Calculations of Potential Energy Surfaces Using Monte Carlo Configuration Interaction

We apply the method of Monte Carlo configuration interaction (MCCI) to calculate ground-state potential energy curves for a range of small molecules and compare the results with full configuration interaction. We show that the MCCI potential energy curve can be calculated to relatively good accuracy, as quantified using the non-parallelity error, using only a very small fraction of the FCI space. In most cases the potential curve is of better accuracy than its constituent single-point energies. We finally test the MCCI program on systems with basis sets beyond full configuration interaction: a lattice of fifty hydrogen atoms and ethylene. The results for ethylene agree fairly well with other computational work while for the lattice of fifty hydrogens we find that the fraction of the full configuration interaction space we were able to consider appears to be too small as, although some qualitative features are reproduced, the potential curve is less accurate.Comment: 14 pages, 22 figure

Monte Carlo configuration interaction applied to multipole moments, ionisation energies and electron affinities

The method of Monte Carlo configuration interaction (MCCI) [1,2] is applied to the calculation of multipole moments. We look at the ground and excited state dipole moments in carbon monoxide. We then consider the dipole of NO, the quadrupole of the nitrogen molecule and of BH. An octupole of methane is also calculated. We consider experimental geometries and also stretched bonds. We show that these non-variational quantities may be found to relatively good accuracy when compared with FCI results, yet using only a small fraction of the full configuration interaction space. MCCI results in the aug-cc-pVDZ basis are seen to generally have reasonably good agreement with experiment. We also investigate the performance of MCCI when applied to ionisation energies and electron affinities of atoms in an aug-cc-pVQZ basis. We compare the MCCI results with full configuration-interaction quantum Monte Carlo [3,4] and `exact' non-relativistic results [3,4]. We show that MCCI could be a useful alternative for the calculation of atomic ionisation energies however electron affinities appear much more challenging for MCCI. Due to the small magnitude of the electron affinities their percentage errors can be high, but with regards to absolute errors MCCI performs similarly for ionisation energies and electron affinities.Comment: 12 pages, 20 figure

Complex permeability of soft magnetic ferrite polyester resin composites at frequencies above 1 MHz

Composite soft magnetic materials consist of magnetic particles in a non-magnetic matrix. The properties of such materials can be modelled using effective medium theory. Measurements have been made of the complex permeability of composites produced using ferrite powder and polyester resin. The success of various effective medium expressions in predicting the variation of complex permeability with composition has been assessed

On log concavity for order-preserving and order-non-reversing maps of partial orders

Stanley used the Aleksandrov-Fenchel inequalities from the theory of nixed volumes to prove the following result. Let P be a partially ordered set with n elements, and let x ∊ P. If Ni* is the number of linear extensions , ⋋ : P + (1 , 2,...,n) satisfying ⋋ (x) = i, then the sequence N*1,…,N*n is log concave (and therefore unimodal). Here the analogous results for both order-preserving and order-non-reversing maps are proved using an explicit injection. Further, if vc is the number of order-preserving maps of P into a chain of length c, then vc is shown to be 1-og concave, and the corresponding result is established for order-non-reversing maps

Correaltion of full-scale drag predictions with flight measurements on the C-141A aircraft. Phase 2: Wind tunnel test, analysis, and prediction techniques. Volume 1: Drag predictions, wind tunnel data analysis and correlation

The degree of cruise drag correlation on the C-141A aircraft is determined between predictions based on wind tunnel test data, and flight test results. An analysis of wind tunnel tests on a 0.0275 scale model at Reynolds number up to 3.05 x 1 million/MAC is reported. Model support interference corrections are evaluated through a series of tests, and fully corrected model data are analyzed to provide details on model component interference factors. It is shown that predicted minimum profile drag for the complete configuration agrees within 0.75% of flight test data, using a wind tunnel extrapolation method based on flat plate skin friction and component shape factors. An alternative method of extrapolation, based on computed profile drag from a subsonic viscous theory, results in a prediction four percent lower than flight test data

Scaling and self-similarity in an unforced flow of inviscid fluid trapped inside a viscous fluid in a Hele-Shaw cell

We investigate quasi-two-dimensional relaxation, by surface tension, of a long straight stripe of inviscid fluid trapped inside a viscous fluid in a Hele-Shaw cell. Combining analytical and numerical solutions, we describe the emergence of a self-similar dumbbell shape and find non-trivial dynamic exponents that characterize scaling behavior of the dumbbell dimensions.Comment: 4 pages, 5 figures, to appear in PR

Suprathermal plasma observed on STS-3 Mission by plasma diagnostics package

Artificially produced electron beams were used extensively during the past decade as a means of probing the magnetosphere, and more recently as a means of actively controlling spacecraft potential. Experimentation in these areas has proven valuable, yet at times confusing, due to the interaction of the electron beam with the ambient plasma. The OSS-1/STS-3 Mission in March 1982 provided a unique opportunity to study beam-plasma interactions at an altitude of 240 km. On board for this mission was a Fast Pulse Electron Generator (FPEG). Measurements made by the Plasma Diagnostics Package (PDP) while extended on the Orbiter RMS show modifications of the ion and electron energy distributions during electron beam injection. Observations made by charged particle detectors are discussed and related to measurements of Orbiter potential. Several of the PDP instruments, the joint PDP/FPEG experiment, and observations made during electron beam injection are described

Legionellosis and biologic therapies

Background Biologic therapies are widely used in inflammatory diseases, and they are associated to an increased infection risk, especially to granulomatous and intracellular infections such as Legionella. Results A review of the literature revealed 105 cases of Legionella pneumonia in patients taking biologic therapies. Sixty-four patients (65.3%) were treated with infliximab, 23 (23.5%) with adalimumab, 5 (5%) with etanercept and 3 (3%) with rituximab. Seventy-one per cent of the patients were treated for rheumatologic diseases and 16% for inflammatory bowel diseases. The majority of the patients received one or more concomitant immunosuppressive drugs, especially steroids (43%). Overall mortality was 19%. Legionella pneumonia might complicate therapy with biologic therapies, especially in patients being treated with infliximab or adalimumab given concomitantly with other immunosuppressive medications during their first 6 months of treatment. Conclusion Physicians should be aware of this potentially severe association. Early recognition and treatment would likely result in reduced morbidity and mortality

Energy use predictions with machine learning during architectural concept design

Studies have shown that the actual energy consumption of buildings once built and in operation is often far greater than the energy consumption predictions made during design—leading to the term “performance gap.” An alternative to traditional, building physics based, prediction methods is an approach based on real-world data, where behavior is learned through observations. Display energy certificates are a source of observed building “behavior” in the United Kingdom, and machine learning, a subset of artificial intelligence, can predict global behavior in complex systems, such as buildings. In view of this, artificial neural networks, a machine learning technique, were trained to predict annual thermal (gas) and electrical energy use of building designs, based on a range of collected design and briefing parameters. As a demonstrative case, the research focused on school design in England. Mean absolute percentage errors of 22.9% and 22.5% for annual thermal and electrical energy use predictions, respectively, were achieved. This is an improvement of 9.1% for the prediction of annual thermal energy use and 24.5% for the prediction of annual electrical energy use when compared to sources evidencing the current performance gap