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Random-effects meta-analyses of observational studies can produce biased estimates if the synthesized studies are subject to unmeasured confounding. We propose sensitivity analyses quantifying the extent to which unmeasured confounding of specified magnitude could reduce to below a certain threshold the proportion of true effect sizes that are scientifically meaningful. We also develop converse methods to estimate the strength of confounding capable of reducing the proportion of scientifically meaningful true effects to below a chosen threshold. These methods apply when a "bias factor" is assumed to be normally distributed across studies or is assessed across a range of fixed values. Our estimators are derived using recently proposed sharp bounds on confounding bias within a single study that do not make assumptions regarding the unmeasured confounders themselves or the functional form of their relationships with the exposure and outcome of interest. We provide an R package, EValue, and a free website that compute point estimates and inference and produce plots for conducting such sensitivity analyses. These methods facilitate principled use of random-effects meta-analyses of observational studies to assess the strength of causal evidence for a hypothesis. for this article are available online. [ABSTRACT FROM AUTHOR]

The grouting technology is an effective and economic method in the grouting industry field. In this paper, a nonlinear model for the grouting dynamic process was established, and the controlling parameters were further modified through a robust method. Moreover, the grouting pressure system for the neural network was also modelled based on a sensitivity analysis algorithm, and in particular, the iterative learning algorithm and Lyapunov asymptotical theory. The results showed that such a robust controlling methodology was better than the normal manual operation method. The subsequent numerical simulations demonstrated that the tuning methodology could meet all the requirements for the grouting control with the maximum pressure variable in the range of 8.1%. The present study and the proposed method could be applied to various engineering projects and especially, to implement in the real control of damming grouting. [ABSTRACT FROM AUTHOR]

The World Health Report 2010 encourages countries to reduce wastage and increase efficiency to achieve Universal Health Coverage (UHC). This research examines the efficiency of divisions (sub-provincial geographic units) in Pakistan in moving towards UHC using Data Envelop Analysis. We have used data from the Pakistan National Accounts 2011–12 and the Pakistan Social Living and Measurement Survey 2012–13 to measure per capita pooled public health spending in the divisions as inputs, and a set of UHC indicators (health service coverage and financial protection) as outputs. Sensitivity analysis for factors outside the health sector influencing health outcomes was conducted to refine the main model specification. Spider radar graphs were generated to illustrate differences between divisions with similar public spending but different performances for UHC. Pearson product-moment correlation was used to explore the strength and direction of the associations between proxy health systems organization variables and efficiency scores. The results showed a large variation in performance of divisions for selected UHC outputs. The results of the sensitivity analysis were also similar. Overall, divisions in Sindh province were better performing and divisions in Balochistan province were the least performing. Access to health care, the responsiveness of health systems, and patients' satisfaction were found to be correlated with efficiency scores. This research suggests that progress towards UHC is possible even at relatively low levels of public spending. Given the devolution of health system responsibilities to the provinces, this analysis will be a timely reference for provinces to gauge the performance of their divisions and plan the ongoing reforms to achieve UHC. [ABSTRACT FROM AUTHOR]

Most catchments in tropical regions are ungauged and data deficient, complicating the simulation of water quantity and quality. Yet, developing and testing hydrological models in data-poor regions is vital to support water management. Here, we used the Soil and Water Assessment Tool (SWAT) to predict stream runoff in Halda Basin in Bangladesh. While the calibrated model's performance was satisfactory (R2 = 0.80, NSE = 0.71), the model was unable to track the extreme low flow peaks due to the temporal and spatial variability of rainfall which may not be fully captured by using data from one rainfall gauging station. Groundwater delay time, baseflow alpha factor and curve number were the most sensitive parameters influencing model performance. This study improves understanding of the key processes of a catchment in a data-poor, monsoon driven, small river basin and could serve as a baseline for scenario modelling for future water management and policy framework. [ABSTRACT FROM AUTHOR]

In this article, we prolate, the coinfection mathematical model for the implication of optimal control. Here the model (Tahir, 0004) AIDS/HIV/TB is extended for an optimal control purpose, and for sensitivity analysis of the dominant parameters involved in infection. For this purpose, initially, we apprized the threshold number of the concern model. Then the sensitivity analysis is discussed for dominant parameters which mostly spread infection in population. Moreover, simulation is shown in simple graph and area graph which shows the effected area of any population in any community by using Matlab programming. Here our mission is to minimize the coinfection problem from any community. Also we defined six control variables with different schemes to control and minimized infection. In the last section, numerical simulation is presented in a new way by Runge-Kutta method of order four, and Matlab programming which is with and without control or vaccination for the concern model. [ABSTRACT FROM AUTHOR]

Most of High-impedance faults (HIFs) occur due to broken conductors falling on high-resistivity desert soil in United Arab Emirates remain undetected due to incredibly low fault currents and endanger lives. A reliable detection method is needed. A live line test conducted on a 11kV distribution line by switching to a remotely laid conductor did not result enough detectable fault currents. This motivated conducting a literature survey and researching for the reasons why the modern numerical protection relays could not detect such low current faults. Hence, a sensitivity analysis of a single-phase-to-ground system is carried out. Mathematical modelling for the sensitivity analysis is performed using differential calculus. Applying definition of sensitivity to a set of power system equations to analyze to provide guidance for detection of high impedance faults is entirely a new approach. This paper provides a comprehensive analysis of single phase to ground fault system by exhibiting profiles of fault currents, line, neutral, and MV bus voltages for a range of fault impedance. The sensitivity analysis results conclude that low-fault current signals should be collected from the neutral line of the distribution system instead of focusing on the line voltage, current, power and other parameters. [ABSTRACT FROM AUTHOR]

A literature survey indicated that high impedance fault (HIF) detection methods employ simulation techniques using very low voltage signals generated using software such as PSCAD, and MATLAB, among others. While software platforms are used to derive satisfactory results, small fault current signals cannot be accurately tapped from high transformation ratio current transformers owing to the dominant load currents in real power systems. Therefore, a sensitivity analysis based on a power system background is considered essential. A set of well-known power system equations are used to analyse and select a parameter to tune this sensitivity, from several available parameters, such as source, transformer, line, neutral, and earth impedance. The analysis reveals that when the detection of HIF in the field is challenging, then fault or neutral impedance emerges as an important parameter for tuning the system. To achieve this, certain rigorous mathematical steps are derived to develop a suitable formulation for sensitivity analysis using differential calculus. Based on the analysis, the sensitivity results and tuning parameters are tabulated, and sensitivity dependency curves are plotted. [ABSTRACT FROM AUTHOR]

In comprehending flood model results, we performed sensitivity analyses and evaluated how different combinations of digital elevation model (DEM) resolution and Manning's roughness affect flood maps produced from a 2D hydraulic model. Moreover, we analysed how the estimation of accuracy can further be influenced by the performance measure and the area's topography. Various combinations of DEM and Manning's n produced different results, in terms of quantified performance in relation to actual flood extent and the generated flood boundaries. High-resolution DEMs performed better with higher Manning's n , while lower n values were better for lower resolution DEMs. Furthermore, although lower resolution DEMs (25 and 50 m) received higher quantified performances, there are more discrepancies in the flood maps and water surface elevations (WSE) produced by them. The current statistical estimators of model performance do not necessarily provide an accurate estimate of which combination of DEM resolution and roughness are more suitable for application to modelling. Different statistical estimates have different assumptions, which can affect the model selection. Therefore, a more holistic approach towards model selection should be adopted that gives equal importance to statistical estimators, as well as the quality of flood inundation extents. [ABSTRACT FROM AUTHOR]

The gut microbiota plays a critical role in the anti-tumor immune response. There is increasing data showing that antibiotics (ATBs) change the composition of the gut microbiota and affect the efficacy of immune checkpoint inhibitors (ICIs). However, this is the first meta-analysis to evaluate the association between ATB use and ICI efficacy in cancer patients to provide a better understanding of the strength of this association. We performed a literature search for relevant studies that evaluated the relationship between ATB use and ICI efficacy using the PubMed, Embase, and conference databases. The primary outcomes consisted of overall survival (OS) and progression-free survival (PFS) measured by hazard ratios (HR) and corresponding 95% confidence intervals (CI). Subgroup and sensitivity analyses were also performed. A total of 19 eligible studies comprising 2,740 cancer patients treated with ICIs were included in the analysis. Our results indicated that ATB use was negatively associated with OS in cancer patients (HR = 2.37; 95% CI = 2.05–2.75; P <.001), without heterogeneity (I2 = 0.0%; P =.851). Moreover, ATB use significantly reduced PFS in patients treated with ICIs (HR = 1.84; 95% CI = 1.49–2.26; P <.001; I2 = 56.2%). Similar results were obtained in the subgroup analyses stratified by the time of ATB use and cancer type. Sensitivity analyses confirmed the stability of our results. Therefore, the findings of our meta-analysis indicated that ATB use is negatively associated with OS and PFS in cancer patients treated with ICI immunotherapy. [ABSTRACT FROM AUTHOR]

An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount, and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate release characteristics and meteorological conditions. However, since release characteristics and meteorological conditions are often unknown, the inverse problem is of great interest, that is, based on all the observations of the plume so far, what can be inferred about the release characteristics? This is the question we seek to answer using plume observations from a controlled release at the Diablo Canyon Nuclear Power Plant in Central California. With access to a large number of evaluations of a computationally expensive particle dispersion simulator that includes continuous and categorical inputs and spatio-temporal output, building a fast statistical surrogate model (or emulator) presents many statistical challenges, but is an essential tool for inverse modeling and sensitivity analysis. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. We use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator, thus finding a posterior distribution of characteristics of the release. Since the release was controlled, these characteristics are known, making it possible to compare our findings to the truth. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants' preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, relying only on an informative prior distribution for the mean reporting rate to supplement the partial information in the data. Covariates are used to inform both the true count-generating process and the under-reporting mechanism, while also allowing for complex spatio-temporal structures. We present several sensitivity analyses based on simulation experiments to aid the elicitation of the prior distribution for the mean reporting rate and decisions relating to the inclusion of covariates. Both prior and posterior predictive model checking are presented, as well as a critical evaluation of the approach. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Nowadays, companies are able to obtain the key to success in global competition by choosing the right suppliers who are more align with their strategies. It is clear that applying appropriate attitudes and criteria have a great importance in choosing suppliers in the process of decision-making by chain managers and especially purchasing managers. In this study tried to apply Lean, Agile, Resilient and Green (L.A.R.G.) approach in a model designed to select the consistence supplier. Accordingly, at first, while reviewing and exploiting the literature, the most main logistics needs of the company concerned in the light of the objectives that followed on the fields of the LARG attitudes, are refined and selected, then their degree of significance is determined through Multi-Objective Performance Analysis (M.O.P.A.). The house of quality (H.O.Q.) matrix is applied to determine the importance degree of the technical characteristics of the suppliers and Taguchi loss function is applied to determine the degree of their performance deviation from the target value in each one of the technical characteristics (ultimate judgment about their competency). The considered suppliers are ranked based on the results of the loss function analysis. A sensitivity analysis was conducted to analyse the impact of different conditions on suppliers' ranking and validation of the ranking results were Satisfied by applying the T.O.P.S.I.S. method. [ABSTRACT FROM AUTHOR]

This study develops an alternative methodology for the risk analysis of information systems security (ISS), an evidential reasoning approach under the Dempster-Shafer theory of belief functions. The approach has the following important dimensions. First, the evidential reasoning approach provides a rigorous, structured manner to incorporate relevant ISS risk factors, related countermeasures, and their interrelationships when estimating ISS risk. Second, the methodology employs the belief function definition of risk—that is, ISS risk is the plausibility of ISS failures. The proposed approach has other appealing features, such as facilitating cost-benefit analyses to help promote efficient ISS risk management. The paper elaborates the theoretical concepts and provides operational guidance for implementing the method. The method is illustrated using a hypothetical example from the perspective of management and a real-world example from the perspective of external assurance providers. Sensitivity analyses are performed to evaluate the impact of important parameters on the model's results. [ABSTRACT FROM AUTHOR]

This article proposes a new quantity called the "sensitivity value," which is defined as the minimum strength of unmeasured confounders needed to change the qualitative conclusions of a naive analysis assuming no unmeasured confounder. We establish the asymptotic normality of the sensitivity value in pair-matched observational studies. The theoretical results are then used to approximate the power of a sensitivity analysis and select the design of a study. We explore the potential to use sensitivity values to screen multiple hypotheses in the presence of unmeasured confounding using a microarray dataset. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The presented ALPHA cost tool is a novel highly flexible bottom-up parametric hybrid cost estimation framework. It combines the benefits of both methods with the aim of providing cost information during all product development phases. The software offers full transparency to the user and advanced two-level uncertainty management to not only understand any project's cost structure but also aid to identify its cost driving parameters. The implementation of sensitivity analysis makes the intrinsic uncertainty inevitable embedded in cost estimation become graspable. Gaussian error propagation offers direct feedback without extra calculation time while classic Monte Carlo Simulation gives detailed insight through post estimation analysis. From the vast number of commercially available or self-developed cost tools many probably already incorporate uncertainty measures similar to those proposed here. But this article shows both the potential of the additionally obtainable information from uncertainty propagation and demonstrates a way of integrating these risk considerations into a self-developed cost tool. [ABSTRACT FROM AUTHOR]

Hydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area. [ABSTRACT FROM AUTHOR]

In this present era of the Anthropocene, human activities affect hydrology and droughts. Quantifying this human influence improves our understanding and builds fundamental knowledge for water resource management. Analysis of observation data is useful in progressing this knowledge as these human activities and feedbacks are intrinsically included. Therefore, here we present an observation-based approach, the upstream-downstream comparison, to quantify changes in hydrological drought downstream of a human activity. We demonstrate this approach in a basin in northern Chile, where a reservoir was introduced. A sensitivity analysis is performed to assess how different choices of drought analysis threshold can affect the results and interpretation. We find that many commonly used choices do not exclude human activities from the threshold and therefore could be underestimating the change detected due to the human influence. The upstream-downstream comparison avoids this through the application of the upstream station threshold rather than the human-influenced downstream station. [ABSTRACT FROM AUTHOR]

Pretreatment selection or censoring ("selection on treatment") can occur when two treatment levels are compared ignoring the third option of neither treatment, in "censoring by death" settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Substance/drug abuse poses a significant threat to the health and socio-economic fabric of individuals and nations. The combined abuse of alcohol and the highly addictive methamphetamine has worsened the drug epidemic in South Africa, especially in the Western Cape province. In this paper, a mathematical model is formulated to model the dynamics of alcohol and methamphetamine co-abuse. We prove that the equilibria of the submodels are locally and globally asymptotically stable when the sub-model threshold parameters are less than unity. The basic reproduction number due to co-abuse is shown to be the maximum of the two sub-model reproduction numbers. Sensitivity analysis reveals that the most sensitive parameters in the co-abuse epidemic are the alcohol and methamphetamine recruitment rates β1 and β2 respectively. The prevalence curve is indicative of a persistent drug problem in the region. Hence, the need to promote social programs that raise awareness of the dangers posed by multiple substance abuse, through educational campaigns in learning institutions, social media and health institutions. Transmission control must focus on enhancing the quitting process while promoting support services to drug users during and after treatment to minimize cases of relapse. [ABSTRACT FROM AUTHOR]

In this article, we extended the co-infection mathematical model [1] for optimal control purpose. We initially derived a threshold number R 0 and found bounded and biological region for the study of the proposed model. Here, we developed a methodology through the considered superinfection problem which is getting abate while neglecting Acquired Immuno deficiency Syndrome (AIDS) because it is a noncurable disease. For this, we developed the following control variables in our model: V 1 , using treatment against TB; V 2 , infection control in health-care center; V 3 , co-treatment of multidrug-resistant TB and HIV or start both HIV antiretroviral and anti-TB drug therapy; and V 4 , avoid close contact with TB patient. These are defined with some different schemes to minimize and control the infection from any community and population. In the last section, numerical simulation is presented which supports the given model. [ABSTRACT FROM AUTHOR]

Nanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a limited range. In this study, 1277 experimental data points of distinct nanofluid relative viscosity (NF-RV) were gathered from a plenary literature review. In order to create a general model, adaptive network-based fuzzy inference system (ANFIS) code was expanded based on the independent variables of temperature, nanoparticle diameter, nanofluid density, volumetric fraction, and viscosity of the base fluid. A statistical analysis of the data for training and testing (with R² = .99997) demonstrates the accuracy of the model. In addition, the results obtained from ANFIS are compared to similar experimental data and show absolute and maximum average relative deviations of about 0.42 and 6.45%, respectively. Comparisons with other theoretical models from previous research is used to verify the model and prove the prediction capabilities of ANFIS. Consequently, this tool can be of huge value in helping chemists and mechanical and chemical engineers - especially those who are dealing with heat transfer applications by nanofluids - by providing highly accurate predictions of NF-RVs. [ABSTRACT FROM AUTHOR]

An operation skill training is useful to enhance the skill of the workers in a shop floor. The number of repetition is a prominent factor in the training and has to be determined in such a way so that the training can be performed cost-effectively. In this research, a novel model is developed to determine the optimal number of repetition in an operation skill training program by integrating learning curves into cost of quality. A real case study in a twist drill manual-sharpening training program is given to show the implementation of the model. A sensitivity analysis is performed to show the effect of the model's parameters to the decision variable and the objective function. Based on the optimization result, the optimal number of repetitions in the case study is found to be 26. It is also found that the developed model can be used to estimate the training cost, and to determine the training's target. [ABSTRACT FROM AUTHOR]

The issue of whether public debt is useful or harmful towards economic growth is one of the most prevailing debates in the literature with no consensus existing on the subject matter. The study employs the ARDL model to examine the long-run and short-run effects of public debt on economic growth for South African data spanning a period between 2002:q1 and 2016:q4. Our sensitivity analysis consists of re-estimating our empirical regressions using two sub-samples dataset corresponding to the post-crisis period (i.e. 2007:q3–2016:q4). All estimated regressions unanimously find negative debt–growth relationship, with the negative relationship strengthening in the post-crisis period. Overall, our empirical results have some useful ramifications towards fiscal policymakers. [ABSTRACT FROM AUTHOR]

Paper-based lateral flow immunoassays (LFIAs) are an interesting on site technique for detecting heavy metals. Pb(II) is one of the toxic heavy metals in polluted environmental water. This paper reports the synthesis and characterization of three Fe3O4-based nanoadsorbents that show different performance to Pb(II), Cu(II), Cd(II), Hg(II), As(V), Zn(II), Fe(III), Ca(II), Mg(II), Mn(II), and Al(III) when a GO or an amino group is introduced. Compared with Fe3O4, Fe3O4-GO showed higher adsorption percentage to many heavy metal ions under slightly acid condition. The negative surface on Fe3O4@NH2 had favourable interactions for four main divalent metal ion adsorption, which followed the order: Pb(II) > Cu(II) > Hg(II) > Cd(II). Combined with antibody-functionalized AuNPs showed high affinity and specificity toward Pb(II), an Fe3O4@NH2-based LFIA with five times greater sensitivity for detecting Pb(II) in lake water was developed. The cut-off value of this Fe3O4@NH2-combined LFIA method for detection Pb(II) in lake water was 20 ng/mL and the detection result for 10 ng/mL Pb(II) was obviously observable by the naked eye or a strip reader machine. This detection strategy could be readily adapted for other small molecules or biomolecules as an ultrasensitive screening tool. [ABSTRACT FROM AUTHOR]

Dengue is endemic in the Philippines and poses a substantial economic burden in the country. In this work, a compartmentalized model which includes healthcare-seeking class is developed. The reproduction number is determined to investigate critical parameters influencing transmission. Partial rank correlation coefficient (PRCC) technique is performed to address how the model output is affected by changes in a specific parameter disregarding the uncertainty over the rest of the parameters. Results show that mosquito biting rate, transmission probability from mosquito to human, respectively, from human to mosquito, and fraction of individuals who seek healthcare at the onset of the disease, posted high PRCC values. In order to obtain the values for the desired parameters, the reported dengue cases by morbidity week in the Philippines for the year 2014 and 2015 are used. The reliability of parameters is then verified via parametric bootstrap. [ABSTRACT FROM AUTHOR]

We propose an epidemic model for the transmission of hepatitis B virus along with the classification of different infection phases and hospitalized class. We formulate the model and discuss its basic mathematical properties, e.g. existence, positivity, and biological feasibility. Exploiting the next generation matrix approach, we find the basic reproductive number of the model. We perform sensitivity analysis to illustrate the effect of various parameters on the transmission of the disease. We investigate stability of the equilibria of the model in terms of the basic reproduction number. Conditions for the stability of the proposed model are obtained using various approaches. Finally, we perform the numerical simulations to discuss sensitivity analysis and to support our analytical work. [ABSTRACT FROM AUTHOR]

We establish a Holling II predator-prey system with pesticide dose response non-linear pulses and then study the global dynamics of the model. First, we construct the Poincaré map in the phase set and discuss its main properties. Second, threshold conditions for the existence and stability of boundary periodic solution and order- periodic solutions have been provided. The results show that the pesticide dose increases when the period of control increases, while it will decrease as threshold increases. Sensitivity analyses reveal that critical condition for the stability of boundary periodic solution is very sensitive to control parameters and pesticide doses. The bifurcation analysis reveals that the proposed model exists complex dynamics. Compared to the model with fixed moments, it demonstrates that the density of pest population not only can be controlled below the threshold but also can avoid some negative effects due to pesticide application, confirming the importance of biological control. [ABSTRACT FROM AUTHOR]

Point data observations are often used to calibrate computable general equilibrium (CGE) models; however, results may be impacted by calibration of an Armington trade specification in a regional CGE (R-CGE) model. This paper calibrates an Armington trade specification with three differently estimated interregional trade data sets. It estimates interregional trade with one survey and two non-survey methods. The resulting three different trade data sets are each used to calibrate REMES, an R-CGE model for Norway. Two regional policy reforms are simulated with the three model versions to analyze the sensitivity of regional manufacturing sector output to trade data estimates. The results show that the trade data estimation method used for calibration significantly affects regional sector output results. Policy analysts and developers should be aware that calibrating an Armington trade specification with differently estimated interregional trade data may have a substantial influence on model results, and hence, on ex-ante and ex-post conclusions on policy impacts. [ABSTRACT FROM AUTHOR]

The paper describes the application of a new multi-criteria decision-making (MCDM) model, MultiAtributive Ideal-Real Comparative Analysis (MAIRCA), used to select a location for the development of a multimodal logistics centre by the Danube River. The MAIRCA method is based on the comparison of theoretical and empirical alternative ratings. Relying on theoretical and empirical ratings the gap (distance) between the empirical and ideal alternative is defined. To determine the weight coefficients of the criteria, the DEMATEL method was applied. In this paper, through a sensitivity analysis, the results of MAIRCA and other MCDM methods - MOORA, TOPSIS, ELECTRE, COPRAS and PROMETHEE - were compared. The analysis showed that a smaller or bigger instability in alternative rankings appears in MOORA, TOPSIS, ELECTRE and COPRAS. On the other hand, the analysis showed that MAIRCA and PROMETHEE offer consistent solutions and have a stable and well-structured analytical framework for ranking the alternatives. By presenting a new method MCDM expands the theoretical framework of expertise in the field of MCDM. This enables the analysis of practical problems with new methodology and creates a basis for further theoretical and practical upgrade. [ABSTRACT FROM AUTHOR]

Objectives: Whether uric acid levels were associated with the progression of chronic kidney disease (CKD) remained controversial. This meta-analysis was aimed to assess the effect of lowering serum uric acid therapy on the progression of CKD to clarify the role of uric acid in the progression of CKD indirectly. Methods: Pubmed, Embase, the Cochrane library, CBM were searched for randomized controlled trials (RCTs) that assessed the efficiency of lowering serum uric acid therapy on the progression of CKD without language restriction. Summary estimates of weighted mean differences (WMDs) and relative risk (RR) were obtained by using random-effect or fixed-effect models. Sensitivity analyses were performed to identify the source of heterogeneity. Results: A total of 12 randomized controlled trials with 832 CKD participants were included in the analysis. Pooled estimate for eGFR was in favor of lowering serum uric acid therapy with a mean difference (MD) of 3.88 ml/min/1.73 m2, 95% CI 1.26-6.49 ml/min/1.73 m2, p = .004 and this was consistent with results for serum creatinine. The risk of worsening of kidney function or ESRD or death was significantly decreased in the treatment group compared to the control group (RR 0.39, 95% CI 0.28-0.52, p< .01). Conclusions: Uric acid-lowering therapy may be effective in retarding the progression of CKD. Further randomized controlled trials should be performed to confirm the effect of lowering serum uric acid therapy on the progression of CKD. [ABSTRACT FROM AUTHOR]

Overhydration and sarcopenia, related to an individual's nutritional status, have been associated with increased cardiovascular mortality and poor prognosis in patients on hemodialysis. The purpose of this study was to investigate the prediction of overhydration and sarcopenia on mortality in patients on hemodialysis using a body composition monitor. We conducted a systematic review and meta-analysis using a random-effects model. We searched the Cochrane Central Register, OVID MEDLINE, EMBASE and PubMed databases for all studies published prior to December 9, 2016 and reviewed the reference lists of relevant reviews, registered trials and relevant conference proceedings. The overhydration group (fluid excess, >15% vs. the normohydration group) and the low lean tissue index group ( <10%) were compared with a reference group. Six trials, consisting of 29,469 patients, were included in the pooled analysis. The pooled hazard ratio for overall survival of the overhydration group, compared with the reference normohydration group was 1.798 (95% confidence interval [CI]: 1.53-2.804, p = .001). The hazard ratio for mortality in the low lean tissue index group was 1.533 (95% CI, 1.411-1.644; p = .001) in the random-effects model. The results from the most recent study showed the greatest heterogeneity in the sensitivity analysis. Low lean tissue index and overhydration, measured using a body composition monitor, were associated with a high mortality rate in patients on hemodialysis. [ABSTRACT FROM AUTHOR]

The primary purpose of decision support systems (DSS) is to help the decision maker develop an understanding of the ill-structured, complex environment represented by the model. This paper concentrates on understanding the modeled environment through model analysis. Specifically, the purpose of this paper is to propose a framework for model analysis based on Perkins's theory of understanding and its basic premise (knowledge as design) and basic components (purpose, models, and arguments). This framework encourages enhanced user understanding in a DSS via the synergistic combination and integration of: (1) cognitive science (theory of understanding), (2) artificial intelligence (machine learning, knowledge extraction, and expert systems), (3) model analysis (deductive and inductive), and (4) DSS (model management, instance management, and knowledge-base management). [ABSTRACT FROM AUTHOR]

Hydrological modelling has undergone constant growth with the increase in information processing capabilities. Hydrological models have traditionally been used to study the effects of climate change on management and land-use changes and for water resources planning, among other purposes. The aim of this study was to determine and analyse the advantages of the HBV and HYMOD models, which are commonly used in hydrology on daily and monthly time scales. A regional sensitivity analysis was used to compare the processes that take on greater importance at different time scales in the two models. As a result, it was found that quick precipitation-runoff processes prove to be better represented in the HBV model, while slow, time-aggregated processes are better represented by the HYMOD model. This study confirms that both models are adequate for rain-dominated basins, such as those of the study area. Additionally, the HBV model proved to be more robust in comparison to HYMOD. [ABSTRACT FROM AUTHOR]

Understanding potential evapotranspiration (PET) changes under climate change is of great importance for hydrological research. The trends of PET changes and their driving forces were investigated in the Lancang (LRB) and Yarlung Zangbo (YRB) river basins, southwest China, using diagnosis graphs and the Mann-Kendall test. Analysis of variance was applied to examine the contribution of different climatic variables to PET. The results show that: (i) there was a statistically significant increase in PET in the period of 1957-2015 in the LRB, while it showed a markedly decreasing trend in the YRB; (ii) PET in both basins is fairly sensitive to wind speed, relative humidity, solar radiation and maximum air temperature, and the interactions between wind speed and relative humidity are also important; and (iii) the increase in PET in the LRB is due mainly to the increase in maximum air temperature and decrease in relative humidity, while declines in wind speed and solar radiation are the main reasons for the decrease in PET in the YRB. [ABSTRACT FROM AUTHOR]

We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that needs to be defined carefully, hidden within a sensitivity analysis for nonrandom assignment is an enormous correction for multiple testing: In the absence of bias, it is extremely improbable that multiple testing alone would create an association insensitive to moderate biases. We propose a new strategy called “cross-screening,” different from but motivated by recent work of Bogomolov and Heller on replicability. Cross-screening splits the data in half at random, uses the first half to plan a study carried out on the second half, then uses the second half to plan a study carried out on the first half, and reports the more favorable conclusions of the two studies correcting using the Bonferroni inequality for having done two studies. If the two studies happen to concur, then they achieve Bogomolov-Heller replicability; however, importantly, replicability is not required for strong control of the family-wise error rate, and either study alone suffices for firm conclusions. In randomized studies with just a few null hypotheses, cross-screening is not an attractive method when compared with conventional methods of multiplicity control. However, cross-screening has substantially higher power when hundreds or thousands of hypotheses are subjected to sensitivity analyses in an observational study of moderate size. We illustrate the technique by comparing 46 biomarkers in individuals who consume large quantities of fish versus little or no fish. The R package CrossScreening on CRAN implements the cross-screening method. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

We demonstrate that the front-door adjustment can be a useful alternative to standard covariate adjustments (i.e., back-door adjustments), even when the assumptions required for the front-door approach do not hold. We do this by providing asymptotic bias formulas for the front-door approach that can be compared directly to bias formulas for the back-door approach. In some cases, this allows the tightening of bounds on treatment effects. We also show that under one-sided noncompliance, the front-door approach does not rely on the use of control units. This finding has implications for the design of studies when treatment cannot be withheld from individuals (perhaps for ethical reasons). We illustrate these points with an application to the National Job Training Partnership Act Study. [ABSTRACT FROM AUTHOR]

Weak instruments produce causal inferences that are sensitive to small failures of the assumptions underlying an instrumental variable, so strong instruments are preferred. The possibility of strengthening an instrument at the price of a reduced sample size has been proposed in the statistical literature and used in the medical literature, but there has not been a theoretical study of the trade-off of instrument strength and sample size. This trade-off and related questions are examined using the Bahadur efficiency of a test or a sensitivity analysis. A moderate increase in instrument strength is worth more than an enormous increase in sample size. This is true with a flawless instrument, and the difference is more pronounced when allowance is made for small unmeasured biases in the instrument. A new method of strengthening an instrument is proposed: it discards half the sample to learn empirically where the instrument is strong, then discards part of the remaining half to avoid areas where the instrument is weak; however, the gains in instrument strength can more than compensate for the loss of sample size. The example is drawn from a study of the effectiveness of high-level neonatal intensive care units in saving the lives of premature infants. [ABSTRACT FROM AUTHOR]

The design of foundation is done in two different stages: geotechnical design and the structural design. The geotechnical design of foundations focuses on three issues: bearing failure, settlement limit and economy. Most of the design concentrates on bearing capacity and settlement criteria ignoring the economy most of time. This paper introduces an optimised design process of foundation using genetic algorithms (GAs) in which the objective function is construction cost, which includes the design parameters and design requirements as the optimisation variables and constraints, respectively. The design process is illustrated using example of shallow isolated column footing in sands. Sensitivity analysis on the soil parameters and design requirements is conducted to find out the key parameters to control the construction cost. [ABSTRACT FROM AUTHOR]

Spatial and seasonal variations of curve number (CN) and initial abstraction ratio (λ) in a watershed can result in inaccurate runoff volume estimations when using the US Natural Resources Conservation Service (SCS-CN) method with constant values for these parameters. In this paper, parameters of CN and λ are considered as calibration parameters and the sensitivity of estimated runoff to these parameters using the SCS-CN method is scrutinized. To incorporate the uncertainty associated with CN and λ, fuzzy linear regression (FLR) is applied to derive the relationships of CN and λ with rainfall depth (P) by employing a large dataset of storm events from four watersheds in Iran. Results indicate that the proposed approach provides more accuracy in estimation of runoff volume compared to the SCS method with constant values of CN and λ, and gives a straightforward technique for evaluating the hydrological effects of CN, λ, and P on runoff volume. [ABSTRACT FROM AUTHOR]

The assessment of net rainfall, defined as the intermediate hydrological variable linked in between the hillslope and the river network, is a challenge. This paper presents a method for net rainfall estimation, using inverse modelling associated to a geomorphology-based transfer function. The analysis is carried out in semi-arid Tunisia, with a dataset from event discharges in a mesoscale dryland basin. A complete sensitivity analysis is developed, along with a discussion of validity limits for simplifying assumptions and the identification of paths for improvement. This work could be relevant for data-scarce areas, thanks to the use of simple dynamic conceptualization and being based on observable geomorphological features, adjusted to the available data and knowledge. [ABSTRACT FROM AUTHOR]

The bone drilling process is indispensable in orthopaedic surgeries and treating bone breakages. It is also very important in dentistry and bone sampling operations. Bone is a very complex material and the process of drilling is very sensitive. Thus, bone drilling is one of the most important, common in the field of biomedical engineering. The bone drilling process can be promoted using automatic drilling machines and surgery-assisting robots. The problematic issue during operation is the high increase in drilling process temperature (higher than 47 °C) which leads to the so-called ‘thermal necrosis’ or cell death, and local burn in bone tissue. Furthermore, imposing higher forces to bone might yield to breaking or cracking, and consequently causes serious damages in bone. In this paper, the tool rotational speed, feed ratio and tool diameter were taken into account as process input parameters, and process temperature and thrust force were taken as output parameters. Design of experiments using response surface methodology was followed. Then, second linear governing equation was assigned to the model and its accuracy was evaluated. Later, Sobol statistical sensitivity analysis was used to ascertain the effect of process input parameters on process temperature and force. The results showed that among all effective input parameters, the tool rotational speed, feed rate and tool diameter have the highest influence respectively on process temperature and force. The behaviour of each output parameter with variation in each input parameter was further investigated. [ABSTRACT FROM PUBLISHER]

Iran, as a seismic country, is situated over the Himalayan-Alpied seismic belt and has faced many destructive earthquakes throughout history. Therefore, it is very important to evaluate the possible damage to the existing infrastructure based on statistical and spatial analysis. In this study, a new model is developed to analyse seismic damages based on seismic hazard assessment and extraction of the vulnerability function for all features of fuel infrastructure. To consider uncertainty analysis in the model, Monte Carlo simulation is used based on 10,000 iterations. The results of hazard analysis indicated that peak ground acceleration is about 0.18 g and there is slight to moderate damages to the desired fuel infrastructure in the study area. Moreover, sensitivity analysis is also performed to determine how median, standard deviation (or beta), grid size, attenuation relationships, liquefaction and landslide susceptibility impact the seismic loss. Last but not least, the effect of input parameters of earthquake scenarios including magnitude, focal depth and focal distance are also analysed in conjunction with regression analysis. The results of the study show that magnitude and focal distance are the most sensitive parameters in which the expected damage to the fuel infrastructure is reduced by about 25% if the epicentre of the earthquake is moved from 10 to 25 km. [ABSTRACT FROM AUTHOR]

To investigate the effect of drag force on stability of residual soil slopes, a modified approach based on the limit equilibrium method is proposed by establishing a nonlinear coupled mathematical model to calculate the seepage velocities in soils and the runoff velocities at a fluid-solid interface. In this mathematical model, the Navier-Stokes equation and Brinkman-extended Darcy equation are used to describe the incompressible surface runoff and seepage in soils, respectively. Then, the drag force can be derived by adopting Newton's law of internal friction, and the factor of safety (FOS) of residual soil slopes considering drag force can be calculated based on the limit equilibrium method. The modified approach was applied to analyse the stability of rainfall-induced landslides in residual soils in Nanjiang, Sichuan, China, 2011. We found that the runoff depth can significantly control the FOS of the residual soil slopes and cause slope failures. The results of sensitivity analysis highlight the significance for considering drag force due to surface runoff when calculating the FOS of the residual soil slope. The FOS obtained from the modified approach is more reasonable, and this modified approach also can be used for evaluating or predicting rainfall-induced slope instability in residual soils. [ABSTRACT FROM AUTHOR]

Geographic information system (GIS)-based multi-criteria decision analysis (MCDA) is commonly used to solve a range of complex spatial problems. We have incorporated an analytical network process (ANP) as a central part of MCDA and used this approach for land subsidence susceptibility mapping (LSSM). We used an ANP approach in combination with the concepts of global sensitivity and uncertainty analyses, in order to minimize any associated errors. This approach was tested for a LSSM application in north-western Iran, using a methodology that consisted of three distinct phases. An ANP approach was used in the first phase to derive criteria weightings by analysing relevant criteria, which included topographic, hydrologic, climatologic, geological, and anthropological indicators. The second phase involved evaluating the uncertainty and sensitivity of areas susceptible to subsidence as a function of the derived weightings, using Monte Carlo simulations. The third phase then used a land subsidence inventory database to validate the results and to measure any improvement in accuracy following sensitivity analysis. Results indicated a 6% improvement in the accuracy of the ANP method as a result of using an integrated global sensitivity analysis. [ABSTRACT FROM AUTHOR]

The control of spread of HIV to reduce its effects on a population is an important role of public health. HIV testing and counselling (HTC) and eventual enrolment of infected individuals on anti retro-viral treatment (ART) as soon as possible to reduce the risk of dying is currently the main intervention against HIV. Mathematical models can be used to study the effects of HIV prevention, testing and treatment with ART on HIV patients. In this study, we employ a deterministic model to provide a quantification of HIV prevention, testing and treatment with ART as public health measurements in the fight against HIV infection. Lyapunov function has been used to derive a condition that ensures that the model system is globally asymptotically stable when R0 is less than unity. Through sensitivity analysis, we determine the relative importance of model parameters for disease transmission. The sensitivity analysis results suggest that the effective contact rates are mechanisms fuelling HIV epidemic proliferation while ART efficacy reduces the incidence. The model is fitted to HIV surveillance data obtained from world in data website. Although the results show a high proportion of individuals with HIV in Kenya, the incidence curve is indicative of a declining HIV infection and settling at an endemic steady state. The results are suggestive of the need to promote preventive mechanism against the occurrence of new infections. Moreover, the results show that the combination of several control mechanisms would significantly reduce the spread of the disease, if we maintain the level of each control high. [ABSTRACT FROM AUTHOR]

The European ‘Floods Directive’ 2007/60/EC focuses on the development of flood risk maps and management plans on the basis of the most appropriate and advanced tools. This pushed a paradigm shift for moving to sustainable development through processes of stakeholder engagement to improve the efficiency and transparency of decision processes. In this context, this research project developed a free and open-source GIS software, calledFloodRisk, to operatively support stakeholders in their compliance with risk map delineation and the management of current and future flood risk based on their needs for multi-purpose applications. In this paper, a high-resolution impact assessment framework based on 2D inundation modelling with different return periods was used, as input, within theFloodRiskmodel to reconstruct the socio-economic damages based on a case study showing how structural and non-structural measures can significantly decrease the cost of floods for households. The sensitivity of theFloodRiskmodel was also examined and it was found to be highly dependent on the selection of damage functions and the economic values of the exposed assets. [ABSTRACT FROM AUTHOR]

Parameter calibration and sensitivity analysis (SA) are usually not straightforward tasks for distributed hydrological models, owing to the complexity of models and the large number of parameters. A two-step SA approach is proposed for analysing hydrological signatures based on the distributed hydrology–soil–vegetation model (DHSVM) in the Jinhua River Basin, East China. A preliminary SA is conducted to obtain influential parameters via analysis of variance. These parameters are further analysed through a variance-based global sensitivity analysis method to achieve robust rankings and parameter contributions. Parallel computing is designed to reduce the computational burden. The results reveal that only a few parameters are significantly sensitive and that interactions between parameters cannot be ignored. When analysing hydrological signatures, it is found that water yield is simulated very well for most samples. Small and medium floods are simulated very well, while slight underestimations happen for large floods. [ABSTRACT FROM AUTHOR]

A quantitative PCR (qPCR) able to differentiate between fieldMycoplasma synoviaeand MS-H vaccine strain was developed, validated and evaluated. It was developed using nucleotide differences in theobggene. Analytical specificity and sensitivity assessed using DNA from 194M. synoviaefield samples, three different batches of MS-H vaccine and from 43 samples representing four other avianMycoplasmaspecies proved to be 100%. The detection limit for fieldM. synoviaeand MS-H vaccine strain was 102-3and 102colony-forming units PCR equivalents/g trachea mucus, respectively. The qPCR was able to detect both, fieldM. synoviaeand MS-H vaccine strain in ratios of 1:100 determined both using spiked and field samples. One hundred and twenty samples fromM. synoviae-infected non-vaccinated birds, 110 samples fromM. synoviae-vaccinated birds from a bird experiment and 224 samples fromM. synoviaenegative (serology and PCR) birds were used to determine the relative sensitivity and specificity using a previously describedM. synoviaePCR as reference. The relative sensitivity and specificity for fieldM. synoviaewere 95.0% and 99.6%, respectively, and 94.6% and 100% for the MS-H-live vaccine, respectively. Field validation and confirmation by multi locus sequence typing revealed that the qPCR correctly distinguished between MS-H and fieldM. synoviae. Evaluation of the differentiatingM. synoviaeqPCR in three commercial flocks suggested transmission of MS-H-live vaccine from vaccinated to non-vaccinated flocks at the same farm. Furthermore, it showed evidence for the colonization with fieldM. synoviaein MS-H-vaccinated flocks. [ABSTRACT FROM AUTHOR]