Comparison of the maternal and neonatal effects of bupivacaine plus fentanyl and ropivacaine plus fentanyl during cesarean delivery

Purpose: The aim of the present study was to compare the anesthetic efficacy, and fetal and maternal effects of 7.5 mg (1 ml) intrathecal 0.75% hyperbaric ropivacaine + 25 ìg (0.5 ml) fentanyl versus 5 mg (l ml) intrathecal 0.5% hyperbaric bupivacaine + 25 ìg (0.5 ml) fentanyl in elective cesarean delivery.Materials and Methods: The study included 40 ASA I–II cases scheduled for cesarean delivery that were randomized into two groups of 20 cases each. Cases in the RF group were administered 0.75% hyperbaric ropivacaine + 25 ìg (0.5 ml) fentanyl and those in the BF group were administered 5 mg (l ml) hyperbaric bupivacaine + 25 ìg (0.5 ml)fentanyl into the spinal space. The time until spinal anesthesia in the T4 dermatome, overall duration of analgesia, hemodynamic parameters, Apgar score of newborns at 1–5 min, fetal blood gas values (pH, PO2, PCO2, HCO3., and BE), maternal side effects, the degree of motor block, maternal need for ephedrine, objective pain scale score, and patient satisfaction were recorded in each group.Results: There were no significant differences between the groups in terms of the parameters evaluated (P > 0.05).Conclusion: In elective cesarean delivery, the combinations of bupivacaine + fentanyl or ropivacaine + fentanyl exhibited similar anesthetic efficacy, and fetal and maternal effects.Key words: Bupivacaine, cesarean, opioid, ropivacain

Design of an Experimental Setup for Testing Multiphysical Effects on High Speed Mini Rotors

Recently, there have been numerous research projects on the development of minirotating machines. These machines mostly operate at speeds above the first critical speed and have special levitation systems. Besides, the multiphysical effects become significant in small scale. Therefore, advanced modeling approaches should be developed and innovative experimental rigs with the foregoing requirements should be constructed in order to test the developed techniques. In the current study, the design of an experimental setup for testing the multiphysical effects has been outlined. First, the previously developed multiphysical models (Dikmen, E., van der Hoogt, P., de Boer, A., and Aarts, R., 2010, “Influence of Multiphysical Effects on the Dynamics of High Speed Minirotors—Part I: Theory,” J. Vibr. Acoust., 132, p. 031010; Dikmen, E., van der Hoogt, P., de Boer, A., and Aarts, R., 2010, “Influence of Multiphysical Effects on the Dynamics of High Speed Minirotors—Part II: Results,” J. Vibr. Acoust., 132, p. 031011) for the analysis of small scale rotors are described briefly for background information. Second, an analysis of the effect of the rotor parameters (diameter, length, rotation speed, etc.) on the dynamics of the rotor under multiphysical effects is presented. Afterward the design process which includes the design decisions based on these results, the availability, simplicity, and applicability of each component is presented in detail. Finally, the experimental results have been presented and the efficiency of the design has been evaluated. In summary, the design requirements for an experimental setup for testing multiphysical effects on minirotors have been analyzed. The design procedure and evaluation of the design have been presented

A Novel Visual Word Co-occurrence Model for Person Re-identification

Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner. The appearance transformation between camera views is encoded by a co-occurrence matrix of visual word joint distributions in probe and gallery images. Our appearance model naturally accounts for spatial similarities and variations caused by pose, illumination & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR and CUHK Campus benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.Comment: Accepted at ECCV Workshop on Visual Surveillance and Re-Identification, 201