The effect of substituted benzene dicarboxylic acid linkers on the optical band gap energy and magnetic coupling in manganese trimer metal organic frameworks

We have systematically studied a series of eight metal-organic frameworks (MOFs) in which the secondary building unit is a manganese trimer cluster, and the linkers are differently substituted benzene dicarboxylic acids (BDC). The optical band gap energy of the compounds vary from 2.62 eV to 3.57 eV, and theoretical studies find that different functional groups result in new states in the conduction band, which lie in the gap and lower the optical band gap energy. The optical absorption between the filled Mn 3d states and the ligands is weak due to minimal overlap of the states, and the measured optical band gap energy is due to transitions on the BDC linker. The Mn atoms in the MOFs have local moments of 5 mu B, and selected MOFs are found to be antiferromagnetic, with weak coupling between the cluster units, and paramagnetic above 10 K

Effect of obesity and thoracic epidural analgesia on perioperative spirometry

Background. Lung volumes in obese patients are reduced significantly in the postoperative period. As the effect of different analgesic regimes on perioperative spirometric tests in obese patients has not yet been studied, we investigated the effect of thoracic epidural analgesia and conventional opioid-based analgesia on perioperative lung volumes measured by spirometry. Methods. Eighty-four patients having midline laparotomy for gynaecological procedures successfully completed the study. Premedication, anaesthesia and analgesia were standardized. The patients were given a free choice between epidural analgesia (EDA) (n=42) or opioids (n=42) for postoperative analgesia. We performed spirometry to measure vital capacity (VC), forced vital capacity, peak expiratory flow, mid-expiratory flow and forced expiratory volume in 1 s at preoperative assessment, 30-60 min after premedication and 20 min, 1 h, 3 h and 6 h after extubation. Results. Baseline values were all within the normal range. All perioperative spirometric values decreased significantly with increasing body mass index (BMI). The greatest reduction in VC occurred directly after extubation, but was less in the EDA group than in the opioid group: mean of −23(sd 8)% versus −30(12)% (P30) the difference in VC was significantly more pronounced than in patients of normal weight (BMI<25): −45(10)% versus −33(4)% (P<0.001). Recovery of spirometric values was significantly quicker in patients receiving EDA, particularly in obese patients. Conclusion. We conclude that EDA should be considered in obese patients undergoing midline laparotomy to improve postoperative spirometr

Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. The degree of LDDs within the generated data can be controlled through the k parameter, length of the generated strings, and by choosing appropriate forbidden strings. In this paper, we explore the capacity of different RNN extensions to model LDDs, by evaluating these models on a sequence of SPk synthesized datasets, where each subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple languages, the presence of LDDs does have significant impact on the performance of recurrent neural architectures, thus making them prime candidate in benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201