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It is widely recognized that countries scoring high in capitalism, democracy, and the rule of law (CDR) tend to have impressive levels of real per capita gross domestic product (GDP) adjusted for purchasing power parity (GDPppp). However, the key to the rule of law is collaboration, and the ability to work together may have been eroded in communities that have experienced past traumas such as forced labor, excessive discrimination and exposure to harmful chemicals. These distressing outcomes can be inherited by future generations through negative epigenetic transgenerational psycho-sequela, leading to poor academic and employment performance, low income, self-harm, negative community relations, and increased aggression. This paper aims to explore the development of a managerial framework for rehabilitating psychological health that aims to revive lost collaboration skills. The originality of this work lies in the managerial framework that facilitates the restoration of collaboration skills, which are fundamental to exceptional economic growth and higher average income countrywide. [ABSTRACT FROM AUTHOR]

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Unlike reactive feedback control, feedforward control is a proactive method by which information about a measurable disturbance is fed, ahead of time, to the manipulated inputs of a process, the output of which is to be controlled, so as to counteract the effect of the disturbance. Discretized observations on the process variable are indexed to form a time series. A time-series model is fitted to the series. The ultrahigh signal-to-noise ratio fitted values are examined by a neural network, for patterns which detect when the future process is expected to become out of control. The neural-network diagnosis forms the basis for corrective action, prior to the process becoming out of control. In principle, this goes beyond SPC to achieve a process which is never actually out of control. Index Terms--Feedforward control, moving window spectral method, neural network, statistical process quality control.

A unique method is presented for evaluating university faculty contribution to student learning. The contribution is obtained by regressing inflation proof re-centered student cumulative grade point average (GPA) on the fraction of the total number of credit hours that students are taught by each professor. We postulate that the resulting regression coefficients measure the average rate at which each professor contributes to student learning as measured by cumulative grade points earned per contact hour of instruction. Since this model is based on grades that are assigned by individual professors, it is in effect a faculty peer evaluation of GPA outcome based learning as defined by the university. A privacy protected anonymous faculty and student body empirical study is conducted. [ABSTRACT FROM AUTHOR]

The regulatory and supervisory financial authorities have tried various methods to find an effective procedure in developing an early warning system of banking crises. Logistic regression models have been used but have shown some weaknesses, so we need new and better methods. The banking crisis occurred in the Dominican Republic between 2002 and 2004 has been used to compare the effectiveness of the logistic regression method over the use of Support Vector Machines (SVM) for the detection of banking crisis. In the analysis 30 financial indicators are used to determine which ones are most appropriate to build a model able to classify banks. In this context, the SVM method produced better results than logistic regression, in detecting problem banks and contradict the findings of other studies that ask about the ineffectiveness of the financial indicators to identify banking crises in emerging economies. [ABSTRACT FROM AUTHOR]