Assessing the Impact of Technology on Aircraft Systems using Technology Impact Forecasting

In today’s atmosphere of constrained defense spending and reduced research budgets, determining how to allocate resources for research and design has become a critical and challenging task. In the area of aircraft design there are many promising technologies to be explored, yet limited funds with which to explore them. In addition, issues concerning uncertainty in technology readiness as well as the quantification of the impact of a technology (or combinations of technologies), are of key importance during the design process. This paper presents a methodology that details a comprehensive and structured process in which to quantitatively explore the effects of technology for a given baseline aircraft. This process, called Technology Impact Forecasting (TIF), involves the creation of a assessment environment for use in conjunction with defined technology scenarios, and will have a significant impact on resource allocation strategies for defense acquisition. The advantages and limitations of the method are discussed. In addition, an example TIF application, that of an Uninhabited Combat Aerial Vehicle, is presented and serves to illustrate the applicability of this methodology to a military system.

Signal enhancement of surface plasmon resonance immunoassay using enzyme precipitation-functionalized gold nanoparticles: A femto molar level measurement of anti-glutamic acid decarboxylase antibody

Colloidal gold nanoparticles (AuNPs) and precipitation of an insoluble product formed by HRP-biocatalyzed oxidation of 3,3'-diaminobenzidine (DAB) in the presence of H2O2 were used to enhance the signal obtained from the surface plasmon resonance (SPR) biosensor. The AuNPs were synthesized and functionalized with HS-OEG(3)-COOH by self assembling technique. Thereafter, the HS-OEG3-COOH functionalized nanoparticles were covalently conjugated with horseradish peroxidase (HRP) and anti IgG antibody to form an enzyme-immunogold complex. Characterizations were performed by several methods: UV-vis absorption, DLS, HR-TEM and Fr-IR. The Au-anti IgG-HRP complex has been applied in enhancement of SPR immunoassay using a sensor chip constructed by 1:9 molar ratio of HS-OEG(6)-COOH and HS-OEG(3)-OH for detection of anti-GAD antibody. As a result, AuNPs showed their enhancement as being consistent with other previous studies while the enzyme precipitation using DAB substrate was applied for the first time and greatly amplified the SPR detection. The limit of detection was found as low as 0.03 ng/ml of anti-GAD antibody (or 200 fM) which is much higher than that of previous reports. This study indicates another way to enhance SPR measurement, and it is generally applicable to other SPR-based immunoassays.

Effects of Gold Nanoparticle and Enzyme Precipitation on Enhancement of Surface Plasmon Resonance Immunoassay: A Super Sensitive Measurement of Anti- Glutamic Acid Decarboxylase Antibody

Introduction: In this study, colloidal gold nanoparticle and precipitation of an insoluble product formed by HRP-biocatalyzed oxidation of 3,3'-diaminobenzidine (DAB) in the presence of H2O2 were used to enhance the signal obtained from the surface plasmon resonance biosensor.

Methods: The colloidal gold nanoparticle was synthesized as described by Turkevitch et al., and their surface was firstly functionalized with HS(CH2)11(OCH2CH2)3COOH (OEG3¬-COOH) by self assembling technique. Thereafter, those OEG3-COOH functionalized nanoparticles were covalently conjugated with horseradish peroxidase (HRP) and anti-IgG antibody (specific to the Fc portion of all human IgG subclasses) to form an enzyme-immunogold complex. Characterization was performed by several methods: UV-Vis absorption, dynamic light scattering (DLS), transmission electron microscopy (TEM) and FTIR. The as-prepared enzyme-immunogold complex has been applied in enhancement of SPR immunoassay. A sensor chip used in the experiment was constructed by using 1:10 molar ratio of HS(CH2)11(OCH2CH2)6COOH and HS(CH2)11(OCH2CH2)3OH. The capture protein, GAD65 (autoantigen) which is recognized by anti-GAD antibody (autoantibody) in the sera of insulin-dependent diabetes mellitus patients, was immobilized onto the 1:10 surface via biotin-streptavidin interaction.

Results and conclusions: In the research, we reported the influences of gold nanoparticle and enzyme precipitation on the enhancement of SPR signal. Gold nanoparticle showed its enhancement as being consistent with other previous studies, while the enzyme precipitation using DAB substrate was applied for the first time and greatly amplified the SPR detection. As the results, anti-GAD antibody could be detected at pg/ml level which is far higher than that of commercial ELISA detection kit. This study indicates another way to enhance SPR measurement, and it is generally applicable to other SPR-based immunoassays.

Landfill cap models under simulated climate change precipitation::Impacts of cracks and root growth

Desiccation crack formation is a key process that needs to be understood in assessment of landfill cap performance under anticipated future climate change scenarios. The objectives of this study were to examine: (a) desiccation cracks and impacts that roots may have on their formation and resealing, and (b) their impacts on hydraulic conductivity under anticipated climate change precipitation scenarios. Visual observations, image analysis of thin sections and hydraulic conductivity tests were carried out on cores collected from two large-scale laboratory trial landfill cap models (∼80 × 80 × 90 cm) during a year of four simulated seasonal precipitation events. Extensive root growth in the topsoil increased percolation of water into the subsurface, and after droughts, roots grew deep into low-permeability layers through major cracks which impeded their resealing. At the end of 1 year, larger cracks had lost resealing ability and one single, large, vertical crack made the climate change precipitation model cap inefficient. Even though the normal precipitation model had developed desiccation cracks, its integrity was preserved better than the climate change precipitation model.

Offshore wind power forecasting error and electricity market implications

Wind energy has been identified as key to the European Union’s 2050 low carbon economy. However, as wind is a variable resource and stochastic by nature, it is difficult to plan and schedule the power system under varying wind power generation. This paper investigates the impacts of offshore wind power forecast error on the operation and management of a pool-based electricity market in 2050. The impact of the magnitude and variance of the offshore wind power forecast error on system generation costs, emission costs, dispatch-down of wind, number of start-ups and system marginal price is analysed. The main findings of this research are that the magnitude of the offshore wind power forecast error has the largest impact on system generation costs and dispatch-down of wind, but the variance of the offshore wind power forecast error has the biggest impact on emissions costs and system marginal price. Overall offshore wind power forecast error variance results in a system marginal price increase of 9.6% in 2050.

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.