Blood, sweat, and tears documenting Thai citizenship

Thailand (Siam) has transformed ancient methods of keepint track of subjects, and adopted modern legislative principles using documentary evidence to discriminate between citizens and outsiders. In the process, it has shaped a complex hierarchical structure with differentiated layers of citizenship, where some groups exist beyond any legal space. At the same time, Thailand has evolved from a society where subjects paid tribute to sovereigns, into democratic polity where entitlement is determined through identity documentation.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.

Cocoon of the silkworm Antheraea pernyi as an example of a thermally insulating biological interface.

Biological materials are hierarchically organized complex composites, which embrace multiple practical functionalities. As an example, the wild silkworm cocoon provides multiple protective functions against environmental and physical hazards, promoting the survival chance of moth pupae that resides inside. In the present investigation, the microstructure and thermal property of the Chinese tussah silkworm (Antheraea pernyi) cocoon in both warm and cold environments under windy conditions have been studied by experimental and numerical methods. A new computational fluid dynamics model has been developed according to the original fibrous structure of the Antheraea pernyi cocoon to simulate the unique heat transfer process through the cocoon wall. The structure of the Antheraea pernyi cocoon wall can promote the disorderness of the interior air, which increases the wind resistance by stopping most of the air flowing into the cocoon. The Antheraea pernyi cocoon is wind-proof due to the mineral crystals deposited on the outer layer surface and its hierarchical structure with low porosity and high tortuosity. The research findings have important implications to enhancing the thermal function of biomimetic protective textiles and clothing.

Improved contact load-bearing capacity of ultra-fine grained titanium : Multilayering and grading

The contact load-bearing response and surface damage resistance of multilayered hierarchical structured (MHSed) titanium were determined and compared to monolithic nanostructured titanium. The MHS structure was formed by combining cryorolling with a subsequent Surface Mechanical Attrition Treatment (SMAT) producing a surface structure consisted of an outer amorphous layer containing nanocrystals, an inner nanostructured layer and finally an ultra-fine grained core. The combination of a hard outer layer, a gradual transition layer and a compliant core results in reduced indentation depth, but a deeper and more diffuse sub-surface plastic deformation zone, compared to the monolithic nanostructured Ti. The redistribution of surface loading between the successive layers in the MHS Ti resulted in the suppression of cracking, whereas the monolithic nanograined (NG) Ti exhibited sub-surface cracks at the boundary of the plastic strain field. Finite element models with discrete layers and mechanically graded layersrepresenting the MHS system confirmed the absence of cracking and revealed a 38% decrease in shear stress in the sub-surface plastic strain field, compared to the monolithic NG Ti. Further, the mechanical gradation achieves a more gradual stress distribution which mitigates the interface failure and increases the interfacial toughness, thus providing strong resistance to loading damage. © 2014 Elsevier Ltd.

Novel ultrathin nanoflake assembled porous MnO2/carbon strip microspheres for superior pseudocapacitors

A novel hierarchical MnO2/carbon strip (MnO2/C) microsphere is synthesized via galvanostatic charge-discharge of a MnO@C matrix precursor where the carbon is from a low-cost citric acid. This hierarchical structure is composed of manganese oxides nanoflakes and inlaid carbon strips. The ultrathin nanoflakes assemble to form porous microspheres with a rippled surface superstructure. Due to its improved conductivity and remarkable increased phase contact area, this novel structure exhibits an excellent electrochemical performance with a specific capacitance of 485.6 F g -1 at a current density of 0.5 A g-1 and an area capacitance as high as 4.23 F cm-2 at a mass loading of 8.7 mg cm-2. It also shows an excellent cycling stability with 88.9% capacity retention after 1000 cycles. It is speculated that the present low-cost novel hierarchical porous microspheres can serve as a promising electrode material for pseudocapacitors. © 2014 American Chemical Society.

Performance enhancement of single-walled nanotube-microwave exfoliated graphene oxide composite electrodes using a stacked electrode configuration

We report the development of a stacked electrode supercapacitor cell using stainless steel meshes as the current collectors and optimised single walled nanotubes (SWNT)-microwave exfoliated graphene oxide (mw rGO) composites as the electrode material. The introduction of mw rGO into a SWNT matrix creates an intertwined porous structure that enhances the electroactive surface area and capacitive performance due to the 3-D hierarchical structure that is formed. The composite structure was optimised by varying the weight ratio of the SWNTs and mw rGO. The best performing ratio was the 90% SWNT-10% mw rGO electrode which achieved a specific capacitance of 306 F g-1 (3 electrode measurement calculated at 20 mV s-1). The 90% SWNT-10% mw rGO was then fabricated into a stacked electrode configuration (SEC) which significantly enhanced the electrode performance per volume (1.43 mW h cm-3, & 6.25 W cm-3). Device testing showed excellent switching capability up to 10 A g-1, and very good stability over 10000 cycles at 1.0 A g-1 with 93% capacity retention. © the Partner Organisations 2014.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

Progress in nanostructured photoanodes for dye-sensitized solar cells

Solar cells represent a principal energy technology to convert light into electricity. Commercial solar cells are at present predominately produced by single- or multi-crystalline silicon wafers. The main drawback to silicon-based solar cells, however, is high material and manufacturing costs. Dye-sensitized solar cells (DSSCs) have attracted much attention during recent years because of the low production cost and other advantages. The photoanode (working electrode) plays a key role in determining the performance of DSSCs. In particular, nanostructured photoanodes with a large surface area, high electron transfer efficiency, and low electron recombination facilitate to prepare DSSCs with high energy conversion efficiency. In this review article, we summarize recent progress in the development of novel photoanodes for DSSCs. Effect of semiconductor material (e.g. TiO2, ZnO, SnO2, N2O5, and nano carbon), preparation, morphology and structure (e.g. nanoparticles, nanorods, nanofibers, nanotubes, fiber/particle composites, and hierarchical structure) on photovoltaic performance of DSSCs is described. The possibility of replacing silicon-based solar cells with DSSCs is discussed.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.

Hierarchical dirichlet process for tracking complex topical structure evolution and its application to autism research literature

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting and merging. The power of the proposed framework is demonstrated on the medical literature corpus concerned with the autism spectrum disorder (ASD) - an increasingly important research subject of significant social and healthcare importance. In addition to the collected ASD literature corpus which we made freely available, our contributions also include two free online tools we built as aids to ASD researchers. These can be used for semantically meaningful navigation and searching, as well as knowledge discovery from this large and rapidly growing corpus of literature.

Hierarchial patterns of invertebrate assemblage structure in stony upland streams change with time and flow permanence

1. Studies in several parts of the world have examined variation in univariate descriptors of macroinvertebrate assemblage structure in perennially flowing stony streams across hierarchies of spatial scale using nested analyses of variance. However, few have investigated whether this spatial variation changes with time or whether these results are representative of habitats other than riffles or of other stream types, such as intermittently flowing streams.

2. We describe patterns in taxon richness and abundance from two sets of samples from stony streams in the Otway Range and the Grampians Range, Victoria, Australia, collected using hierarchical designs. Sampling of riffles was repeated in the Otways, to determine whether spatial patterns were consistent among times. In the Grampians, spatial patterns were compared between intermittent and perennially flowing streams (stream type) by sampling pools.

3. In the Otways streams, most variation in the dependent variables occurred between sample units. Patterns of variation among the other scales (streams, segments, riffles, groups of stones) were not consistent between sampling times, suggesting that they may have little ecological significance.

4. In the Grampians streams, variation in macroinvertebrate taxon richness and abundance differed significantly between replicate streams within each stream type but not between stream types or pools. The largest source of variation in taxon richness was stream type. Little variation occurred among sample units.

5. The pattern of most variation occurring among sample units is robust both to differences in the method of sampling and different dependent variables among studies and increasingly appears to be a property of riffles in stony, perennial upland streams. High variation among sample units (residual variation) limits the explanatory power of linear models and therefore, where samples are from a single sampling time, small but significant components of variation are unlikely to represent features of assemblage structure that will be stable over time.

Phase behavior, crystallization, and hierarchical nanostructures in self-organized thermoset blends of epoxy resin and amphiphilic poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) triblock copolymers

Nanostructured thermoset blends of bisphenol A-type epoxy resin (ER) and amphiphilic poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) (PEO-PPO-PEO) triblock copolymers were successfully prepared. Two samples of PEO-PPO-PEO triblock copolymer with different ethylene oxide (EO) contents, denoted as EO30 with 30 wt % EO content and EO80 with 80 wt % EO content, were used to form the self-organized thermoset blends of varying compositions using 4,4'-methylenedianiline (MDA) as curing agent. The phase behavior, crystallization, and morphology were investigated by differential scanning calorimetry (DSC), transmission electron microscopy (TEM), atomic force microscopy (AFM), and small-angle X-ray scattering (SAXS). It was found that macroscopic phase separation took place in the MDA-cured ER/EO30 blends containing 60-80 wt % EO30 triblock copolymer. The MDA-cured ER/EO30 blends with EO30 content up to 50 wt % do not show macroscopic phase separation but exhibit nanostructures on the order of 10-30 nm as revealed by both the TEM and SAXS studies. The AFM study further shows that the ER/EO30 blend at some composition displays structural inhomogeneity at two different nanoscales and is hierarchically nanostructured. The spherical PPO domains with an average size of about 10 nm are uniformly dispersed in the 80/20 ER/EO30 blend; meanwhile, a structural inhomogeneity on the order of 50-200 nm is observed. The ER/EO80 blends are not macroscopically phase-separated over the entire composition range because of the much higher PEO content of the EO80 triblock copolymer. However, the ER/EO80 blends show composition-dependent nanostructures on the order of 10-100 nm. The 80/20 ER/EO80 blend displays hierarchical structures at two different nanoscales, i.e., a bicontinuous microphase structure on the order of about 100 nm and spherical domains of 10-20 nm in diameter uniformly dispersed in both the continuous microphases. The blends with 60 wt % and higher EO80 content are completely volume-filled with spherulites. Bundles of PEO lamellae with spacing of 20-30 nm interwoven with a microphase structure on the order of about 100 nm are revealed by AFM study for the 30/70 ER/EO80 blend.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.

Hierarchical hidden Markov models with general state hierarchy

The hierarchical hidden Markov model (HHMM) is an extension of the hidden Markov model to include a hierarchy of the hidden states. This form of hierarchical modeling has been found useful in applications such as handwritten character recognition, behavior recognition, video indexing, and text retrieval. Nevertheless, the state hierarchy in the original HHMM is restricted to a tree structure. This prohibits two different states from having the same child, and thus does not allow for sharing of common substructures in the model. In this paper, we present a general HHMM in which the state hierarchy can be a lattice allowing arbitrary sharing of substructures. Furthermore, we provide a method for numerical scaling to avoid underflow, an important issue in dealing with long observation sequences. We demonstrate the working of our method in a simulated environment where a hierarchical behavioral model is automatically learned and later used for recognition.