Multiple Biomarkers for the prediction of ischemic stroke: the PRIME study

Objective: To simultaneously evaluate 14 biomarkers from distinct biological pathways for risk prediction of ischemic stroke, including biomarkers of hemostasis, inflammation, and endothelial activation as well as chemokines and adipocytokines.
Methods and Results: The Prospective Epidemiological Study on Myocardial Infarction (PRIME) is a cohort of 9771 healthy men 50 to 59 years of age who were followed up over 10 years. In a nested case–control study, 95 ischemic stroke cases were matched with 190 controls. After multivariable adjustment for traditional risk factors, fibrinogen (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.03–2.28), E-selectin (OR, 1.76; 95% CI, 1.06–2.93), interferon-γ-inducible-protein-10 (OR, 1.72; 95% CI, 1.06–2.78), resistin (OR, 2.86; 95% CI, 1.30–6.27), and total adiponectin (OR, 1.82; 95% CI, 1.04–3.19) were significantly associated with ischemic stroke. Adding E-selectin and resistin to a traditional risk factor model significantly increased the area under the receiver-operating characteristic curve from 0.679 (95% CI, 0.612–0.745) to 0.785 and 0.788, respectively, and yielded a categorical net reclassification improvement of 29.9% (P=0.001) and 28.4% (P=0.002), respectively. Their simultaneous inclusion in the traditional risk factor model increased the area under the receiver-operating characteristic curve to 0.824 (95% CI, 0.770–0.877) and resulted in an net reclassification improvement of 41.4% (P<0.001). Results were confirmed when using continuous net reclassification improvement.
Conclusion: Among multiple biomarkers from distinct biological pathways, E-selectin and resistin provided incremental and additive value to traditional risk factors in predicting ischemic stroke.

Verification of a novel material model to predict damage in composite structures

An intralaminar damage model (IDM), based on continuum damage mechanics, was developed for the simulation of composite structures subjected to damaging loads. This model can capture the complex intralaminar damage mechanisms, accounting for mode interactions, and delaminations. Its development is driven by a requirement for reliable crush simulations to design composite structures with a high specific energy absorption. This IDM was implemented as a user subroutine within the commercial finite element package, Abaqus/Explicit[1]. In this paper, the validation of the IDM is presented using two test cases. Firstly, the IDM is benchmarked against published data for a blunt notched specimen under uniaxial tensile loading, comparing the failure strength as well as showing the damage. Secondly, the crush response of a set of tulip-triggered composite cylinders was obtained experimentally. The crush loading and the associated energy of the specimen is compared with the FE model prediction. These test cases show that the developed IDM is able to capture the structural response with satisfactory accuracy

Experimentally validated partial least squares model for dynamic line rating

This study presents a model based on partial least squares (PLS) regression for dynamic line rating (DLR). The model has been verified using data from field measurements, lab tests and outdoor experiments. Outdoor experimentation has been conducted both to verify the model predicted DLR and also to provide training data not available from field measurements, mainly heavily loaded conditions. The proposed model, unlike the direct measurement based DLR techniques, enables prediction of line rating for periods ahead of time whenever a reliable weather forecast is available. The PLS approach yields a very simple statistical model that accurately captures the physical performance of the conductor within a given environment without requiring a predetermination of parameters as required by many physical modelling techniques. Accuracy of the PLS model has been tested by predicting the conductor temperature for measurement sets other than those used for training. Being a linear model, it is straightforward to estimate the conductor ampacity for a set of predicted weather parameters. The PLS estimated ampacity has proven its accuracy through an outdoor experiment on a piece of the line conductor in real weather conditions.

Low pressure methane solubility in lithium-ion batteries based solvents and electrolytes as a function of temperature. Measurement and prediction

The methane solubility in five pure electrolyte solvents and one binary solvent mixture for lithium ion batteries – such as ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC) and the (50:50 wt%) mixture of EC:DMC was studied experimentally at pressures close to atmospheric and as a function of temperature between (280 and 343) K by using an isochoric saturation technique. The effect of the selected anions of a lithium salt LiX (X = hexafluorophosphate,

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).

Spatio-temporal Change in Crowned (Propithecus coronatus) and Decken’s Sifaka (Propithecus deckenii) Habitat in the Mahavavy-Kinkony Wetland Complex, Madagascar.

The crowned sifaka (Propithecus coronatus) and Decken’s sifaka (Propithecus deckenii) are Endangered lemurs endemic to west and central Madagascar. Both have suffered habitat loss and fragmentation throughout their ranges. The goal
of this study, conducted in the Mahavavy-Kinkony Wetland Complex (MKWC) in northwestern Madagascar, was to assess the effects of historical change in the species’ habitats, and to model the potential impact of further land-use change on their habitats. The IDRISI Andes Geographical Information System and image-processing software was used for satellite-image classifiation, and the Land Change Modeler was used to compare the natural habitat of the species from 1973 to 2005, and to predict available habitat for 2050. We analyzed two forests in the MKWC occupied by P. coronatus (Antsilaiza and Anjohibe), and three forests occupied by P. deckenii (Tsiombikibo, Marofandroboka and Andohaomby). The two forests occupied by P. coronatus contracted during the period 1949–1973, but then expanded to exceed their 1949 area by 28% in 2005. However, the land change model predicted that they will contract again to match their 1949 area by 2050, and will again lose their corridor connection, meaning that the conservation gains for this species in the complex are at risk of being reversed. The three forests occupied by P. deckenii have declined in area steadily since 1949, losing 20% of their original area by 2005, and are predicted to lose a further 15% of their original area by 2050. Both species are therefore at risk of becoming even more threatened if land-use change continues within the complex. Improved conservation of the remaining forest is recommended to avoid further loss, as well as ecological restoration and reforestation to promote connectivity between the forests. A new strategy for controlling agriculture and forest use is required in order to avoid further destruction of the forest.

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.

A new comparator account of auditory verbal hallucinations: how motor prediction can plausibly contribute to the sense of agency for inner speech

The comparator account holds that processes of motor prediction contribute to the sense of agency by attenuating incoming sensory information and that disruptions to this process contribute to misattributions of agency in schizophrenia. Over the last 25 years this simple and powerful model has gained widespread support not only as it relates to bodily actions but also as an account of misattributions of agency for inner speech, potentially explaining the etiology of auditory verbal hallucination (AVH). In this paper we provide a detailed analysis of the traditional comparator account for inner speech, pointing out serious problems with the specification of inner speech on which it is based and highlighting inconsistencies in the interpretation of the electrophysiological evidence commonly cited in its favor. In light of these analyses we propose a new comparator account of misattributed inner speech. The new account follows leading models of motor imagery in proposing that inner speech is not attenuated by motor prediction, but rather derived directly from it. We describe how failures of motor prediction would therefore directly affect the phenomenology of inner speech and trigger a mismatch in the comparison between motor prediction and motor intention, contributing to abnormal feelings of agency. We argue that the new account fits with the emerging phenomenological evidence that AVHs are both distinct from ordinary inner speech and heterogeneous. Finally, we explore the possibility that the new comparator account may extend to explain disruptions across a range of imagistic modalities, and outline avenues for future research.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

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.

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.

A field and reactive transport model study of arsenic in a basaltic rock aquifer

The use of geothermal energy as a source for electricity and district heating has increased over recent decades. Dissolved As can be an important constituent of the geothermal fluids brought to the Earth's surface. Here the field application of laboratory measured adsorption coefficients of aqueous As species on basaltic glass surfaces is discussed. The mobility of As species in the basaltic aquifer in the Nesjavellir geothermal system, Iceland was modelled by the one-dimensional (1D) reactive transport model PHREEQC ver. 2, constrained by a long time series of field measurements with the chemical composition of geothermal effluent fluids, pH, Eh and, occasionally, Fe- and As-dissolved species measurements. Di-, tri- and tetrathioarsenic species (As(OH)S22-, AsS₃H^2-, AsS33- and As(SH)4-) were the dominant form of dissolved As in geothermal waters exiting the power plant (2.556μM total As) but converted to some extent to arsenite (H₃AsO₃) and arsenate HAsO42- oxyanions coinciding with rapid oxidation of S2- to S2O32- and finally to SO42- during surface runoff before feeding into a basaltic lava field with a total As concentration of 0.882μM following dilution with other surface waters. A continuous 25-a data set monitoring groundwater chemistry along a cross section of warm springs on the Lake Thingvallavatn shoreline allowed calibration of the 1D model. Furthermore, a series of ground water wells located in the basaltic lava field, provided access along the line of flow of the geothermal effluent waters towards the lake. The conservative ion Cl^- moved through the basaltic lava field (4100m) in less than10a but As was retarded considerably due to surface reactions and has entered a groundwater well 850m down the flow path as arsenate in accordance to the prediction of the 1D model. The 1D model predicted a complete breakthrough of arsenate in the year 2100. In a reduced system arsenite should be retained for about 1ka. © 2011 Elsevier Ltd.

A 1-D Vaneless Diffuser Model Accounting for the Effects of Spanwise Flow Stratification

The radial vaneless diffuser, though comparatively simple in terms of geometry, poses a significant challenge in obtaining an accurate 1-D based performance prediction due to the swirling, unsteady and distorted nature of the flow field. Turbocharger compressors specifically, with the ever increasing focus on achieving a wide operating range, have been recognised to operate with significant regions of spanwise separated flow, particularly at off design conditions.
Using a combination of single passage Computational Fluid Dynamics (CFD) simulations and extensive gas stand test data for three geometries, the current study aims to evaluate the onset and impact of spanwise flow stratification in radial vaneless diffusers, and how the extent of the aerodynamic blockage presented to the flow throughout the diffuser varies with both geometry and operating condition. Having analysed the governing performance parameters and flow phenomena, a novel 1-D modelling method is presented and compared to an existing baseline method as well as test data to quantify the improvement in prediction accuracy achieved.

A 1-D radial vaneless diffuser model accounting for the effects of aerodynamic blockage

The radial vaneless diffuser, though comparatively simple in terms of geometry, poses a significant challenge in obtaining an accurate 1-D based performance prediction due to the swirling, unsteady and distorted nature of the flow field. Turbocharger compressors specifically, with the ever increasing focus on achieving a wide operating range, have been recognised to operate with significant regions of spanwise separated flow, particularly at off-design conditions.

Using a combination of single passage Computational Fluid Dynamics (CFD) simulations and extensive gas stand test data for three geometries, the current study aims to evaluate the onset and impact of spanwise aerodynamic blockage in radial vaneless diffusers, and how the extent of the blocked region throughout the diffuser varies with both geometry and operating condition. Having analysed the governing performance parameters and flow phenomena, a novel 1-D modelling method is presented and compared to an existing baseline method as well as test data to quantify the improvement in prediction accuracy achieved.