Current tools for prediction of arteriovenous fistula outcomes

It remains challenging to accurately predict whether an individual arteriovenous fistula (AVF) will mature and be useable for haemodialysis vascular access. Current best practice involves the use of routine clinical assessment and ultrasonography complemented by selective venography and magnetic resonance imaging. The purpose of this literature review is to describe current practices in relation to pre-operative assessment prior to AVF formation and highlight potential areas for future research to improve the clinical prediction of AVF outcomes.

Numerical prediction of the low-velocity impact damage and compression after impact strength of composite laminates

Low-velocity impact damage can drastically reduce the residual mechanical properties of the composite structure even when there is barely visible impact damage. The ability to computationally predict the extent of damage and compression after impact (CAI) strength of a composite structure can potentially lead to the exploration of a larger design space without incurring significant development time and cost penalties. A three-dimensional damage model, to predict both low-velocity impact damage and compression after impact CAI strength of composite laminates, has been developed and implemented as a user material subroutine in the commercial finite element package, ABAQUS/Explicit. The virtual tests were executed in two steps, one to capture the impact damage and the other to predict the CAI strength. The observed intra-laminar damage features, delamination damage area as well as residual strength are discussed. It is shown that the predicted results for impact damage and CAI strength correlated well with experimental testing.

Impact of finite element idealisation on the prediction of welded fuselage stiffened panel buckling

Lap joints are widely used in the manufacture of stiffened panels and influence local panel sub-component stability, defining buckling unit dimensions and boundary conditions. Using the Finite Element method it is possible to model joints in great detail and predict panel buckling behaviour with accuracy. However, when modelling large panel structures such detailed analysis becomes computationally expensive. Moreover, the impact of local behaviour on global panel performance may reduce as the scale of the modelled structure increases. Thus this study presents coupled computational and experimental analysis, aimed at developing relationships between modelling fidelity and the size of the modelled structure, when the global static load to cause initial buckling is the required analysis output. Small, medium and large specimens representing welded lap-joined fuselage panel structure are examined. Two element types, shell and solid-shell, are employed to model each specimen, highlighting the impact of idealisation on the prediction of welded stiffened panel initial skin buckling.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Assessing 1D Loss Models for the Off-Design Performance Prediction of Automotive Turbocharger Compressors

Single-Zone modelling is used to assess three 1D impeller loss model collections. An automotive turbocharger centrifugal compressor is used for evaluation. The individual 1D losses are presented relative to each other at three tip speeds to provide a visual description of each author’s perception of the relative importance of each loss. The losses are compared with their resulting prediction of pressure ratio and efficiency, which is further compared with test data; upon comparison, a combination of the 1D loss collections is identified as providing the best performance prediction. 3D CFD simulations have also been carried out for the same geometry using a single passage model. A method of extracting 1D losses from CFD is described and utilised to draw further comparisons with the 1D losses. A 1D scroll volute model has been added to the single passage CFD results; good agreement with the test data is achieved. Short-comings in the existing 1D loss models are identified as a result of the comparisons with 3D CFD losses. Further comparisons are drawn between the predicted 1D data, 3D CFD simulation results, and the test data using a nondimensional method to highlight where the current errors exist in the 1D prediction.

An Evaluation of 1D Loss Model Collections for the Off-Design Performance Prediction of an Automotive Turbocharger Compressor

Abstract. Single-zone modelling is used to assess different collections of impeller 1D loss models. Three collections of loss models have been identified in literature, and the background to each of these collections is discussed. Each collection is evaluated using three modern automotive turbocharger style centrifugal compressors; comparisons of performance for each of the collections are made. An empirical data set taken from standard hot gas stand tests for each turbocharger is used as a baseline for comparison. Compressor range is predicted in this study; impeller diffusion ratio is shown to be a useful method of predicting compressor surge in 1D, and choke is predicted using basic compressible flow theory. The compressor designer can use this as a guide to identify the most compatible collection of losses for turbocharger compressor design applications. The analysis indicates the most appropriate collection for the design of automotive turbocharger centrifugal compressors.

An Evaluation of 1D Design Methods for the Off-Design Performance Prediction of Automotive Turbocharger Compressors

Several one-dimensional design methods have been used to predict the off-design performance of three modern centrifugal compressors for automotive turbocharging. The three methods used are single-zone, two-zone, and a more recent statistical method. The predicted results from each method are compared against empirical data taken from standard hot gas stand tests for each turbocharger. Each of the automotive turbochargers considered in this study have notably different geometries and are of varying application. Due to the non-adiabatic test conditions, the empirical data has been corrected for the effect of heat transfer to ensure comparability with the 1D models. Each method is evaluated for usability and accuracy in both pressure ratio and efficiency prediction. The paper presents an insight into the limitations of each of these models when applied to one-dimensional automotive turbocharger design, and proposes that a corrected single-zone modelling approach has the greatest potential for further development, whilst the statistical method could be immediately introduced to a design process where design variations are limited.

Comparative Study of Different Methods for the Prediction of Drug− Polymer Solubility

In this study, a comparison of different methods to predict drug−polymer solubility was carried out on binary systems consisting of five model drugs (paracetamol, chloramphenicol, celecoxib, indomethacin, and felodipine) and polyvinylpyrrolidone/vinyl acetate copolymers (PVP/VA) of different monomer weight ratios. The drug−polymer solubility at 25 °C was predicted using the Flory−Huggins model, from data obtained at elevated temperature using thermal analysis methods based on the recrystallization of a supersaturated amorphous solid dispersion and two variations of the melting point depression method. These predictions were compared with the solubility in the low molecular weight liquid analogues of the PVP/VA copolymer (N-vinylpyrrolidone and vinyl acetate). The predicted solubilities at 25 °C varied considerably depending on the method used. However, the three thermal analysis methods ranked the predicted solubilities in the same order, except for the felodipine−PVP system. Furthermore, the magnitude of the predicted solubilities from the recrystallization method and melting point depression method correlated well with the estimates based on the solubility in the liquid analogues, which suggests that this method can be used as an initial screening tool if a liquid analogue is available. The learnings of this important comparative study provided general guidance for the selection of the most suitable method(s) for the screening of drug−polymer solubility.

An Evaluation of Vaneless Diffuser Modelling Methods as a Means of Improving the Off-Design Performance Prediction of Centrifugal Compressors

An evaluation of existing 1-D vaneless diffuser design tools in the context of improving the off-design performance prediction of automotive turbocharger centrifugal compressors is described. A combination of extensive gas stand test data and single passage CFD simulations have been employed in order to permit evaluation of the different methods, allowing conclusions about the relative benefits and deficiencies of each of the different approaches to be determined. The vaneless diffuser itself has been isolated from the incumbent limitations in the accuracy of 1-D impeller modelling tools through development of a method to fully specify impeller exit conditions (in terms of mean quantities) using only standard test stand data with additional interstage static pressure measurements at the entrance to the diffuser. This method allowed a direct comparison between the test data and 1-D methods through sharing common inputs, thus achieving the aim of diffuser isolation.

Crucial to the accuracy of determining the performance of each of the vaneless diffuser configurations was the ability to quantify the presence and extent of the spanwise aerodynamic blockage present at the diffuser inlet section. A method to evaluate this critical parameter using CFD data is described herein, along with a correlation for blockage related to a new diffuser inlet flow parameter ⚡, equal to the quotient of the local flow coefficient and impeller tip speed Mach number. The resulting correlation permitted the variation of blockage with operating condition to be captured.

Monitoring of lineage-specific chimaerism allows early prediction of response following donor lymphocyte infusions for relapsed chronic myeloid leukaemia

Donor lymphocyte infusions (DLI) have been shown to enhance the graft-versus-leukaemia (GVL) effect and induce haematological and molecular remission in patients with relapsed CML following allogeneic bone marrow transplantation (BMT). The potent donor cell-mediated cytolysis following DLI may lead to a short period of aplasia before the re-establishment of donor haematopoiesis. The absence of detectable donor cells in patients prior to DLI infusion may result in permanent aplasia in certain patients. We report on four patients who relapsed 1, 3, 6.5 and 7 years post-BMT for chronic phase CML and were treated with DLI from their original BMT donor. Polymorphic short tandem repeats (STRs) were used to assess haematological chimaerism both prior to and following DLI. At the time of relapse, STR-PCR indicated the presence of donor cells in all four patients, at levels ranging from 1-40%. A clinical and molecular response was seen in 4/4 patients following a short period of cytopenia and all patients remain in clinical remission with a follow-up of 2 months-3 years post-DLI. STR-PCR indicated that a response was occurring during the period of pancytopenia when metaphase analysis was unsuccessful. Lineage-specific analysis of the cellular response to DLI was monitored using STR-PCR of peripheral blood (PB) and bone marrow (BM) lymphocyte-enriched fractions and CD2-positive and -negative T cell fractions. In one patient BM and PB CD34-positive and -negative fractions were also assessed. A change in the ratio of donor:recipient cells in the PB lymphocyte fraction was the earliest molecular indication of an anti-leukaemic response. Subsequent conversion to donor chimaerism occurred in the other lineages and the granulocyte fraction was the last lineage to convert. In conclusion, lineage-specific STR-PCR permits detailed monitoring of subtle changes in donor/recipient cell dynamics in specific lineages following DLI during the crucial pancytopenic phase and may be a useful predictor of haematological response to DLI therapy.

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.