Building simulation: Ten challenges

Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. The formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.     

The formation of an apatite coating on carboxylated polyphosphazenes via a biomimetic process

The biomimetic deposition of an apatite coating on polyphosphazene was investigated. A polyphosphazene having carboxylatophenoxy side groups was synthesized as a candidate polymer with heterogeneous nucleation sites for apatite deposition. Poly[bis(carboxylatophenoxy)phosphazene] (PCPP) was synthesized by a two-step synthetic route involving the synthesis of poly(dichlorophosphazene) (PDP) followed by nucleophilic replacement of the chlorine atoms of PDP with carboxylatophenoxy groups. The surface of the carboxylated polyphosphazene induced the formation of a dense layer of apatite on the surface after incubation in simulated body fluid at 37 °C with an ionic concentration approximately the same as that of human blood plasma. The results suggested an early precipitation of octacalcium phosphate and calcium phosphate intermediates on the surface, both of which are known to promote osteoblast differentiation and proliferation.     

Biocompatibility of native and functionalized single-walled carbon nanotubes for neuronal interface

Single-walled carbon nanotubes (SWNTs) have unique mechanical, electrical, and optical properties and can be easily chemically modified; features that make them excellent candidate materials for applications as sensors and stimulators in neuronal tissue engineering. The purpose of this study was to demonstrate that SWNTs can support neuronal attachment and growth, that simple chemical modifications can be employed to control cell growth, that SWNTs do not interfere with ongoing neuronal function, and that neurons can be electrically coupled to SWNTs. Growth and attachment of the neuroblastoma*glioma NG108, a model neuronal cell, was assessed on unmodified SWNT substrates or substrates from SWNTs modified with 4-benzoic acid or 4-tert-butylphenyl functional groups using a simple functionalization method. SWNT films support cell growth, but at a reduced level compared to tissue culture-treated polystyrene. The order of viability and cell attachment was tissue culture treated polystyrene > SWNTs > 4-tert-butylphenyl-functionalized SWNTs > 4-benzoic acid-functionalized SWNTs. Decreased cell growth after culture on untreated (non adherent) polystyrene suggested that cell attachment was a critical determinant of proliferation and cell growth on SWNTs. Fluorescence and scanning electron microscopy revealed decreased neurite outgrowth in NG108 grown on SWNT substrates. We are also among the first groups to demonstrate electrical coupling of SWNTs and neurons by demonstrating that NG108 and rat primary peripheral neurons showed robust voltage-activated currents when electrically stimulated through transparent, conductive SWNT films. Our data suggest that SWNTs are flexible resource materials for tissue engineering application involving electrically excitable tissues such as muscles and nerves.     

Facile synthesis and growth mechanism of Ni-catalyzed GaAs nanowires on non-crystalline substrates

GaAs nanowires (NWs) have been extensively explored for next generation electronics, photonics and photovoltaics due to their direct bandgap and excellent carrier mobility. Typically, these NWs are grown epitaxially on crystalline substrates, which could limit potential applications requiring high growth yield to be printable or transferable on amorphous and flexible substrates. Here, utilizing Ni as a catalytic seed, we successfully demonstrate the synthesis of highly crystalline, stoichiometric and dense GaAs NWs on amorphous SiO(2) substrates. Notably, the NWs are found to grow via the vapor-solid-solid (VSS) mechanism with non-spherical NiGa catalytic tips and low defect densities while exhibiting a narrow distribution of diameter (21.0 ± 3.9 nm) uniformly along the entire length of the NW (>10 μm). The NWs are then configured into field-effect transistors showing impressive electrical characteristics with I(ON)/I(OFF) > 10(3), which further demonstrates the purity and crystal quality of NWs obtained with this simple synthesis technique, compared to the conventional MBE or MOCVD grown GaAs NWs.     

Impaired Higher Order Implicit Sequence Learning on the Verbal Version of the Serial Reaction Time Task in Patients With Parkinson's Disease.

Although neuroimaging studies have strongly implicated basal ganglia involvement in implicit sequence learning, serial reaction time (SRT) studies with Parkinson's disease (PD) patients have yielded mixed results. The present research sought to examine the ability of people with PD to implicitly learn sequences with different sequential structures and to objectively assess explicit knowledge. A version of the SRT task that reduces motor demands was used to compare 19 patients with PD but not dementia and 37 matched controls. PD patients showed less implicit sequence-specific learning for both sequences and reduced response time improvement over sequential trials for the more complex sequence. A closer examination revealed that the deficit involved higher order sequential associations as well as the learning of pairwise information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data

The estimation of orientation distribution functions (ODFs) from discrete orientation data, as produced by electron backscatter diffraction or crystal plasticity micromechanical simulations, is typically achieved via techniques such as the Williams–Imhof–Matthies–Vinel (WIMV) algorithm or generalized spherical harmonic expansions, which were originally developed for computing an ODF from pole figures measured by X-ray or neutron diffraction. These techniques rely on ad-hoc methods for choosing parameters, such as smoothing half-width and bandwidth, and for enforcing positivity constraints and appropriate normalization. In general, such approaches provide little or no information-theoretic guarantees as to their optimality in describing the given dataset. In the current study, an unsupervised learning algorithm is proposed which uses a finite mixture of Bingham distributions for the estimation of ODFs from discrete orientation data. The Bingham distribution is an antipodally-symmetric, max-entropy distribution on the unit quaternion hypersphere. The proposed algorithm also introduces a minimum message length criterion, a common tool in information theory for balancing data likelihood with model complexity, to determine the number of components in the Bingham mixture. This criterion leads to ODFs which are less likely to overfit (or underfit) the data, eliminating the need for a priori parameter choices.     

Social skill deficit interventions for substance abusers.

Because substance abusers demonstrate significant cognitive and behavioral skill deficits, 2 social competency models of habilitation are proposed to increase abusers' life management skills and coping abilities. Social problem-solving training programs focus on generic cognitive problem-solving deficits, while social skills training programs address specific behavioral deficiencies. Social problem solving is a cognitive-behavioral approach that teaches people how to think, emphasizing thinking that is alternative, consequential, means-end, perspective taking, and social-causal. Social skills training may be taught using modeling, role playing, rehearsal, coaching, and feedback. Both approaches have been successful for increasing social adjustment and diminishing psychopathology among high-risk substance abusers. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Magnetic quadrupole doublet focusing system for high energy ions

A high energy focused ion beam microprobe using a doublet arrangement of short magnetic quadrupole lenses was used to focus 1-3 MeV protons to spot sizes of 1x1 microm2 and 1-4.5 MeV carbon and silicon ion beams to spot sizes of 1.5x1.5 microm2. The results presented clearly demonstrate that this simple doublet configuration can provide high energy microbeams for microanalysis and microfabrication applications.