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Accurately assessing the health risks posed by major contaminants is essential for protecting groundwater. However, the complexity of pollution sources and the uncertainty of parameters pose challenges for quantitative health risk assessment. In this study, a source-oriented groundwater risk evaluation process was improved by screening key pollutants, employing a combined hydrochemical and positive matrix factorization (PMF) approach for source apportionment, and incorporating two-dimensional Monte Carlo simulation for risk characterization. The application of this process to groundwater assessment in Central Jiangxi Province identified NO 3 - , F - , Se and Mn as the key pollutants. The pollution sources were anthropogenic activities, rock dissolution, regional geological processes, and ion exchange. Anthropogenic sources contributed 36.8 % and 28.8 % of the pollution during the wet season and dry season, respectively, and accounted for more than half of the health risks. NO 3 - from anthropogenic sources was the primary controlling pollutant. Additionally, the risk assessment indicated that children were at the highest health risk during the dry season, with ingestion rate suggested to be controlled below 1.062 L·day -1 to make the health risk within an acceptable range. The improved assessment methodology could provide more accurate results and recommended intakes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The win ratio (WR) is an emerging alternative for reporting composite outcomes, prioritizing clinically significant events such as mortality while incorporating surrogate measures. However, its benefits should be weighed against limitations, particularly the influence of lower hierarchical outcomes. This secondary analysis of the PARAGLIDE-HF trial performed a WR sensitivity analysis using a modified hierarchical composite outcome to assess the utility of WR sensitivity analysis and the efficacy of sacubitril/valsartan versus valsartan., Methods: PARAGLIDE-HF compared sacubitril/valsartan with valsartan in heart failure (HF) patients with ejection fraction >40% (N=466). A hierarchical outcome in the primary analysis included cardiovascular death, HF hospitalizations, urgent HF visits, and change in N-terminal pro-B-type natriuretic peptide (NT-proBNP), with a 25% decrease considered a win. In the pre-specified subgroup with ejection fraction ≤60% (N=357), sacubitril/valsartan showed a treatment effect on the hierarchical outcome (WR, 1.46; 95% CI, 1.08-1.97). Sensitivity analyses for this subgroup included: 1) excluding NT-proBNP change, 2) substituting the 25% proportion change of NT-proBNP with 10% or 50%, and 3) including renal outcomes within the hierarchical outcome. In addition to the WR, the win odds (WO), in which 50% of the ties are allocated to both the numerator and denominator of the WR-a potentially more suitable modification of the WR that accounts for the presence of ties-were presented., Results: Excluding NT-proBNP (WR, 1.49; 95% CI, 1.00-2.22; WO, 1.12; 95% CI, 1.00-1.26), adjusting the NT-proBNP threshold from 25% to 10% or 50% (WR, 1.41; 95% CI, 1.06-1.89; WO, 1.27; 95% CI, 1.04-1.56 for 10%; and WR, 1.54; 95% CI, 1.11-2.12; WO, 1.25; 95% CI, 1.06-1.48 for 50%), and incorporating renal outcomes (WR, 1.44; 95% CI, 1.07-1.94; WO, 1.28; 95% CI, 1.05-1.56) consistently favored sacubitril/valsartan., Conclusions: Multiple WR sensitivity analyses support a consistent treatment benefit of sacubitril/valsartan versus valsartan in patients with ejection fraction >40% to 60%. Future studies could consider prespecifying WR sensitivity analysis for comprehensive assessment of treatment effects., Trial Registration: PARAGLIDE-HF; ClinicalTrials.gov ID, NCT03988634 (https://clinicaltrials.gov/study/NCT03988634)., Competing Interests: Declaration of competing interest None, (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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 [Formula: see text], to quantify the invasion threshold for these behaviors. The findings reveal that the crime/substance-free equilibrium is globally asymptotically stable when [Formula: see text]. At the same time, entrenched equilibria become stable where [Formula: see text]. 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 [Formula: see text], 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., (© 2024. The Author(s).)

The Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Pacific-North American Pattern (PNA) cause climate variability in the Northern Hemisphere (NH), which affects the carbon cycle of terrestrial ecosystems. Based on a dynamic global vegetation model, we analysed the impacts of the AO, NAO and PNA on changes in terrestrial climate and carbon cycle dynamics from 1980 to 2017. The positive AO (pAO), positive NAO (pNAO), and positive PNA (pPNA) mainly led to warmer and more humid conditions in the North Asia (NA) and Europe (EUR), whereas the negative AO (nAO), negative NAO (nNAO), and negative PNA (nPNA) resulted in colder and drier conditions. Furthermore, the nAO, nNAO, and nPNA increased the carbon sinks of terrestrial ecosystems, whereas the pAO, pNAO, and pPNA reduced the carbon sinks, especially in EUR. We also quantified the direct impacts of the oscillations in the concurrent season and their legacy impacts from the preceding season separately. Increased AO and NAO indices increased the carbon sinks in the East Asia (EA) and EUR, whereas an increased PNA index reduced the carbon sinks in most parts of the NH. With respect to legacy impacts, increased AO and PNA indices enhanced the carbon sinks in the Central-Western Asia and North Africa (CWN), Temperate North America (TNA) and Boreal North America (BNA), whereas an increased NAO index strengthened the carbon source capacity in the CWN, EUR, TNA, BNA. These results provide a framework for conducting further research on the mechanisms of interannual variability of the terrestrial carbon cycle., (Copyright © 2024. Published by Elsevier B.V.)

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, ANN Hybrid , 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 ANN Hybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANN Hybrid 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 R 2  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 ANN Hybrid 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., (© 2024. The Author(s).)

Heat transfer in the human eyeball, a complex organ, is significantly influenced by various pathophysiological and external parameters. Particularly, heat transfer critically affects fluid behavior within the eye and ocular drug delivery processes. Overcoming the challenges of experimental analysis, this study introduces a comprehensive three-dimensional mathematical and computational model to simulate the heat transfer in a realistic geometry. Our work includes an extensive sensitivity analysis to address uncertainties and delineate the impact of different variables on heat distribution in ocular tissues. To manage the model's complexity, we employed a very fast model reduction technique with certified sharp error bounds, ensuring computational efficiency without compromising accuracy. Our results demonstrate remarkable consistency with experimental observations and align closely with existing numerical findings in the literature. Crucially, our findings underscore the significant role of blood flow and environmental conditions, particularly in the eye's internal tissues. Clinically, this model offers a promising tool for examining the temperature-related effects of various therapeutic interventions on the eye. Such insights are invaluable for optimizing treatment strategies in ophthalmology., (© 2024 The Author(s). International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

Biogeochemical catchment models are often developed for a single catchment and, as a result, often generalize poorly beyond this. Evaluating their transferability is an important step in improving their predictive power and application range. We assess the transferability of a recently developed Bayesian Belief Network (BBN) that simulated monthly stream phosphorus (P) concentrations in a poorly-drained grassland catchment through application to three further catchments with different hydrological regimes and agricultural land uses. In all catchments, flow and turbidity were measured sub-hourly from 2009 to 2016 and supplemented with 400-500 soil P test measurements. In addition to a previously parameterized BBN, five further model structures were implemented to incorporate in a stepwise way: in-stream P removal using expert elicitation, additional groundwater P stores and delivery, and the presence or absence of septic tank treatment, and, in one case, Sewage Treatment Works. Model performance was tested through comparison of predicted and observed total reactive P (TRP) concentrations and percentage bias (PBIAS). The original BBN accurately simulated the absolute values of observed flow and TRP concentrations in the poorly and moderately drained catchments (albeit with poor apparent percentage bias scores; 76 % ≤ PBIAS≤94 %) irrespective of the dominant land use, but performed less well in the groundwater-dominated catchments. However, including groundwater total dissolved P (TDP) and Sewage Treatment Works (STWs) inputs, and in-stream P uptake improved model performance (-5 % ≤ PBIAS≤18 %). A sensitivity analysis identified redundant variables further helping to streamline the model applications. An enhanced BBN model capable for wider application and generalisation resulted., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© 2024 The Author(s). International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

In this study, we examine the third-order fractional nonlinear Schrödinger equation (FNLSE) in [Formula: see text]-dimensional, by employing the analytical methodology of the new extended direct algebraic method (NEDAM) alongside optical soliton solutions. In order to better understand high-order nonlinear wave behaviors in such systems, the researched model captures the physical and mathematical properties of nonlinear dispersive waves, with applications in plasma physics and optics. With the aid of above mentioned approach, we rigorously assess the novel optical soliton solutions in the form of dark, bright-dark, dark-bright, periodic, singular, rational, mixed trigonometric and hyperbolic forms. Additionally, stability assessments using conserved quantities, such as Hamiltonian property, and consistency checks were used to validate the solutions. The dynamic structure of the governing model is further examined using chaos, bifurcation, and sensitivity analysis. With the appropriate parameter values, 2D, 3D, and contour plots can all be utilized to graphically show the data. This work advances our knowledge of nonlinear wave propagation in Bose-Einstein condensates, ultrafast fibre optics, and plasma physics, among other areas with higher-order chromatic effects., (© 2024. The Author(s).)

This article presents a case where the issue was to determine who was the driver and who was the passenger at the time of a fatal car accident involving two persons, one of whom died in the accident. The presence of the two persons in the car was not contested, only the mechanisms that led to the deposition of the DNA (i.e., the activities) were. To our knowledge, few cases are evaluated considering the alleged activities. The reasons for this include the lack of knowledge, and data, as well as the difficulties encountered for the formulation of conclusions. In this case report, we present the architecture of the Bayesian Network (BN) used to evaluate the DNA results of the traces recovered from the steering wheel, driver's and passenger's airbags. The following propositions were considered: "The person of interest (POI) was driving the car and the alternative person (AP) was the passenger at the time of the accident" or vice versa. We discuss the assumptions that were made and how data from the literature was used to parametrize into the BN. A likelihood ratio of the order of 90 was finally assigned. The statement proposed to the mandating authority indicated that, given the information that was made available to us, our observations were of the order of 90 times more probable if the POI was driving the car at the time of the accident rather than if the AP was. A sensitivity analysis was performed (5000 simulations): this shows that our likelihood ratio is robust., Competing Interests: Declaration of Competing Interest No conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

A main goal of epidemiology is to provide an impact of an exposure on health outcomes. The attributable fraction (AF) is a widely used measure for quantifying its contribution. Various methods have been developed to estimate AF, including standardization, inverse probability of treatment weighting, and doubly robust methods. However, the validity of these methods is established based on the conditional exchangeability assumption, which cannot be tested using only observed data. To assess how vulnerable the research findings are to departures from this assumption, researchers need to conduct a sensitivity analysis. In this study, we propose novel sensitivity analysis methods for AF. Sensitivity analysis problems are formulated as optimization problems, and analytic solutions for the problem are derived. We illustrate our proposed sensitivity analysis methods with a publicly available dataset and examine how the AF of the mother's smoking status during pregnancy for low birth weight changes to the degree of unmeasured confounding., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Evaluating 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., Materials and Methods: 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: 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., Discussion: 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., Conclusion: 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.

Organophosphate esters (OPEs) have proven to be pervasive in aquatic environments globally. However, understanding their partitioning behavior and mechanisms at the sediment-water interface remains limited. This study elucidated the spatial heterogeneity, interfacial exchange, and diffusion mechanisms of 14 OPEs (∑ 14 OPEs) from river to coastal aquatic system. The transport tendencies of OPEs at the sediment-water interface were quantitatively assessed using fugacity methods. The total ∑ 14 OPEs concentrations in water and sediments ranged from 154 ng/L to 528 ng/L and 2.41 ng/g dry weight (dw) to 230 ng/g dw, respectively. This result indicated a descending spatial tendency with moderate variability. OPE distribution was primarily influenced by temperature, pH, and dissolved oxygen levels. As the carbon atom number increased, alkyl and chlorinated OPEs transitioned from diffusion towards the aqueous phase to equilibrium. In contrast, aryl OPEs and triphenylphosphine oxide, which had equivalent carbon atom counts, maintained equilibrium throughout. Diffusion trends of individual OPE congener at the sediment-water interface varied at the same total organic carbon contents (f oc ). As the f oc increased, the fugacity fraction values for all OPE homologs showed a declining trend. The distinct molecular structure of each OPE monomer might lead to unique diffusive behaviors at the sediment-water interface. Higher soot carbon content had a more pronounced effect on the distribution patterns of OPEs. The sediment-water distribution of OPEs was primarily influenced by total organic carbon, sediment particle size, dry density, and moisture content. OPEs displayed the highest sensitivity to fluctuations in ammonium and dissolved organic carbon. This study holds significant scientific and theoretical implications for elucidating the interfacial transport and driving forces of OPEs and comprehending their fate and endogenous release in aquatic ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rewilding abandoned farmlands provides a nature-based climate solution via carbon (C) offsetting; however, the C-cycle-climate feedback in such restored ecosystems is poorly understood. Therefore, we conducted a 2-year field experiment in Loess Plateau, China, to determine the impacts of warming (∼1.4 °C) and altered precipitation (±25 %, ±50 %, and ambient), alone or in concert on soil C pools and associated C fluxes. Experimental warming significantly enhanced soil respiration without affecting the ecosystem net C uptake and soil C storage; these variables tended to increase along the manipulated precipitation gradient. Their interactions increased ecosystem net C uptake (synergism) but decreased soil respiration and soil C accumulation (antagonism) compared with a single warming or altered precipitation. Additionally, most variables related to the C cycle tended to be more responsive to increased precipitation, but the ecosystem net C uptake responded intensely to warming and decreased precipitation. Overall, ecosystem net C uptake and soil C storage increased by 94.4 % and 8.2 %, respectively, under the warmer-wetter scenario; however, phosphorus deficiency restricted soil C accumulation under these climatic conditions. By contrast, ecosystem net C uptake and soil C storage decreased by 56.6 % and 13.6 %, respectively, when exposed to the warmer-drier climate, intensifying its tendency toward a C source. Therefore, the C sink function of semiarid abandoned farmland was unsustainable. Our findings emphasize the need for management of post-abandonment regeneration to sustain ecosystem C sequestration in the context of climate change, aiding policymakers in the development of C-neutral routes in abandoned regions., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2024. The Author(s).)

Mastitis, a prevalent and economically important disease in the dairy industry, poses substantial challenges to dairy cow health, milk quality, and farm profitability worldwide. Mastitis is predominantly caused by bacterial infections. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of bacterial culture and the VetMAX™ MastiType Multi Kit PCR in identified clinical mastitis pathogens. A total of 396 quarter-level milk samples were collected from 396 cows with clinical mastitis on 29 farms in the subtropical dairy region of Australia between March and December 2021. These samples were cultured and tested by PCR, and analysed using Bayesian latent class analysis under the assumption of one population two tests and also of three populations two tests, by dividing the population into subpopulations based on regions. Informative priors used in the analysis were calculated from published evidence. Models were compared using the Deviance Information Criterion (DIC). Sensitivity analysis was performed to evaluate the impact of changes in priors. The most common isolates cultured and detected by PCR were Streptococcus uberis (17.4 % and 27.3 %, respectively) and Escherichia coli (12.6 % and 25.0 %, respectively). Under the assumption of one population two tests, the Se of PCR (at cycle threshold (Ct) ≤ 37) was higher than that of bacterial culture for both pathogens: for E. coli, the Se was 50.2 % (95 % posterior probability interval (PPI): 37.4; 74.1) for bacterial culture, and 93.7 % (95 % PPI: 85.5; 98.4) for PCR. For S. uberis, the Se was 50.4 % (95 % PPI: 40.9; 61.3) for bacterial culture, and 81.5 % (73.0; 88.9) for PCR. Conversely, the Sp of bacterial culture was higher than that of PCR for both pathogens: for E. coli, the Sp was 99.2 % (97.8; 100) for bacterial culture, and 95.1 % (87.8; 99.4) for PCR. For S. uberis, the Sp was 99.2 % (95 % PPI: 97.6; 100) for bacterial culture, and 96.7 % (95 % PPI: 92.1; 99.2) for PCR. Bayesian latent class analysis with three populations two tests was only performed for S. uberis. For E. coli, this could not be performed because there were no PCR-positive results in one subpopulation. Under the assumption of three populations two tests, for S. uberis, the Se was 49.6 % (40.6; 59.4) for bacterial culture, and 81.1 % (72.6; 88.6) for PCR; and the Sp for bacterial culture was 99.1 % (97.7; 100), and for PCR was 96.9 % (93.0; 99.3). The DIC for the one population two tests model was lower than the DIC for the three populations two tests model. The sensitivity analysis for the one population two tests model demonstrated that a 10 % reduction in priors led to substantial changes in Se of both bacterial culture and PCR tests for E. coli and S. uberis, with overlap percentages ranging from 80.6 % to 92.2 %. In contrast, the Sp of bacterial culture and PCR tests remained relatively stable despite changes in priors, except for the Sp of PCR test for E. coli. In summary, the VetMAX™ MastiType Multi Kit demonstrated higher Se compared to bacterial culture, suggesting its potential as a routine test for identifying mastitis pathogens in milk samples from cows with clinical mastitis. While the bacterial culture method offered higher Sp in pathogen detection; results obtained following bacterial culture and subsequent susceptibility testing remain valuable, particularly in guiding antimicrobial treatment for mastitis., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

Reservoir carbon emissions reflect greenhouse gases emitted by flooded land post-construction. However, pre-construction flooded land has been overlooked in previous assessments. Utilizing annual land cover data from 1990 to 2022 and pertinent parameters of cascade reservoirs in the Lancang River (LCR), we calculated the actual flooded areas of these reservoirs. Subsequently, the Tier1 model was employed to estimate the carbon emissions during the reservoir's life cycle and the annual carbon emissions from newly flooded land during construction. Our findings indicate that the LCR cascade reservoir's carbon emission throughout its life cycle is 4.324Tg CO 2eq (1.818-8.879Tg CO 2eq ). When compared with previous results, our estimated figures (0.496-2.106 g CO 2eq /(kw·h)) fall below the global hydroelectric carbon footprint's average threshold range (IPCC: 4-14 g CO 2eq /(kW·h)). This implies that the previously estimated carbon emissions from the reservoir may be inflated due to the flooded land prior to reservoir construction. Notably, the nutrient state of the water body predominantly governs reservoir carbon emissions. This research sheds light on the intricacies of carbon emissions from cascade reservoirs and underscores the importance of accurately delineating reservoir boundaries and managing nutrient inputs to mitigate carbon emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2024. The Author(s).)

Introduction: Renal transplant biopsies provide insights into graft health and support decision making. The current evidence on links between biopsy scores and transplant outcomes suggests there may be numerous factors affecting biopsy scores. Here we adopt measurement science approach to investigate the sources of uncertainty in biopsy assessment and suggest techniques to improve its robustness., Methods: Histological assessments, Remuzzi scores, biopsy processing and clinical variables are obtained from 144 repeat biopsies originating from 16 deceased-donor kidneys. We conducted sensitivity analysis to find the morphometric features with highest discriminating power and studied the dependencies of these features on biopsy and stain type. The analysis results formed a basis for recommendations on reducing the assessment variability., Results: Most morphometric variables are influenced by the biopsy and stain types. The variables with the highest discriminatory power are sclerotic glomeruli counts, healthy glomeruli counts per unit area, percentages of interstitial fibrosis and tubular atrophy as well as diameter and lumen of the worst artery. A revised glomeruli adequacy score is proposed to improve the robustness of the glomeruli statistics, whereby a minimum of 104 µm 2 of cortex tissue is recommended to keep type 1 and type 2 error probabilities below 0.15 and 0.2., Discussion: The findings are transferable to several biopsy scoring systems. We hope that this work will help practitioners to understand the sources of statistical uncertainty and improve the utility of renal biopsy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Loizeau, Romanchikova, Thomas, Alsuleman, Ayorinde and Pettigrew.)

Introduction: Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by comparing its costs to conventional rehabilitation pathways., Methods: Since technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. The aim of the study is to compare the costs of a "mixed" rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings., Results: We found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one., Conclusion: Through a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Gower, Aprile, Falchini, Fasano, Germanotta, Randazzo, Spinelli, Trieste, Gramatica and Turchetti.)

We conducted sensitivity analysis (SA) of the INRA 2018 feeding system for ruminants applied to dairy cows. We evaluated which dietary input variables contribute most to changes in each output variable, considering the potential interactions presence among input variables. A quantitative analysis (one-at-a-time analysis, OAT; i.e., local SA) and a relative comparative analysis (global SA, GSA) through variance-based SA considering potential interactions and non-monotonicity were applied. The 5 likely influential dietary input variables were selected: CP, Gross energy (GE), OM apparent digestibility (OMd), effective degradability of nitrogen assuming a passage rate of 6%/h (ED6&#95;N) and true intestinal digestibility of nitrogen. The sensitivity of 5 selected animal responses (output variables) to input variables was analyzed: DMI, milk protein yield (MPY), energy in methane (ECH4), nitrogen utilization efficiency (NUE), and ratio between urine and total N excretion (UN/TN). Six diets for dairy cattle, reflecting the diversity of diets commonly used in practice, were formulated to meet 95% of the potential milk production (37.5 kg/d) of a multiparous dairy cow at wk 14 of lactation. For each diet, the 5 input variables were randomly sampled around the INRA 2018 feed table values (reference point), and the animal responses around this reference situation were calculated using the rationing software INRAtion®V5. In OAT, the sensitivity of animal responses was quantified by calculating the normalized tangent value at the reference point, and in GSA, the Sobol indices were calculated for relative influence of each input and their interaction. The influence of the 5 key input variables on the 5 main animal responses predicted from the INRA feeding system was consistent across both SA approaches. With the 6 diets, GE and OMd appeared as the main contributors to changes in DMI, MPY, ECH4, and NUE. Crude protein was the main contributor to changes in UN/TN and another major contributor to changes in NUE. When considering OAT, the sensitivity of outputs showed differences depending on diet, more particularly for DMI and MPY. With grass hay-based diets (GH diets), DMI was less sensitive and MPY was more sensitive to variations in input variables than other diets. When considering GSA, interactions between input variables were also noticeable for DMI and MPY; the interactions were high with the GH diets for DMI, and with fresh ryegrass and grass silage diets for MPY. On the other hand, for MPY, the non-GH diets were less sensitive to variations in input variables and the interaction between inputs was higher than with GH diets. In both cases, the interactions were mainly related to energy-related inputs (i.e., OMd and GE). Our results support the hypothesis that MPY, unlike DMI, is more responsive to energy-related factors at high PDI/UFL ratio (i.e., between metabolizable protein and NEL, e.g., GH diets >117 g PDI/UFL), than at lower PDI/UFL ratio. Hence, hybridizing the SA methods can help to interpret the system and facilitate a more precise evaluation thereof, especially GSA, which is amenable to non-monotonic models such as those characterizing complex feeding systems integrating multiple nutritional and animal factors., (© 2025, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

This study proposes a novel model employing nonlinear ordinary differential equations to dissect HCV dynamics. Six distinct population groups are delineated: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. A detailed numerical analysis of this model was conducted, tracking the predicted trends over a span of 20 years. The primary objective of this analysis is to assess and confirm the model's predictive accuracy and its potential to supplant invasive diagnostic methods in monitoring the progression of liver fibrosis. By incorporating various control parameters, namely u 1 ( t ) , u 2 ( t ) , and u 3 ( t ) , the model offers a nuanced perspective on disease progression and treatment outcomes. Parameter u 1 ( t ) modulates treatment-induced fibrosis progression, providing a crucial lever for mitigating treatment-related side effects. u 2 ( t ) reflects treatment effectiveness, capturing the proportion of responders within the treatment cohort. Meanwhile, u 3 ( t ) governs fibrosis progression in non-responders, shedding light on the disease's natural trajectory without effective treatment.

Putative surrogate endpoints must undergo a rigorous statistical evaluation before they can be used in clinical trials. Numerous frameworks have been introduced for this purpose. In this study, we extend the scope of the information-theoretic causal-inference approach to encompass scenarios where both outcomes are time-to-event endpoints, using the flexibility provided by D-vine copulas. We evaluate the quality of the putative surrogate using the individual causal association (ICA)-a measure based on the mutual information between the individual causal treatment effects. However, in spite of its appealing mathematical properties, the ICA may be ill defined for composite endpoints. Therefore, we also propose an alternative rank-based metric for assessing the ICA. Due to the fundamental problem of causal inference, the joint distribution of all potential outcomes is only partially identifiable and, consequently, the ICA cannot be estimated without strong unverifiable assumptions. This is addressed by a formal sensitivity analysis that is summarized by the so-called intervals of ignorance and uncertainty. The frequentist properties of these intervals are discussed in detail. Finally, the proposed methods are illustrated with an analysis of pooled data from two advanced colorectal cancer trials. The newly developed techniques have been implemented in the R package Surrogate., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in ∼ 3 min) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values for LV contractility indexed by the end-systolic elastance E es with a 1% error, which suggests that this parameter is unique. The estimated E es is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose/aim: This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method., Materials and Methods: An unrestricted public Computed Tomography (CT) dataset, with binary organ segmentation, was used to develop a multiclass segmentation model. To start the optimization process, a preliminary manual search of hyperparameters was conducted and from there a grid search identified the most influential result metrics. A total of 658 different models were trained in 2100 h, using 13 160 effective patients. The quantity of results was analyzed using random forest regression, identifying relative hyperparameter impact., Results: Metric implied segmentation quality (accuracy 96.8%, precision 95.1%) and visual inspection were found to be mismatched. In this work batch normalization was most important, but performance varied with hyperparameters and metrics selected. Targeted grid-search optimization and random forest analysis of relative hyperparameter importance, was an easily implementable sensitivity analysis approach., Conclusion: The proposed optimization method gives a systematic and quantitative approach to something intuitively understood, that hyperparameters change model performance. Even just grid search optimization with random forest analysis presented here can be informative within hardware and data quality/availability limitations, adding confidence to model validity and minimize decision-making risks. By providing a guided methodology, this work helps medical physicists to improve their model optimization, irrespective of specific challenges posed by datasets and model design., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of The American Association of Physicists in Medicine.)

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO 3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567 mg/L, with a median value of 4.3 mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

To compare the environmental impact and carbon footprint of gray hydrogen, blue hydrogen, and green hydrogen, inventories were obtained through literature research. Some inventories that were not available in China were obtained through foreign inventories combined with localized power conversion. The localized end-point destructive life cycle impact assessment method was used to calculate the environmental impact potential of the raw material acquisition, transportation, and hydrogen production stages of five hydrogen products. The carbon footprint was calculated, and the sensitivity analysis and uncertainty analysis were carried out and compared with the ReCiPe method. The results showed that： ① The environmental impact from large to small was： gray hydrogen （coal） （1 203 mPt·kg -1 ） &gt; blue hydrogen （coal） （876 mPt·kg -1 ） &gt; gray hydrogen （gas） （492 mPt·kg -1 ） &gt; green hydrogen （323 mPt·kg -1 ） &gt; blue hydrogen （gas） （252 mPt·kg -1 ）. The environmental impacts of gray hydrogen and blue hydrogen were mainly concentrated in climate change, fine particulate matter formation, and fossil fuels. The environmental impacts of green hydrogen were mainly concentrated in climate change, fine particulate matter formation, fossil fuels, and mineral resources. ② The carbon footprint from large to small was： gray hydrogen （coal） （23.79 kg·kg -1 , measured by CO 2 eq, the same below） &gt; blue hydrogen （coal） （11.07 kg·kg -1 ） &gt; gray hydrogen （gas） （10.97 kg·kg -1 ） &gt; blue hydrogen （gas） （3.47 kg·kg -1 ） &gt; green hydrogen （1.97 kg·kg -1 ）. Direct carbon emissions in the production process of gray hydrogen and blue hydrogen accounted for the largest proportion, whereas that of green hydrogen accounted for a large proportion of power input. ③ Measures to reduce environmental impact and carbon emissions include reducing direct emissions of pollutants and greenhouse gases, reducing power consumption, and strengthening raw material substitution and reduction.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most., Competing Interests: The authors declare that they have no competing interests., (© 2024 Abas et al.)

The difference in restricted mean survival time has been increasingly used as an alternative measure to the hazard ratio in survival analysis. Although some statistical methods have been developed for estimating the difference in restricted mean survival time adjusted for measured confounders in observational studies, the impact of unmeasured confounding on the estimate has rarely been assessed. We develop a novel sensitivity analysis for the estimate of the difference in restricted mean survival time with respect to unmeasured confounding. After formulating the sensitivity analysis problem as an optimization problem, we explain how to obtain the sensitivity range of the difference in restricted mean survival time efficiently and assess its uncertainty using the percentile bootstrap confidence interval. Analytic results are provided for some important survival settings. Simulation studies show that the proposed methods perform well in various settings. We illustrate the proposed sensitivity analysis method by analyzing data from the German Breast Cancer Study Group study., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Coral reefs, among the most diverse ecosystems on Earth, currently face major threats from pollution, unsustainable fishing practices , and perturbations in environmental parameters brought on by climate change. Corals also sustain regular wounding from other sea life and human activity. Recent reef restoration practices have even involved intentional wounding by systematically breaking coral fragments and relocating them to revitalize damaged reefs, a practice known as microfragmentation. Despite its importance, very little research has explored the inner mechanisms of wound healing in corals. Some reef-building corals have been observed to initiate an immunological response to wounding similar to that observed in mammalian species. Utilizing prior models of wound healing in mammalian species as the mathematical basis, we formulated a mechanistic model of wound healing, including observations of the immune response and tissue repair in scleractinian corals for the species Pocillopora damicornis. The model consists of four differential equations which track changes in remaining wound debris, number of cells involved in inflammation, number of cells involved in proliferation, and amount of wound closure through re-epithelialization. The model is fit to experimental wound size data from linear and circular shaped wounds on a live coral fragment. Mathematical methods, including numerical simulations and local sensitivity analysis, were used to analyze the resulting model. The parameter space was also explored to investigate drivers of other possible wound outcomes. This model serves as a first step in generating mathematical models for wound healing in corals that will not only aid in the understanding of wound healing as a whole, but also help optimize reef restoration practices and predict recovery behavior after major wounding events., Competing Interests: Declaration of competing interest None, (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: In terms of predicting surgery mortality, it is controversial whether red blood cell width works independently. In non-cardiac surgery patients older than 18 years, we intend to examine the relationship between red blood cell width and postoperative 30-day mortality., Methods: In this retrospective cohort study, 90,785 Singapore General Hospital patients were matched by propensity score between January 1, 2012 and October 31, 2016. It was determined that red blood cell width at baseline and mortality within 30 days after surgery were the independent and dependent variables. We used a non-parametric multivariate logistic regression to balance the confounders among 7807 patients with high RDW and 7807 patients with non-high RDW in the propensity score matching. We investigated the association between RDW and 30-day mortality after surgery using the doubly robust estimation method., Results: Cohorts matched according to propensity score, the risk of 30-day mortality after surgery increased by 114.6.0% among the high RDW group (OR = 2.146, 95% CI 1.645-2.799, P < 0.00001). In the crude model, there was a significant association between RDW and 30-day mortality after surgery (OR = 1.877, 95% CI 1.476-2.388, P < 0.00001). In the propensity-score adjusted model, the risk of 30-day mortality after surgery in the high RDW group compared to the control group was not as high as in the non-adjusted model (OR = 1.867, 95% CI 1.467-2.376, P < 0.00001). Compared to non-high RDW group, the risk of 30-day mortality after surgery increased by 117.0% and 127.7% among high RDW group in the original cohort (OR 2.170, 95% CI 1.754-2.683, P < 0.00001) and the weighted cohort (OR 2.272, 95% CI 2.009-2.580, P < 0.00001), respectively., Conclusions: According to the results of this observational, propensity score-matched cohort study, uncontrolled high RDW before surgery is associated with an increased risk of death within 30 days after surgery, that is to say, patients over the age of 18 with high preoperative RDW who undergo non-cardiac surgery have a worse postoperative prognosis than those with normal RDW., (© 2024. The Author(s).)

Wazwaz Kaur Boussinesq (WKB) equation can effectively simulate the behavior of water waves in shallow water, including the nonlinear effect and dispersion phenomenon of waves, which is of great significance for understanding the dynamic process of ocean, river and other water bodies. To enrich the wave equation theory, the (3+1)-dimensional integer order derivative of WKB equation is changed to the fractional one with beta derivative. The current work deals with the fractional (3+1)-dimensional WKB equation for discussing its chaotic behavior and establishing some new analytic solutions. The chaotic properties of the equation are verified by the trend of evolution along with time, Lyapunov exponents and initial sensitivity analysis. And then complete discrimination system for polynomial method is applied to derive some trigonometric, hyperbolic, Jacobi elliptic and other solutions. The graphical demonstrations are provided for part of these solutions. From these visualized graphs, the solitary, periodic and quasi-periodic wave are shown and the effect of fractional derivatives on the equation can be seen intuitively., (© 2024. The Author(s).)

Brain waste is largely cleared via diffusion and advection in cerebrospinal fluid (CSF). CSF flows through a pathway referred to as the glymphatic system, which is also being targeted for delivering drugs to the brain. Despite the importance of solute transport, no brain-wide models for predicting clearance and delivery through perivascular pathways and adjacent parenchyma existed. We devised such a model by upgrading an existing model of CSF flow in the mouse brain to additionally solve advection-diffusion equations, thereby estimating solute transport. We simulated steady-state transport of 3 kDa dextran injected proximal to the perivascular space (PVS) of the middle cerebral artery, mimicking in vivo experiments. We performed a sensitivity analysis of 11 biological properties of PVSs and brain parenchyma by repeatedly simulating solute transport with varying parameter values. Parameter combinations that led to a large total pressure gradient, poor CSF perfusion or a steep solute gradient were deemed unrealistic. Solute concentrations in parenchyma were most sensitive to changes in pial PVS size, as this parameter linearly affects volume flow rates. We also found that realistic transport requires both highly permeable penetrating PVSs and high-resistance parenchyma. This study highlights the potential of brain-wide models to provide insights into solute transport processes.

Objective: We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23., Method: The articles identified through a keyword search using Pubmed and Scopus were included in the study. The articles obtained from this search were eliminated according to the exclusion criteria, and the articles in which QBA analysis was applied were included in the detailed evaluation., Results: It can be said that the application of QBA analysis has gradually increased over the 13-year period. Accordingly, the number of articles in which simple is used as a method in QBA analysis is 9 (9.89%), the number of articles in which the multidimensional approach is used is 10 (10.99%), the number of articles in which the probabilistic approach is used is 60 (65.93%) and the number of articles in which the method is not specified is 12 (13.19%). The number of articles with misclassification bias model is 44 (48.35%), the number of articles with uncontrolled confounder(s) bias model is 32 (35.16%), the number of articles with selection bias model is 7 (7.69%) and the number of articles using more than one bias model is 8 (8.79%). Of the 49 (53.85%) articles in which the bias parameter source was specified, 19 (38.78%) used internal validation, 26 (53.06%) used external validation and 4 (8.16%) used educated guess, data constraints and hypothetical data. Probabilistic approach was used as a bias method in 60 (65.93%) of the articles, and mostly beta (8 [13.33%)], normal (9 [15.00%]) and uniform (8 [13.33%]) distributions were selected., Conclusion: The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors., (© 2024 John Wiley & Sons Ltd.)

Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models-on the treatment assignment, the principal strata, censoring, and the outcome-is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA)., Methods: Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study., Results: In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these., Conclusions: These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters., Highlights: It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is funded by the National Institute for Health Research (NIHR) Policy Research Programme (grant NIHR201728). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

During nuclear accidents, large quantities of radionuclides will be released into the environment, posing serious health hazards to local residents. The screening of high-risk nuclides is critical for the development of subsequent nuclear emergency response measures. In order to overcome the shortcomings of traditional screening methods, a machine learning method was proposed to screen high-risk nuclides and predict their contamination to groundwater more effectively. The performances of Support Vector Machine (SVM), Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms were compared, and sensitivity analyses of the initial leakage concentration ratio (C 0 /C p ), distribution coefficient (K d ) and decay coefficient (λ) on the model outputs were performed. Results showed that RF classification model achieved the highest prediction accuracy for screening high-risk nuclides. The contribution of the input parameters ranked as K d > λ > C 0 /C p . BPNN regression model was found to be the best for predicting when high-risk nuclides would pollute groundwater. The output was negatively correlated with C 0 /C p and positively correlated with K d and λ, with the parameter influence ranking as K d > C 0 /C p > λ. The contribution of K d mainly came from itself, and the contribution of C 0 /C p and λ mainly due to their interaction with other parameters., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The trace metals (TMs) accumulated in urban park soils can pose potential threats to human health, making the management of soil quality based on health risks critically important. Based on the human health risk assessment (HHRA) model coupled with Monte Carlo Simulation, this study improved the contaminated land exposure assessment (CLEA) model. Combined with local parameters, the Soil Environmental Criteria (SEC) for high-risk trace metals (TMs) in urban park soils were calculated. Results indicated that all the mean TCR (Total carcinogenic risk) values of seven TMs exceeded the risk threshold of 1E-06, suggesting a higher likelihood of carcinogenic risks for all populations. As and Cr presented the highest potential carcinogenic risks, and were identified as high-risk TMs in the study area. The traditional CLEA model was enhanced by incorporating region-specific data, optimizing exposure parameter calculations, and addressing parameter sensitivity and uncertainty. Using the improved CLEA model, the SEC values for high-risk TMs were calculated, revealing that the SEC values gradually increased from ages 1 to 18, while significantly decreased for individuals over 80 years old. This study effectively addresses issues of parameter uncertainty and sensitivity in the CLEA model, offering new insights for the development of soil environmental quality standards., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To analyze the quantitative mapping relationship between the geometric errors of the five-axis CNC machine tool and the tooth surface deviation of spiral bevel gears, and identify the key geometric errors that affect the machining deviation of the spiral bevel gear tooth surface, a sensitivity analysis method for tooth surface machining deviation of spiral bevel gear based on screw theory and EFAST method is proposed. Firstly, the machining model of the spiral bevel gear tooth surface based on the five-axis CNC machine tool is established. Then, the geometric error transfer model of the machine tool is established based on the screw theory. Combined with the EFAST global sensitivity analysis method, the key geometric errors that affect tooth surface deviation are identified. Finally, the reliability and effectiveness tests are performed by comparing the results to the local sensitivity analysis method and simulating compensation for the key geometric errors. The result of compensation shows that the tooth surface deviation is reduced significantly according to global sensitivity analysis. The feasibility of applying sensitivity analysis to control the tooth surface deviation of spiral bevel gears has been demonstrated., (© 2024. The Author(s).)

Background: Since May 7 2022, mpox has been endemic in many countries which has attracted the attention of health authorities in various countries and made control decisions, in which vaccination is the mainstream strategy. However, the shortage of vaccine doses and the reduction of protective efficacy have led to unresolved issues such as vaccine allocation decisions and evaluation of transmission scale., Methods: We developed an epidemiological model to describe the prevalence of the mpox virus in New York City and calibrated the model to match surveillance data from May 19 to November 3, 2022. Finally, we adjusted the model to simulate and compare several scenarios of non-vaccination and pre-pandemic vaccination., Results: Relative to the status quo, if vaccination is not carried out, the number of new infections increases to about 385%, and the transmission time will be extended to about 350%, while if vaccinated before the epidemic, the number of new infections decreases to 94.2-96%., Conclusions: The mpox outbreak in New York City may be linked to the Pride event. However, with current vaccine coverage, there will be no more large-scale outbreaks of mpox, even if there is another similar activity. For areas with limited vaccines, priority is given to high-risk groups in the age group [34-45] years as soon as possible., (© 2024. The Author(s).)

In this study, the modified Sardar sub-equation method is capitalised to secure soliton solutions to the ( 1 + 1 ) -dimensional chiral nonlinear Schrödinger (NLS) equation. Chiral soliton propagation in nuclear physics is an extremely attractive field because of its wide applications in communications and ultrafast signal routing systems. Additionally, we perform bifurcation analysis to gain a deeper understanding of the dynamics of the chiral NLS equation. This highlights the complex behaviour of the system and exposes the conditions under which various types of bifurcations occur. Additionally, a sensitivity analysis is performed to assess how small changes in initial conditions and parameters influence the solutions, offering valuable perspectives on the stability and dependability of the acquired solutions. By employing the above-mentioned methodology, we derive a variety of exact solutions, including periodic, singular, dark, bright, mixed trigonometric, exponential, hyperbolic, and rational wave solutions. The study's findings advance our theoretical knowledge of chiral NLS equations and have potential applications in optical communication and related fields., (© 2024. The Author(s).)

Anaerobic digestion (AD) is one of the most extensively accepted processes for organic waste cleanup, and production of both bioenergy and organic fertilizer. Numerous mathematical models have been conceived for modeling the anaerobic process. In this study, a new modified dynamic mathematical model for the simulation of the biochemical and physicochemical processes involved in the AD process for biogas production was proposed. The model was validated, and a sensitivity analysis based on the OAT approach (one-at-a-time) was carried out as a screening technique to identify the most sensitive parameters. The model was developed by updating the bio-chemical framework and including more details concerning the physico-chemical process. The fraction X P was incorporated into the model as a particulate inert product arising from biomass decay (inoculum). New components were included to distinguish between the substrate and inoculum, and a surface-based kinetics was used to model the substrate disintegration. Additionally, the sulfate reduction process and hydrogen sulfide production have been included. The model was validated using data extracted from the literature. The model's ability to generate accurate predictions was testified using statistical metrics. The model exhibited excellent performance in forecasting the parameters related to the biogas process, with measurements falling within a reasonable error margin. The relative absolute error (rAE) and root mean square error (RMSE) were both less than 5 %, indicating a high ability of the current model in comparison with the literature. Additionally, the scatter index (SI) was below 10 %, and the Nash-Sutcliffe efficiency (NES) approached one, which affirms the model's accuracy and reliability. Finally, the model was applied to investigate the performances of the AD of food waste (FW). The findings of this study support the robustness of the developed model and its applicability as a virtual platform to evaluate the efficiency of the AD treatment and to forecast biogas production and its quality, CO 2 emission, and energy potential across various organic solid waste types., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics., (© 2024. The Author(s).)

Hand, foot and mouth disease (HFMD) is a Class C infectious disease that carries particularly high risk for preschool children and is a leading cause of childhood death in some countries. We mimic the periodic outbreak of HFMD over a 2-year period-with differing amplitudes-and propose a dynamic HFMD model that differentiates transmission between mature and immature individuals and uses two possible optimal-control strategies to minimize case numbers, total costs and deaths. We parameterized the model by fitting it to HFMD data in mainland China from January 2011 to December 2018, and the basic reproduction number was estimated as 0.9599. Sensitivity analysis demonstrates that transmission between immature and mature individuals contributes substantially to new infections. Increasing the isolation rates of infectious individuals-particularly mature infectious individuals-could greatly reduce the outbreak risk and potentially eradicate the disease in a relatively short time period. It follows that we have a reasonable chance of controlling HFMD if we can reduce transmission in children under 7 and isolate older infectious individuals., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

As global concern over the negative impacts of global warming, primarily caused by using passenger vehicles (PVs), the transition to hydrogen fuel cell vehicles (HFCVs) is an essential alternative for reducing greenhouse (GHG) emissions. This research employs a bottom-up approach to analyze road vehicle fleet's GHG emissions. We calculated GHG emissions from PVs in 15 Group of Twenty (G20) countries based on four scenarios adopting the global HFCVs from 2024 to 2050. This paper introduces business-as-usual (BaU), moderate, aggressive, and non-HFCVs scenario. The results show that the aggressive scenario has the highest sales, estimated between 62,000 and 29.48 million vehicles by 2050, with global hydrogen market penetration rates 48.48%. Building on countries' respective national strategies, the findings highlight China and India as the leading markets for hydrogen demand, with Germany and Japan also showing significant interest. The aggressive scenario further demonstrates that transitioning from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and HFCVs can significantly reduce annual GHG emissions. Ultimately, this study finds that the transition to HFCVs could reduce emissions by up to 67.09% by 2050., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: Cancer is inversely associated with cognitive impairment. Whether this is due to statistical handling of attrition (death and censoring) is unknown., Methods: We quantified associations between cancer history and incident cognitive impairment among Health, Aging, and Body Composition Study participants without baseline cognitive impairment or stroke (n = 2604) using multiple competing-risks models and their corresponding estimands: cause-specific, subdistribution, and marginal hazards, plus composite-outcome (cognitive impairment or all-cause mortality) hazards. All-cause mortality was also modeled., Results: After covariate adjustment (demographics, apolipoprotein E ε4, lifestyle, health conditions), cause-specific and marginal hazard ratios (HRs) were similar to each other (≈ 0.84; P values < 0.05). The subdistribution HR was 0.764 (95% confidence interval [CI] = 0.645-0.906), and composite-outcome Cox model HR was 1.149 (95% CI = 1.016-1.299). Cancer history was positively associated with all-cause mortality (HR = 1.813; 95% CI = 1.525-2.156)., Discussion: Cause-specific, subdistribution, and marginal hazards models produced inverse associations between cancer and cognitive impairment. Competing risk models answer slightly different questions, and estimand choice influenced findings here., Highlights: Cancer history is inversely associated with incident cognitive impairment. Findings were robust to handling of competing risks of death. All models also addressed possible informative censoring bias. Cancer history was associated with 16% lower hazard of cognitive impairment. Cancer history was associated with 81% higher all-cause mortality hazard., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

To ensure consistent performance of additively manufactured metal parts, it is advantageous to identify alloys that are robust to process variations. This paper investigates the effect of steel alloy composition on mechanical property robustness in laser-directed energy deposition (L-DED). In situ blending of ultra-high-strength low-alloy steel (UHSLA) and pure iron powders produced 10 compositions containing 10-100 wt% UHSLA. Samples were deposited using a novel configuration that enabled rapid collection of hardness data. The Vickers hardness sensitivity of each alloy was evaluated with respect to laser power and interlayer delay time. Yield strength (YS) and ultimate tensile strength (UTS) sensitivities of five select alloys were investigated in a subsequent experiment. Microstructure analysis revealed that cooling rate-driven phase fluctuations between lath martensite and upper bainite were a key factor leading to high hardness sensitivity. By keeping the UHSLA content ≤20% or ≥70%, the microstructure transformed primarily to ferrite or martensite, respectively, which generally corresponded to improved robustness. Above 70% UHSLA, the YS sensitivity remained low while the UTS sensitivity increased. This finding, coupled with the observation of auto-tempered martensite at lower cooling rates, may suggest a strong response of the work hardening capability to auto-tempering at higher alloy contents. This work demonstrates a methodology for incorporating robust design into the development of alloys for additive manufacturing.

The West Coast New Area is a typical city in China where water supply is predominantly sourced from reservoirs. Heavy metal pollution in these reservoirs directly impacts the safety of drinking water and human health. Therefore, this study comprehensively evaluated the status of heavy metal pollution in the water environment and sediment of the main water supply reservoir in the study area, revealing the interaction relationship and pollution sources, as well as assessing the probabilistic health risks to human beings. The results show that there are different degrees of pollution in the main water supply reservoirs in the study area, and the pollution increases with the increase of water depth. The heavy metal pollution index was up to 2681, indicating heavily pollution. The main polluting elements were Mn and Fe, and the maximum contents were 4.11 mg L -1 and 0.68 mg L -1 , respectively, which far exceeded the Class III standard limit of drinking water in China. The main source of pollution is human activities, and Mn release from sediment aggravates deep water pollution. The non-carcinogenic risk index of heavy metals in the reservoir, ranging from 4% to 14%, is higher than 1, indicating a potential non-carcinogenic threat. Furthermore, heavy metals have a much greater impact on children compared to adults, among which Mn is the main contributor to human non-carcinogenic risk, contributing more than 60%. Therefore, controlling the content of Mn and Fe can effectively reduce the heavy metal pollution of reservoir and human health risk. The research results are of great significance for the utilization of reservoir water resources and the protection of the ecological environment in the study area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD I L 2 Z, is developed for the early detection and treatment of lung cancer. This model incorporates I L 2 cytokine and anti-PD-L1 inhibitors, enhancing the immune system's anticancer response within five epidemiological compartments. The TCD I L 2 Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number ( R 0 ), stability, and sensitivity analysis. Next-generation techniques based on R 0 that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with I L 2 and anti-PD-L1 inhibitors admiring the Lyapunov functions' applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler's method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with I L 2 and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes.