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This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This article proposes a strategy to make the testing step easier, generating user acceptance tests (UATs) in an automatic way from requirements artifacts.

This strategy is based on two modeling frameworks: scenarios and task/method paradigm. Scenarios are a requirement artifact used to describe business processes and requirements, and task/method paradigm is a modeling paradigm coming from the artificial intelligence field. The proposed strategy is composed of four steps. In the first step, scenarios are described through a semantic wiki website. Then scenarios are automatically translated into a task/method model (step two). In the third step, the task/method model obtained in step two is executed in order to produce and store all possible achievements of tasks and thus scenarios. The stored achievements are saved in a data structure called execution tree (ET). Finally, from this ET (step four), the UATs are generated.

The feasibility of this strategy is shown through a case study coming from the agriculture production systems field.

Generally, test design approaches deal with a small number of variables describing one specific situation where a decision table or workflow is used to design tests. The proposed approach can deal with many variables because the authors rely on scenarios that can be composed in order to obtain a tree with all the testing paths that can arise from their description.

The Reynolds-averaged Navier–Stokes (RANS) equations represent the computational workhorse for engineering design, despite their numerous flaws. Improving and quantifying the uncertainties associated with RANS models is particularly critical in view of the analysis and optimization of complex turbomachinery flows.

First, an efficient strategy is introduced for calibrating turbulence model coefficients from high-fidelity data. The results are highly sensitive to the flow configuration (called a calibration scenario) used to inform the coefficients. Second, the bias introduced by the choice of a specific turbulence model is reduced by constructing a mixture model by means of Bayesian model-scenario averaging (BMSA). The BMSA model makes predictions of flows not included in the calibration scenarios as a probability-weighted average of a set of competing turbulence models, each supplemented with multiple sets of closure coefficients inferred from alternative calibration scenarios.

Different choices for the scenario probabilities are assessed for the prediction of the NACA65 V103 cascade at off-design conditions. In all cases, BMSA improves the solution accuracy with respect to the baseline turbulence models, and the estimated uncertainty intervals encompass reasonably well the reference data. The BMSA results were found to be little sensitive to the user-defined scenario-weighting criterion, both in terms of average prediction and of estimated confidence intervals.

A delicate step in the BMSA is the selection of suitable scenario-weighting criteria, i.e. suitable prior probability mass functions (PMFs) for the calibration scenarios. The role of such PMFs is to assign higher probability to calibration scenarios more likely to provide an accurate estimate of model coefficients for the new flow. In this paper, three mixture models are constructed, based on alternative choices of the scenario probabilities. The authors then compare the capabilities of three different criteria.

Recent advances in hydrological impact studies point that the response of specific catchments to climate change scenario using a single model approach is questionable. This study was aimed at investigating the impact of climate change on three river basins in China, Ethiopia and Norway using WASMOD and HBV hydrological models.

First, hydrological models' parameters were determined using current hydro‐climatic data inputs. Second, the historical time series of climatic data was adjusted according to the climate change scenarios. Third, the hydrological characteristics of the catchments under the adjusted climatic conditions were simulated using the calibrated hydrological models. Finally, comparisons of the model simulations of the current and possible future hydrological characteristics were performed. Responses were evaluated in terms of runoff, actual evapotranspiration and soil moisture change for incremental precipitation and temperature change scenarios.

From the results obtained, it can be inferred that two equally well calibrated models gave different hydrological response to hypothetical climatic scenarios. The authors' findings support the concern that climate change analysis using lumped hydrological models may lead to unreliable conclusions.

Extrapolation of driving forces (temperature and precipitation) beyond the range of parameter calibration yields unreliable response. It is beyond the scope of this study to reduce this model ambiguity, but reduction of uncertainty is a challenge for further research.

The research was conducted based on the primary time series data using the existing two hydrological models to test the magnitude differences one can expect when using different hydrological models to simulate hydrological response of climate changes in different climate zones.

This paper proposes a conceptualization of the e-waste domain, formalized through a metamodel, to express complex e-waste realities in a simple manner. This also enables the transition from a structural model to a behavioral model to implement analysis techniques.

The methodology used is design science research (DSR), a problem-solving paradigm which seeks to construct a working artifact and prove its relevance. The artifact, a metamodel for the e-waste domain, was constructed through an iterative manner and later analyzed to conclude its theoretical relevance and contributions in this domain. As part of the approach, the authors used supplementary techniques such as systematic literature review (SLR), conceptual modeling (CM) and system dynamics (SD).

The application in the e-waste domain of CM techniques such as metamodeling, model-to-model transformation and simulation is valuable for supporting decision-making, especially when combined with SD. The approach presented in this paper, the conceptual tools and different simulation techniques could also be applied in other complex domains to obtain similar results.

The modeling method to apply simulation techniques is targeted toward the e-waste domain experts to understand, design, implement, measure and improve strategies and public policies.

The use of CM techniques to model and analyze structural and behavioral e-waste scenarios.

In the context of the trade war in full swing, the global supply chain systems have experienced a serious shock and become very vulnerable. The purpose of this paper is to explore the intertwining effects between the export-supporting subsidy policy and the import-deterring tariff policy to develop better insights for trade policy-making under the intra-industry-trade (IIT) conflicts. The research results may provide the trade policy makers and international businesses with better insights in making rational trade policy and business decisions.

Two-stage game-/bargaining-theoretical models for the dual competing international supply chains with a unilateral/bilateral tariff imposing or subsidy implementing under six different scenarios of IIT conflict are developed, analyzed and compared. On this basis, the corresponding numerical analyses are conducted to assess the impact of the tariff and subsidy policies and derive the trade policy implications and business insights.

The research results indicate that: (1) the bilateral subsidy implementing from both governments is the best policy for all stakeholders in two countries, which would lead to the highest profits, social welfare and consumer surplus than those of the other scenarios; (2) the bilateral tariff imposing of both governments is the worst policy for all stakeholders in two countries, which would lead to the lowest profits, social welfare and consumer surplus than those of the other scenarios; (3) the fair trade scenario without tariff imposing and subsidy implementing turns out to be the second-best trade policy for the governments. Under the World Trade Organization rule and fair-trade principles, the bilateral subsidy policy is not allowed in most of the cases. Thus, adopting a fair-trade policy may be the most appropriate trade policy for two trading countries.

The modeling approach developed for this study is original and innovative due to the following characteristics. First, based on three trade policy alternatives – fair trade, tariff imposing and subsidy implementing – of two generic governments under IIT conflict, nine different combinations of three policy alternatives are defined. Second, excluding the symmetrical combinations, six IIT conflict scenarios under various tariff/subsidy policy pairs ranging from no conflict to high conflict are assumed for two dual competing international supply chains. Third, a novel two-stage game-/bargaining-theoretical modeling approach is applied to investigate the optimal/equilibrium decisions regarding pricing, ordering quantity and their critical economic outcomes for all possible trade policy scenarios. Fourth, this study lays down a research foundation for the future trade conflict study using a game-theoretical modeling approach.

This research studies a plastic recycling system from a reverse logistics angle and investigates the potential benefits of a multimodality strategy to the network design of plastic recycling. This research aims to quantify the impact of multimodality on the network, to provide decision support for the design of more sustainable plastic recycling networks in the future.

A MILP model is developed to assess different plastic waste collection, treatment and transportation scenarios. Comprehensive costs of the network are considered, including emission costs. A baseline scenario represents the optimized current situation while other scenarios allow multimodality options (barge and train) to be applied.

Results show that transportation cost contributes to about 7 percent of the total cost and multimodality can bring a reduction of almost 20 percent in transportation costs (CO₂‐eq emissions included). In our illustrative case with two plastic separation methods, the post‐separation channel benefits more from a multimodality strategy than the source‐separation channel. This relates to the locations and availability of intermediate facilities and the quantity of waste transported on each route.

This study applies a reverse logistics network model to design a plastic recycling network with special structures and incorporates a multimodality strategy to improve sustainability. Emission costs (carbon emission equivalents times carbon tax) are added to the total cost of the network to be optimized.

By incorporating senior management's scenarios—alternative stories about the competition, markets, capital investments, new technologies—into the planning process, executives can test and build a consensus on the implicit and explicit mental models of their business.

Iran Nanotechnology Initiative Council could significantly increase the production of scientific articles in the field by imposing ten-year incentive policies so that Iran ranked 7 in this area in the year 2015. But this progress was insufficient to speed up the production and commercialization of nanotechnology products and Iran ranked 44 with a share of 0.03 per cent of nanotechnology production in the world. Therefore, Iran Nanotechnology Initiative Council as a governmental policymaker institution in this area has sought for the policy threefold increase of funding to speed up the production and commercialization of products in this field. But given that the result was not so clear, this research was formed in the form of modeling Iran Nanotechnology Innovation Network and testing various scenarios to increase its efficiency.

This paper uses simulation framework of innovation networks (SKIN) in an attempt to model the production innovation network in Iran in the field of nanotechnology that can measure the effect of incentive policies in changing the network structure and, consequently, increasing the level and pace of innovation in it. Given that the volume of articles produced in Iranian universities in the field of Nanotechnology had a high speed and volume in comparison with the volume of technical knowledge produced by companies, and because the SKIN framework did not consider the distinction between the two, in the first step, the framework is developed using the model of absorptive capacity of knowledge provided by Cohen and Levinthal (1990) and then the developed model was used to model the Innovation Network.

Finally, two policies of threefold increase of budget (Scenario 1) and increasing the support for joint projects (with maintaining the current budget level) (Scenario 2) were tested in this model. The social network analysis method was used to analyze the results of the two scenarios, where innovation network topology was compared (as an index to measure the network efficiency) in three states of current status of the network (the baseline scenario), implementing the first and second scenarios of extraction and with each other.

This paper models Iranian Nanotechnology Innovation Network for studying the evolution of the network as a result of executing different supportive scenarios.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.