Feasibility of agent-based modelling of articular cartilage including a conceptual representation of its structure

Articular cartilage is a complex structure with an architecture in which fluid-swollen proteoglycans constrained within a 3D network of collagen fibrils. Because of the complexity of the cartilage structure, the relationship between its mechanical behaviours at the macroscale level and its components at the micro-scale level are not completely understood. The research objective in this thesis is to create a new model of articular cartilage that can be used to simulate and obtain insight into the micro-macro-interaction and mechanisms underlying its mechanical responses during physiological function. The new model of articular cartilage has two characteristics, namely: i) not use fibre-reinforced composite material idealization ii) Provide a framework for that it does probing the micro mechanism of the fluid-solid interaction underlying the deformation of articular cartilage using simple rules of repartition instead of constitutive / physical laws and intuitive curve-fitting. Even though there are various microstructural and mechanical behaviours that can be studied, the scope of this thesis is limited to osmotic pressure formation and distribution and their influence on cartilage fluid diffusion and percolation, which in turn governs the deformation of the compression-loaded tissue. The study can be divided into two stages. In the first stage, the distributions and concentrations of proteoglycans, collagen and water were investigated using histological protocols. Based on this, the structure of cartilage was conceptualised as microscopic osmotic units that consist of these constituents that were distributed according to histological results. These units were repeated three-dimensionally to form the structural model of articular cartilage. In the second stage, cellular automata were incorporated into the resulting matrix (lattice) to simulate the osmotic pressure of the fluid and the movement of water within and out of the matrix; following the osmotic pressure gradient in accordance with the chosen rule of repartition of the pressure. The outcome of this study is the new model of articular cartilage that can be used to simulate and study the micromechanical behaviours of cartilage under different conditions of health and loading. These behaviours are illuminated at the microscale level using the socalled neighbourhood rules developed in the thesis in accordance with the typical requirements of cellular automata modelling. Using these rules and relevant Boundary Conditions to simulate pressure distribution and related fluid motion produced significant results that provided the following insight into the relationships between osmotic pressure gradient and associated fluid micromovement, and the deformation of the matrix. For example, it could be concluded that: 1. It is possible to model articular cartilage with the agent-based model of cellular automata and the Margolus neighbourhood rule. 2. The concept of 3D inter connected osmotic units is a viable structural model for the extracellular matrix of articular cartilage. 3. Different rules of osmotic pressure advection lead to different patterns of deformation in the cartilage matrix, enabling an insight into how this micromechanism influences macromechanical deformation. 4. When features such as transition coefficient were changed, permeability (representing change) is altered due to the change in concentrations of collagen, proteoglycans (i.e. degenerative conditions), the deformation process is impacted. 5. The boundary conditions also influence the relationship between osmotic pressure gradient and fluid movement at the micro-scale level. The outcomes are important to cartilage research since we can use these to study the microscale damage in the cartilage matrix. From this, we are able to monitor related diseases and their progression leading to potential insight into drug-cartilage interaction for treatment. This innovative model is an incremental progress on attempts at creating further computational modelling approaches to cartilage research and other fluid-saturated tissues and material systems.

The borate mineral jeremejevite Al6(BO3)5(F,OH)3 – A vibrational spectroscopic study

Jeremejevite is a borate mineral of aluminium and is of variable colour, making the mineral and important inexpensive jewel. The mineral contains variable amounts of F and OH, depending on origin. A comparison of the vibrational spectroscopic data is made with the published data of borate minerals. Raman spectra were averaged over a range of crystal orientations. Two intense Raman bands observed at 961 and 1067 cm−1 are assigned to the symmetric stretching and antisymmetric stretching modes of trigonal boron. Infrared spectrum, bands observed at 1229, 1304, 1350, 1388 and 1448 cm−1 are attributed to BOH in-plane bending modes. Intense Raman band found at 372 cm−1 with other bands of significant intensity at 327 and 417 cm−1 is assigned to trigonal borate bending modes. A quite intense Raman band is found at 3673 cm−1 with other sharp Raman bands found at 3521, 3625 and 3703 cm−1 are assigned to the stretching modes of OH. Raman and infrared spectroscopy has been used to assess the molecular structure of the mineral jeremejevite. Such research is important in the study of borate based nanomaterials.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.

Increased charge transfer of Poly (ethylene oxide) based electrolyte by addition of small molecule and its application in dye-sensitized solar cells

A Poly (ethylene oxide) based polymer electrolyte impregnated with 2-Mercapto benzimidazole was comprehensively characterized by XRD, UV–visible spectroscopy, FTIR as well as electrochemical impedance spectroscopy. It was found that the crystallization of PEO was dramatically reduced and the ionic conductivity of the electrolyte was increased 4.5 fold by addition of 2-Mercapto benzimidazole. UV–visible and FTIR spectroscopes indicated the formation of charge transfer complex between 2-Mercapto benzimidazole and iodine of the electrolyte. Dye-sensitized solar cells with the polymer electrolytes were assembled. It was found that both the photocurrent density and photovoltage were enhanced with respect to the DSC without 2-Mercapto benzimidazole, leading to a 60% increase of the performance of the cell.

Ion transport in small-molecule electrolytes based on LiI/3-hydroxypropionitrile with high salt contents

Abnormal “polymer-in-salt” conduction behavior is observed in a solid electrolyte composed of lithium iodide (LiI) and 3-hydroxypropionitrile (HPN). Based on comprehensive investigations by X-ray diffraction (XRD) and Raman and infrared spectroscopy, this abnormal conduction behavior is attributed to the formation of new ionic associates [Lim +In−]· · ·N C (m> n) and the reinforced hydrogen bonding of I· · ·HO in the electrolyte at high LiI concentrations.

Cohort normalization based sparse representation for undersampled face recognition

Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition.

Video-based training combined with flotation tank recovery does not improve three-point shooting in basketball

Video-based training combined with flotation tank recovery may provide an additional stimulus for improving shooting in basketball. A pre-post controlled trial was conducted to assess the effectiveness of a 3 wk intervention combining video-based training and flotation tank recovery on three-point shooting performance in elite female basketball players. Players were assigned to an experimental (n=10) and control group (n=9). A 3 wk intervention consisted of 2 x 30 min float sessions a week which included 10 min of video-based training footage, followed by a 3 wk retention phase. A total of 100 three-point shots were taken from 5 designated positions on the court at each week to assess three-point shooting performance. There was no clear difference in the mean change in the number of successful three-point shots between the groups (-3%; ±18%, mean; ±90% confidence limits). Video-based training combined with flotation recovery had little effect on three-point shooting performance.

Vibrational spectroscopic characterization of the phosphate mineral hureaulite – (Mn, Fe)5(PO4)2(HPO4)2·4(H2O)

This research was done on hureaulite samples from the Cigana claim, a lithium bearing pegmatite with triphylite and spodumene. The mine is located in Conselheiro Pena, east of Minas Gerais. Chemical analysis was carried out by Electron Microprobe analysis and indicated a manganese rich phase with partial substitution of iron. The calculated chemical formula of the studied sample is: (Mn3.23, Fe1.04, Ca0.19, Mg0.13)(PO4)2.7(HPO4)2.6(OH)4.78. The Raman spectrum of hureaulite is dominated by an intense sharp band at 959 cm−1 assigned to PO stretching vibrations of HPO42− units. The Raman band at 989 cm−1 is assigned to the PO43− stretching vibration. Raman bands at 1007, 1024, 1047, and 1083 cm−1 are attributed to both the HOP and PO antisymmetric stretching vibrations of HPO42− and PO43− units. A set of Raman bands at 531, 543, 564 and 582 cm−1 are assigned to the ν4 bending modes of the HPO42− and PO43− units. Raman bands observed at 414, and 455 cm−1 are attributed to the ν2 HPO42− and PO43− units. The intense A series of Raman and infrared bands in the OH stretching region are assigned to water stretching vibrations. Based upon the position of these bands hydrogen bond distances are calculated. Hydrogen bond distances are short indicating very strong hydrogen bonding in the hureaulite structure. A combination of Raman and infrared spectroscopy enabled aspects of the molecular structure of the mineral hureaulite to be understood.

Video-based road commentary training improves hazard perception of young drivers in a dual task

This study used a video-based hazard perception dual task to compare the hazard perception skills of young drivers with middle aged, more experienced drivers and to determine if these skills can be improved with video-based road commentary training. The primary task required the participants to detect and verbally identify immediate hazard on video-based traffic scenarios while concurrently performing a secondary tracking task, simulating the steering of real driving. The results showed that the young drivers perceived fewer immediate hazards (mean = 75.2%, n = 24, 19 females) than the more experienced drivers (mean = 87.5%, n = 8, all females), and had longer hazard perception times, but performed better in the secondary tracking task. After the road commentary training, the mean percentage of hazards detected and identified by the young drivers improved to the level of the experienced drivers and was significantly higher than that of an age and driving experience matched control group. The results will be discussed in the context of psychological theories of hazard perception and in relation to road commentary as an evidence-based training intervention that seems to improve many aspects of unsafe driving behaviour in young drivers.

Hydrogen gas sensors based on thermally evaporated nanostructured MoO3 Schottky diode : a comparative study

In this paper, a comparative study of Pt/nanostructured MoO3/SiC Schottky diode based hydrogen gas sensors is presented. MoO3 nanostructured films with three different morphologies (nanoplatelets, nanoplateletsnanowires and nano-flowers) were deposited on SiC by thermal evaporation. We compare the current-voltage characteristics and the dynamic response of these sensors as they are exposed to hydrogen gas at temperatures up to 250°C. Results indicate that the sensor based on MoO3 nanoflowers exhibited the highest sensitivity (in terms of a 5.79V voltage shift) towards 1% hydrogen; while the sensor based on MoO3 nanoplatelets showed the quickest response (t90%- 40s).

Pt/MoO3 nano-flower/SiC Schottky diode based hydrogen gas sensor

In this paper, we report the development of a novel Pt/MoO3 nano-flower/SiC Schottky diode based device for hydrogen gas sensing applications. The MoO3 nanostructured thin films were deposited on SiC substrates via thermal evaporation. Morphological characterization of the nanostructured MoO3 by scanning electron microscopy revealed randomly orientated thin nanoplatelets in a densely packed formation of nano-flowers with dimensions ranging from 250 nm to 1 μm. Current-voltage characteristics of the sensor were measured at temperatures from 25°C to 250°C. The sensor showed greater sensitivity in a reverse bias condition than in forward bias. Dynamic response of the sensor was investigated towards different concentrations of hydrogen gas in a synthetic air mixture at 250°C and a large voltage shift of 5.7 V was recorded upon exposure to 1% hydrogen.

Pt/anodized TiO2/SiC-based MOS device for hydrocarbon sensing

Pt/anodized TiO2/SiC based metal-oxide-semiconductor (MOS) devices were fabricated and characterized for their sensitivity towards propene (C3H6). Titanium (Ti) thin films were deposited onto the SiC substrates using a filtered cathodic vacuum arc (FCVA) method. Fluoride ions containing neutral electrolyte (0.5 wt% NH4F in ethylene glycol)were used to anodize the Ti films. The anodized films were subsequently annealed at 600 °C for 4 hrs in an oxygen rich environment to obtain TiO2. The current-voltage(I-V) characteristics of the Pt/TiO2/SiC devices were measured in different concentrations of propene. Exposure to the analyte gas caused a change in the Schottky barrier height and hence a lateral shift in the I-V characteristics. The effective change in the barrier height for 1% propene was calculated as 32.8 meV at 620°C. The dynamic response of the sensors was also investigated and a voltage shift of 157 mV was measured at 620°C during exposure to 1% propene.

Graphene-like nano-sheets based LiTaO3 surface acoustic wave NO2 gas sensor

Thin films consisting of graphene-like nano-sheets were deposited onto LiTaO3 surface acoustic wave transducers. A thickness of less than 10 nm and the existence of C-C bond were observed during the characterization of graphene-like nano-sheets. Frequency shift of 18.7 kHz and 14.9 kHz towards 8.5 ppm NO2 at two different operating temperature, 40°C and 25°C, respectively, was observed.

Towards a capabilities database to inform inclusive design : Experimental investigation of effective survey-based predictors of human-product interaction

A key issue in the field of inclusive design is the ability to provide designers with an understanding of people's range of capabilities. Since it is not feasible to assess product interactions with a large sample, this paper assesses a range of proxy measures of design-relevant capabilities. It describes a study that was conducted to identify which measures provide the best prediction of people's abilities to use a range of products. A detailed investigation with 100 respondents aged 50-80 years was undertaken to examine how they manage typical household products. Predictor variables included self-report and performance measures across a variety of capabilities (vision, hearing, dexterity and cognitive function), component activities used in product interactions (e.g. using a remote control, touch screen) and psychological characteristics (e.g. self-efficacy, confidence with using electronic devices). Results showed, as expected, a higher prevalence of visual, hearing, dexterity, cognitive and product interaction difficulties in the 65-80 age group. Regression analyses showed that, in addition to age, performance measures of vision (acuity, contrast sensitivity) and hearing (hearing threshold) and self-report and performance measures of component activities are strong predictors of successful product interactions. These findings will guide the choice of measures to be used in a subsequent national survey of design-relevant capabilities, which will lead to the creation of a capability database. This will be converted into a tool for designers to understand the implications of their design decisions, so that they can design products in a more inclusive way.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.