Blue electroluminescent polymers with dopant-host systems and molecular dispersion features: polyfluorene as the deep blue host and 1,8-naphthalimide derivative units as the light blue dopants

We developed a series of highly efficient blue electroluminescent polymers with dopant-host systems and molecular dispersion features by selecting 1,8-naphthalimide derivatives as the light blue emissive dopant units, choosing polyfluorene as the deep blue emissive polymer host and covalently attaching the dopant units to the side chain of the polymer host. The polymers' EL spectra exhibited both deep blue emission from the polymer host and light blue emission from the dopant units because of the energy transfer and charge trapping from the polymer host to the dopant units.

Synthesis of titanium silicalites in different template systems and their catalytic performance

Titanium silicalites have been synthesized in the TPABr+ammonia, TPABr+hexanediamine, TPABr+ethylenediamine, TPABr+diethylamine, TPABr+TEAOH, TPABr+n-butylamine, TPABr+TBAOH and TBAOH+n-butylamine systems. As-synthesized titanium silicalites were characterized by XRD, IR and C-13 CP MAS NMR. Catalytic performance in epoxidation of propylene and template effect was investigated. It has been shown that both TPABr and TBAOH serve as templating agent in TPABr+TBAOH system. But in other systems, when there is enough TPABr, organic amines or ammoniums only act as the bases. TEAOH or n-butylamine can take the role of template when less TPABr is added. It indicates that the ability of organic amines or ammoniums to direct the Pentasil structure decreases as follows: TPA(+)>TBA(+)>TEA(+)>n-butylamine. Catalysts exhibiting good performance in epoxidation of propylene can be attained using TPABr as the template and ammonia, n-butylamine, diethylamine, hexanediamine or TBAOH as bases. (C) 1999 Elsevier Science B.V. All rights reserved.

The Organization for Security and Co-operation in Europe and European Security Law

Odello, Marco, The Organization for Security and Co-operation in Europe and European Security Law, In: European Security Law, Oxford University Press, pp. 295-328, 2007. RAE2008

HIV/AIDS and national security

McInnes, C., 'HIV/AIDS and national security', in: AIDS and Governance, N. Poku, A. Whiteside and B. Sandkjaer (eds.),(Aldershot: Ashgate, 2007), pp.93-111 RAE2008

Small angle scattering characterisation of micellar systems and templated architectures

The work described in this thesis reports the structural changes induced on micelles under a variety of conditions. The micelles of a liquid crystal film and dilute solutions of micelles were subjected to high pressure CO2 and selected hydrocarbon environments. Using small angle neutron scattering (SANS) techniques the spacing between liquid crystal micelles was measured in-situ. The liquid crystals studied were templated from different surfactants with varying structural characteristics. Micelles of a dilute surfactant solution were also subjected to elevated pressures of varying gas atmospheres. Detailed modelling of the in-situ SANS experiments revealed information of the size and shape of the micelles at a number of different pressures. Also reported in this thesis is the characterisation of mesoporous materials in the confined channels of larger porous materials. Periodic mesoporous organosilicas (PMOs) were synthesised within the channels of anodic alumina membranes (AAM) under different conditions, including drying rates and precursor concentrations. In-situ small angle x-ray scattering (SAXS) and transmission electron microscopy (TEM) was used to determine the pore morphology of the PMO within the AAM channels. PMO materials were also used as templates in the deposition of gold nanoparticles and subsequently used in the synthesis of germanium nanostructures. Polymer thin films were also employed as templates for the directed deposition of gold nanoparticles which were again used as seeds for the production of germanium nanostructures. A supercritical CO2 (sc-CO2) technique was successfully used during the production of the germanium nanostructures.

Novel ingredients from brewers' spent grain - bioactivity in cell culture model systems and bioactivity retention in fortified food products

Functional food ingredients, with scientifically proven and validated bioactive effects, present an effective means of inferring physiological health benefits to consumers to reduce the risk of certain diseases. The search for novel bioactive compounds for incorporation into functional foods is particularly active, with brewers’ spent grain (BSG, a brewing industry co-product) representing a unique source of potentially bioactive compounds. The DNA protective, antioxidant and immunomodulatory effects of phenolic extracts from both pale (P1 - P4) and black (B1 – B4) BSG were examined. Black BSG extracts significantly (P < 0.05) protected against DNA damage induced by hydrogen peroxide (H2O2) and extracts with the highest total phenolic content (TPC) protected against 3-morpholinosydnonimine hydrochloride (SIN-1)-induced oxidative DNA damage, measured by the comet assay. Cellular antioxidant activity assays were used to measured antioxidant potential in the U937 cell line. Extracts P1 – P3 and B2 - B4 demonstrated significant (P < 0.05) antioxidant activity, measured by the superoxide dismutase (SOD) activity, catalase (CAT) activity and gluatathione (GSH) content assays. Phenolic extracts P2 and P3 from pale BSG possess anti-inflammatory activity measured in concanavalin-A (conA) stimulated Jurkat T cells by an enzyme-linked immunosorbent assay (ELISA); significantly (P < 0.05) reducing production of interleukin-2 (IL-2), interleukin-4 (IL-4, P2 only), interleukin-10 (IL-10) and interferon-γ (IFN-γ). Black BSG phenolic extracts did not exhibit anti-inflammatory effects in vitro. Hydroxycinnamic acids (HA) have previously been shown to be the phenolic acids present at highest concentration in BSG; therefore the HA profile of the phenolic extracts used in this research, the original barley (before brewing) and whole BSG was characterised and quantified using high performance liquid chromatography (HPLC). The concentration of HA present in the samples was in the order of ferulic acid (FA) > p-coumaric acid (p-CA) derivatives > FA derivatives > p-CA > caffeic acid (CA) > CA derivatives. Results suggested that brewing and roasting decreased the HA content. Protein hydrolysates from BSG were also screened for their antioxidant and anti-inflammatory potential. A total of 34 BSG protein samples were tested. Initial analyses of samples A – J found the protein samples did not exert DNA protective effects (except hydrolysate H) or antioxidant effects by the comet and SOD assays, respectively. Samples D, E, F and J selectively reduced IFN-γ production (P < 0.05) in Jurkat T cells, measured using enzyme linked immunosorbent assay (ELISA). Further testing of hydrolysates K – W, including fractionated hydrolysates with molecular weight < 3, < 5 and > 5 kDa, found that higher molecular weight (> 5 kDa) and unfractionated hydrolysates demonstrate greatest anti-inflammatory effects, while fractionated hydrolysates were also shown to have antioxidant activity, by the SOD activity assay. A commercially available yogurt drink (Actimel) and snack-bar and chocolate-drink formulations were fortified with the most bioactive phenolic and protein samples – P2, B2, W, W < 3 kDa, W < 5 kDa, W > 5 kDa. All fortified foods were subjected to a simulated gastrointestinal in vitro digestion procedure and bioactivity retention in the digestates was determined using the comet and ELISA assays. Yogurt fortified with B2 digestate significantly (P < 0.05) protected against H2O2-induced DNA damage in Caco-2 cells. Greatest immunomodulatory activity was demonstrated by the snack-bar formulation, significantly (P < 0.05) reducing IFN-γ production in con-A stimulated Jurkat T cells. Hydrolysate W significantly (P < 0.05) increased the IFN-γ reducing capacity of the snack-bar. Addition of fractionated hydrolysate W < 3 kDa and W < 5 kDa to yogurt also reduced IL-2 production to a greater extent than the unfortified yogurt (P < 0.05).

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

The Use of Singular Systems and other Inversion Methods in Weighted L2- spaces

info:eu-repo/semantics/published

Model-based pilot and data power adaptation in psam with periodic delayed feedback

We consider the optimum design of pilot-symbol-assisted modulation (PSAM) schemes with feedback. The received signal is periodically fed back to the transmitter through a noiseless delayed link and the time-varying channel is modeled as a Gauss-Markov process. We optimize a lower bound on the channel capacity which incorporates the PSAM parameters and Kalman-based channel estimation and prediction. The parameters available for the capacity optimization are the data power adaptation strategy, pilot spacing and pilot power ratio, subject to an average power constraint. Compared to the optimized open-loop PSAM (i.e., the case where no feedback is provided from the receiver), our results show that even in the presence of feedback delay, the optimized power adaptation provides higher information rates at low signal-to-noise ratios (SNR) in medium-rate fading channels. However, in fast fading channels, even the presence of modest feedback delay dissipates the advantages of power adaptation.

Hazard and risk assessment from Hurricane Ivan (2004) in Grenada using Geographical Information Systems and remote sensing

Hurricanes are destructive storms with strong winds, intense storm surges, and heavy rainfall. The resulting impact from a hurricane can include structural damage to buildings and infrastructure, flooding, and ultimately loss of human life. This paper seeks to identify the impact of Hurricane Ivan on the aected population of Grenada, one of the Caribbean islands. Hurricane Ivan made landfall on 7th September 2004 and resulted in 80% of the population being adversely aected. The methods that were used to model these impacts involved performing hazard and risk assessments using GIS and remote sensing techniques. Spatial analyses were used to create a hazard and a risk map. Hazards were identied initially as those caused by storm surges, severe winds speeds, and flooding events related to Hurricane Ivan. These estimated hazards were then used to create a risk map. An innovative approach was adopted, including the use of hillshading to assess the damage caused by high wind speeds. This paper explains in detail the methodology used and the results produced.