This is a pioneer study that presents two branches of computational intelligence techniques, namely linear genetic programming (LGP) and radial basis function (RBF) neural network to build models for bankruptcy prediction. The main goal is to classify samples of 140 bankrupt and non-bankrupt Iranian corporations by means LGP and RBF. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection analysis. In order to benchmark the proposed models, a log-log regression analysis is further performed. A comparative study on the classification accuracy of the LGP, RBF and regression-based models is conducted. The results indicate that the proposed models effectively let estimate any enterprise in the aspect of bankruptcy. The LGP models have a significantly better prediction performance in comparison with the RBF and regression models.

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This investigation aimed to compare spinopelvic kinematics during rowing on an ergometer vs. in a rowing tank and to evaluate changes with progressing fatigue. Spinal and pelvic kinematics of 8 competitive scull rowers (19.0 6 2.1 years, 179.9 6 7.6 cm, and 74.8 6 8.1 kg) were collected during 1 hour of rowing on an ergometer and in a rowing tank using a routine training protocol. Kinematics of the upper thoracic spine, lower thoracic spine, lumbar spine, and pelvis were determined using an infrared camera system (Vicon, Oxford, United Kingdom). There was a greater lumbar range of motion (ROM) and less posterior pelvic tilt at the catch during rowing on the ergometer compared with in the rowing tank (p 5 0.001-0.048), but no differences in pelvic ROM. In the rowing tank, the pelvic ROM increased over time (p 5 0.002) and the ROM of the lower thoracic spine decreased (p 5 0.002). In addition, there was an extended drive phase (when the rower applies pressure to the oar levering the boat forward) and an abbreviated recovery phase (setting up the rower's body for the next stroke) in the rowing tank (p 5 0.032). Different rowing training methods lead to differences in spinopelvic kinematics, which may lead to substantially different spinal loading situations. Greater pelvic rotation and lesser lumbar ROM are considered ideal; therefore, the present results indicate that rowing in the rowing tank might facilitate the maintenance of this targeted spinopelvic posture, which might help protect the lower back. Rowers, coaches, and researchers should consider the differences between rowing training methods, especially when giving training recommendations.