Combining global climate and regional landscape models to improve prediction of invasion risk

Aim
It is widely acknowledged that species distributions result from a variety of biotic and abiotic factors operating at different spatial scales. Here, we aimed to (1) determine the extent to which global climate niche models (CNMs) can be improved by the addition of fine-scale regional data; (2) examine climatic and environmental factors influencing the range of 15 invasive aquatic plant species; and (3) provide a case study for the use of such models in invasion management on an island.

Location
Global, with a case study of species invasions in Ireland.

Methods
Climate niche models of global extent (including climate only) and regional environmental niche models (with additional factors such as human influence, land use and soil characteristics) were generated using maxent for 15 invasive aquatic plants. The performance of these models within the invaded range of the study species in Ireland was assessed, and potential hotspots of invasion suitability were determined. Models were projected forward up to 2080 based on two climate scenarios.

Results
While climate variables are important in defining the global range of species, factors related to land use and nutrient level were of greater importance in regional projections. Global climatic models were significantly improved at the island scale by the addition of fine-scale environmental variables (area under the curve values increased by 0.18 and true skill statistic values by 0.36), and projected ranges decreased from an average of 86% to 36% of the island.

Main conclusions
Refining CNMs with regional data on land use, human influence and landscape may have a substantial impact on predictive capacity, providing greater value for prioritization of conservation management at subregional or local scales.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Prediction of Limit Cycle Oscillations Using an Implicit Aeroelastic-Harmonic Balance Method

This work proposes a extends a novel approach to compute tran sonic Limit Cycle Oscillations using high fidelity analysis. CFD based Harmonic Balance methods have proven to be efficient tools to predict periodic phenomena. This paper’s contribution is to present a methodology to determine the unknown frequency of oscillations using an implicit for- mulation of the HB method to accurately capture Limit Cycle Oscillations (LCOs); this is achieved by defining a frequency updating procedure based on a coupled CFD/CSD Harmonic Balance formulation to find the LCO condition. A pitch/plunge aerofoil and respective linear structural models is used to exercise the new method. Results show consistent agreement between the proposed and time-marching methods for both LCO amplitude and frequency.

Prediction of limit cycle oscillations under uncertainty using a Harmonic Balance method

The Harmonic Balance method is an attractive solution for computing periodic responses and can be an alternative to time domain methods, at a reduced computational cost. The current paper investigates using a Harmonic Balance method for simulating limit cycle oscillations under uncertainty. The Harmonic Balance method is used in conjunction with a non-intrusive polynomial-chaos approach to propagate variability and is validated against Monte Carlo analysis. Results show the potential of the approach for a range of nonlinear dynamical systems, including a full wing configuration exhibiting supercritical and subcritical bifurcations, at a fraction of the cost of performing time domain simulations.

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models - Multiple regression, Random Forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply Random Forest or Quantile regression techniques to the machining domain. The performance of these models was compared to each other to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).

Mechanisms of reduced susceptibility and genotypic prediction of antibiotic resistance in Prevotella isolated from cystic fibrosis (CF) and non-CF patients

OBJECTIVES: To investigate mechanisms of reduced susceptibility to commonly used antibiotics in Prevotella cultured from patients with cystic fibrosis (CF), patients with invasive infection and healthy control subjects and to determine whether genotype can be used to predict phenotypic resistance.

METHODS: The susceptibility of 157 Prevotella isolates to seven antibiotics was compared, with detection of resistance genes (cfxA-type gene, ermF and tetQ), mutations within the CfxA-type β-lactamase and expression of efflux pumps.

RESULTS: Prevotella isolates positive for a cfxA-type gene had higher MICs of amoxicillin and ceftazidime compared with isolates negative for this gene (P < 0.001). A mutation within the CfxA-type β-lactamase (Y239D) was associated with ceftazidime resistance (P = 0.011). The UK CF isolates were 5.3-fold, 2.7-fold and 5.7-fold more likely to harbour ermF compared with the US CF, UK invasive and UK healthy control isolates, respectively. Higher concentrations of azithromycin (P < 0.001) and clindamycin (P < 0.001) were also required to inhibit the growth of the ermF-positive isolates compared with ermF-negative isolates. Furthermore, tetQ-positive Prevotella isolates had higher MICs of tetracycline (P = 0.001) and doxycycline (P < 0.001) compared with tetQ-negative isolates. Prevotella spp. were also shown, for the first time, to express resistance nodulation division (RND)-type efflux pumps.

CONCLUSIONS: This study has demonstrated that Prevotella isolated from various sources harbour a common pool of resistance genes and possess RND-type efflux pumps, which may contribute to tetracycline resistance. The findings indicate that antibiotic resistance is common in Prevotella spp., but the genotypic traits investigated do not reflect phenotypic antibiotic resistance in every instance.

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.

StaRProtein: a web server for prediction of stability of repeat proteins

Repeat proteins have become increasingly important due to their capability to bind to almost any proteins and the potential as alternative therapy to monoclonal antibodies. In the past decade repeat proteins have been designed to mediate specific protein-protein interactions. The tetratricopeptide and ankyrin repeat proteins are two classes of helical repeat proteins that form different binding pockets to accommodate various partners. It is important to understand the factors that define folding and stability of repeat proteins in order to prioritize the most stable designed repeat proteins to further explore their potential binding affinities. Here we developed distance-dependant statistical potentials using two classes of alpha-helical repeat proteins, tetratricopeptide and ankyrin repeat proteins respectively, and evaluated their efficiency in predicting the stability of repeat proteins. We demonstrated that the repeat-specific statistical potentials based on these two classes of repeat proteins showed paramount accuracy compared with non-specific statistical potentials in: 1) discriminate correct vs. incorrect models 2) rank the stability of designed repeat proteins. In particular, the statistical scores correlate closely with the equilibrium unfolding free energies of repeat proteins and therefore would serve as a novel tool in quickly prioritizing the designed repeat proteins with high stability. StaRProtein web server was developed for predicting the stability of repeat proteins.

Prediction of Transonic Limit-Cycle Oscillations Using an Aeroelastic Harmonic Balance Method

This work proposes a novel approach to compute transonic limit-cycle oscillations using high-fidelity analysis. Computational-Fluid-Dynamics based harmonic balance methods have proven to be efficient tools to predict periodic phenomena. This paper’s contribution is to present a new methodology to determine the unknown frequency of oscillations, enabling harmonic balance methods to accurately capture limit-cycle oscillations; this is achieved by defining a frequency-updating procedure based on a coupled computational-fluid-dynamics/computational-structural-dynamics harmonic balance formulation to find the limit-cycle oscillation condition. A pitch/plunge airfoil and delta wing aerodynamic and respective linear structural models are used to validate the new method against conventional time-domain simulations. Results show consistent agreement between the proposed and time-marching methods for both limit-cycle oscillation amplitude and frequency while producing at least a one-order-of-magnitude reduction in computational time.

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.

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.