Effect of two opposing changes in photoperiod upon age at first egg in layer-hybrid pullets

An experiment was designed to test the response of growing pullets to two changes in photoperiod (an increase from 8 to 14 h followed 5 weeks later by the reverse change, or a decrease from 14 to 8 h followed by an increase). The first change was made either at 35 days or at 56 days of age, to test the influence of age on the responses observed. Control groups were kept oil constant 8-h and constant 14-h photoperiods and the responses to appropriate single changes were also tested. Mean age at first egg varied from 111 days for birds given a single increment at 56 days to 166 days for pullets given an increase in photoperiod at 35 days followed by a reduction at 70 days. Responses to the single changes confirmed earlier reports that sensitivity to change in photoperiod varies with age ill a manner that is quantitatively predictable. Responses to the double changes could be explained by Postulating that the initial change altered the 'physiological age' of the bird to all extent that was also quantitatively predictable. An early increase in photoperiod advances sexual development and makes the bird more sensitive to a subsequent decrease than would be expected by reference to its chronological age. An early decrease in photoperiod delays sexual development, which can have the effect of making the bird more or less sensitive to a subsequent increase since, ill layer-strain pullets, sensitivity to an increment in photoperiod normally increases Lip to about 9 weeks of age but decreases thereafter. Mean age at first egg predicted using these concepts was very highly correlated with observed age at first egg. The results provide a rational basis for constructing a model to predict age at first egg for any combination of increases and decreases in photoperiod applied to growing pullets.

Proposing a hybrid-intelligent framework to secure e-government web applications

One of the essential needs to implement a successful e-Government web application is security. Web application firewalls (WAF) are the most important tool to secure web applications against the increasing number of web application attacks nowadays. WAFs work in different modes depending on the web traffic filtering approach used, such as positive security mode, negative security mode, session-based mode, or mixed modes. The proposed WAF, which is called (HiWAF), is a web application firewall that works in three modes: positive, negative and session based security modes. The new approach that distinguishes this WAF among other WAFs is that it utilizes the concepts of Artificial Intelligence (AI) instead of regular expressions or other traditional pattern matching techniques as its filtering engine. Both artificial neural networks and fuzzy logic concepts will be used to implement a hybrid intelligent web application firewall that works in three security modes.

Both black and symbolically white: The 'Bajan-Brit' return migrant as post-colonial hybrid

The research presented in this article centres on an under-researched demographic group of young return migrants, namely, second-generation Barbadians, or 'Bajan-Brits', who have decided to 'return' to the birthplace of their parents. Based on 51 in-depth interviews, the essay examines the experiences of second-generation return migrants from an interpretative perspective framed within post-colonial discourse. The article first considers the Bajan-Brits and issues of race in the UK before their decision to migrate. It is then demonstrated that on 'return', in certain respects, these young, black English migrants occupy a liminal position of cultural, racial and economic privilege, based on their 'symbolic' or 'token' whiteness within the post-colonial context of Barbados. But this very hybridity and inbetweeness means that they also face difficulties and associated feelings of social alienation and discrimination. The ambivalent status of this transnational group of migrants serves to challenge traditional notions of Barbadian racial identity.

Evaluation of two hybrid metric-conceptual models, for simulating phosphorus transfer from agricultural land in the river enborne, a lowland UK catchment

Across Europe, elevated phosphorus (P) concentrations in lowland rivers have made them particularly susceptible to eutrophication. This is compounded in southern and central UK by increasing pressures on water resources, which may be further enhanced by the potential effects of climate change. The EU Water Framework Directive requires an integrated approach to water resources management at the catchment scale and highlights the need for modelling tools that can distinguish relative contributions from multiple nutrient sources and are consistent with the information content of the available data. Two such models are introduced and evaluated within a stochastic framework using daily flow and total phosphorus concentrations recorded in a clay catchment typical of many areas of the lowland UK. Both models disaggregate empirical annual load estimates, derived from land use data, as a function of surface/near surface runoff, generated using a simple conceptual rainfall-runoff model. Estimates of the daily load from agricultural land, together with those from baseflow and point sources, feed into an in-stream routing algorithm. The first model assumes constant concentrations in runoff via surface/near surface pathways and incorporates an additional P store in the river-bed sediments, depleted above a critical discharge, to explicitly simulate resuspension. The second model, which is simpler, simulates P concentrations as a function of surface/near surface runoff, thus emphasising the influence of non-point source loads during flow peaks and mixing of baseflow and point sources during low flows. The temporal consistency of parameter estimates and thus the suitability of each approach is assessed dynamically following a new approach based on Monte-Carlo analysis. (c) 2004 Elsevier B.V. All rights reserved.

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.