196 resultados para Intelligent Environments


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This paper reports an approach by which laboratory based testing and numerical modelling can be combined to predict the long term performance of a range of concretes exposed to marine environments. Firstly, a critical review of the test methods for assessing the chloride penetration resistance of concrete is given. The repeatability of the different test results is also included. In addition to the test methods, a numerical simulation model is used to explore the test data further to obtain long-term chloride ingress trends. The combined use of testing and modelling is validated with the help of long-term chloride ingress data from a North Sea exposure site. In summary, the paper outlines a methodology for determining the long term performance of concrete in marine environments.

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Microbial habitats that contain an excess of carbohydrate in the form of sugar are widespread in the microbial biosphere. Depending on the type of sugar, prevailing water activity and other substances present, sugar-rich environments can be highly dynamic or relatively stable, osmotically stressful, and/or destabilizing for macromolecular systems, and can thereby strongly impact the microbial ecology. Here, we review the microbiology of different high-sugar habitats, including their microbial diversity and physicochemical parameters, which act to impact microbial community assembly and constrain the ecosystem. Saturated sugar beet juice and floral nectar are used as case studies to explore the differences between the microbial ecologies of low and higher water-activity habitats respectively. Nectar is a paradigm of an open, dynamic and biodiverse habitat populated by many microbial taxa, often yeasts and bacteria such as, amongst many others, Metschnikowia spp. and Acinetobacter spp., respectively. By contrast, thick juice is a relatively stable, species-poor habitat and is typically dominated by a single, xerotolerant bacterium (Tetragenococcus halophilus). A number of high-sugar habitats contain chaotropic solutes (e.g. ethyl acetate, phenols, ethanol, fructose and glycerol) and hydrophobic stressors (e.g. ethyl octanoate, hexane, octanol and isoamyl acetate), all of which can induce chaotropicity-mediated stresses that inhibit or prevent multiplication of microbes. Additionally, temperature, pH, nutrition, microbial dispersion and habitat history can determine or constrain the microbiology of high-sugar milieux. Findings are discussed in relation to a number of unanswered scientific questions.

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There is now a strong body of research that suggests that the form of the built environment can influence levels of physical activity, leading to an increasing interest in incorporating health objectives into spatial planning and regeneration policies and projects. There have been a number of strands to this research, one of which has sought to develop “objective” measurements of the built environment using Geographic Information Science (GIS) involving measures of connectivity and proximity to compare the relative “walkability” of different neighbourhoods. The development of the “walkability index” (e.g. Leslie et al 2007, Frank et al 2010) has become a popular indicator of spatial distribution of those features of the built environment that are considered to have the greatest positive influence on levels of physical activity. The success of this measure is built on its ability to succinctly capture built environment correlates of physical activity using routinely available spatial data, which includes using road centre lines as a basis of a proxy for connectivity.

This paper discusses two key aspects of the walkability index. First, it follows the suggestion of Chin et al (2008) that the use of a footpath network (where available), rather than road centre lines, may be far more effective in evaluating walkability. This may be particularly important for assessing changes in walkability arising from pedestrian-focused infrastructure projects, such as greenways. Second, the paper explores the implication of this for how connectivity can be measured. The paper takes six different measures of connectivity and first analyses the relationships between them and then tests their correlation with actual levels of physical activity of local residents in Belfast, Northern Ireland. The analysis finds that the best measurements appear to be intersection density and metric reach and uses this finding to discuss the implications of this for developing tools that may better support decision-making in spatial planning.

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It has previously been reported that rice grown in regions of Bangladesh with low-arsenic (As) concentrations in irrigation water can have relatively high concentrations of As within their grains. This study aims to determine how widespread this issue is, and determine the seasonal variation in grain As in these regions. Levels of As were measured in shallow tube well (STW) water, soils, and rice grains collected during the Boro (dry) and Aman (wet) seasons from six Upazilas (sub-districts) of Bangladesh where As levels in groundwater were known to be low. In all the Upazilas, the As concentrations in STW water were <50 mu g L-1. The As levels in soil samples collected from the Upazilas ranged between 0.2-4.0 mgkg(-1) in the sam-ples collected during the Boro season, and 0.4-5.7 mg kg(-1) in the samples collected in the Aman season. Levels of As in both Boro and Aman rice grain varied widely: in Boro 0.02-0.45 mg kg(-1), and in Aman 0.01-0.29 mg kg(-1). Additionally, a household survey of dietary habits was also conducted in one Upazila by estimating As ingestion by 15 head female members. On average, the women consumed 3.1 L of water, 1.1 kg of cooked rice, and 42 g dry weight of curry per day. The total As ingestion rates ranged from 31.1-129.3 mu g day(-1) (mean 63.5 mu g kg(-1)). These findings indicate that the major route of As ingestion in low groundwater As areas of Bangladesh is rice, followed by curry and then water.

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Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

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This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control. 

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We examined variability in hierarchical beta diversity across ecosystems, geographical gradients, and organism groups using multivariate spatial mixed modeling analysis of two independent data sets. The larger data set comprised reported ratios of regional species richness (RSR) to local species richness (LSR) and the second data set consisted of RSR: LSR ratios derived from nested species-area relationships. There was a negative, albeit relatively weak, relationship between beta diversity and latitude. We found only relatively subtle differences in beta diversity among the realms, yet beta diversity was lower in marine systems than in terrestrial or freshwater realms. Beta diversity varied significantly among organisms' major characteristics such as body mass, trophic position, and dispersal type in the larger data set. Organisms that disperse via seeds had highest beta diversity, and passively dispersed organisms showed the lowest beta diversity. Furthermore, autotrophs had lower beta diversity than organisms higher up the food web; omnivores and carnivores had consistently higher beta diversity. This is evidence that beta diversity is simultaneously controlled by extrinsic factors related to geography and environment, and by intrinsic factors related to organism characteristics.

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While current speech recognisers give acceptable performance in carefully controlled environments, their performance degrades rapidly when they are applied in more realistic situations. Generally, the environmental noise may be classified into two classes: the wide-band noise and narrow band noise. While the multi-band model has been shown to be capable of dealing with speech corrupted by narrow-band noise, it is ineffective for wide-band noise. In this paper, we suggest a combination of the frequency-filtering technique with the probabilistic union model in the multi-band approach. The new system has been tested on the TIDIGITS database, corrupted by white noise, noise collected from a railway station, and narrow-band noise, respectively. The results have shown that this approach is capable of dealing with noise of narrow-band or wide-band characteristics, assuming no knowledge about the noisy environment.

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AIMS: To assess quantitatively variations in the extent of capillary basement membrane (BM) thickening between different retinal layers and within arterial and venous environments during diabetes.

METHODS: One year after induction of experimental (streptozotocin) diabetes in rats, six diabetic animals together with six age-matched control animals were sacrificed and the retinas fixed for transmission electron microscopy (TEM). Blocks of retina straddling the major arteries and veins in the central retinal were dissected out, embedded in resin, and sectioned. Capillaries in close proximity to arteries or veins were designated as residing in either an arterial (AE) or a venous (VE) environment respectively, and the retinal layer in which each capillary was located was also noted. The thickness of the BM was then measured on an image analyser based two dimensional morphometric analysis system.

RESULTS: In both diabetics and controls the AE capillaries had consistently thicker BMs than the VE capillaries. The BMs of both AE and VE capillaries from diabetics were thicker than those of capillaries in the corresponding retinal layer from the normal rats (p < or = 0.005). Also, in normal AE and VE capillaries and diabetic AE capillaries the BM in the nerve fibre layer (NFL) was thicker than that in either the inner (IPL) or outer (OPL) plexiform layers (p < or = 0.001). However, in diabetic VE capillaries the BMs of capillaries in the NFL were thicker than those of capillaries in the IPL (p < or = 0.05) which, in turn, had thicker BMs than capillaries in the OPL (p < or = 0.005).

CONCLUSIONS: The variation in the extent of capillary BM thickening between different retinal layers within AE and VE environments may be related to differences in levels of oxygen tension and oxidative stress in the retina around arteries compared with that around veins.