Arsenic in Rice Grown in Low-Arsenic Environments in Bangladesh

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.

An Intelligent Threat Prevention Framework with Heterogeneous Information

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.

Intelligent potentiostat for identification of heavy metals in situ

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. 

A multivariate analysis of beta diversity across organisms and environments

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.

Combining multi-band and frequency-filtering techniques for speech recognition in noisy environments

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.

Diabetic retinopathy:morphometric analysis of basement membrane thickening of capillaries in different retinal layers within arterial and venous environments

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.

Knowledge Exchange, Spatial Analysis & Healthy Urban Environments (KESUE): Project Report

The Knowledge Exchange, Spatial Analysis and Healthy Urban Environments (KESUE) project has extended work previously undertaken by a QUB team of inter-disciplinary researchers engaged with the Physical Activity in the Regeneration of Connswater (PARC) project (Tully et al, 2013). The PARC project focussed on parts of East Belfast to assess the health impact of the Connswater Community Greenway. The KESUE project has aimed to extend some of the tools used initially in East Belfast so that they have data coverage of all of Belfast and Derry-Londonderry. The purpose of this has been to enable the development of evidence and policy tools that link features of the built environment with physical activity in these two cities. The project has used this data to help shape policy decisions in areas such as physical activity, park management, public transport and planning.

Working with a range of local partners who part-funded the project (City Councils in Belfast and Derry-Londonderry, Public Health Agency, Belfast Healthy Cities and Department of Regional Development), this project has mapped all the footpaths in the two cities (covering 37% of the NI population) and employed this to develop evidence used in strategies related to healthy urban planning. Using Geographic Information Systems (GIS), the footpath network has been used as a basis for a wide range of policy-relevant analyses including pedestrian accessibility to public facilities, site options for new infrastructure and assessing how vulnerable groups can access services such as pharmacies. Key outputs have been Accessibility Atlases and maps showing how walkability of the built environment varies across the two cities.

In addition to generating this useful data, the project included intense engagement with potential users of the research, which has led to its continued uptake in a number of policies and strategies, creating a virtuous circle of research, implementation and feedback. The project has proved so valuable to Belfast City Council that they have now taken on one of the researchers to continue the work in-house.

The Cyrenaican Prehistory Project 2011: Late-Holocene environments and human activity from a cave fill in Cyrenaica, Libya

Little is known about late Holocene environmental change in Cyrenaica. The late Holocene sequence in the Haua Fteah, the key regional site, is highly discontinuous and characterised by stable-burning deposits. The geoarchaeology of the late-Holocene cave fill of a small cave, CP1565, located close to the Haua Fteah, is described. The well-stratified sequence, dating from the fourth century AD to the present day, provides a glimpse of life at the bottom of the settlement hierarchy and of changing environments over the last 1600 years, with degraded vegetation and aridity in the ‘Little Ice Age’.

The Robustness of Periodic Orchestrations in Uncertain Evolving Environments

A framework for assessing the robustness of long-duration repetitive orchestrations in uncertain evolving environments is proposed. The model assumes that service-based evaluation environments are stable over short time-frames only; over longer periods service-based environments evolve as demand fluctuates and contention for shared resources varies. The behaviour of a short-duration orchestration E in a stable environment is assessed by an uncertainty profile U and a corresponding zero-sum angel-daemon game Γ(U) [2]. Here the angel-daemon approach is extended to assess evolving environments by means of a subfamily of stochastic games. These games are called strategy oblivious because their transition probabilities are strategy independent. It is shown that the value of a strategy oblivious stochastic game is well defined and that it can be computed by solving a linear system. Finally, the proposed stochastic framework is used to assess the evolution of the Gabrmn IT system.

Handling Sequential Observations in Intelligent Surveillance

Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality, many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.