Quantitative ventilation assessments of idealized urban canopy layers with various urban layouts and the same building packing density

This paper investigates urban canopy layers (UCL) ventilation under neutral atmospheric condition with the same building area density (λp=0.25) and frontal area density (λf=0.25) but various urban sizes, building height variations, overall urban forms and wind directions. Turbulent airflows are first predicted by CFD simulations with standard k-ε model evaluated by wind tunnel data. Then air change rates per hour (ACH) and canopy purging flow rate (PFR) are numerically analyzed to quantify the rate of air exchange and the net ventilation capacity induced by mean flows and turbulence. With a parallel approaching wind (θ=0o), the velocity ratio first decreases in the adjustment region, followed by the fully-developed region where the flow reaches a balance. Although the flow quantities macroscopically keep constant, however ACH decreases and overall UCL ventilation becomes worse if urban size rises from 390m to 5km. Theoretically if urban size is infinite, ACH may reach a minimum value depending on local roof ventilation, and it rises from 1.7 to 7.5 if the standard deviation of building height variations increases (0% to 83.3%). Overall UCL ventilation capacity (PFR) with a square overall urban form (Lx=Ly=390m) is better as θ=0o than oblique winds (θ=15o, 30o, 45o), and it exceeds that of a staggered urban form under all wind directions (θ=0o to 45o), but is less than that of a rectangular urban form (Lx=570m, Ly=270m) under most wind directions (θ=30o to 90o). Further investigations are still required to quantify the net ventilation efficiency induced by mean flows and turbulence.

Drug permeability in 16HBE14o- airway cell layers correlates with absorption from the isolated perfused rat lung

The permeability of the lung is critical in determining the disposition of inhaled drugs and the respiratory epithelium provides the main physical barrier to drug absorption. The 16HBE14o- human bronchial epithelial cell line has been developed recently as a model of the airway epithelium. In this study, the transport of 10 low molecular weight compounds was measured in the 16HBE14o- cell layers, with apical to basolateral (absorptive) apparent permeability coefficients (P(app)) ranging from 0.4 x 10(-6)cms(-1) for Tyr-D-Arg-Phe-Phe-NH(2) to 25.2x10(-6)cms(-1) for metoprolol. Permeability in 16HBE14o- cells was found to correlate with previously reported P(app) in Caco-2 cells and absorption rates in the isolated perfused rat lung (k(a,lung)) and the rat lung in vivo (k(a,in vivo)). Log linear relationships were established between P(app) in 16HBE14o- cells and P(app) in Caco-2 cells (r(2)=0.82), k(a,lung) (r(2)=0.78) and k(a,in vivo) (r(2)=0.68). The findings suggest that permeability in 16HBE14o- cells may be useful to predict the permeability of compounds in the lung, although no advantage of using the organ-specific cell line 16HBE14o- compared to Caco-2 cells was found in this study.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Serum protein layers on parylene-C and silicon oxide: effect on cell adhesion

Among the range of materials used in bioengineering, parylene-C has been used in combination with silicon oxide and in presence of the serum proteins, in cell patterning. However, the structural properties of adsorbed serum proteins on these substrates still remain elusive. In this study, we use an optical biosensing technique to decipher the properties of fibronectin (Fn) and serum albumin adsorbed on parylene-C and silicon oxide substrates. Our results show the formation of layers with distinct structural and adhesive properties. Thin, dense layers are formed on parylene-C, whereas thicker, more diffuse layers are formed on silicon oxide. These results suggest that Fn acquires a compact structure on parylene-C and a more extended structure on silicon oxide. Nonetheless, parylene-C and silicon oxide substrates coated with Fn host cell populations that exhibit focal adhesion complexes and good cell attachment. Albumin adopts a deformed structure on parylene-C and a globular structure on silicon oxide, and does not support significant cell attachment on either surface. Interestingly, the co-incubation of Fn and albumin at the ratio found in serum, results in the preferential adsorption of albumin on parylene-C and Fn on silicon oxide. This finding is supported by the exclusive formation of focal adhesion complexes in differentiated mouse embryonic stem cells (CGR8), cultured on Fn/albumin coated silicon oxide, but not on parylene-C. The detailed information provided in this study on the distinct properties of layers of serum proteins on substrates such as parylene-C and silicon oxide is highly significant in developing methods for cell patterning.

Hidden Markov analysis describes dive patterns in semiaquatic animals

Existing methods of dive analysis, developed for fully aquatic animals, tend to focus on frequency of behaviors rather than transitions between them. They, therefore, do not account for the variability of behavior of semiaquatic animals, and the switching between terrestrial and aquatic environments. This is the first study to use hidden Markov models (HMM) to divide dives of a semiaquatic animal into clusters and thus identify the environmental predictors of transition between behavioral modes. We used 18 existing data sets of the dives of 14 American mink (Neovison vison) fitted with time-depth recorders in lowland England. Using HMM, we identified 3 behavioral states (1, temporal cluster of dives; 2, more loosely aggregated diving within aquatic activity; and 3, terminal dive of a cluster or a single, isolated dive). Based on the higher than expected proportion of dives in State 1, we conclude that mink tend to dive in clusters. We found no relationship between temperature and the proportion of dives in each state or between temperature and the rate of transition between states, meaning that in our study area, mink are apparently not adopting different diving strategies at different temperatures. Transition analysis between states has shown that there is no correlation between ambient temperature and the likelihood of mink switching from one state to another, that is, changing foraging modes. The variables provided good discrimination and grouped into consistent states well, indicating promise for further application of HMM and other state transition analyses in studies of semiaquatic animals.

Hidden

The exhibition Hidden researches the relationship between an artist book and paintings that use the form of a book. The exhibition investigates the overlaps between reading and looking.

Decomposition of surface electromyographic signal using Hidden Markov Model

The detection of physiological signals from the motor system (electromyographic signals) is being utilized in the practice clinic to guide the therapist in a more precise and accurate diagnosis of motor disorders. In this context, the process of decomposition of EMG (electromyographic) signals that includes the identification and classification of MUAP (Motor Unit Action Potential) of a EMG signal, is very important to help the therapist in the evaluation of motor disorders. The EMG decomposition is a complex task due to EMG features depend on the electrode type (needle or surface), its placement related to the muscle, the contraction level and the health of the Neuromuscular System. To date, the majority of researches on EMG decomposition utilize EMG signals acquired by needle electrodes, due to their advantages in processing this type of signal. However, relatively few researches have been conducted using surface EMG signals. Thus, this article aims to contribute to the clinical practice by presenting a technique that permit the decomposition of surface EMG signal via the use of Hidden Markov Models. This process is supported by the use of differential evolution and spectral clustering techniques. The developed system presented coherent results in: (1) identification of the number of Motor Units actives in the EMG signal; (2) presentation of the morphological patterns of MUAPs in the EMG signal; (3) identification of the firing sequence of the Motor Units. The model proposed in this work is an advance in the research area of decomposition of surface EMG signals.

Outwitted by the hidden: unsure emotions

In this paper the authors consider natural, feigned or absence of emotions in text-based dialogues. The dialogues occurred during interactions between human Judges/Interrogators and hidden entities in practical Turing tests implemented at Bletchley Park in June 2012. The authors focus on the interactions that left the Interrogator unable to say whether they were talking to a human or a machine after five minutes of questioning; the hidden interlocutor received an ‘unsure’ classification. In cases where the Judge has provided post-event feedback the authors present their rationale from three viva voce one-to-one Turing tests. The authors find that emoticons and other visual devices used to express feelings in text-based interaction were missing in the conversations between the Interrogators and hidden interlocutors.

Working through the layers: curriculum implementation in language education

This chapter presents findings on English Language instruction at the lower primary level in the context of policies for curricular innovation at national, school and classroom levels. The focus is on policies which connect national and school levels, and on how they might be interpreted when implemented in multiple schools within Singapore’s educational system. Referring to case studies in two schools and to individual lesson observations in 10 schools, we found much agreement with national policies in terms of curriculum (i.e. lesson content and activity selection),leading to great uniformity in the lessons taught by different teachers in different schools. In addition, we found that schools had an important mediating influence on implementation of national policies. However, adoptions and adaptations of policy innovations at the classroom level were somewhat superficial as they were more related to changes in educational facilities and procedures than in philosophies.

The hidden organic carbon in deep mineral soils

Aims Current estimates of soil organic carbon (SOC) are based largely on surficial measurements to depths of 0.3 to 1 m. Many of the world’s soils greatly exceed 1 m depth and there are numerous reports of biological activity to depths of many metres. Although SOC storage to depths of up to 8 m has been previously reported, the extent to which SOC is stored at deeper depths in soil profiles is currently unknown. This paper aims to provide the first detailed analysis of these previously unreported stores of SOC. Methods Soils from five sites in the deeply weathered regolith in the Yilgarn Craton of south-western Australia were sampled and analysed for total organic carbon by combustion chromatography. These soils ranged between 5 and 38 m (mean 21 m) depth to bedrock and had been either recently reforested with Pinus pinaster or were under agriculture. Sites had a mean annual rainfall of between 399 and 583 mm yr−1. Results The mean SOC concentration across all sites was 2.30 ± 0.26 % (s.e.), 0.41 ± 0.05 % and 0.23 ± 0.04 % in the surface 0.1, 0.1–0.5 and 0.5 to 1.0 m increments, respectively. The mean value between 1 and 5 m was 0.12 ± 0.01 %, whereas between 5 and 35 m the values decreased from 0.04 ± 0.002 % to 0.03 ± 0.003 %. Mean SOC mass densities for each of the five locations varied from 21.8–37.5 kg C m−2, and were in toto two to five times greater than would be reported with sampling to a depth of 0.5 m. Conclusions This finding may have major implications for estimates of global carbon storage and modelling of the potential global impacts of climate change and land-use change on carbon cycles. The paper demonstrates the need for a reassessment of the current arbitrary shallow soil sampling depths for assessing carbon stocks, a revision of global SOC estimates and elucidation of the composition and fate of deep carbon in response to land use and climate change

Electronic structure of Pd multi-layers on Re(0001): the role of charge transfer

Understanding the origin of the properties of metal-supported metal thin films is important for the rational design of bimetallic catalysts and other applications, but it is generally difficult to separate effects related to strain from those arising from interface interactions. Here we use density functional (DFT) theory to examine the structure and electronic behavior of few-layer palladium films on the rhenium (0001) surface, where there is negligible interfacial strain and therefore other effects can be isolated. Our DFT calculations predict stacking sequences and interlayer separations in excellent agreement with quantitative low-energy electron diffraction experiments. By theoretically simulating the Pd core-level X-ray photoemission spectra (XPS) of the films, we are able to interpret and assign the basic features of both low-resolution and high-resolution XPS measurements. The core levels at the interface shift to more negative energies, rigidly following the shifts in the same direction of the valence d-band center. We demonstrate that the valence band shift at the interface is caused by charge transfer from Re to Pd, which occurs mainly to valence states of hybridized s-p character rather than to the Pd d-band. Since the d-band filling is roughly constant, there is a correlation between the d-band center shift and its bandwidth. The resulting effect of this charge transfer on the valence d-band is thus analogous to the application of a lateral compressive strain on the adlayers. Our analysis suggests that charge transfer should be considered when describing the origin of core and valence band shifts in other metal / metal adlayer systems.

The experience of the hidden curriculum for autistic girls at mainstream primary schools

This article presents the findings of ethnographic case studies of three girls on the autistic spectrum attending mainstream primary schools and illustrates the difficulties they experience and the ways in which these are often unrecognised. The observations of the girls and subsequent individual interviews with their mothers, class teachers, SENCO’s and ultimately themselves, reveal the personal adjustments the girls make in response to the hidden curriculum and the ways in which these go unnoticed, effectively masking their need for support, and contributing to their underachievement in school. The research also identifies a misunderstanding of autism in girls by some teachers that contributes to a lack of support for their needs, despite their diagnosis. Teachers need to understand how autistic girls present, and how they learn, if they are to recognise the need to illuminate the hidden curriculum. The implications of these findings are that without this awareness autistic girls in mainstream settings are also at risk of limited access to the known curriculum and of social isolation.