Finite element analysis of an embankment on a soft estuarine deposit using an elastic–viscoplastic soil model

A new elastic–viscoplastic (EVP) soil model has been used to simulate the measured deformation response of a soft estuarine soil loaded by a stage-constructed embankment. The simulation incorporates prefabricated vertical drains installed in the foundation soils and reinforcement installed at the base of the embankment. The numerical simulations closely matched the temporal changes in surface settlement beneath the centerline and shoulder of the embankment. More importantly, the elastic–viscoplastic model simulated the pattern and magnitudes of the lateral deformations beneath the toe of the embankment — a notoriously difficult aspect of modelling the deformation response of soft soils. Simulation of the excess pore-water pressure proved more difficult because of the heterogeneous nature of the estuarine deposit. Excess pore-water pressures were, however, mapped reasonably well at three of the six monitoring locations. The simulations were achieved using a small set of material constants that can easily be obtained from standard laboratory tests. This study validates the use of the EVP model for problems involving soft soil deposits beneath loading from a geotechnical structure.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

Towards Surface Plasmon Resonance biosensing combined with bioaffinity-assisted nano HILIC Liquid Chromatography Time-of-flight Mass Spectrometry identification of Paralytic Shellfish Poisons

The potential for coupling technologies to deliver new, improved forms of bioanalysis is still in its infancy. We review a number of examples in which coupling has been successful, with special emphasis on combining surface-plasmon-resonance biosensors with mass spectrometry. We give an overview of current progress towards combining biosensor-based bioanalysis with chemical analysis for confirmation of paralytic shellfish poisons that are marine toxins. This comprehensive approach could be an alternative to the official methods currently used (e.g., animal testing and high-performance liquid chromatography with fluorescence detection) and could serve as a model for many more such applications. (C) 2009 Elsevier Ltd. All rights reserved.

The effect of simvastatin on bone formation and ceramic resorption in a peri-implant defect model

Experimental use of statins as stimulators of bone formation suggests they may have widespread applicability in the field of orthopaedics. With their combined effects on osteoblasts and osteoclasts, statins have the potential to enhance resorption of synthetic materials and improve bone ingrowth. In this study, the effect of oral and local administration of simvastatin to a 0 tricalcium phosphate (beta TCP)-filled defect around an implant was compared with recombinant human bone morphogenetic protein 2 (rhBMP2). On hundred and sixty-two Sprague-Dawley rats were assigned to treatment groups: local application of 0.1, 0.9 or 1.7 mg of simvastatin, oral simvastatin at 5, 10 or 50 mg kg(-1) day(-1) for 20 days, local delivery of I or 10 mu g of rhBMP2, or control. At 6 weeks rhBMP2 increased serum tartrate-resistant acid phosphatase 5b levels and reduced PTCP area fraction, particle size and number compared with control, suggesting increased osteoclast activity. There was reduced stiffness and increased mechanical strength with this treatment. Local simvastatin resulted in a decreased mineral apposition rate at 6 weeks and increased fibrous area fraction, PTCP area fraction, particle size and number at 26 weeks. Oral simvastatin had no effect compared with control. Local application of rhBMP2 increased resorption and improved mechanical strength whereas simvastatin was detrimental to healing. Oral simvastatin was ineffective at promoting either ceramic resorption or bone formation. The effect of statins on the repair of bone defects with graft substitute materials is influenced by its bioavailability. Thus, further studies on the optimal delivery system are needed. (C) 2007 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.

Evaluation of effective groundwater recharge of freshwater lens in small islands by the combined modeling of geoelectrical data and water heads

In small islands, a freshwater lens can develop due to the recharge induced by rain. Magnitude and spatial distribution of this recharge control the elevation of freshwater and the depth of its interface with salt water. Therefore, the study of lens morphology gives useful information on both the recharge and water uptake due to evapotranspiration by vegetation. Electrical resistivity tomography was applied on a small coral reef island, giving relevant information on the lens structure. Variable density groundwater flow models were then applied to simulate freshwater behavior. Cross validation of the geoelectrical model and the groundwater model showed that recharge exceeds water uptake in dunes with little vegetation, allowing the lens to develop. Conversely, in the low-lying and densely vegetated sectors, where water uptake exceeds recharge, the lens cannot develop and seawater intrusion occurs. This combined modeling method constitutes an original approach to evaluate effective groundwater recharge in such environments.
[Comte, J.-C., O. Banton, J.-L. Join, and G. Cabioch (2010), Evaluation of effective groundwater recharge of freshwater lens in small islands by the combined modeling of geoelectrical data and water heads, Water Resour. Res., 46, W06601, doi:10.1029/2009WR008058.]

High incidence of proviral integrations in the Hoxa locus in a new model of E2a-PBX1-induced B-cell leukemia

Relevant mouse models of E2a-PBX1-induced pre-B cell leukemia are still elusive. We now report the generation of a pre-B leukemia model using E2a-PBX1 transgenic mice, which lack mature and precursor T-cells as a result of engineered loss of CD3epsilon expression (CD3epsilon(-/-)). Using insertional mutagenesis and inverse-PCR, we show that B-cell leukemia development in the E2a-PBX1 x CD3epsilon(-/-) compound transgenic animals is significantly accelerated when compared to control littermates, and document several known and novel integrations in these tumors. Of all common integration sites, a small region of 19 kb in the Hoxa gene locus, mostly between Hoxa6 and Hoxa10, represented 18% of all integrations in the E2a-PBX1 B-cell leukemia and was targeted in 86% of these leukemias compared to 17% in control tumors. Q-PCR assessment of expression levels for most Hoxa cluster genes in these tumors revealed an unprecedented impact of the proviral integrations on Hoxa gene expression, with tumors having one to seven different Hoxa genes overexpressed at levels up to 6600-fold above control values. Together our studies set the stage for modeling E2a-PBX1-induced B-cell leukemia and shed new light on the complexity pertaining to Hox gene regulation. In addition, our results show that the Hoxa gene cluster is preferentially targeted in E2a-PBX1-induced tumors, thus suggesting functional collaboration between these oncogenes in pre-B-cell tumors.

Prognostic Significance of TRAIL Signaling Molecules in Stage II and III Colorectal Cancer

Purpose: We previously found that cellular FLICE-inhibitory protein (c-FLIP), caspase 8, and tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) receptor 2 (DR5) are major regulators of cell viability and chemotherapy-induced apoptosis in colorectal cancer. In this study, we determined the prognostic significance of c-FLIP, caspase 8, TRAIL and DR5 expression in tissues from patients with stage II and III colorectal cancer.

Experimental Design: Tissue microarrays were constructed from matched normal and tumor tissue derived from patients (n = 253) enrolled in a phase III trial of adjuvant 5-fluorouracil–based chemotherapy versus postoperative observation alone. TRAIL, DR5, caspase 8, and c-FLIP expression levels were determined by immunohistochemistry.

Results: Colorectal tumors displayed significantly higher expression levels of c-FLIP (P < 0.001), caspase 8 (P = 0.01), and DR5 (P < 0.001), but lower levels of TRAIL (P < 0.001) compared with matched normal tissue. In univariate analysis, higher TRAIL expression in the tumor was associated with worse overall survival (P = 0.026), with a trend to decreased relapse-free survival (RFS; P = 0.06), and higher tumor c-FLIP expression was associated with a significantly decreased RFS (P = 0.015). Using multivariate predictive modeling for RFS in all patients and including all biomarkers, age, treatment, and stage, we found that the model was significant when the mean tumor c-FLIP expression score and disease stage were included (P < 0.001). As regards overall survival, the overall model was predictive when both TRAIL expression and disease stage were included (P < 0.001).

Conclusions: High c-FLIP and TRAIL expression may be independent adverse prognostic markers in stage II and III colorectal cancer and might identify patients most at risk of relapse.

Microcomputed tomography imaging in a rat model of delayed union/non-union fracture

We aimed to develop a clinically relevant delayed union/non-union fracture model to evaluate a cell therapy intervention repair strategy. Histology, three-dimensional (3D) micro-computed tomography (micro-CT) imaging and mechanical testing were utilized to develop an analytical protocol for qualitative and quantitative assessment of fracture repair. An open femoral diaphyseal osteotomy, combined with periosteal diathermy and endosteal excision, was held in compression by a four pin unilateral external fixator. Three delayed union/non-union fracture groups established at 6 weeks-(a) a control group, (b) a cell therapy group, and (c) a group receiving phosphate-buffered saline (PBS) injection alone-were examined subsequently at 8 and 14 weeks. The histological response was combined fibrous and cartilaginous non-unions in groups A and B with fibrous non-unions in group C. Mineralized callus volume/total volume percentage showed no statistically significant differences between groups. Endosteal calcified tissue volume/endosteal tissue volume, at the center of the fracture site, displayed statistically significant differences between 8 and 14 weeks for cell and PBS intervention groups but not for the control group. The percentage load to failure was significantly lower in the control and cell treatment groups than in the PBS alone group. High-resolution micro-CT imaging provides a powerful tool to augment characterization of repair in delayed union/non-union fractures together with outcomes such as histology and mechanical strength measurement. Accurate, nondestructive, 3D identification of mineralization progression in repairing fractures is enabled in the presence or absence of intervention strategies. (c) 2007 Orthopaedic Research Society.

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

The four I-model for scaffolding the professional development of experienced teachers in the use of virtual learning environments for classroom teaching.

Models of professional development for teachers have been criticized for not being embedded in the context in which teachers are familiar, namely their own classrooms. This paper discusses an adapted-Continuous Practice Improvement model, which qualitative findings indicate was effective in facilitating the transfer of creative and innovative teaching approaches from the expert or Resident Teacher’s school to the novice or Visiting Teachers’ classrooms over the duration of the project. The cultural shift needed to embed and extend the use of online teaching across the school was achieved through the positive support and commitment of the principals in the Visiting Teachers’ schools, combined with the success of the professional development activities offered by the Visiting Teachers to their school-based colleagues.

Application-based workload model for wireless sensor node computing platforms

Wireless sensor node platforms are very diversified and very constrained, particularly in power consumption. When choosing or sizing a platform for a given application, it is necessary to be able to evaluate in an early design stage the impact of those choices. Applied to the computing platform implemented on the sensor node, it requires a good understanding of the workload it must perform. Nevertheless, this workload is highly application-dependent. It depends on the data sampling frequency together with application-specific data processing and management. It is thus necessary to have a model that can represent the workload of applications with various needs and characteristics. In this paper, we propose a workload model for wireless sensor node computing platforms. This model is based on a synthetic application that models the different computational tasks that the computing platform will perform to process sensor data. It allows to model the workload of various different applications by tuning data sampling rate and processing. A case study is performed by modeling different applications and by showing how it can be used for workload characterization. © 2011 IEEE.

The p-hub Model with Hub-catchment Areas, Existing Hubs, and Simulation: A Case Study of Serbian Intermodal Terminals

This paper addresses the problem of optimally locating intermodal freight terminals in Serbia. To solve this problem and determine the effects of the resulting scenarios, two modeling approaches were combined. The first approach is based on multiple-assignment hub-network design, and the second is based on simulation. The multiple-assignment p-hub network location model was used to determine the optimal location of intermodal terminals. Simulation was used as a tool to estimate intermodal transport flow volumes, due to the unreliability and unavailability of specific statistical data, and as a method for quantitatively analyzing the economic, time, and environmental effects of different scenarios of intermodal terminal development. The results presented here represent a summary, with some extension, of the research realized in the IMOD-X project (Intermodal Solutions for Competitive Transport in Serbia).

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art