Palladium nanoparticles supported on fluorine-doped tin oxide as an efficient heterogeneous catalyst for Suzuki coupling and 4-nitrophenol reduction

Immobilization of palladium nanoparticles onto the fluorine-doped tin oxide (FTO) as support Pd/FTO, resulted in a highly active heterogeneous catalyst for Suzuki-Miyaura cross-coupling reactions and 4-nitrophenol reduction. The Pd/FTO catalyst has been synthesized by immobilization of palladium nanoparticles onto FTO via a simple impregnation method. ICP-MS analysis confirmed that there is 0.11 mmol/g of palladium was loaded successfully on FTO support. The crystallinity, morphologies, compositions and surface properties of Pd/FTO were fully characterized by various techniques. It was further examined for its catalytic activity and robustness in Suzuki coupling reaction with different aryl halides and solvents. The yields obtained from Suzuki coupling reactions were basically over 80%. The prepared catalyst was also tested on mild reaction such as reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). Pd/FTO catalyst exhibited high catalytic activity towards 4-NP reduction with a rate constant of 1.776 min(-1) and turnover frequency (TOF) value of 29.1 hr(-1). The findings revealed that Pd/FTO also maintained its high stability for five consecutive runs in Suzuki reactions and 4-NP reductions. The catalyst showed excellent catalytic activities by using a small amount of Pd/FTO for the Suzuki coupling reaction and 4-NP reduction

Results of the autopsy in Bahrami Children Hospital

Background: The aims of this study were to evaluate the trend and clinical utility of the autopsy in Bahrami Children's Hospital in Tehran, Iran. Methods: In this retrospective descriptive-analytic survey during a six years course from 1998 to 2003, autopsies in the hospital were studied. The clinical and autopsy diagnoses were compared and categorized as follows: 1. Change (Clinical and Autopsy diagnoses discordant), 2. Add (Significant unexpected findings noted on the autopsy, although the clinical diagnosis was not altered), 3. Confirm (Clinical and Autopsy diagnoses concordant), 4. Autopsy inconclusive Findings: Eighty four autopsies were studied. Out of 350 neonatal deaths, autopsy was performed in 74 neonates (21%) and of 249 under 5 year deaths (except neonates) autopsy was performed in only 10 cases (4%). The autopsy rate declined during these years. In 61 cases (73%) the autopsy diagnoses confirmed the clinical diagnosis, in 10 cases (12%) it changed the clinical diagnoses, in 11 cases (13%) it significantly added to the clinical diagnoses and in 2 cases (2%) it was inconclusive.Conclusions: This study reveals a decline in neonatal and infant autopsy during a six year period. This study also demonstrates that neonatal and infant autopsy continues to provide clinically useful data in 25% of cases and remains a valuable tool in pediatric medicine

Combining artificial neural networks and genetic algorithms for rock cuttings slopes stability condition identification

Keeping large-scale transportation infrastructure networks, such as railway net-works, operational under all conditions is one of the major challenges today. The budgetary constraints for maintenance and network operation and the network dimension are two of the main factors that make the management of a transporta-tion network such a challenging task. Hence, aiming to assist the management of a transportation network, a data-driven model is proposed for stability condition identification of rock cuttings slopes. It should be noted that one of the key points of the proposed system is to avoid data from complex monitoring equipment or laboratory expensive testes. Accordingly, only information taken from routine in-spections (visual information) and complemented with geometric and geologic data will be used to feed the models. Therefore, in this work the flexible learning capabilities of Artificial Neural Networks (ANN) were used to fit a data-driven model for Earthwork Hazard Category (EHC) identification. Considering the high number of parameters involved in EHC identification, Genetic Algorithms (GA) were applied for input feature selection purposes. The proposed models were addressed following a nominal classification strategy. In addition, to over-come the problem of imbalanced data (since typically good conditions are much common than bad ones), three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved modelling results are pre-sented and discussed, detailing GA effectiveness and ANNs performance.This work was supported by FCT - “Fundação para a Ciência e a Tecnologia”, within Institute for Sustainability and Innovation in Structural Engineering (ISISE), project UID/ECI/04029/2013 as well Project Scope: UID/CEC/00319/2013 and through the post-doctoral Grant fellowship with reference SFRH/BPD/94792/2013. This work was also partly financed by FEDER (Fundo Europeu de Desenvolvimento Regional) funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT within the scope of the project POCI-01-0145-451 FEDER-007633. This work has been also supported by COMPETE: POCI-01-0145- FEDER-007043. A special thanks goes to NetworkRail that kindly made available the data (basic earthworks examination data and the Earthworks Hazard Condition scores) used in this work