Partial least squares model for dynamic rating of overhead line in wind intensive areas based on field measurements

This paper presents a statistical model for the thermal behaviour of the line model based on lab tests and field measurements. This model is based on Partial Least Squares (PLS) multi regression and is used for the Dynamic Line Rating (DLR) in a wind intensive area. DLR provides extra capacity to the line, over the traditional seasonal static rating, which makes it possible to defer the need for reinforcement the existing network or building new lines. The proposed PLS model has a number of appealing features; the model is linear, so it is straightforward to use for predicting the line rating for future periods using the available weather forecast. Unlike the available physical models, the proposed model does not require any physical parameters of the line, which avoids the inaccuracies resulting from the errors and/or variations in these parameters. The developed model is compared with physical model, the Cigre model, and has shown very good accuracy in predicting the conductor temperature as well as in determining the line rating for future time periods. 

Interferon regulatory factor (IRF) 3 is critical for the development of experimental autoimmune encephalomyelitis.

BACKGROUND: Experimental autoimmune encephalomyelitis (EAE) is an animal model of autoimmune inflammatory demyelination that is mediated by Th1 and Th17 cells. The transcription factor interferon regulatory factor 3 (IRF3) is activated by pathogen recognition receptors and induces interferon-beta production.

METHODS: To determine the role of IRF3 in autoimmune inflammation, we immunised wild-type (WT) and irf3-/- mice to induce EAE. Splenocytes from WT and irf3-/- mice were also activated in vitro in Th17-polarising conditions.

RESULTS: Clinical signs of disease were significantly lower in mice lacking IRF3, with reduced Th1 and Th17 cells in the central nervous system. Peripheral T-cell responses were also diminished, including impaired proliferation and Th17 development in irf3-/- mice. Myelin-reactive CD4+ cells lacking IRF3 completely failed to transfer EAE in Th17-polarised models as did WT cells transferred into irf3-/- recipients. Furthermore, IRF3 deficiency in non-CD4+ cells conferred impairment of Th17 development in antigen-activated cultures.

CONCLUSION: These data show that IRF3 plays a crucial role in development of Th17 responses and EAE and warrants investigation in human multiple sclerosis.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Development of dynamic structural testing to current state-of-the-art hybrid testing

Even though computational power used for structural analysis is ever increasing, there is still a fundamental need for testing in structural engineering, either for validation of complex numerical models or to assess material behaviour. In addition to analysis of structures using scale models, many structural engineers are aware to some extent of cyclic and shake-table test methods, but less so of ‘hybrid testing’. The latter is a combination of physical testing (e.g. hydraulic
actuators) and computational modelling (e.g. finite element modelling). Over the past 40 years, hybrid testing of engineering structures has developed from concept through to maturity to become a reliable and accurate dynamic testing technique. The hybrid test method provides users with some additional benefits that standard dynamic testing methods do not, and the method is more cost-effective in comparison to shake-table testing. This article aims to provide the reader with a basic understanding of the hybrid test method, including its contextual development and potential as a dynamic testing technique.

Dynamic identification of building structures: new strategies:Meccanica dei materiali e delle strutture

In this paper the evolution of a time domain dynamic identification technique based on a statistical moment approach is presented. This technique can be used in the case of structures under base random excitations in the linear state and in the non linear one. By applying Itoˆ stochastic calculus, special algebraic equations can be obtained depending on the statistical moments of the response of the system to be identified. Such equations can be used for the dynamic identification of the mechanical parameters and of the input. The above equations, differently from many techniques in the literature, show the possibility of obtaining the identification of the dissipation characteristics independently from the input. Through the paper the first formulation of this technique, applicable to non linear systems, based on the use of a restricted class of the potential models, is presented. Further a second formulation of the technique in object, applicable to each kind of linear systems and based on the use of a class of linear models, characterized by a mass proportional damping matrix, is described.

Modelling Static and Dynamic FRP-Concrete Bond Behaviour Using a Local Concrete Damage Model

This paper presents a study on the bond behaviour of FRP-concrete bonded joints under static and dynamic loadings, by developing a meso-scale finite element model using the K&C concrete damage model in LS-DYNA. A significant number of single shear experiments under static pull-off loading were modelled with an extensive parametric study covering key factors in the K&C model, including the crack band width, the compressive fracture energy and the shear dilatation factor. It is demonstrated that the developed model can satisfactorily simulate the static debonding behaviour, in terms of mesh objectivity, the load-carrying capacity and the local bond-slip behaviour, provided that proper consideration is given to the selection of crack band width and shear dilatation factor. A preliminary study of the effect of the dynamic loading rate on the debonding behaviour was also conducted by considering a dynamic increase factor (DIF) for the concrete strength as a function of strain rate. It is shown that a higher loading rate leads to a higher load-carrying capacity, a longer effective bond length, and a larger damaged area of concrete in the single shear loading scenario.

Photo-dynamic biocidal action of methylene blue and hydrogen peroxide on the cyanobacterium Synechococcus leopoliensis under visible light irradiation

Biofilm growth on stone surfaces is a significant contributing factor to stone biodeterioration. Current market based biocides are hazardous to the environment and to public health. We have investigated the photo-dynamic effect of methylene blue (MB) in the presence of hydrogen peroxide (H₂O₂) on the destruction of the cyanobacterium Synechococcus leopoliensis (S. leopoliensis) under irradiation with visible light. Data presented in this paper illustrate that illumination of S. leopoliensis in the presence of a photosensitiser (MB) and H₂O₂ results in the decomposition of both the cyanobacterium and the photosensitiser. The presence of MB and H₂O₂ affects the viability of the photosensitiser and the cyanobacterium with the fluorescence of both decreasing by 80% over the irradiation time investigated. The photo-dynamic effect was observed under aerobic and anaerobic conditions indicating that oxygen was not necessary for the process. This novel combination could be effective for the remediation of biofilm colonised stone surfaces

Blind Characterisation of Sensors with Second-Order Dynamic Response

Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.

Late Holocene sea-level fall and turn-off of reef flat carbonate production: Rethinking bucket fill and coral reef growth models

Relative sea-level rise has been a major factor driving the evolution of reef systems during the Holocene. Most models of reef evolution suggest that reefs preferentially grow vertically during rising sea level then laterally from windward to leeward, once the reef flat reaches sea level. Continuous lagoonal sedimentation ("bucket fill") and sand apron progradation eventually lead to reef systems with totally filled lagoons. Lagoonal infilling of One Tree Reef (southern Great Barrier Reef) through sand apron accretion was examined in the context of late Holocene relative sea-level change. This analysis was conducted using sedimentological and digital terrain data supported by 50 radiocarbon ages from fossil microatolls, buried patch reefs, foraminifera and shells in sediment cores, and recalibrated previously published radiocarbon ages. This data set challenges the conceptual model of geologically continuous sediment infill during the Holocene through sand apron accretion. Rapid sand apron accretion occurred between 6000 and 3000 calibrated yr before present B.P. (cal. yr B.P.); followed by only small amounts of sedimentation between 3000 cal. yr B.P. and present, with no significant sand apron accretion in the past 2 k.y. This hiatus in sediment infill coincides with a sea-level fall of similar to 1-1.3 m during the late Holocene (ca. 2000 cal. yr B.P.), which would have caused the turn-off of highly productive live coral growth on the reef flats currently dominated by less productive rubble and algal flats, resulting in a reduced sediment input to back-reef environments and the cessation in sand apron accretion. Given that relative sea-level variations of similar to 1 m were common throughout the Holocene, we suggest that this mode of sand apron development and carbonate production is applicable to most reef systems.

The Prenatal Distress Questionnaire:An investigation of factor structure in a high risk population

Background: The Prenatal Distress Questionnaire (PDQ) is a short measure designed to assess specific worries and concerns related to pregnancy. The aim of this study was to confirm the factor structure of the PDQ in a group of pregnant women with a small for gestational age infant (< 10th centile). Methods: The first PDQ assessment for each of 337 pregnant women participating in the Prospective Observational Trial to Optimise paediatric health (PORTO) study was analysed. All women enrolled in the study were identified as having a small for gestational age foetus (< 10th centile), thus representing an 'elevated risk' group. Data were analysed using confirmatory factor analysis (CFA). Three models of the PDQ were evaluated and compared in the current study: a theoretical uni-dimensional measurement model, a bi-dimensional model, and a three-factor model solution. Results: The three-factor model offered the best fit to the data while maintaining sound theoretical grounds(χ² (51df) = 128.52; CFI = 0.97; TLI = 0.96; RMSEA = 0.07). Factor 1 contained items reflecting concerns about birth and the baby, factor 2 concerns about physical symptoms and body image and factor 3 concerns about emotions and relationships. Conclusions: CFA confirmed that the three-factor model provided the best fit, with the items in each factor reflecting the findings of an earlier exploratory data analysis. © 2013 Society for Reproductive and Infant Psychology.

On the diversity of superluminous supernovae: ejected mass as the dominant factor

We assemble a sample of 24 hydrogen-poor superluminous supernovae(SLSNe). Parameterizing the light-curve shape through rise and declinetime-scales shows that the two are highly correlated. Magnetar-poweredmodels can reproduce the correlation, with the diversity in rise anddecline rates driven by the diffusion time-scale. Circumstellarinteraction models can exhibit a similar rise-decline relation, but onlyfor a narrow range of densities, which may be problematic for thesemodels. We find that SLSNe are approximately 3.5 mag brighter and havelight curves three times broader than SNe Ibc, but that the intrinsicshapes are similar. There are a number of SLSNe with particularly broadlight curves, possibly indicating two progenitor channels, butstatistical tests do not cleanly separate two populations. The generalspectral evolution is also presented. Velocities measured from Fe II aresimilar for SLSNe and SNe Ibc, suggesting that diffusion timedifferences are dominated by mass or opacity. Flat velocity evolution inmost SLSNe suggests a dense shell of ejecta. If opacities in SLSNe aresimilar to other SNe Ibc, the average ejected mass is higher by a factor2-3. Assuming κ = 0.1 cm2 g-1, we estimate amean (median) SLSN ejecta mass of 10 M⊙ (6M⊙), with a range of 3-30 M⊙. Doubling theassumed opacity brings the masses closer to normal SNe Ibc, but with ahigh-mass tail. The most probable mechanism for generating SLSNe seemsto be the core collapse of a very massive hydrogen-poor star, forming amillisecond magnetar.

Dynamic modelling of die melt temperature profile in polymer extrusion: Effects of process settings, screw geometry and material

Extrusion is one of the major methods for processing polymeric materials and the thermal homogeneity of the process output is a major concern for manufacture of high quality extruded products. Therefore, accurate process thermal monitoring and control are important for product quality control. However, most industrial extruders use single point thermocouples for the temperature monitoring/control although their measurements are highly affected by the barrel metal wall temperature. Currently, no industrially established thermal profile measurement technique is available. Furthermore, it has been shown that the melt temperature changes considerably with the die radial position and hence point/bulk measurements are not sufficient for monitoring and control of the temperature across the melt flow. The majority of process thermal control methods are based on linear models which are not capable of dealing with process nonlinearities. In this work, the die melt temperature profile of a single screw extruder was monitored by a thermocouple mesh technique. The data obtained was used to develop a novel approach of modelling the extruder die melt temperature profile under dynamic conditions (i.e. for predicting the die melt temperature profile in real-time). These newly proposed models were in good agreement with the measured unseen data. They were then used to explore the effects of process settings, material and screw geometry on the die melt temperature profile. The results showed that the process thermal homogeneity was affected in a complex manner by changing the process settings, screw geometry and material.

Dynamic Modeling and Interaction of Hybrid Natural Gas and Electricity Supply System in Microgrid

Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.

Activated protein C inhibits tumor necrosis factor and macrophage migration inhibitory factor production in monocytes

The precise regulatory mechanisms of amplification and downregulation of the pro- and anti-inflammatory cytokines in the inflammatory response have not been fully delineated. Although activated protein C (APC) and its precursor protein C (PC) have recently been reported to be promising therapeutic agents in the management of meningococcal sepsis, direct evidence for the anti-inflammatory effect remains scarce. We report that APC inhibits in vitro the release of tumor necrosis factor (TNF) and macrophage migration inhibitory factor (MIF), two known cytokine mediators of bacterial septic shock, from lipopolysaccharide (LPS)-stimulated human monocytes. The THP-1 monocytic cell line, when stimulated with LPS and concomitant APC, exhibited a marked reduction in the release of TNF and MIF protein in a concentration-dependent manner compared to cells stimulated with LPS alone. This effect was observed only when incubations were performed in serum-free media, but not in the presence of 1-10% serum. Serum-mediated inhibition could only be overcome by increasing APC concentrations to far beyond physiological levels, suggesting the presence of endogenous serum-derived APC inhibitors. Inhibition of MIF release by APC was found to be independent of TNF, as stimulation of MIF release by LPS was unaltered in the presence of anti-TNF antibodies. Our data confirm that the suggested anti-inflammatory properties of APC are due to direct inhibition of the release of the pro-inflammatory monokine TNF, and imply that the anti-inflammatory action of APC is also mediated via inhibition of MIF release.

Activated protein C inhibits lipopolysaccharide-induced nuclear translocation of nuclear factor kappaB (NF-kappaB) and tumour necrosis factor alpha (TNF-alpha) production in the THP-1 monocytic cell line

Activated protein C (APC) protects against sepsis in animal models and inhibits the lipopolysacharide (LPS)-induced elaboration of proinflammatory cytokines from monocytes. The molecular mechanism responsible for this property is unknown. We assessed the effect of APC on LPS-induced tumour necrosis factor alpha (TNF-alpha) production and on the activation of the central proinflammatory transcription factor nuclear factor-kappaB (NF-kappaB) in a THP-1 cell line. Cells were preincubated with varying concentrations of APC (200 microg/ml, 100 microg/ml and 20 microg/ml) before addition of LPS (100 ng/ml and 10 microg/ml). APC inhibited LPS-induced production of TNF-alpha both in the presence and absence of fetal calf serum (FCS), although the effect was less marked with 10% FCS. APC also inhibited LPS-induced activation of NF-kappaB, with APC (200 microg/ml) abolishing the effect of LPS (100 ng/ml). The ability of APC to inhibit LPS-induced translocation of NF-kappaB is likely to be a significant event given the critical role of the latter in the host inflammatory response.