation analysis. Each key factor, including the propagation and recovery rates, influences congestion propagation differently. The results of this study may provide theoretical support for the efficient operation and management of multi-mode rail transit systems. [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]

The arboviral disease dengue is a life-threatening global public health problem due to its high prevalence rate, morbidity and mortality. Recent studies suggest that morbidity during secondary dengue infection is higher than during primary infection. To deepen our understanding, we have explored a two-serotype co-infected dengue model with bilinear incidence for primary infection and saturated incidence for secondary infection. In the model, we have also included four control measures: mosquito bite prevention control, treatment control, vaccine control and mosquito killing control. The existence and local stability of the disease free, serotype II free, serotype I free and endemic equilibrium points have been studied. The effect of inhibitory or awareness factors on disease dynamics has also been exhaustively examined. By using sensitivity analysis we have identified and quantified the most influential parameters to prevent as well as to control the dengue outbreak. To optimize our strategies, we have characterized the time-dependent optimal control to reduce dengue prevalence and the total implementation cost of the aforementioned controls. Efficiency analysis has been done to identify the best effective combination of the applied controls to reduce dengue infection. Mosquito bites with treatment control is the most effective combined strategy to control dengue infection. [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]

To investigate the effects of hydrogen addition on the ignition characteristics and engine performance of ammonia-hydrogen fuel, a reaction kinetics mechanism suitable for high-pressure engine conditions was proposed. The study explores the influence of hydrogen blending ratios on the ignition delay time (IDT) of ammonia-hydrogen fuel, revealing the sensitive reactions and evolution characteristics of reaction pathways during the ignition process of ammonia-hydrogen fuel promoted by hydrogen. The study also investigates the impact of hydrogen blending ratios on the engine performance of ammonia-hydrogen fuel. The results show that the IDT of ammonia significantly decreases with increasing hydrogen blending ratios and temperature. The addition of hydrogen to ammonia significantly lowers the autoignition temperature boundary of the mixture. Under low-temperature and low-pressure conditions, ammonia, which would not ignite originally, ignites due to the addition of hydrogen. The addition of hydrogen shifts the sensitive reactions of the mixture from NH 3 and NH 2 -related reactions to H 2 /O 2 -related reactions, without altering the reaction pathway of ammonia, only changing the pathway flux of components such as NH 2 , H 2 NN, H 2 NO, and HONO. In a spark-ignition (SI) engine, maintaining the total fuel heat value and ignition timing constant, an increase in hydrogen blending ratio significantly raises the peak cylinder pressure. However, due to the forward movement of the heat release process, the power performance of engine experiences varying degrees of reduction. Therefore, it is necessary to delay the ignition timing of engine with the increase in hydrogen blending ratio, moving the combustion center beyond the top dead center (TDC). Furthermore, increasing the hydrogen concentration in the blend significantly raises the temperature and pressure during the engine combustion process, leading to an increase in the concentration of thermal NO X in the emissions. • A reaction kinetic mechanism for ammonia-hydrogen fuel was constructed, which included 38 species and 343 reactions. • The variation trend of the autoignition boundary of ammonia-hydrogen fuel is revealed quantitatively. • The effects of hydrogen on ignition sensitivity and fuel reaction pathway of ammonia-hydrogen fuel were explored. • The effect of hydrogen blending ratio on the performance of SI engine is analyzed at fixed fuel energy and ignition time. [ABSTRACT FROM AUTHOR]

This paper focuses on a dual-fuel injector to characterize the ignition delay time (IDT), flame propagation, and flame spectral distribution of diesel and methane combustion. To gain an insight into the combustion characteristics of diesel pilot-ignited methane dual-fuel combustion with a high-pressure direct injection (HPDI) strategy under various conditions, the ignition and combustion processes are obtained by a high-speed color camera. The variables such as diesel injection pressure (DIP), diesel injection duration (DID), fuel relative injection interval time (RIT), ambient temperature (AT), and ambient pressure (AP) are varied according to engine-like conditions for a better understanding of the relation between the operating conditions and combustion characteristics. The results show that methane jets inhibited the IDT of diesel, while the conditions and ignition of diesel also determined the ignition of methane. Premixed flame always exists at the initial combustion, the development of the flame propagation indicates that the entrainment between diesel and methane plays a crucial role in the flame velocity and combustion intensity. According to sensitivity analysis, the IDT, Flame tip penetration (P), Flame tip velocity (V), and Flame area (FA) are most sensitive to the AT, and at least 4, 3, 2, and 7 times to the other variables each. However, the area ratio of chemiluminescence to soot incandescence (B/R) is most sensitive to AP, which is at least 14 times the other variables. • The effects of 5 variables on diesel ignition high-pressure methane combustion characteristics are studied. • A segmentation method for chemiluminescence and soot incandescence has been proposed. • The sensitivity of combustion characteristic parameters to variables is quantitatively analyzed. [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]

A cylindrical-pillar-based new meta-surface-assisted ultra-wideband window is proposed, designed and simulated, in the present paper, for a wideband gyro-traveling-wave tube. The return-loss characteristics were analyzed by simulation in a high-frequency structure simulator to calculate the bandwidth at an allowable return loss of −17 dB and the center frequency of 44.42 GHz of the operating bandwidth of 32.10 GHz (28.31–60.47 GHz) which is 72.26% relative bandwidth for the operating circular TE0,1 mode. The results with respect to which parameters/characteristics were validated within 5% against those obtained using CST Microwave Studio. The sensitivity analysis was also carried out to obtain the manufacturing tolerances. Interestingly, an improvement of 8.03% (from 64.23% to 72.26%) at an allowable return-loss of −17 dB was obtained in the proposed window over the bandwidth achieved in previously reported square-pillar with cylindrical-tip-based meta-surface-assisted window. [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]

Major depression is a public health problem facing the world. This study aimed to identify the risk factors for major depression and clarify their causal effects. Data from the National Health and Nutrition Examination Survey (NHANES). Multifactorial logistic regression analysis was used to calculate the effect of each variable on major depression. Subgroup analyses and interaction tests were conducted to observe the stability of the association between them. Nonlinear correlations were explored using restricted cubic spline plots. The causal effects of serum Klotho on major depression were assessed using Mendelian randomization (MR) analysis. A total of 8359 participated in the study. After adjusting for all covariates, the risk of having major depression was 1.47 times higher for each unit rise in serum Klotho (OR = 1.47, 95 % CI = 1.07–2.02; P = 0.0183). MR analysis showed no causal relationship between serum Klotho levels and risk of major depression (OR = 1.09, 95 % CI = 0.91–1.30; P = 0.4120). Sensitivity analysis verified the reliability of the results. Serum Klotho is positively associated with an increased risk of major depression in the U.S. population, but MR analyses did not show genetic causality between Klotho and major depression in individuals of European ancestry. Based on the results of the current study, no indication maintaining high levels of Klotho may increase the risk of major depression. The main limitation of this study is the inconsistency of the cross-sectional study and the MR population. • Serum Klotho was first observed as a risk factor for major depression, but MR findings suggest no causal relationship. • J-shaped association between serum Klotho and major depression. • This study combines comprehensive data from a large-sample cross-sectional survey and a genomic analysis system. • No indication that serum Klotho may help improve major depression. [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]