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This study explores the influence individual characteristics identified in prior research have on ethical choice in a financial reporting task. An action-based, multi-metric dependent variable is developed to measure ethical reporting choice. Intermediate accounting students participate in the task as part of a curricular assignment in a revenue recognition module. Results demonstrate that several, but not all, individual characteristics found in prior research do influence accounting students’ ethical revenue recognition choices. Specifically, the external locus-of-control, idealism, consequentialist, and Machiavellian characteristics are found to influence ethical reporting choice.

The purpose of this paper is to propose a methodology that may aid in assessing ecological quality multi‐trait screening through the use of simple and robust tools while exerting minimal effort in conducting trials and interpreting results.

Response data for two popular site‐monitoring environmental indicators, chemical oxygen demand (COD) and biochemical oxygen demand (BOD), are arranged by implementing an 8‐run saturated orthogonal array proposed by Taguchi. Unreplicated data consolidation is performed through the Super‐Ranking translation. This permits converting the two eco‐traits to a single master response which becomes much easier to manipulate statistically. Distribution‐free multi‐factor contrasting provides the data reduction engine to filter‐out non‐active eco‐design variables for a waste treatment unit in a large dairy‐products plant.

Environmental quality improvement is achieved by accumulating structured eco‐data sets through an unreplicated‐saturated L₈(2⁷) Taguchi design. Concurrent minimization of the two selected eco‐responses, COD and BOD, promotes in a statistically significant fashion the quantity of incoming wastes, set at its minimum load, as the sole active eco‐factor.

Brief but robust experimentation is exploited in gaining information about the phenomenological behavior of environmental quality indicators, namely COD and BOD, in facilities that manage wastewater treatment. Design for environment is enforced through standard DOE planning schemes. Collected multi‐metric eco‐quality data are translated non‐parametrically in an easy‐to‐comprehend manner that requires no assist from software aids while bypassing more statistical intensive techniques which may demand involvement of more experienced personnel. The methodology is accessible to any level of statistical competence seamlessly intertwined to optimization demands for rapid inference needs.

The method mixes up three distinctive “design‐and‐analysis” elements in order to provide optimal solution in a design‐for‐environment project. The sampling capabilities of Taguchi's orthogonal arrays in concert with Super‐Ranking transformation fuse multi‐eco‐characteristics to a single easy‐to‐handle master unitless eco‐response. Order statistics tables recently published in terms of true probabilities have been adopted for supplying the proper cutoff points to be utilized for gauging against observed rank sums for an 8‐run orthogonal array screening. Quality managers and environmental engineers who contribute routinely to continuous eco‐improvement projects in their Total Environmental Quality Management (TEQM) program may find this approach attractive and viable en route to a typical industrial pollution prevention control deployment.

Happiness is an important mental emotion and yet becoming a major health concern nowadays. For this reason, better recognizing the objective understanding of how humans respond to event-related observations in their daily lives is especially important.

This paper uses non-intrusive technology (hyperspectral imaging [HSI]) for happiness recognition. Experimental setup is conducted for data collection in real-life environments where observers are showing spontaneous expressions of emotions (calm, happy, unhappy: angry) during the experimental process. Based on facial imaging captured from HSI, this work collects our emotional database defined as SWU Happiness DB and studies whether the physiological signal (i.e. tissue oxygen saturation [StO2], obtained by an optical absorption model) can be used to recognize observer happiness automatically. It proposes a novel method to capture local dynamic patterns (LDP) in facial regions, introducing local variations in facial StO₂ to fully use physiological characteristics with regard to hyperspectral patterns. Further, it applies a linear discriminant analysis-based support vector machine to recognize happiness patterns.

The results show that the best classification accuracy is 97.89 per cent, objectively demonstrating a feasible application of LDP features on happiness recognition.

This paper proposes a novel feature (i.e. LDP) to represent the local variations in facial StO₂ for modeling the active happiness. It provides a possible extension to the promising practical application.