Proposed Sensitivity Analysis Research Articles

Sensitivity analysis on transient pressure of entrapped air pocket in deep stormwater tunnel system

In deep stormwater tunnel systems (DSTSs), entrapped air pockets are prone to pressurization and deformation during rapid filling, resulting in pressure surges that threaten system security. This study investigates the effects of structural and inflow parameters on peak pressures. To simulate the pressure surges of entrapped air pockets, a rigid-column model was developed for a simplified DSTS configuration comprising two shafts and one tunnel. The global sensitivities of the air pocket, system structure, and inflow parameters were calculated using the Extended Fourier Amplitude Sensitivity Test (EFAST), which employed indicators of the maximum pressure and relative increment. The results indicate that parameters related to the shape and initial state of the air pocket exert a substantial and direct effect on pressure surges in rapid filling, whereas tunnel system parameters tend to exhibit minimal influence. Notably, compared to the total inflow discharge, the flow difference between two shafts imposes a more significant and direct impact. For the maximum pressure of air pocket, the initial pressure and the maximum water level height of shafts demonstrate more pronounced effects. The proposed sensitivity analysis could be integrated into methodologies for system safety assessment, while the rigid-column model may be extended to accommodate multiple shafts and air pockets.

Prioritizing rear-end crash explanatory factors for injury severity level using deep learning and global sensitivity analysis

Traffic accidents are usually unique events with unpredictable geographical and temporal dimensions; thus, accident injury severity level (INJ-SL) analysis presents formidable categorization and data stability problems. Classical statistical models are limited in their ability to correctly model INJ-SL, whilst sophisticated machine learning approaches do not appear to have any equations to prioritize/analyze multiple contributing factors to forecast accidents accompanying INJ-SLs. In addition, the intercorrelations between the input variables may render the conclusions of a formal sensitivity analysis incorrectly. Rear-end collisions are the most common form of traffic accidents; consequently, their linked INJ-SL requires more research. This paper provides a complex technique based on a deep learning paradigm paired with different indicators of Global Sensitivity Analysis to address all of these concerns. Unlike existing neural network designs, this technique presents a deep residual neural network structure that employs residual shortcuts (i.e., connections). The connections enable the DRNNs to sidestep a few levels of the deep network architecture, evading the regular training with high accuracy issues. Using the trained DRNNs model, a Latin Hypercube sampling simulation was undertaken to determine each explanatory component's influence on the resulting INJ-SL. The latest available data from 2011 to 2018 is used to assess all rear-end collisions in North Carolina. A comparison was made between the performance of two different schemes of data categorization using a set of global sensitivity metrics. It was determined that the devised technique overcame the data heterogeneity problems to achieve an accuracy of 87%. In addition, the proposed sensitivity analysis identified the most relevant factors associated with INJ-SL rear-end collisions.

Environmental Consequences of Shelf Life Extension: Conventional versus Active Packaging for Fresh-Cut Salads

The use of active coatings in fresh food packaging is an innovative technique that optimizes the functional properties of films, resulting in a longer product shelf life and reduced food waste. But, which is more sustainable, active packaging (AP) or conventional packaging (CP) for the packaging of fresh-cut products? To answer this research question, this study analyzes the environmental performance of AP during its life cycle for packaging a minimally processed fresh salad mix compared with CP, in terms of its manufacture and use. The AP is a bag that includes a bioactive component, oregano essential oil (OEO), which is an inhibitor of microbial growth, incorporated into an ethylene vinyl alcohol copolymer (EVOH) coating on a conventional polypropylene (PP) film. To this end, a Life Cycle Assessment (LCA) was carried out based on ISO 14040 and 14044, using the ReCiPe methodology. The results showed that using active packaging has a beneficial affect, reducing the amount of produced food by 30% compared with conventional packaging over the same period. The reductions in the studied impact categories were greater than 50% in most of them, with a 62% reduction in global warming. The proposed sensitivity analysis showed the difference between the disposal or treatment of waste generated by the packaging production process and the packaged product, indicating that this step is of great importance for the environmental impacts and sustainability of this process. In 80% of the scenarios analyzed, the AP achieved better results than the CP in terms of damage categories.

EMsFEM based concurrent topology optimization method for hierarchical structure with multiple substructures

An efficient concurrent topology optimization method based on the Extended Multiscale Finite Element Method (EMsFEM) is proposed to design the hierarchical structure with multiple substructures in this paper. Firstly, the mathematical description model for the topology of hierarchical structures and the interpolation model for material are established at both the macro and substructural levels. Then the concurrent topology optimization formulation for hierarchical structures is proposed based on EMsFEM. After that, the sensitivity analysis scheme of the objective function and constraint functions is given, based on which the Method of Moving Asymptotes (MMA) is employed to update design variables. The assumption of separation of length scales is not required in the proposed method, which avoids the disconnection between adjacent substructures. Besides, without limiting the topology and spatial distribution of substructures in advance, the concurrent topology optimization for hierarchical structures with multiple substructures can be realized using this method. The provided numerical examples verify the correctness and effectiveness of the proposed sensitivity analysis method as well as the feasibility, effectiveness and scalability of the proposed concurrent topology optimization method for hierarchical structures. Finally, the influence of the initial substructure configurations and the number of substructure types on the final configuration and mechanical performance of hierarchical structures are explored with a series of numerical examples.

Sensitivity analysis for the interactive effects of internal bias and publication bias in meta-analyses.

Meta-analyses can be compromised by studies' internal biases (e.g., confounding in nonrandomized studies) as well as publication bias. These biases often operate nonadditively: publication bias that favors significant, positive results selects indirectly for studies with more internal bias. We propose sensitivity analyses that address two questions: (1) "For a given severity of internal bias across studies and of publication bias, how much could the results change?"; and (2) "For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?" These methods consider the average internal bias across studies, obviating specifying the bias in each study individually. The analyst can assume that internal bias affects all studies, or alternatively that it only affects a known subset (e.g., nonrandomized studies). The internal bias can be of unknown origin or, for certain types of bias in causal estimates, can be bounded analytically. The analyst can specify the severity of publication bias or, alternatively, consider a "worst-case" form of publication bias. Robust estimation methods accommodate non-normal effects, small meta-analyses, and clustered estimates. As we illustrate by re-analyzing published meta-analyses, the methods can provide insights that are not captured by simply considering each bias in turn. An R package implementing the methods is available (multibiasmeta).

A Novel Error Sensitivity Analysis Method for a Parallel Spindle Head

Geometric errors are the main factors affecting the output accuracy of the parallel spindle head, and it is necessary to perform a sensitivity analysis to extract the critical geometric errors. The traditional sensitivity analysis method analyzes the output position and orientation errors independently, defining multiple sensitivity indices and making it difficult to determine critical geometric errors. In this paper, we propose sensitivity indices that can comprehensively consider position and orientation errors. First, the configuration of the hybrid machine tool is introduced, and the TCP position error model is derived. Then, the tool radius and the effective cutting length are introduced, and the sensitivity indices are defined. After that, the sensitivity analysis of the 3-DOF parallel spindle head is performed using the proposed sensitivity indices, and six critical geometric errors are extracted. The machining accuracy of the parallel spindle head can be greatly improved by improving the critical geometric errors. The proposed sensitivity analysis method can provide important guidance for machine tool accuracy design.

Sensitivity analysis index to determine the optimal location of multi-objective UPFC for improvement of power quality parameters

Today, advanced technologies are used for the reliable operation of power systems. In order to access a reliable and practical function in the systems, it is necessary to use precise control systems with higher efficiency in the systems to minimize the problems of the distribution system. Devices that are made on the basis of power electronics have wide applications in power systems today. These devices, which are called FACTS devices, provide the possibility of improving energy transmission with the least investment cost, as well as quick control of power system problems. FACTS devices are placed in series, parallel or series-parallel transmission lines and control the operation of distribution systems in permanent states as well as the dynamic behavior of the system in transient states. FACTS devices are an effective method to solve the problems and limitations of lines. Transmission and replacement networks are used to create new lines in the network. From the point of view of power distribution control, FACTS controllers can be placed anywhere in the transmission line, but the elements are most effectively used when they are placed at the most critical point. Unified Power Flow Controller (UPFC) is one of the most important FACTS devices that can improve parameters. The quality of power used in the power system depends on the effect of this controller in improving the loss reduction, reducing the total harmonic distortion (THD), and removing the clogged lines to their proper location in the power system. This device is capable of all different effective parameters at a time. These parameters usually include voltage, impedance, and phase. This paper uses a new sensitivity analysis index to determine the optimal size and location of the UPFC to improve the power quality parameters of the distribution system. The proposed method is evaluated on the IEEE 14-bus network in the Simulink environment of MATLAB software. Finally, according to the simulation results and the results obtained from the base network, it is determined that using the proposed sensitivity analysis index in UPFC location, losses Network, total harmonic distortions (THD) and eclipses Network lines are much improved. The results indicate that the proposed method has provided a good answer to determine the optimal size and location of UPFC to improve power quality parameters. Using the results of the sensitivity analysis method, the best place to install UPFC in the power grid, mode number 6 between buses 2 and 11 is obtained. and The amount of network power losses after sensitivity analysis, compared to the base network Active losses of about 55% and reactive losses of about 11% deceased, Due to the circular structure of the network, the current passing through the lines is different depending on the amount of load in the bases near that line, and with the presence of UPFC, the network is optimized and the current passing through the lines is also reduced. 4) Percentage of total harmonic distortion (THD) mains voltage before the presence of UPFC is about 27.12%, which after installation in the 6th place with a maximum size has been reduced to about 11.11%.

A multiple imputation-based sensitivity analysis approachfor regression analysis with a missing notatrandom covariate.

Missing covariate problems are common in biomedical and electrical medical record data studies while evaluating the relationship between a biomarker and certain clinical outcome, when biomarker data are not collected for all subjects. However, missingness mechanism is unverifiable based on observed data. If there is a suspicion of missing not at random (MNAR), researchers often perform sensitivity analysis to evaluate the impact of various missingness mechanisms. Under the selection modeling framework, we propose a sensitivity analysis approach with a standardized sensitivity parameter using a nonparametric multiple imputation strategy. The proposed approach requires fitting two working models to derive two predictive scores: one for predicting missing covariate values and the other for predicting missingness probabilities. For each missing covariate observation, the two predictive scores along with the pre-specified sensitivity parameter are used to define an imputing set. The proposed approach is expected to be robust against mis-specifications of the selection model and the sensitivity parameter since the selection model and the sensitivity parameter are not directly used to impute missing covariate values. A simulation study is conducted to study the performance of the proposed approach when MNAR is induced by Heckman's selection model. Simulation results show the proposed approach can produce plausible regression coefficient estimates. The proposed sensitivity analysis approach is also applied to evaluate the impact of MNAR on the relationship between post-operative outcomes and incomplete pre-operative Hemoglobin A1c level for patients who underwent carotid intervetion for advanced atherosclerotic disease.

Sensitivity and Uncertainty Analysis for Two-stream Capture-Recapture Methods in Disease Surveillance.

Capture-recapture methods are widely applied in estimating the number ( ) of prevalent or cumulatively incident cases in disease surveillance. Here, we focus the bulk of our attention on the common case in which there are 2 data streams. We propose a sensitivity and uncertainty analysis framework grounded in multinomial distribution-based maximum likelihood, hinging on a key dependence parameter that is typically nonidentifiable but is epidemiologically interpretable. Focusing on the epidemiologically meaningful parameter unlocks appealing data visualizations for sensitivity analysis and provides an intuitively accessible framework for uncertainty analysis designed to leverage the practicing epidemiologist's understanding of the implementation of the surveillance streams as the basis for assumptions driving estimation of . By illustrating the proposed sensitivity analysis using publicly available HIV surveillance data, we emphasize both the need to admit the lack of information in the observed data and the appeal of incorporating expert opinion about the key dependence parameter. The proposed uncertainty analysis is a simulation-based approach designed to more realistically acknowledge variability in the estimated associated with uncertainty in an expert's opinion about the nonidentifiable parameter, together with the statistical uncertainty. We demonstrate how such an approach can also facilitate an appealing general interval estimation procedure to accompany capture-recapture methods. Simulation studies illustrate the reliable performance of the proposed approach for quantifying uncertainties in estimating in various contexts. Finally, we demonstrate how the recommended paradigm has the potential to be directly extended for application to data from >2 surveillance streams.

Sensitivity analysis of a three-invariant plasticity model with different sampling algorithms

ABSTRACT A Sobol sensitivity analysis is employed to identify the importance of parameters in the three-invariant soil model (GeoModel) for finite element simulation. Three commonly used sampling techniques, namely, pseudo-random, quasi-random (QR), and Latin-hypercube sampling (LHS), are investigated to determine the efficiency of each sampling technique for sensitivity analysis and identify the critical parameters for the soil model. Among the sampling methods, LHS shows the best sampling uniformity and offers more stable results and faster convergence than other methods. The analysis results also show that Young’s modulus and the initial value for the shear-yield surface are the most critical parameters for the designed outputs, which quantitatively measure the maximum total deformation (MD), plastic deformation (PV), and plastic zone size (VY). The proposed sensitivity analysis framework in this study can be applied to finite element simulations of similar engineering and scientific problems for assessing model parameters’ influence.

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package "compound.Cox". The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.

Size-biased sensitivity analysis for matched pairs design to assess the impact of healthcare-associated infections

Abstract: Healthcare-associated infections are serious adverse events that occur during a hospital admission. Quantifying the impact of these infections on inpatient length of stay and cost has important policy implications due to the Hospital-Acquired Conditions Reduction Program in the United States. However, most studies on this topic are flawed because they do not account for when a healthcare-associated infection occurred during a hospital admission. Such an approach leads to selection bias because patients with longer hospital stays are more likely to experience an infection due to their increased exposure time. Time of infection is often not incorporated into the estimation strategy because this information is unknown, yet there are no methods that account for the selection bias in this scenario. To address this problem, we propose a sensitivity analysis for matched pairs designs for assessing the effect of healthcare-associated infections on length of stay and cost when time of infection is unknown. The approach models the probability of infection, or the assignment mechanism, as proportional to a power function of the uninfected length of stay, where the sensitivity parameter is the value of the power. The general idea is to incorporate the degree of exposure into the probability of an infection occurring. Under this size-biased assignment mechanism, we develop hypothesis tests under a sharp null hypothesis of constant multiplicative effects. The approach is demonstrated on a pediatric cohort of inpatient encounters and compared to benchmark estimates that properly account for time of infection. The results reaffirm the severe degree of bias when not accounting for time of infection and also show that the proposed sensitivity analysis captures the benchmark estimates for plausible and theoretically justified values of the sensitivity parameter.

Aggregation operators of quadripartitioned single-valued neutrosophic Z-numbers with applications to diverse COVID-19 scenarios

Z-number, proposed by Zadeh is an ordered pair of fuzzy numbers which have the capability to depict both the reliability and certainty of any available information. Likewise, based upon Belnap’s four-valued logic, Quadripartitioned Single-Valued Neutrosophic Sets (QSVNSs) are characterized by four independent components of truth, contradiction, ignorance, and falsity degrees to represent uncertain information or data at hand. In fact, QSVNSs are extensions of Single-Valued Neutrosophic Sets (SVNSs) where the indeterminacy component is further partitioned into two parts — contradiction and ignorance. However, QSVNSs alone cannot reflect the reliability measure of decision maker’s preferences or allocations. Thus, for better modeling of uncertainty and to combine the information of truth, contradiction, ignorance, and falsity degrees with their respective reliability attributes, we put forward a hybrid framework for the first time in this study. Hence, we propose a notion of Quadripartitioned Single-Valued Neutrosophic Z-number (QSVNZN), which is constructed as a generalization of the Z-number and the QSVNSs. Some basic operations and a score function are also defined for ranking QSVNZNs. Moreover, we also aggregate QSVN information with the help of three weighted aggregation operators viz., QSVNZN weighted arithmetic averaging (QSVNZNWAA) operator, QSVNZN weighted geometric averaging (QSVNZNWGA) operator, and QSVNZN weighted hybrid averaging (QSVNZNWHA) operator. Several suitable properties and relations between these operators are also presented. The applicability of our newly proposed operators and the score function is demonstrated in three multi-criteria decision making (MCDM) occasions specifically in the COVID-19 context. Meticulous comparative analysis, the validity of our proposed approaches, sensitivity analysis, and runtime analysis is also carried out to depict the legitimacy, veracity, and feasibility of our theoretical construct. The obtained results shall greatly benefit the decision-makers to deal with indeterminate and inconsistent information efficiently. Or in other words, the new framework shall exhibit sufficient descriptive ability from the human-cognition-based perspective.

Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses

BackgroundIncreasing attention is being given to assessing treatment effect heterogeneity among individuals belonging to qualitatively different latent subgroups. Inference routinely proceeds by first partitioning the individuals into subgroups, then estimating the subgroup-specific average treatment effects. However, because the subgroups are only latently associated with the observed variables, the actual individual subgroup memberships are rarely known with certainty in practice and thus have to be imputed. Ignoring the uncertainty in the imputed memberships precludes misclassification errors, potentially leading to biased results and incorrect conclusions.MethodsWe propose a strategy for assessing the sensitivity of inference to classification uncertainty when using such classify-analyze approaches for subgroup effect analyses. We exploit each individual’s typically nonzero predictive or posterior subgroup membership probabilities to gauge the stability of the resultant subgroup-specific average causal effects estimates over different, carefully selected subsets of the individuals. Because the membership probabilities are subject to sampling variability, we propose Monte Carlo confidence intervals that explicitly acknowledge the imprecision in the estimated subgroup memberships via perturbations using a parametric bootstrap. The proposal is widely applicable and avoids stringent causal or structural assumptions that existing bias-adjustment or bias-correction methods rely on.ResultsUsing two different publicly available real-world datasets, we illustrate how the proposed strategy supplements existing latent subgroup effect analyses to shed light on the potential impact of classification uncertainty on inference. First, individuals are partitioned into latent subgroups based on their medical and health history. Then within each fixed latent subgroup, the average treatment effect is assessed using an augmented inverse propensity score weighted estimator. Finally, utilizing the proposed sensitivity analysis reveals different subgroup-specific effects that are mostly insensitive to potential misclassification.ConclusionsOur proposed sensitivity analysis is straightforward to implement, provides both graphical and numerical summaries, and readily permits assessing the sensitivity of any machine learning-based causal effect estimator to classification uncertainty. We recommend making such sensitivity analyses more routine in latent subgroup effect analyses.

Sensitivity-Based Topology Optimization of Squirrel-Cage Induction Motor in Time Domain Using Multi-Material Level-Set Method

Although the output power of an induction motor (IM) is generally lower than that of the interior permanent magnet synchronous motor, IMs are widely used in industrial world due to their solidity, low cost, self-starting, etc. For designing high-performance IMs, a computer-aided engineering approach using the finite-element method is essential. However, because a finite-element analysis of IMs takes a long time due to the long transient state in the time domain, it is difficult to perform the optimization using evolutionary algorithms. To overcome this difficulty, a sensitivity-based topology optimization (TO) method for IMs in the frequency domain was proposed. However, this method has a drawback: because the magnetic nonlinearity in the rotor and stator cores cannot be considered, the physical quantities also cannot be accurately estimated. To consider the magnetic nonlinearity in the TO of the IM, the sensitivity analysis with the rotor rotation of the IM using the time-domain adjoint variable method is proposed here. The proposed sensitivity analysis method for the steady state of the IM was evaluated, and it was applied to the TO of a squirrel-cage IM. The effective rotor structure, which is composed of the iron core and secondary conductor, was derived from the multi-material level-set method.

Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions.

Studies leveraging gene-environment (GxE) interactions within Mendelian randomization (MR) analyses have prompted the emergence of two similar methodologies: MR-GxE and MR-GENIUS. Such methods are attractive in allowing for pleiotropic bias to be corrected when using individual instruments. Specifically, MR-GxE requires an interaction to be explicitly identified, while MR-GENIUS does not. We critically examine the assumptions of MR-GxE and MR-GENIUS in the absence of a pre-defined covariate, and propose sensitivity analyses to evaluate their performance. Finally, we explore the effect of body mass index (BMI) upon systolic blood pressure (SBP) using data from the UK Biobank, finding evidence of a positive effect of BMI on SBP. We find both approaches share similar assumptions, though differences between the approaches lend themselves to differing research settings. Where a suitable gene-by-covariate interaction is observed MR-GxE can produce unbiased causal effect estimates. MR-GENIUS can circumvent the need to identify interactions, but as a consequence relies on either the MR-GxE assumptions holding globally, or additional information with respect to the distribution of pleiotropic effects in the absence of an explicitly defined interaction covariate.

Uncertainty Analysis and Sensitivity Estimation on an Artillery External Ballistic System

Abstract In the design of artillery external ballistics, sensitivity analysis can effectively quantify the influence of multi-source uncertain parameters on the dispersion of projectile landing points to improve the precise attack ability of artillery. However, for a complicated artillery external ballistic system containing multiple inputs and outputs, its mapping relationships are not definite under uncertainty and it is difficult to estimate a comprehensive sensitivity index due to involving the calculation of high dimensional integral. Therefore, a sensitivity analysis method based on the combination of variance and covariance decomposition with the approximate high dimensional model representation (AHDMR) is proposed to measure the influence of muzzle state parameters, projectile characteristic parameters, etc. on projectile landing points under uncertainty in this paper. First, we establish the numerical simulation model of artillery external ballistics by combing the external ballistic theory and Runge–Kutta algorithm to acquire the mapping relationships between the uncertain input parameters and the dispersion of projectile landing points and implement uncertainty analysis under different uncertainty levels (UL) and distributions. Then, with the use of a set of orthogonal polynomials for uniform and Gaussian distribution, respectively, the high dimensional model representation of the mapping relationship is approximately expressed and the compressive sensitivity indices can be effectively estimated based on the Monte Carlo simulation. Moreover, the comparison results of two numerical examples indicate the proposed sensitivity analysis method is accurate and practical. Finally, through the method, the importance rankings of multi-uncertain parameters on projectile landing points for two distributions are effectively quantified under the UL = [0.01, 0.02, 0.03, 0.04, 0.05].

Sensitivity Analysis of Switched Reluctance Motor for Potential Application in Electric Vehicles Considering Weight Factor

The switched reluctance motor (SRM), an environmentally friendly machine, is suitable for application in electric vehicles (EV) in recent days. This paper presents an SRM sensitivity analysis focussing on weight of the materials used for stator and rotor for use in electric vehicle application. Two SRM structures are considered, namely normal stator tooth and tapered stator tooth. Thirteen materials widely used in the SRM are considered for these two structures to perform the proposed sensitivity analysis. The major outputs of torque, speed, power, iron loss, efficiency, copper loss, and power factor are gathered through finite element analysis (FEA) simulation. Each of the 13 materials are examined for normal tooth and tapered tooth geometrical structures. The results are discussed, focusing on the fitness of the considered SRM structures for EV application. As weight-sensitive vehicles, the materials used in EVs are compared against the respective motor weights obtained through analysis to recommend the lightest motor for production.

ENABLING TECHNOLOGIES CHALLENGES OF GREEN INTERNET OF THINGS (IOT) TOWARDS SUSTAINABLE DEVELOPMENT IN THE ERA OF INDUSTRY 4.0

The extensive adoption of the Internet of Things (IoT) has increased the carbon footprint on a large scale across the globe. To handle this challenge, scholars and policymakers are making efforts to propose novel energy-efficient solutions to provide a desirable environment for green-IoT (G-IoT). Additionally, further research is required to analyze the G-IoT-related challenges to elucidate the difficulties of its implementation for researchers. Moreover, the GIoT requirements have been considered in different network levels, namely software, hardware, architecture, communication. To present a comprehensive framework to identify the challenges of G-IoT, a survey using literature review and expert’s opinion is carried. Total 23 challenges are taken to evaluate and implement G-IoT technologies towards sustainable development achievements (SDA). Consequently, this article aims to rank and evaluate the challenges to implement the G-IoT towards the SDA. An integrated approach is proposed with stepwise weight assessment ratio analysis (SWARA) and additive ratio assessment (ARAS) under Pythagorean fuzzy sets. As a result, an machine-to-machine (M2M) standardization protocol with a weight value of 0.0508 has the first rank, followed by adaptation to natural energy sources with a weight value of 0.0479, information security and privacy protection with a weight value of 0.0469, and internet protocol version-6 (IPv6) for low-end devices with weight 0.0467. To validate the proposed method, sensitivity analysis and comparison using existing methods have been conducted.

Sensitivity analysis for unmeasured confounding in the estimation of marginal causal effects

Summary One of the main threats to the validity of causal effect estimates from observational data is the existence of unmeasured confounders. A plethora of methods has been proposed to quantify deviation from conditional exchangeability, which arises when confounding is not properly accounted for, with each method having its own set of limitations and underlying assumptions. Few methods both scale well with the increasing complexity of potential measured confounders and avoid making strong simplifying assumptions about the effect of the unmeasured confounder within strata of the measured confounders. For binary outcomes, we propose a quantification of the deviation from conditional exchangeability, based on standardization within levels of the exposure, which can accommodate any type of measured and unmeasured confounders or desired estimand. In the case of binary exposure, this amounts to varying two parameters across a grid of values, no matter how complex the measured confounding. We propose three methods of estimation for the causal estimand of interest under our proposed sensitivity analysis. This allows for an easily applied, easily interpreted sensitivity analysis that makes minimal assumptions about the type of unmeasured confounding and places no limits on the complexity of the potential measured confounders.