Fetal biophysical profile SCORING is a sonographic-based method of fetal assessment first described by Manning and Platt in 1980.The biophysical profile score was developed as a method to integrate real-time observations of the fetus and his/her intrauterine environment in order to more comprehensively assess the fetal condition. These findings must be evaluated in the context of maternal/fetal history (i.e., chronic hypertension, post-dates, intrauterine growth restriction, etc), fetal structural integrity (presence or absence of congenital anomalies), and the functionality of fetal support structures (placental and umbilical cord). For example, acute asphyxia due to placental abruption may result in an absence of the acute variables of the biophysical profile score (fetal breathing movements, fetal movement, fetal tone, and fetal heart rate reactivity) with a normal amniotic fluid volume. With post maturity the asphyxial event may be intermittent and chronic resulting in a decrease in amniotic fluid volume, but with the acute variables remaining normal.While the 5 components of the biophysical profile score have remained unchanged since 1980 (Manning, 1980), the definitions of a normal and abnormal parameter have evolved with increasing experience.In 1984 the definition of oligohydramnios was increased from < 1cm pocket of fluid to < 2.0 x 1.0 cm pocket. Oligohydramnios is now defined as a pocket of amniotic fluid < 2.0 x 2.0 cm (Manning, 1995a)If the four ultrasound variables are normal, the accuracy of the biophysical profile score was not found to be significantly improved by adding the non-stress test. As a result, in 1987 the profile score was modified to incorporate the non-stress test only when one of the ultrasound variables was abnormal (Manning 1987). Table 1 outlines the current definitions for quantifying a variable as present or absent.Each of the 5 components of the biophysical profile score does not have equal significance. Fetal breathing movements, amniotic fluid volume, and the non-stress test are the most powerful variables. For example, when the biophysical profile score is 2, the perinatal mortality varies between 428/1000 with only fetal movement present to 66/1000 if the non-stress test is reactive and all of the ultrasound parameters are absent (Manning 1990b). Some authors have, therefore, proposed utilization of a modified biophysical profile that incorporates only the non-stress test and amniotic fluid volume (Miller 1996). Although the positive predictive value of these 2 tests is equivalent to a biophysical profile score of 6, the perinatal mortality is still increased over a normal test score of 8 or 10 (Manning 1990b). The false positive rate with the modified biophysical profile score is also substantially higher.Conclusions: The fetus expresses its well being or compromised status through a number of different biophysical activities that are controlled by different central nervous system centers. The utilization of the biophysical score for ante partum surveillance in high-risk patients has resulted in a reduction in perinatal mortality when compared to historical controls. The appropriate management of the viable fetus with an abnormal biophysical profile score may also decrease long-term neurological morbidity (Manning 1998).It is unlikely that in the future additional variables will be added to the biophysical profile score. However, perhaps the incorporation of the fetal state (i.e., eye movements) and Doppler flow studies of specific fetal vessels (umbilical artery, middle cerebral artery, ductus venosus) will be incorporated into a complete assessment of the fetal conditionTable 1. Components of the 30 Minute Biophysical Profile ScoreComponent                                DefinitionFetal movements                       ³3 body or limb movementsFetal tone                                  One episode of active extension and flexion of the limbs; opening and                                                closing of handFetal breathing movements         ³1 episode of ³30 seconds in 30 minutesAmniotic fluid volume                 - Hiccups are considered breathing activity                                                A single 2 cm x 2 cm pocket is considered adequateNon-stress test                          2 accelerations > 15 beats per minute of at least 15 seconds duration

SCORING rules are common methods for incentivizing experts to present the opinions consistent with their beliefs. Information markets such as prediction markets and decision markets use SCORING rules for eliciting the most accurate predictions experts can make. In these markets, experts are invited to buy and sell contracts, according to which they will be paid if their prediction about a future uncertain event is true. The final trading prices can be interpreted as an aggregation of their prediction for an uncertain future event. In this paper, we propose new mechanisms (prediction oriented and decision oriented) for predicting the value of uncertain continuous variables in the future. These mechanisms in their basic form, are SCORING rules with a new paradigm. This paper also includes the results of performing a case study (a prediction oriented mechanism) for predicting the outcome of the 11th presidential election of Iran in 2013 to analyze the performance of our model. Besides the fact that the mechanism's average absolute error in predicting nominees' percentages was low, about 6. 53%, it also predicted the final outcome order of all nominees correctly.

Credit SCORING is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is used for reclassifying client to reject or accept class for case of real data sets of an Iranian bank branch. For this reason, two DEA models are solved. Also, the reject and accept frontiers and overlapping region among two frontiers are obtained. Then a goal programming problem is solved for finding coefficients of the discriminant hyper-plane. The results are obtained from the samples are kept from the main dataset, clarify that the classified hyper-plane obtained from the used method provides an almost profitable classification for payment loan.