Is foot ulcer simulation training (FUST) really effective? Participants’ supervisors speak out

Background Foot ulcers are a common reason for diabetes-related hospitalisation. Foot ulcer simulation training (FUST) programs have increased podiatry participants self-confidence to manage foot ulcers. However, supervisors’ perspectives on their participants attending these simulation programs have not been investigated. This mixed method (quantitative and qualitative) study aimed to investigate home clinical supervisors’ perspectives on any changes to their participants’ competence and practice following FUST. Methods Clinical supervisors of fifteen podiatrists, who participated in a two-day Foot Ulcer Simulation Training (FUST) course, were recruited. Supervisors completed quantitative surveys evaluating their participants’ foot ulcer competence pre-FUST and 6-months post-FUST, via a purposed designed 21-item survey using a five-point Likert scale (1=Very limited, 5=Highly competent). Supervisors also attended a semi-structured qualitative group interview to investigate supervisors’ perspectives on FUST. Results Supervisors surveys returned were pre-FUST (n=10) and post-FUST (n=12). Significant competence improvements were observed at the 6-month survey (mean scores 2.84 cf. 3.72, p < 0.05). Five supervisors attended the group interview. Five sub-themes emerged: i) FUST provided a good foundation for future learning, ii) FUST modelled good clinical behaviour, iii) clinical practice improvement was evident in most participants, iv) clinical improvements were dependent on participant’s willingness to change and existing workplace culture, v) FUST needs to be reinforced back in the home clinic. Conclusion Overall, supervisors of FUST participants indicated that the course improved their participants’ competence and clinical practice. However, the degree of improvement appears dependant on the participants’ home workplace culture and willingness to embrace change.

Foot ulcer simulation training (FUST) : are podiatrists FUST with long-term clinical confidence?

Background Foot ulcers are a leading cause of diabetes-related hospitalisations. Clinical training has been shown to be beneficial in foot ulcer management. Recently, improved self-confidence in podiatrists was reported immediately after foot ulcer simulation training (FUST) pilot programs. This study aimed to investigate the longer-term impacts of the FUST program on podiatrists’ self-confidence over 12 months in a larger sample. Methods Participants were podiatrists attending a two-day FUST course comprising web-based interactive learning, low-fidelity part-tasks and high-fidelity full clinical scenarios. Primary outcome measures included participants’ self-confidence measured pre-, (immediately) post-, 6-month post- and 12-month post-course via a purpose designed 21-item survey using a five-point Likert scale (1=Very limited, 5=Highly confident). Participants’ perceptions of knowledge gained, satisfaction, relevance and fidelity were also investigated. ANOVA and post hoc tests were used to test any differences between groups. Results Thirty-four participants completed FUST. Survey response rates were 100% (pre), 82% (post), 74% (6-month post), and 47% (12-month post). Overall mean scores were 3.13 (pre), 4.49 (post), 4.35 (6-month post) and 4.30 (12-month post) (p < 0.05); post hoc tests indicated no differences between the immediately, 6-month and 12-month post group scores (p > 0.05). Satisfaction, knowledge, relevance and fidelity were all rated highly. Conclusion This study suggests that significant short-term improvements in self-confidence to manage foot ulcers via simulation training are retained over the longer term. It is likely that improved self-confidence leads to improved foot ulcer clinical practice and outcomes; although this requires further research.

Correlating flow induced shear stress and chondrocytes activity in micro-porous scaffold using computational fluid dynamics and rapid prototyping

Flow induced shear stress plays an important role in regulating cell growth and distribution in scaffolds. This study sought to correlate wall shear stress and chondrocytes activity for engineering design of micro-porous osteochondral grafts based on the hypothesis that it is possible to capture and discriminate between the transmitted force and cell response at the inner irregularities. Unlike common tissue engineering therapies with perfusion bioreactors in which flow-mediated stress is the controlling parameter, this work assigned the associated stress as a function of porosity to influence in vitro proliferation of chondrocytes. D-optimality criterion was used to accommodate three pore characteristics for appraisal in a mixed level fractional design of experiment (DOE); namely, pore size (4 levels), distribution pattern (2 levels) and density (3 levels). Micro-porous scaffolds (n=12) were fabricated according to the DOE using rapid prototyping of an acrylic-based bio-photopolymer. Computational fluid dynamics (CFD) models were created correspondingly and used on an idealized boundary condition with a Newtonian fluid domain to simulate the dynamic microenvironment inside the pores. In vitro condition was reproduced for the 3D printed constructs seeded by high pellet densities of human chondrocytes and cultured for 72 hours. The results showed that cell proliferation was significantly different in the constructs (p<0.05). Inlet fluid velocity of 3×10-2mms-1 and average shear stress of 5.65×10-2 Pa corresponded with increased cell proliferation for scaffolds with smaller pores in hexagonal pattern and lower densities. Although the analytical solution of a Poiseuille flow inside the pores was found insufficient for the description of the flow profile probably due to the outside flow induced turbulence, it showed that the shear stress would increase with cell growth and decrease with pore size. This correlation demonstrated the basis for determining the relation between the induced stress and chondrocyte activity to optimize microfabrication of engineered cartilaginous constructs.

Atomistic simulation of interfacial behaviour in graphene-polymer nanocomposites

Graphene–polymer nanocomposites have promising properties as new structural and functional materials. The remarkable mechanical property enhancement in these nanocomposites is generally attributed to exceptional mechanical property of graphene and possible load transfer between graphene and polymer matrix. However, the underlying strengthening and toughening mechanisms have not been well understood. In this work, the interfacial behavior of graphene-polyethylene (PE) was investigated using molecular dynamics (MD) method. The interfacial shear force (ISF) and interfacial shear stress (ISS) between graphene and PE matrix were evaluated, taking into account graphene size, the number of graphene layers and the structural defects in graphene. MD results show that the ISS at graphene-PE interface mainly distributes at each end of the graphene nanofiller within the range of 1 nm, and much larger than that at carbon nanotube (CNT)-PE interface. Moreover, it was found that the ISS at graphene-PE interface is sensitive to the layer number.

Effect of defects on fracture strength of graphene sheets

With a hexagonal monolayer network of carbon atoms, graphene has demonstrated exceptional electrical 22 and mechanical properties. In this work, the fracture of graphene sheets with Stone–Wales type defects and vacancies were investigated using molecular dynamics simulations at different temperatures. The initiation of defects via bond rotation was also investigated. The results indicate that the defects and vacancies can cause significant strength loss in graphene. The fracture strength of graphene is also affected by temperature and loading directions. The simulation results were compared with the prediction from the quantized fracture mechanics.

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters : theory and application to microfilaments

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.

Electrocatalytically switchable CO2 capture : first principle computational exploration of carbon nanotubes with pyridinic nitrogen

Carbon nanotubes with specific nitrogen doping are proposed for controllable, highly selective, and reversible CO2 capture. Using density functional theory incorporating long-range dispersion corrections, we investigated the adsorption behavior of CO2 on (7,7) single-walled carbon nanotubes (CNTs) with several nitrogen doping configurations and varying charge states. Pyridinic-nitrogen incorporation in CNTs is found to induce an increasing CO2 adsorption strength with electron injecting, leading to a highly selective CO2 adsorption in comparison with N2. This functionality could induce intrinsically reversible CO2 adsorption as capture/release can be controlled by switching the charge carrying state of the system on/off. This phenomenon is verified for a number of different models and theoretical methods, with clear ramifications for the possibility of implementation with a broader class of graphene-based materials. A scheme for the implementation of this remarkable reversible electrocatalytic CO2-capture phenomenon is considered.

Tensile properties of graphene nanotube hybrid structures : a molecular dynamics study

Graphene has been reported with record-breaking properties which have opened up huge potential applications. A considerable research has been devoted to manipulate or modify the properties of graphene to target a more smart nanoscale device. Graphene and carbon nanotube hybrid structure (GNHS) is one of the promising graphene derivates, while their mechanical properties have been rarely discussed in literature. Therefore, such a studied is conducted in this paper basing on the large-scale molecular dynamics simulation. The target GNHS is constructed by considering two separate graphene layers that being connected by single-wall carbon nanotubes (SWCNTs) according to the experimental observations. It is found that the GNHSs exhibit a much lower yield strength, Young’s modulus, and earlier yielding comparing with a bilayer graphene sheet. Fracture of studied GNHSs is found to fracture located at the connecting region between carbon nanotubes (CNTs) and graphene. After failure, monatomic chains are normally observed at the front of the failure region, and the two graphene layers at the failure region without connecting CNTs will adhere to each other, generating a bilayer graphene sheet scheme (with a layer distance about 3.4 Å). This study will enrich the current understanding of the mechanical performance of GNHS, which will guide the design of GNHS and shed lights on its various applications.

Blast response of reinforced concrete pile using fully coupled computer simulation techniques

Pile foundations transfer loads from superstructures to stronger sub soil. Their strength and stability can hence affect structural safety. This paper treats the response of reinforced concrete pile in saturated sand to a buried explosion. Fully coupled computer simulation techniques are used together with five different material models. Influence of reinforcement on pile response is investigated and important safety parameters of horizontal deformations and tensile stresses in the pile are evaluated. Results indicate that adequate longitudinal reinforcement and proper detailing of transverse reinforcement can reduce pile damage. Present findings can serve as a benchmark reference for future analysis and design.

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.

A computational study of carbon dioxide adsorption on solid boron

Capturing and sequestering carbon dioxide (CO2) can provide a route to partial mitigation of climate change associated with anthropogenic CO2 emissions. Here we report a comprehensive theoretical study of CO2 adsorption on two phases of boron, α-B12 and γ-B28. The theoretical results demonstrate that the electron deficient boron materials, such as α-B12 and γ-B28, can bond strongly with CO2 due to Lewis acid-base interactions because the electron density is higher on their surfaces. In order to evaluate the capacity of these boron materials for CO2 capture, we also performed calculations with various degrees of CO2 coverage. The computational results indicate CO2 capture on the boron phases is a kinetically and thermodynamically feasible process, and therefore from this perspective these boron materials are predicted to be good candidates for CO2 capture.

Numerical study of lid-driven square cavity with heat generation using LBM

In this study, the mixed convection heat transfer and fluid flow behaviors in a lid-driven square cavity filled with high Prandtl number fluid (Pr = 5400, ν = 1.2×10-4 m2/s) at low Reynolds number is studied using thermal Lattice Boltzmann method (TLBM) where ν is the viscosity of the fluid. The LBM has built up on the D2Q9 model and the single relaxation time method called the Lattice-BGK (Bhatnagar-Gross-Krook) model. The effects of the variations of non dimensional mixed convection parameter called Richardson number(Ri) with and without heat generating source on the thermal and flow behavior of the fluid inside the cavity are investigated. The results are presented as velocity and temperature profiles as well as stream function and temperature contours for Ri ranging from 0.1 to 5.0 with other controlling parameters that present in this study. It is found that LBM has good potential to simulate mixed convection heat transfer and fluid flow problem. Finally the simulation results have been compared with the previous numerical and experimental results and it is found to be in good agreement.

On-line Demand Management of Low Voltage Residential Distribution Networks in Smart Grids

A novel intelligent online demand management system is discussed in this chapter for peak load management in low voltage residential distribution networks based on the smart grid concept. The discussed system also regulates the network voltage, balances the power in three phases and coordinates the energy storage within the network. This method uses low cost controllers, with two-way communication interfaces, installed in costumers’ premises and at distribution transformers to manage the peak load while maximizing customer satisfaction. A multi-objective decision making process is proposed to select the load(s) to be delayed or controlled. The efficacy of the proposed control system is verified by a MATLAB-based simulation which includes detailed modeling of residential loads and the network.

Comparison of functionals for metal hexaboride band structure calculations

Density functional calculations of the electronic band structure for superconducting and semi-conducting metal hexaborides are compared using a consistent suite of assumptions and with emphasis on the physical implications of computed models. Spin polarization enhances mathematical accuracy of the functional approximations and adds significant physical meaning to model interpretation. For YB6 and LaB6, differences in alpha and beta projections occur near the Fermi energy. These differences are pronounced for superconducting hexaborides but do not occur for other metal hexaborides.