Alternative Modeling Approach Research Articles

An empirical mean-field model of symmetry-breaking in a turbulent wake.

Improved turbulence modeling remains a major open problem in mathematical physics. Turbulence is notoriously challenging, in part due to its multiscale nature and the fact that large-scale coherent structures cannot be disentangled from small-scale fluctuations. This closure problem is emblematic of a greater challenge in complex systems, where coarse-graining and statistical mechanics descriptions break down. This work demonstrates an alternative data-driven modeling approach to learn nonlinear models of the coherent structures, approximating turbulent fluctuations as state-dependent stochastic forcing. We demonstrate this approach on a high–Reynolds number turbulent wake experiment, showing that our model reproduces empirical power spectra and probability distributions. The model is interpretable, providing insights into the physical mechanisms underlying the symmetry-breaking behavior in the wake. This work suggests a path toward low-dimensional models of globally unstable turbulent flows from experimental measurements, with broad implications for other multiscale systems.

DO BUSINESSES THAT DISCLOSE CLIMATE CHANGE INFORMATION EMIT LESS CARBON? EVIDENCE FROM S&P 500 FIRMS

Unlike prior literature, this paper exploits heterogeneity in the substantive contents of firms’ voluntary carbon disclosure to estimate the impact of their participation in the CDP (formerly the Carbon Disclosure Project) on firms’ entity-wide carbon footprints. Heterogeneity in firms’ substantive contents of disclosures pertains to their level of climate mitigation activity, ranging from basic transparency to carbon management and to higher levels of climate action, such as third-party verification of entity-wide carbon emissions. This paper makes use of the U.S. Environmental Protection Agency’s introduction of the Clean Power Plan, which during 2011–2016 exerted regulatory pressure on GHG-intensive firms, and the interaction between regulatory pressure and corporate management, to model firms’ decision to participate in the CDP and their subsequent environmental performance. Results based on a difference-in-difference-in-differences estimator, nested in a two-stage endogenous binary-variable model, indicate that there is evidence of increases in total carbon emissions by firms that have instituted carbon management practices but did not certify their carbon disclosures with third-party audits, among other more advanced forms of carbon management. Despite this, by one measure of carbon emissions intensity, firms’ basic carbon transparency is linked to increased carbon efficiency. Sector regressions show that consumer-oriented sectors engaged in “cheap talk” by participating in voluntary carbon disclosure at high levels irrespective of their carbon footprints, whereas firms that were already carbon efficient in GHG-intensive sectors used their participation in the CDP to signal their climate leadership to regulators and other stakeholders. Findings are robust to alternative specifications and an alternative modeling approach.

The capital ratio and the interest rate spread: A panel threshold regression approach

Using annual observations of insured US commercial banks, this paper investigates the possible asymmetric impacts of capital ratios on the interest rate spread where the asymmetry depends on the magnitude and also the channel the bank used to increase the capital ratio. To this end, we employ a panel threshold regression approach with one and two threshold variables, where the change in the capital ratio itself and the main components of change in the capital ratio, namely, change in the capital or change in risk-weighted assets have been used as the threshold variables. The results show a significant threshold effect. In the single threshold models, banks that correspond to regimes with a higher increase in the capital ratio, a higher contribution of capital to the increase in the capital ratio, and a higher contribution of risk-weighted assets to the increase in the capital ratio also show a higher impact of capital ratios on the interest rate spread compared to their counterparts. In the panel threshold regression model with two threshold variables and four regimes, we find similar results. Our baseline results also hold stable using the panel smooth transition regression approach as an alternative modeling approach, using alternative proxies as threshold variables and performing the main tests in different bank size categories.

Dynamic simulation of the energy consumption and carbon emissions for domestic hot water production in a touristic region

At a buildings’ level, domestic hot water consumption is amongst the most relevant examples of the water-energy nexus. As such, understanding its drivers and forecasting domestic hot water and associated energy consumption are of paramount importance for decision making from the individual household level to a national, or even international, sustainability policy level. However, detailed end-use measurements of either energy or water are seldom available and large scale (city, region or country) estimates are often done based on annual average values. The present research effort aims at large scale estimation of the energy use and carbon emissions due to domestic hot water consumption in two, predominantly residential, locations in a touristic region in Portugal. A novel dynamic modelling approach is proposed and used, which accounts for the variability of: i) the cold water temperature based on measured data; ii) the total amount of water consumed based on measured data; and iii) the fraction of the water consumed as domestic hot water corrected by the air temperature. The results are compared with alternative modelling approaches of the same degree of complexity, revealing differences ranging between −18% and +11% in both net energy required to produce the domestic hot water and the corresponding carbon emissions. Differences in the results are found to exist not only due to the modelling approach, but also between the locations, highlighting the importance of both the simulation approach and the particularities of the context being captured.

Efficient modeling of adsorption chillers: Avoiding discretization by operator splitting

Reliable and computationally efficient models are crucial to improve the performance of adsorption chillers. However, modeling of adsorption chillers is challenging due to the intrinsic process dynamics. Currently, adsorption chillers are most often represented by 1-d, lumped-parameter models that use lumped models for all components but resolve the heat exchangers in one spatial dimension. Still, accurate simulations require fine discretization leading to poor computational efficiency. To increase computational efficiency, here, an alternative modeling approach is proposed that avoids discretization by applying operator splitting.The benefits of the proposed modeling approach are demonstrated in two real-world case studies. First, the resulting adsorption chiller model is calibrated and validated with experimental data of a lab-scale one-bed adsorption chiller: The proposed model retains the accuracy of a state-of-the-art model while increasing computational efficiency by up to 70%. Second, the proposed model is applied to a commercial two-bed adsorption chiller with excellent accuracy. Finally, the validity of model is tested by varying the ratio between the overall heat transfer coefficient and the heat capacity rate of the fluid in the heat exchangers. The proposed model is shown to be well suited for the conditions present in most adsorption chillers.

Parametric analysis and machine learning-based parametric modeling of wire laser metal deposition induced porosity

Additive manufacturing is an appealing solution to produce geometrically complex parts, difficult to manufacture using traditional technologies. The extreme process conditions, in particular the high temperature, complex interactions and couplings, rich metallurgical transformations and combinatorial deposition trajectories, induce numerous process defects and in particular porosity. Simulating numerically porosity appearance remains extremely complex because of the multiple physics induced by the laser-material interaction, the multiple space and time scales, with a strong impact on the simulation efficiency and performances. Moreover, when analyzing parts build-up by using the wire laser metal deposition —wLMD— technology it can be noticed a significant variability in the porosity size and distribution even when process parameters remain unchanged. For these reasons the present paper aims at proposing an alternative modeling approach based on the use of neural networks to express the porosity as a function of different process parameters that will be extracted from the process analysis.

Are the LAS Models Accurate Predictors of Mortality?

<h3>Purpose</h3> The LAS is used in the United States to prioritize candidates for lung transplant yet elements of model design may limit its predictive accuracy and classification of outcomes. This study evaluated the overall- and subpopulation-level prediction accuracy of the LAS models and compared the results to alternative modeling approaches. <h3>Methods</h3> Scientific Registry for Transplant Recipients data (2015-2020 for waitlist [WL] and 2010-2020 for posttransplant [PT] models) were used to build the current LAS, Cox proportional hazards regression, random survival forest, and residual cohort models. Discrimination performance was evaluated by comparing forecasted WL survival rates at 1, 3, and 6 months and PT survival rates at 1, 3, and 5 years using area under receiver operating characteristic curve (AUC) estimates. Calibration curves of estimated models were compared. <h3>Results</h3> All models performed similarly within each cohort. For WL, discrimination performance was best in the baseline cohort for the shorter forecast times (1 month) (LAS 0.92, others 0.93) and diminished with increasing length of forecast time and time on the waiting list (6-month cohort, 1 month forecast 0.81 LAS, others 0.69-0.77). WL model was well-calibrated for risk rates under 0.75 but underpredicted risk of death for most candidates while other models varied either over or under-predicting (Figure). PT discrimination performance was similar across models (0.58-0.60) and remained stable with increasing forecasting times (0.57-0.60) in the baseline cohort and performance eroded with residual cohorts. PT model was well calibrated for risk rates below 0.25 but overpredicted at higher risk levels. <h3>Conclusion</h3> The LAS accurately discriminates high vs. low WL mortality risk but underpredicts PT risk in many candidates. Comparator models did not perform better than the LAS. Strategies to optimize predictive accuracy and calibration in lung allocation models are essential to accurate prediction of transplant benefit in transplant candidates. <h3>Funding</h3> NIH R01HL153175

A temporal assessment of distracted driving injury severities using alternate unobserved-heterogeneity modeling approaches

This study explores temporal shifts in the effects of explanatory variables on the injury severity outcomes of crashes involving distracted driving. Using data from distracted driving crashes on Kansas State highways over a four-year period (from 2014 to 2017 inclusive), separate yearly models of driver-injury severities (with possible outcomes of severe injury, minor injury, and no injury) were estimated using two alternate modeling approaches to account for possible unobserved heterogeneity: a latent-class multinomial logit with class probability functions and a random parameters logit with possible heterogeneity in the means and variances of random parameters. Likelihood ratio tests were conducted to determine if model parameter estimates have shifted over time. A wide range of variables were found to statistically influence driver-injury severities and the findings show that were statistically significant temporal shifts in parameter estimates in both the random parameters and latent class modeling approaches. These shifts are likely the result of changes in driver behavior, improvements in vehicle and highway safety features, changes in communication technologies, and other temporally shifting trends. However, while out-of-sample simulations show that the two modeling approaches both indicate that distracted driving crashes have become less severe over time, the alternate approaches produced substantially different injury-severity predictions, suggesting the need for future research to explore how unobserved heterogeneity can best be modeled in temporal contexts.

Impact of history of mental disorders on short-term mortality among hospitalized patients with sepsis: A population-based cohort study.

BackgroundMental disorders are associated with markedly reduced life expectancy, in part due to an increased risk of death due to infection, likely reflecting sepsis-associated mortality. Patients with mental disorders are at an increased risk of sepsis, but data on the prognostic impact of mental disorders in sepsis are sparse, showing conflicting findings.MethodsWe used statewide data to identify hospitalizations aged ≥18 years with sepsis in Texas during 2014–2017. Mental disorders, including mood, anxiety, psychosis, and personality disorders were identified using Clinical Classification Software codes. Multilevel, multivariable logistic regression with propensity adjustment (primary model), with propensity score matching, and multivariable logistic regression as alternative models, were used to estimate the association between mental disorders and short-term mortality (defined as in-hospital mortality or discharge to hospice). Additional models were fitted for sensitivity analyses and to estimate the prognostic associations of individual categories of mental disorders.ResultsAmong 283,025 hospitalizations with sepsis, 56,904 (20.1%) had mental disorders. Hospitalizations with vs without mental disorders were younger (age 18–44 years 12.2% vs 10.6%), more commonly white (61.0% vs 49.8%), with lower burden of comorbidities (mean [SD] Deyo comorbidity index 2.53 [2.27] vs 2.73 [2.47]), and with lower need for organ support (mechanical ventilation 32.8% vs 36.0%); p<0.0001 for all comparisons. Crude short-term mortality among sepsis hospitalizations with and without mental disorders was 25.0% vs 32.8%, respectively. On adjusted analyses, mental disorders remained associated with lower odds of short-term mortality (adjusted odds ratio 0.792 [95% CI 0.772–0.812]). This finding was consistent on the alternative modeling approaches, sensitivity analyses, and examination of individual categories of mental disorders.ConclusionsMental disorders were associated, unexpectedly, with markedly lower risk of short-term mortality in sepsis. Further studies to examine the mechanisms underlying these findings may inform future efforts to improve sepsis outcomes.

Retesting the EKC hypothesis through transmission of the ARMEY curve model: an alternative composite model approach with theory and policy implications for NAFTA countries.

We investigate the validity of the environmental Kuznets curve (EKC) hypothesis for the NAFTA countries. In this investigation, we approach this hypothesis from a different methodology and propose employing the ARMEY curve hypothesis since the mathematical-functional propositions of both hypotheses were constructed on the same inverted U-shaped relationships. Thus, for the first time, it can be interpreted that the EKC hypothesis is empirically tested through a transmission mechanism of the ARMEY curve hypothesis in a single composite model. Therefore, this approach makes our study different from all empirical studies in the relevant literature. We apply the Augmented Mean Group (AMG) estimator to this aim. Empirical findings indicate that the ARMEY curve hypothesis was verified only for the USA. However, this new approach proposed in this study cannot test the EKC hypothesis through the ARMEY curve model for any NAFTA country since this approach requires verification of the ARMEY curve hypothesis and a significant composite model for the same NAFTA country. If our composite model was significant, it might make it possible to numerically determine a maximum real GDP per capita level that would minimize or maximize CO2 emission levels for the USA. Therefore, this study introduces-proposes this new methodology as an alternative way of testing the EKC hypothesis in the relevant literature for future empirical studies.

Assessment of the spatial-temporal distribution of groundwater recharge in data-scarce large-scale African river basin.

The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negative consequences. However, such analyses might not be possible when the models require detailed hydro-climate and hydrogeological data in data-scarce regions. Hence, this calls for alternate suitable modeling approaches that are applicable with the limited data and, however, includes the detailed assessment of the spatial-temporal distribution of different water balance components especially the GWR component. This paper aimed at investigating the spatial and temporal distribution of the GWR at monthly, seasonal and annual scales using the WetSpass-M physically distributed hydrological model, which is not requiring the detailed catchment information. In addition, the study conducted the sensitivity analysis of model parameters to assess the significant variation of GWR. The large-scale river basins such as the Omo river basin, Ethiopia, were chosen to demonstrate the potential of the WetSpass-M model under limited data conditions. From the modeling results, it was found that the maximum average monthly GWR of 13.4 mm occurs in July. The estimated average seasonal GWR is 32.5 mm/yr and 47.6 mm/yr in the summer and winter seasons, respectively. Further, it was found that GWR is highly sensitive to the parameter such as average rainfall intensity factor.

Estimation of downhole cuttings concentration from experimental data – Comparison of empirical and fuzzy logic models

Proper selection of drilling parameters during planning phase to ensure an adequately clean well is the key to avoid costly drilling operational problems such as mechanical stuck pipe and high drillstring torque and drag resulting in not reaching the drilling target for highly deviated/ERD wells. Cuttings transport is a complex phenomenon influenced by different parameters such as flow rate, drillstring rotation, rate of penetration, eccentricity, well configuration, drilling fluid properties and cuttings properties. The complexity of the cuttings transport process has led to the use of alternative modeling approaches. In this paper, a computational intelligence method, fuzzy logic (FL), is chosen to develop a model to estimate downhole cuttings concentration. FL, resembling human reasoning, is capable of modeling non-linear functions of arbitrary complexity and dealing with imprecision in data. The FL model developed in the current work is based on 509 experimental observations involving 11 independent test parameters each. 25% of the collected dataset (excluding the test dataset) is used as cross-validation dataset and the rest is used as training dataset to avoid overfitting. The test dataset comprised of nine observations is used for comparing FL model estimates for downhole cuttings concentration to those made by two published empirical models which are based on dimensional analysis and critical drilling fluid velocity concept respectively. Comparison of the models on the test dataset shows that the developed FL model performs better than the two empirical models in all the three statistical measurements namely coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE).

Theoretical Background of Occupational-Exposure Models-Report of an Expert Workshop of the ISES Europe Working Group "Exposure Models".

On 20 October 2020, the Working Group “Exposure Models” of the Europe Regional Chapter of the International Society of Exposure Science (ISES Europe) organised an online workshop to discuss the theoretical background of models for the assessment of occupational exposure to chemicals. In this report, participants of the workshop with an active role before and during the workshop summarise the most relevant discussion points and conclusions of this well-attended workshop. ISES Europe has identified exposure modelling as one priority area for the strategic development of exposure science in Europe in the coming years. This specific workshop aimed to discuss the main challenges in developing, validating, and using occupational-exposure models for regulatory purposes. The theoretical background, application domain, and limitations of different modelling approaches were presented and discussed, focusing on empirical “modifying-factor” or “mass-balance-based” approaches. During the discussions, these approaches were compared and analysed. Possibilities to address the discussed challenges could be a validation study involving alternative modelling approaches. The wider discussion touched upon the close relationship between modelling and monitoring and the need for better linkage of the methods and the need for common monitoring databases that include data on model parameters.

Sample Size and Model Prediction Accuracy in EQ-5D-5L Valuations Studies: Expected Out-of-Sample Accuracy Based on Resampling with Different Sample Sizes and Alternative Model Specifications.

Background. National valuation studies are costly, with ∼1000 face-to-face interviews recommended, and some countries may deem such studies infeasible. Building on previous studies exploring sample size, we determined the effect of sample size and alternative model specifications on prediction accuracy of modeled coefficients in EQ-5D-5L value set generating regression analyses. Methods. Data sets (n = 50 to ∼1000) were simulated from 3 valuation studies, resampled at the respondent level and randomly drawn 1000 times with replacement. We estimated utilities for each subsample with leave-one-out at the block level using regression models (8 or 20 parameter; with or without a random intercept; time tradeoff [TTO] data only or TTO + discrete choice experiment [DCE] data). Prediction accuracy, root mean square error (RMSE), was calculated by comparing to censored mean predicted values to the left-out block in the full data set. Linear regression was used to estimate the relative effect of changes in sample size and each model specification. Results. Results showed that doubling the sample size decreased RMSE by on average 0.012. Effects of other model specifications were smaller but can when combined compensate for loss in prediction accuracy from a small sample size. For models using TTO data only, 8-parameter models clearly outperformed 20-parameter models. Adding a random intercept, or including DCE responses, also improved mean RMSE, most prominently for variants of the 20-parameter models. Conclusions. The prediction accuracy impact of further increases in sample size after 300 to 500 were smaller than the impact of combining alternative modeling choices. Hybrid modeling, use of constrained models, and inclusion of random intercepts all substantially improve the expected prediction accuracy. Beyond a minimum of 300 to 500 respondents, the sample size may be better informed by other considerations, such as legitimacy and representativeness, than by the technical prediction accuracy achievable.HighlightsIncreases in sample size beyond a minimum in the range of 300 to 500 respondents provide smaller gains in expected prediction accuracy than alternative modeling approaches.Constrained, nonlinear models; time tradeoff + discrete choice experiment hybrid modeling; and including a random intercept all improved the prediction accuracy of models estimating values for the EQ-5D-5L based on data from 3 different valuation studies.The tested modeling choices can compensate for smaller sample sizes.

Artificial neural network modelling of green synthesis of silver nanoparticles by honey

Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with Levenberg–Marquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.

Effects of vertical anisotropy on optimization of multilateral well geometry

Multilateral wells have been increasingly used in recent years by different industries such as the oil- and gas industry (incl. coal-bed methane (CBM)) and geothermal energy production. The common purpose of these wells in the oil industry and partially in the geothermal industry is to achieve a higher production rate per well without increasing the hydraulic gradient significantly. In geothermal systems the ultimate objective is to improve heat extraction performance by enhancing productivity but also by increasing the heat transfer area. Optimal design of multilateral wells has been the focus of numerous publications in CBM and unconventional oil- and gas production, but as a result of cross-fertilization and technology-transfer among different sectors in the energy industry in the last few years, the focus of research on multilateral wells has turned to enhanced geothermal systems (EGS). Recently, several papers have been published on the use of multilateral wells in deep geothermal reservoirs. Coaxial closed-loop geothermal systems with multilateral wells utilize only the increased heat transfer area due to the laterals while EGS's with multilateral injection and production wells also benefit from the hydrodynamic advantages of such well configurations. However, substantial emphasis is – understandably – placed on the effect of artificial and natural fracture networks of EGS reservoirs and less focus is devoted to the impact of vertical permeability anisotropy which can seriously influence the flow pattern of a given configuration. Similarly, in CBM-related studies, the effect of horizontal permeability anisotropy along the face cleats to the butt cleats on multilateral well-design was investigated and vertical permeability anisotropy was not considered. In the current work we present a generalized approach for evaluating the effects of vertical permeability anisotropy on multilateral well geometry by numerical hydrodynamic modelling in an idealized, homogeneous hydrogeological setting. Current paper proposes an alternative modelling approach for representing the non-horizontal branches of multilateral wells by introducing a thin layer for the laterals with the same inclination as the well branches. This approach is then is used to investigate the effect of anisotropy on the flow pattern and near-wellbore drawdown and hydraulic gradient in the case of different branch deviations. Results suggest that the benefits of highly deviated or horizontal laterals emerge when vertical anisotropy is high. Evaluation of branch deviation vs. anisotropy indicates that above approx. 60° there is no significant benefit in increasing deviation which implies that very highly deviated or horizontal laterals might not necessarily pay off the associated technical challenges and extra costs.

Predicting the master curves of bituminous mastics with micromechanical modelling

The performance of asphalt mixtures is significantly affected by the viscoelastic properties of their mastic phase. The analytical approaches used to predict the properties of mastics from their constituents’ properties are limited in their accuracy and potential to handle non-linear material behaviour. An alternative micromechanical finite element modelling approach to calculate the master curves of mastics from the binder and filler phase properties is presented, where the representative volume elements of mastics consist of linear-viscoelastic bitumen matrices and elastic spherical filler particles. For validation, shear relaxation moduli of bitumen and bitumen-filler mastics are measured at °C°C. Additionally, the model is evaluated and compared with the existing analytical solutions. The results indicate that the proposed approach is advantageous as compared to the analytical solutions, as it allows predicting the mastics’ properties over wider temperature, frequency and material ranges at better agreement with the measurements while giving insight into the micromechanical behaviour.

A New Model Ice for Wave-Ice Interaction

The interaction of waves and ice is of significant relevance for engineers, oceanographers and climate scientists. In-situ measurements are costly and bear uncertainties due to unknown boundary conditions. Therefore, physical laboratory experiments in ice tanks are an important alternative to validate theories or investigate particular effects of interest. Ice tanks use model ice which has down-scaled sea ice properties. This model ice in ice tanks holds disadvantages due to its low stiffness and non-linear behavior which is not in scale to sea ice, but is of particular relevance in wave-ice interactions. With decreasing stiffness steeper waves are required to reach critical stresses for ice breaking, while the non-linear, respectively non-elastic, deformation behavior is associated with high wave damping. Both are scale effects and do not allow the direct transfer of model scale test results to scenarios with sea ice. Therefore, the alternative modeling approach of Model Ice of Virtual Equivalent Thickness (MIVET) is introduced. Its performance is tested in physical experiments and compared to conventional model ice. The results show that the excessive damping of conventional model ice can be reduced successfully, while the scaling of the wave induced ice break-up still requires research and testing. In conclusion, the results obtained are considered a proof of concept of MIVET for wave-ice interaction problems.

Network Models for Multiobjective Discrete Optimization

This paper provides a novel framework for solving multiobjective discrete optimization problems with an arbitrary number of objectives. Our framework represents these problems as network models, in that enumerating the Pareto frontier amounts to solving a multicriteria shortest-path problem in an auxiliary network. We design techniques for exploiting network models in order to accelerate the identification of the Pareto frontier, most notably a number of operations to simplify the network by removing nodes and arcs while preserving the set of nondominated solutions. We show that the proposed framework yields orders-of-magnitude performance improvements over existing state-of-the-art algorithms on five problem classes containing both linear and nonlinear objective functions. Summary of Contribution: Multiobjective optimization has a long history of research with applications in several domains. Our paper provides an alternative modeling and solution approach for multiobjective discrete optimization problems by leveraging graphical structures. Specifically, we encode the decision space of a problem as a layered network and propose graph reduction operators to preserve only solutions whose image are part of the Pareto frontier. The nondominated solutions can then be extracted through shortest-path algorithms on such a network. Numerical results comparing our method with state-of-the-art approaches on several problem classes, including the knapsack, set covering, and the traveling salesperson problem (TSP), suggest orders-of-magnitude runtime speed-ups for exactly enumerating the Pareto frontier, especially when the number of objective functions grows.

An examination of model fit and measurement invariance of general mental ability and personality measures used in the multilingual context of the Swiss Armed Forces: A Bayesian structural equation modeling approach

ABSTRACT Measurement invariance of psychological test batteries is an essential quality criterion when the test batteries are administered in different cultural and language contexts. The purpose of this study was to examine to what extent measurement model fit and measurement invariance across the two largest language groups in Switzerland (i.e., German and French speakers) can be assumed for selected general mental ability and personality tests used in the Swiss Armed Forces’ cadre selection process. For the model fit and invariance testing, we used Bayesian structural equation modeling (BSEM). Because the sizes of the language group samples were unbalanced, we reran the invariance testing with the subsampling procedure as a robustness check. The results showed that at least partial approximate scalar invariance can be assumed for the constructs. However, comparisons in the full sample and subsamples also showed that certain test items function differently across the language groups. The results are discussed regarding the three following issues: First, we critically discuss the applied criterion and alternative effect size measures for assessing the practical importance of non-invariances. Second, we highlight potential remedies and further testing options, that can be applied, once certain items have been detected to function differently. Third, we discuss alternative modeling and invariance testing approaches to BSEM and outline future research avenues.