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.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as 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=293.0328.0K. 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 over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Neural Network Based Attack on a Masked Implementation of AES

Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.

Pro-neural transcription factors as cancer markers

BACKGROUND: The aberrant transcription in cancer of genes normally associated with embryonic tissue differentiation at various organ sites may be a hallmark of tumour progression. For example, neuroendocrine differentiation is found more commonly in cancers destined to progress, including prostate and lung. We sought to identify proteins which are involved in neuroendocrine differentiation and differentially expressed in aggressive/metastatic tumours.

RESULTS: Expression arrays were used to identify up-regulated transcripts in a neuroendocrine (NE) transgenic mouse model of prostate cancer. Amongst these were several genes normally expressed in neural tissues, including the pro-neural transcription factors Ascl1 and Hes6. Using quantitative RT-PCR and immuno-histochemistry we showed that these same genes were highly expressed in castrate resistant, metastatic LNCaP cell-lines. Finally we performed a meta-analysis on expression array datasets from human clinical material. The expression of these pro-neural transcripts effectively segregates metastatic from localised prostate cancer and benign tissue as well as sub-clustering a variety of other human cancers.

CONCLUSION: By focussing on transcription factors known to drive normal tissue development and comparing expression signatures for normal and malignant mouse tissues we have identified two transcription factors, Ascl1 and Hes6, which appear effective markers for an aggressive phenotype in all prostate models and tissues examined. We suggest that the aberrant initiation of differentiation programs may confer a selective advantage on cells in all contexts and this approach to identify biomarkers therefore has the potential to uncover proteins equally applicable to pre-clinical and clinical cancer biology.

Targeting translocator protein (18 kDa) (TSPO) dampens pro-inflammatory microglia reactivity in the retina and protects from degeneration

BACKGROUND: Reactive microglia are commonly seen in retinal degenerative diseases, and neurotoxic microglia responses can contribute to photoreceptor cell death. We and others have previously shown that translocator protein (18 kDa) (TSPO) is highly induced in retinal degenerations and that the selective TSPO ligand XBD173 (AC-5216, emapunil) exerts strong anti-inflammatory effects on microglia in vitro and ex vivo. Here, we investigated whether targeting TSPO with XBD173 has immuno-modulatory and neuroprotective functions in two mouse models of acute retinal degeneration using bright white light exposure.

METHODS: BALB/cJ and Cx3cr1 (GFP/+) mice received intraperitoneal injections of 10 mg/kg XBD173 or vehicle for five consecutive days, starting 1 day prior to exposure to either 15,000 lux white light for 1 h or 50,000 lux focal light for 10 min, respectively. The effects of XBD173 treatment on microglia and Müller cell reactivity were analyzed by immuno-stainings of retinal sections and flat mounts, fluorescence-activated cell sorting (FACS) analysis, and mRNA expression of microglia markers using quantitative real-time PCR (qRT-PCR). Optical coherence tomography (OCT), terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) stainings, and morphometric analyses were used to quantify the extent of retinal degeneration and photoreceptor apoptosis.

RESULTS: Four days after the mice were challenged with bright white light, a large number of amoeboid-shaped alerted microglia appeared in the degenerating outer retina, which was nearly completely prevented by treatment with XBD173. This treatment also down-regulated the expression of TSPO protein in microglia but did not change the TSPO levels in the retinal pigment epithelium (RPE). RT-PCR analysis showed that the microglia/macrophage markers Cd68 and activated microglia/macrophage whey acidic protein (Amwap) as well as the pro-inflammatory genes Ccl2 and Il6 were reduced after XBD173 treatment. Light-induced degeneration of the outer retina was nearly fully blocked by XBD173 treatment. We further confirmed these findings in an independent mouse model of focal light damage. Retinas of animals receiving XBD173 therapy displayed significantly more ramified non-reactive microglia and more viable arrestin-positive cone photoreceptors than vehicle controls.

CONCLUSIONS: Targeting TSPO with XBD173 effectively counter-regulates microgliosis and ameliorates light-induced retinal damage, highlighting a new pharmacological concept for the treatment of retinal degenerations.