The Role of Water Content and Paste Proportion on Physico-mechanical Properties of Alkali Activated Fly Ash–Ggbs Concrete

The growth of the construction industry worldwide poses a serious concern on the sustainability of the building material production chain, mainly due to the carbon emissions related to the production of Portland cement. On the other hand, valuable materials from waste streams, particularly from the metallurgical industry, are not used at their full potential. Alkali activated concrete (AAC) has emerged in the last years as a promising alternative to traditional Portland cement based concrete for some applications. However, despite showing remarkable strength and durability potential, its utilisation is not widespread, mainly due to the lack of broadly accepted standards for the selection of suitable mix recipes fulfilling design requirements, in particular workability, setting time and strength. In this paper, a contribution towards the design development of AAC synthetized from pulverised fuel ash (60%) and ground granulated blast furnace slag (40%) activated with a solution of sodium hydroxide and sodium silicate is proposed. Results from a first batch of mixes indicated that water content influences the setting time and that paste content is a key parameter for controlling strength development and workability. The investigation indicated that, for the given raw materials and activator compositions, a minimum water to solid (w/s) ratio of 0.37 was needed for an initial setting time of about 1 hour. Further work with paste content in the range of 30% to 33% determined the relationship between workability and strength development and w/s ratio and paste content. Strengths in the range of 50 - 60 MPa were achieved.

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.

Alkali activated fuel ash and slag mixes:optimization study from paste to concrete building blocks

Alkali activated binders, based on ash and slag, also known as geopolymers, can play a key role in reducing the carbon footprint of the construction sector by replacing ordinary Portland cement in some concretes. Since 1970s, research effort has been ongoing in many research institutions. In this study, pulverized fuel ash (pfa) from a UK power plant, ground granulated blast furnace slag (ggbs) and combinations of the two have been investigated as geopolymer binders for concrete applications. Activators used were sodium hydroxide and sodium silicate solutions. Mortars with sand/binder ratio of 2.75 with several pfa and ggbs combinations have been mixed and tested. The optimization of alkali dosage (defined as the Na2O/binder mass ratio) and modulus (defined as the Na2O/SiO2 mass ratio) resulted in strengths in excess of 70 MPa for tested mortars. Setting time and workability have been considered for the identification of the best combination of pfa/ggbs and alkali activator dosage for different precast concrete products. Geopolymer concrete building blocks have been replicated in laboratory and a real scale factory trial has been successfully carried out. Ongoing microstructural characterization is aiming to identify reaction products arising from pfa/ggbs combinations.

Inhibition of endosome fusion by wortmannin persists in the presence of activated Rab5

Rab5-dependent endosome fusion is sensitive to the phosphoinositide 3-kinase inhibitor, wortmannin. It has been proposed that phosphoinositide 3-kinase activity may be required for activation of rab5 by influencing its nucleotide cycle such as to promote its active GTP state. In this report we demonstrate that endosome fusion remains sensitive to wortmannin despite preloading of endosomes with stimulatory levels of a GTPase-defective mutant rab5(Q79L) or of a xanthosine triphosphate-binding mutant, rab5(D136N), in the presence of the nonhydrolysable analogue XTPgammaS. These results suggest that activation of rab5 cannot be the principal function of the wortmannin-sensitive factor on the endosome fusion pathway. This result is extrapolated to all GTPases by demonstrating that endosome fusion remains wortmannin sensitive despite prior incubation with the nonhydrolysable nucleotide analogue GTPgammaS. Consistent with these results, direct measurement of clathrin-coated vesicle-stimulated nucleotide dissociation from exogenous rab5 was insensitive to the presence of wortmannin. A large excess of rab5(Q79L), beyond levels required for maximal stimulation of the fusion assay, afforded protection against wortmannin inhibition, and partial protection was also observed with an excess of wild-type rab5 independent of GTPgammaS.

The CXCR2 antagonist, SCH-527123, shows antitumor activity and sensitizes cells to oxaliplatin in preclinical colon cancer models

Colorectal cancer is the second most common cause of cancer-related death in the United States. Recent studies showed that interleukin-8 (IL-8) and its receptors (CXCR1 and CXCR2) are significantly upregulated in both the tumor and its microenvironment, and act as key regulators of proliferation, angiogenesis, and metastasis. Our previous study showed that IL-8 overexpression in colorectal cancer cells triggers the upregulation of the CXCR2-mediated proliferative pathway. The aim of this study was to investigate whether the CXCR2 antagonist, SCH-527123, inhibits colorectal cancer proliferation and if it can sensitize colorectal cancer cells to oxaliplatin both in vitro and in vivo. SCH-527123 showed concentration-dependent antiproliferative effects in HCT116, Caco2, and their respective IL-8-overexpressing variants colorectal cancer cell lines. Moreover, SCH-527123 was able to suppress CXCR2-mediated signal transduction as shown through decreased phosphorylation of the NF-κB/mitogen-activated protein kinase (MAPK)/AKT pathway. These findings corresponded with decreased cell migration and invasion, while increased apoptosis in colorectal cancer cell lines. In vivo results verified that SCH-527123 treatment decreased tumor growth and microvessel density when compared with vehicle-treated tumors. Importantly, these preclinical studies showed that the combination of SCH-527123 and oxaliplatin resulted in a greater decrease in cell proliferation, tumor growth, apoptosis, and angiogenesis that was superior to single-agent treatment. Taken together, these findings suggest that targeting CXCR2 may block tumor proliferation, migration, invasion, and angiogenesis. In addition, CXCR2 blockade may further sensitize colorectal cancer to oxaliplatin treatment.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Detection of Bridge Dynamic Parameters using an Instrumented Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure to provide adequate maintenance and guarantee the required levels of transport service and safety. In Europe, this is now a legal requirement - a European Directive requires all member states of the European Union to implement a Bridge Management System. However, the process is expensive, requiring the installation of sensing equipment and data acquisition electronics on the bridge. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges as an indicator of its structural condition. This approach eliminates the need for any on-site installation of measurement equipment. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the possibility of extracting the dynamic parameters of the bridge from the spectra of the vehicle accelerations. The effect of vehicle speed, vehicle mass and bridge span length on the detection of the bridge dynamic parameters are investigated. The algorithm is highly sensitive to the condition of the road profile and simulations are carried out for both smooth and rough profiles

Monitoring Bridge Dynamic Behaviour using an Instrumented Two Axle Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.

Experimental Investigation of the Detection of Bridge Dynamic Parameters using a Moving Vehicle

This paper investigates the feasibility of using an instrumented vehicle to detect bridge dynamic parameters, such as natural frequency and structural damping, in a scaled laboratory experiment. In the experiment, a scaled vehicle model crosses a steel girder which has been adopted as the bridge model. The bridge model also includes a scaled road surface profile. The effects of varying vehicle model mass and speed are investigated. The damping of the girder is also varied. The bridge frequency and changes in damping are detected in the vehicle acceleration response in the presence of a rough road surface profile.

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Extracting statistically dominant modes of a steel truss bridge from vehicle-induced vibrations

This study is intended to investigate the validity of the stability diagram (SD) aided multivariate autoregressive (MAR) analysis for identifying modal parameters of a real truss bridge. The MAR models are adopted to fit the time series of the dynamic accelerations recorded from a number of observation points on the bridge; then the modal parameters are extracted from the MAR model coefficient matrix. The SD is adopted to determine statistically dominant modes. In plotting the SD, a number of stability criteria are further adopted for filtering out those modes with unstable modal parameters. By the present method, the first five modal frequencies and mode shapes are identified with very high precision, while the damping ratios are identified with high precision for the 1st mode but with poorer precision for higher modes. Moreover, the ability of the SD in selecting structural modes without getting involved in any model-order optimization problem is highlighted through a comparison study.