Second-order inelastic analysis of composite framed structures based on the refined plastic hinge method

Composite steel-concrete structures experience non-linear effects which arise from both instability-related geometric non-linearity and from material non-linearity in all of their component members. Because of this, conventional design procedures cannot capture the true behaviour of a composite frame throughout its full loading range, and so a procedure to account for those non-linearities is much needed. This paper therefore presents a numerical procedure capable of addressing geometric and material non-linearities at the strength limit state based on the refined plastic hinge method. Different material non-linearity for different composite structural components such as T-beams, concrete-filled tubular (CFT) and steel-encased reinforced concrete (SRC) sections can be treated using a routine numerical procedure for their section properties in this plastic hinge approach. Simple and conservative initial and full yield surfaces for general composite sections are proposed in this paper. The refined plastic hinge approach models springs at the ends of the element which are activated when the surface defining the interaction of bending and axial force at first yield is reached; a transition from the first yield interaction surface to the fully plastic interaction surface is postulated based on a proposed refined spring stiffness, which formulates the load-displacement relation for material non-linearity under the interaction of bending and axial actions. This produces a benign method for a beam-column composite element under general loading cases. Another main feature of this paper is that, for members containing a point of contraflexure, its location is determined with a simple application of the method herein and a node is then located at this position to reproduce the real flexural behaviour and associated material non-linearity of the member. Recourse is made to an updated Lagrangian formulation to consider geometric non-linear behaviour and to develop a non-linear solution strategy. The formulation with the refined plastic hinge approach is efficacious and robust, and so a full frame analysis incorporating geometric and material non-linearity is tractable. By way of contrast, the plastic zone approach possesses the drawback of strain-based procedures which rely on determining plastic zones within a cross-section and which require lengthwise integration. Following development of the theory, its application is illustrated with a number of varied examples.

Tuning the surface structure of nitrogen-doped TiO2Nanofibres : an effective method to rnhance photocatalytic activities of visible-light-driven green synthesis and degradation

Nitrogen-doped TiO2 nanofibres of anatase and TiO2(B) phases were synthesised by a reaction between titanate nanofibres of a layered structure and gaseous NH3 at 400–700 °C, following a different mechanism than that for the direct nitrogen doping from TiO2. The surface of the N-doped TiO2 nanofibres can be tuned by facial calcination in air to remove the surface-bonded N species, whereas the core remains N doped. N-Doped TiO2 nanofibres, only after calcination in air, became effective photocatalysts for the decomposition of sulforhodamine B under visible-light irradiation. The surface-oxidised surface layer was proven to be very effective for organic molecule adsorption, and the activation of oxygen molecules, whereas the remaining N-doped interior of the fibres strongly absorbed visible light, resulting in the generation of electrons and holes. The N-doped nanofibres were also used as supports of gold nanoparticle (Au NP) photocatalysts for visible-light-driven hydroamination of phenylacetylene with aniline. Phenylacetylene was activated on the N-doped surface of the nanofibres and aniline on the Au NPs. The Au NPs adsorbed on N-doped TiO2(B) nanofibres exhibited much better conversion (80 % of phenylacetylene) than when adsorbed on undoped fibres (46 %) at 40 °C and 95 % of the product is the desired imine. The surface N species can prevent the adsorption of O2 that is unfavourable for the hydroamination reaction, and thus, improve the photocatalytic activity. Removal of the surface N species resulted in a sharp decrease of the photocatalytic activity. These photocatalysts are feasible for practical applications, because they can be easily dispersed into solution and separated from a liquid by filtration, sedimentation or centrifugation due to their fibril morphology.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Gas-Phase Transformation of Phosphatidylcholine Cations to Structurally Informative Anions via Ion/Ion Chemistry

Gas-phase transformation of synthetic phosphatidylcholine (PC) monocations to structurally informative anions is demonstrated via ion/ion reactions with doubly deprotonated 1,4-phenylenedipropionic acid (PDPA). Two synthetic PC isomers, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (PC16:0/18:1) and 1-oleoyl-2-palmitoyl-sn-glycero-3-phosphocholine (PC18:1/16:0), were subjected to this ion/ion chemistry. The product of the ion/ion reaction is a negatively charged complex, \[PC + PDPA - H](-). Collisional activation of the long-lived complex causes transfer of a proton and methyl cation to PDPA, generating \[PC - CH3](-). Subsequent collisional activation of the demethylated PC anions produces abundant fatty acid carboxylate anions and low-abundance acyl neutral losses as free acids and ketenes. Product ion spectra of \[PC - CH3](-) suggest favorable cleavage at the sn-2 position over the sn-1 due to distinct differences in the relative abundances. In contrast, collisional activation of PC cations is absent of abundant fatty acid chain-related product ions and typically indicates only the lipid class via formation of the phosphocholine cation. A solution phase method to produce the gas-phase adducted PC anion is also demonstrated. Product ion spectra derived from the solution phase method are similar to the results generated via ion/ion chemistry. This work demonstrates a gas-phase means to increase structural characterization of phosphatidylcholines via ion/ion chemistry. Grant Number ARC/CE0561607, ARC/DP120102922

A finite volume method for solving the two-sided time-space fractional advection-dispersion equation

We present a finite volume method to solve the time-space two-sided fractional advection-dispersion equation on a one-dimensional domain. The spatial discretisation employs fractionally-shifted Grünwald formulas to discretise the Riemann-Liouville fractional derivatives at control volume faces in terms of function values at the nodes. We demonstrate how the finite volume formulation provides a natural, convenient and accurate means of discretising this equation in conservative form, compared to using a conventional finite difference approach. Results of numerical experiments are presented to demonstrate the effectiveness of the approach.

Police officer in-vehicle discomfort : appointments carriage method and vehicle seat features

Musculoskeletal pain is commonly reported by police officers. A potential cause of officer discomfort is a mismatch between vehicle seats and the method used for carrying appointments. Twenty-five police officers rated their discomfort while seated in: (1) a standard police vehicle seat, and (2) a vehicle seat custom-designed for police use. Discomfort was recorded in both seats while wearing police appointments on: (1) a traditional appointments belt, and (2) a load-bearing vest / belt combination (LBV). Sitting in the standard vehicle seat and carrying appointments on a traditional appointments belt were both associated with significantly elevated discomfort. Four vehicle seat features were most implicated as contributing to discomfort: back rest bolster prominence; lumbar region support; seat cushion width; and seat cushion bolster depth. Authorising the carriage of appointments using a LBV is a lower cost solution with potential to reduce officer discomfort. Furthermore, the introduction of custom-designed vehicle seats should be considered.

Organo-LDH synthesized via tricalcium aluminate hydration in the present of Na-dodecylbenzenesulfate aqueous solution and subsequent investigated by near-infrared and mid-infrared

Na-dodecylbenzenesulfate (SDBS), a natural anionic surfactant, has been successfully intercalated into a Ca based LDH host structure during tricalcium aluminate hydration in the presence of SDBS aqueous solution (CaAl-SDBS-LDH). The resulting product was characterized by powder X-ray diffraction (XRD), mid-infrared (MIR) spectroscopy combined with near-infrared (NIR) spectroscopy technique, thermal analysis (TG–DTA) and scan electron microscopy (SEM). The XRD results revealed that the interlayer distance of resultant product was expanded to 30.46 Å. MIR combined with NIR spectra offered an effective method to illustrate this intercalation. The NIR spectra (6000–5500 cm−1) displayed prominent bands to expound SDBS intercalated into hydration product of C3A. And the bands around 8300 cm−1 were assigned to the second overtone of the first fundamental of CH stretching vibrations of SDBS. In addition, thermal analysis showed that the dehydration and dehydroxylation took place at ca. 220 °C and 348 °C, respectively. The SEM results appeared approximately hexagonal platy crystallites morphology for CaAl-SDBS-LDH, with particle size smaller and thinner.

A new approach to prepare ZVI and its application in removal of Cr(VI) from aqueous solution

Zero valent iron (ZVI) was prepared by reducing natural goethite (NG-ZVI) and synthetic goethite (SG-ZVI) in hydrogen at 550 °C. XRD, TEM, FESEM/EDS and specific surface area (SSA) and pore analyser were used to characterize goethites and reduced goethites. Both NG-ZVI and SG-ZVI with a size of nanoscale to several hundreds of nanometers were obtained by reducing goethites at 550 °C. The reductive capacity of the ZVIs was assessed by removal of Cr(VI) at ambient temperature in comparison with that of commercial iron powder (CIP). The effect of contact time, initial concentration and reaction temperature on Cr(VI) removal was investigated. Furthermore, the uptake mechanism was discussed according to isotherms, thermodynamic analysis and the results of XPS. The results showed that SG-ZVI had the best reductive capacity to Cr(VI) and reduced Cr(VI) to Cr(III). The results suggest that hydrogen reduction is a good approach to prepare ZVI and this type of ZVI is potentially useful in remediating heavy metals as a material of permeable reaction barrier.

Supramolecular selection in molecular and framework co-crystals

An ongoing challenge in chemistry and crystal engineering is the synthesis of functional materials with predictable structures and customisable properties. This may be achieved by crystallising mixtures of different compounds. Co-crystals formed through this method have predictable structures and their properties may be tuned by varying the ratio of the compounds in the crystallising solution. This thesis examines single crystals formed by the co-crystallisation of metal complexes that have similar structures but different physical or chemical properties. A variety of new compounds with interesting properties were prepared, characterised and their significance in the context of crystal engineering was explored.

Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

Z-source-inverter-based flexible distributed generation system solution for grid power quality improvement

Distributed generation (DG) systems are usually connected to the grid using power electronic converters. Power delivered from such DG sources depends on factors like energy availability and load demand. The converters used in power conversion do not operate with their full capacity all the time. The unused or remaining capacity of the converters could be used to provide some ancillary functions like harmonic and unbalance mitigation of the power distribution system. As some of these DG sources have wide operating ranges, they need special power converters for grid interfacing. Being a single-stage buck-boost inverter, recently proposed Z-source inverter (ZSI) is a good candidate for future DG systems. This paper presents a controller design for a ZSI-based DG system to improve power quality of distribution systems. The proposed control method is tested with simulation results obtained using Matlab/Simulink/PLECS and subsequently it is experimentally validated using a laboratory prototype.

A finite volume scheme with preconditioned Lanczos method for two-dimensional space-fractional reaction–diffusion equations

Fractional differential equations have been increasingly used as a powerful tool to model the non-locality and spatial heterogeneity inherent in many real-world problems. However, a constant challenge faced by researchers in this area is the high computational expense of obtaining numerical solutions of these fractional models, owing to the non-local nature of fractional derivatives. In this paper, we introduce a finite volume scheme with preconditioned Lanczos method as an attractive and high-efficiency approach for solving two-dimensional space-fractional reaction–diffusion equations. The computational heart of this approach is the efficient computation of a matrix-function-vector product f(A)bf(A)b, where A A is the matrix representation of the Laplacian obtained from the finite volume method and is non-symmetric. A key aspect of our proposed approach is that the popular Lanczos method for symmetric matrices is applied to this non-symmetric problem, after a suitable transformation. Furthermore, the convergence of the Lanczos method is greatly improved by incorporating a preconditioner. Our approach is show-cased by solving the fractional Fisher equation including a validation of the solution and an analysis of the behaviour of the model.

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

A Combined Renormalization Group-Multiple Scale Method for Singularly Perturbed Problems

This paper introduces a straightforward method to asymptotically solve a variety of initial and boundary value problems for singularly perturbed ordinary differential equations whose solution structure can be anticipated. The approach is simpler than conventional methods, including those based on asymptotic matching or on eliminating secular terms. © 2010 by the Massachusetts Institute of Technology.