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This study examines the use of linear regressions that include interaction terms, finding frequent interpretation errors in published accounting research. We provide insights on how to estimate, interpret, and present interactive regression models, and explain seldom-used but easily-implemented methods to report conditional marginal effects. We also examine the use of interaction terms in tax and financial reporting trade-off studies, evaluating the conceptual fit between a regression model with interactions and alternative definitions of trade-off. Although we advocate the use of interactive models, noise levels common in accounting research greatly reduce the ability to detect interaction effects.

The purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation.

In this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

The advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process.

In this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples.

The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error.

This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts.

The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation.

This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

The purpose of this paper is to study how consumers process price frames of product bundles (product plus surcharge) and discount offers to weigh contentious positions between the weighted-additive and the reference-dependent models. Further, some research suggests bundling, while others suggest partitioning to be a more effective pricing strategy. This research evaluated the relative influences of different price frames to examine which model is supported and what are the boundary conditions for price framing.

Two online studies were conducted on Indian adults who had prior experiences of online purchases. They were asked to judge attractiveness of bundles (product along with shipping surcharge). Discounts were shown on the product, the surcharge or on the overall bundle either as partitioned prices or as a bundle.

Across two studies on low- and high-priced products, discounts on shipping surcharge increased attractiveness of the bundle compared to a similar discount on the product or on the overall bundle, supporting the reference-dependent model. Further, for a low-priced product, bundling increased attractiveness, while for a high-priced product, partitioning was more attractive.

More research is needed to examine whether these results translate to other kinds of products, surcharges or discount promotions and in different populations.

This research makes important contributions to theoretical and practical aspects of bundling and partitioned pricing research. It also adds much needed data about evaluation of product bundles with shipping surcharges among Indian customers.

For over three decades, vector autoregressions have played a central role in empirical macroeconomics. These models are general, can capture sophisticated dynamic behavior, and can be extended to include features such as structural instability, time-varying parameters, dynamic factors, threshold-crossing behavior, and discrete outcomes. Building upon growing evidence that the assumption of linearity may be undesirable in modeling certain macroeconomic relationships, this article seeks to add to recent advances in VAR modeling by proposing a nonparametric dynamic model for multivariate time series. In this model, the problems of modeling and estimation are approached from a hierarchical Bayesian perspective. The article considers the issues of identification, estimation, and model comparison, enabling nonparametric VAR (or NPVAR) models to be fit efficiently by Markov chain Monte Carlo (MCMC) algorithms and compared to parametric and semiparametric alternatives by marginal likelihoods and Bayes factors. Among other benefits, the methodology allows for a more careful study of structural instability while guarding against the possibility of unaccounted nonlinearity in otherwise stable economic relationships. Extensions of the proposed nonparametric model to settings with heteroskedasticity and other important modeling features are also considered. The techniques are employed to study the postwar U.S. economy, confirming the presence of distinct volatility regimes and supporting the contention that certain nonlinear relationships in the data can remain undetected by standard models.

Additive manufacturing (AM) is revolutionizing the manufacturing industry due to several advantages and capabilities, including use of rapid prototyping, fabrication of complex geometries, reduction of product development cycles and minimization of material waste. As metal AM becomes increasingly popular for aerospace and defense original equipment manufacturers (OEMs), a major barrier that remains is rapid qualification of components. Several potential defects (such as porosity, residual stress and microstructural inhomogeneity) occur during layer-by-layer processing. Current methods to qualify AM parts heavily rely on experimental testing, which is economically inefficient and technically insufficient to comprehensively evaluate components. Approaches for high fidelity qualification of AM parts are necessary.

This review summarizes the existing powder-based fusion computational models and their feasibility in AM processes through discrete aspects, including process and microstructure modeling.

Current progresses and challenges in high fidelity modeling of AM processes are presented.

Potential opportunities are discussed toward high-level assurance of AM component quality through a comprehensive computational tool.

The purpose of this paper is to propose a new approach of using additively manufactured parametric models in the wind tunnel test-based aerodynamic shape optimization (ASO) framework and to present its applicability test results obtained from a realistic aircraft design problem.

For aircraft shape optimization, the following three methodologies were used. First, as a validation study, the possibility of using rapid prototyping (RP) model in the wind tunnel test was verified. Second, through the wind tunnel test-based ASO, the application and feasibility of the real fighter aircraft shape optimization were verified. A generic fighter configuration is parameterized to generate various test models using additive manufacturing. Wind tunnel tests are conducted to measure their stability criteria in high angle of attack (AOA). Finally, a computational fluid dynamics (CFD) study was performed and analysis procedures, costs and results compared to the wind tunnel test were compared and reviewed.

RP technology can significantly reduce the time and cost of generating parametric wind tunnel models and can open up new possibilities for wind tunnel tests to be used in the rigorous aerodynamic design loop. There was a slight difference between the results of the RP model and the metallic model because of rigidity and surface roughness. However, the tendency of the aerodynamic characteristics was very similarly predictable. Although there are limitations to obtaining precise aerodynamic data, it is a suitable method to be applied to comparative studies on various shapes with large geo-metric changes in the early phase of design. The CFD analysis indicates that the wind tunnel-based ASO using the RP model shows the efficiency corresponding to the CFD shape optimization.

The RP parametric models may have various assembly error sources and rigidity problems. The proposed methodology may not be suitable for collecting the accurate aerodynamic database of a final design; rather, the methodology is more suitable to screen out many configurations having fairly large shape variation in the early stage of the design process.

The wind tunnel test-based ASO can replace or supplement CFD-based ASO. In areas where CFD accuracy is low, such as high AOA flight characteristics, RP model wind tunnel-based ASO can be a research method that can secure both efficiency and accuracy advantages, providing ten times more effective in terms of cost and time. The wind tunnel test is used to obtain aerodynamic data at the final stage of shape design. It can be extended to a comparative study of several shapes in the early design phase. This procedure can be applied for both industrial level and educational aircraft design activities.

This study is the application to be applied as a parametric study on the whole aircraft, rather than using the RP model applying a simple partial control surface or configuration change of a part of the wing. The possibility of using the RP model was confirmed by comparing and verifying each other in a medium-sized wind tunnel using a relatively large RP model and a metallic model. It was verified that it can be applied in the shape design process, not the shape verification in the traditional design procedure, and a comparison with the CFD method was also performed. With further development and validation efforts, the new design framework may become an industrial standard for future aircraft development.

One of the most basic pieces of information useful to hospitality operations is gross sales, and the ability to forecast them is strategically important. These forecasts could provide powerful information to cut costs, increase efficient use of resources, and improve the ability to compete in a constantly changing environment. This study tests sophisticated, yet simple‐to‐use time series models to forecast sales. The results show that, with slight re‐arrangement of historical sales data, easy‐to‐use time series models can accurately forecast gross sales.

Due to the complexity of and variations in additive manufacturing (AM) processes, there is a level of uncertainty that creates critical issues in quality assurance (QA), which must be addressed by time-consuming and cost-intensive tasks. This deteriorates the process repeatability, reliability and part reproducibility. So far, many AM efforts have been performed in an isolated and scattered way over several decades. In this paper, a systematically integrated holistic view is proposed to achieve QA for AM.

A systematically integrated view is presented to ensure the predefined part properties before/during/after the AM process. It consists of four stages, namely, QA plan, prospective validation, concurrent validation and retrospective validation. As a foundation for QA planning, a functional workflow and the required information flows are proposed by using functional design models: Icam DEFinition for Function Modeling.

The functional design model of the QA plan provides the systematically integrated view that can be the basis for inspection of AM processes for the repeatability and qualification of AM parts for reproducibility.

A powder bed fusion process was used to validate the feasibility of this QA plan. Feasibility was demonstrated under many assumptions; real validation is not included in this study.

This study provides an innovative and transformative methodology that can lead to greater productivity and improved quality of AM parts across industries. Furthermore, the QA guidelines and functional design models provide the foundation for the development of a QA architecture and management system.

This systematically integrated view and the corresponding QA plan can pose fundamental questions to the AM community and initiate new research efforts in the in-situ digital inspection of AM processes and parts.