Holocene alluvial environments at Barking, Lower Thames Valley (London, UK)

Deposit modelling based on archived borehole logs supplemented by a small number of dedicated boreholes is used to reconstruct the main boundary surfaces and the thickness of the main sediment units within the succession of Holocene alluvial deposits underlying the floodplain in the Barking Reach of the Lower Thames Valley. The basis of the modelling exercise is discussed and the models are used to assess the significance of floodplain relief in determining patterns of sedimentation. This evidence is combined with the results of biostratigraphical and geochronological investigations to reconstruct the environmental conditions associated with each successive stage of floodplain aggradation. The two main factors affecting the history and spatial pattern of Holocene sedimentation are shown to be the regional behaviour of relative sea level and the pattern of relief on the surface of the sub-alluvial, Late Devensian Shepperton Gravel. As is generally the case in the Lower Thames Valley, three main stratigraphic units are recognised, the Lower Alluvium, a peat bed broadly equivalent to the Tilbury III peat of Devoy (1979) and an Upper Alluvium. There is no evidence to suggest that the floodplain was substantially re-shaped by erosion during the Holocene. Instead, the relief inherited from the Shepperton Gravel surface was gradually buried either by the accumulation of peat or by deposition of fine-grained sediment from suspension in standing or slow-moving water. The palaeoenvironmental record from Barking confirms important details of the Holocene record observed elsewhere in the Lower Thames Valley, including the presence of Taxus in the valley-floor fen carr woodland between about 5000 and 4000 cal BP, and the subsequent growth of Ulmus on the peat surface.

An evaluation of the pedestrian classification in a multi-domain multi-modality setup

The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains. The experiments show that features families, intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP) and histogram of oriented gradients (HOG), computed from FIR and visible domains are highly complementary, but their relative performance varies across different modalities. In our experiments, the FIR domain has proven superior to the visible one for the task of pedestrian classification, but the overall best results are obtained by a multi-domain multi-modality multi-feature fusion.

A PCR-RFLP assay for gender assignment in the three-toed sloths (Bradypus, Pilosa, Bradypodidae)

The three-toed sloths (Bradypus) are slow-moving arboreal neotropical mammals. Understanding demographic variables (such as sex ratio) of populations is a key for conservation purposes. Nevertheless, gender assignment of Bradypus is particularly challenging because of the lack of sexual dimorphism in infants and in adults, particularly B. torquatus, the most endangered of the three-toed sloths, in which sex is attributed by visual observation of the reproductively active males. Here, we standardized a method for sexing Bradypus individuals using PCR-RFLP of sex-linked genes ZFX/ZFY. This assay was validated with known-gender animals and proved accurate to assign gender on three Bradypus species.

Number of particle creation and decoherence in the nonideal dynamical Casimir effect at finite temperature

In this work we investigate the dynamical Casimir effect in a nonideal cavity by deriving an effective Hamiltonian. We first compute a general expression for the average number of particle creation, applicable for any law of motion of the cavity boundary, under the only restriction of small velocities. We also compute a general expression for the linear entropy of an arbitrary state prepared in a selected mode, also applicable for any law of motion of a slow moving boundary. As an application of our results we have analyzed both the average number of particle creation and linear entropy within a particular oscillatory motion of the cavity boundary. On the basis of these expressions we develop a comprehensive analysis of the resonances in the number of particle creation in the nonideal dynamical Casimir effect. We also demonstrate the occurrence of resonances in the loss of purity of the initial state and estimate the decoherence times associated with these resonances. Since our results were obtained in the framework of the perturbation theory, they are restricted, under resonant conditions, to a short-time approximation. (C) 2009 Elsevier Inc. All rights reserved.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.

Visual tracking of vehicles using multiresolution analysis and neural network

This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.

A unified 2D-3D video scene change detection framework for mobile camera platforms

In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

Environmental and grazing influence on spatial variability of intertidal biofilm on subtropical rocky shores

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Foraging by the omnivorous crab Pachygrapsus transversus affects the structure of assemblages on sub-tropical rocky shores

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An assessment of the economic and social impacts of climate change on the health sector in the Caribbean

Climate change affects the fundamental bases of good human health, which are clean air, safe drinking water, sufficient food, and secure shelter. Climate change is known to impact health through three climate dimensions: extreme heat, natural disasters, and infections and diseases. The temporal and spatial climatic changes that will affect the biology and ecology of vectors and intermediate hosts are likely to increase the risks of disease transmission. The greatest effect of climate change on disease transmission is likely to be observed at the extremes of the range of temperatures at which transmission typically occurs. Caribbean countries are marked by unique geographical and geological features. When combined with their physical, infrastructural development, these features make them relatively more prone to negative impacts from changes in climatic conditions. The increased variability of climate associated with slow-moving tropical depressions has implications for water quality through flooding as well as hurricanes. Caribbean countries often have problems with water and sanitation. These problems are exacerbated whenever there is excess rainfall, or no rainfall. The current report aims to prepare the Caribbean to respond better to the anticipated impact of climate change on the health sector, while fostering a subregional Caribbean approach to reducing carbon emissions by 2050. It provides a major advance on the analytical and contextual issues surrounding the impact of climate change on health in the Caribbean by focusing on the vector-borne and waterborne diseases that are anticipated to be impacted directly by climate change. The ultimate goal is to quantify both the direct and indirect costs associated with each disease, and to present adaptation strategies that can address these health concerns effectively to benefit the populations of the Caribbean.

Aplicação da distribuição de poisson para determinação de estoque mínimo de itens de MRO de baixo giro aplicados em manutenção de ativos industriais

Pós-graduação em Engenharia de Produção - FEB

Entwicklung eines Ansatzes zur proaktiven Identifikation und Bestandsplanung von langsamdrehenden Materialien

Dem Bestandsmanagement wird in Unternehmen eine stetig steigende Bedeutung beigemessen. Die Möglichkeit, durch ein effizientes Bestandsmanagement Kosten zu reduzieren, ist für viele Unternehmen im Hinblick auf einen langfristigen Unternehmenserfolg wichtig. Im Fokus des Bestandsmanagements stehen oft schnelldrehende Materialien, die sich durch geringe Reichweiten und hohe Lagerumschläge auszeichnen. Das Potenzial eines systematischen Managements von langsamdrehenden Materialien wurde bisher noch nicht untersucht. Dieses Paper greift diese Thematik auf und liefert einen Beitrag zum Bestandsmanagement für langsamdrehende Materialien.

Spatial Patterns in Mass Balance of the Siple Coast and Amundsen Sea Sectors, West Antarctica

Local rates of change in ice-sheet thickness were calculated at IS sites in West Antarctica using the submergence velocity technique. This method entails a comparison of the vertical velocity of the ice sheet, measured using repeat global positioning system surveys of markers, and local long-term rates of snow accumulation obtained using firn-core stratigraphy. Any significant difference between these two quantities represents a thickness change with time. Measurements were conducted at sites located similar to 100-200 km apart along US ITASE traverse routes, and at several isolated locations. All but one of the sites are distributed in the Siple Coast and the Amundsen Sea basin along contours of constant elevation, along flowlines, across ice divides and close to regions of enhanced flow. Calculated rates of thickness change are different from site to site. Most of the large rates of change in ice thickness (similar to 10 cm a(-1) or larger) are observed in or close to regions of rapid flow, and are probably related to ice-dynamics effects. Near-steady-state conditions are calculated mostly at sites in the slow-moving ice-sheet interior and near the main West Antarctic ice divide. These results are consistent with regional estimates of ice-sheet change derived from remote-sensing measurements at similar locations in West Antarctica.

In vitro cell motility as a potential mesenchymal stem cell marker for multipotency.

Mesenchymal stem cells (MSCs) are expected to have a fundamental role in future cell-based therapies because of their high proliferative ability, multilineage potential, and immunomodulatory properties. Autologous transplantations have the "elephant in the room" problem of wide donor variability, reflected by variability in MSC quality and characteristics, leading to uncertain outcomes in the use of these cells. We propose life imaging as a tool to characterize populations of human MSCs. Bone marrow MSCs from various donors and in vitro passages were evaluated for their in vitro motility, and the distances were correlated to the adipogenic, chondrogenic, and osteogenic differentiation potentials and the levels of senescence and cell size. Using life-image measuring of track lengths of 70 cells per population for a period of 24 hours, we observed that slow-moving cells had the higher proportion of senescent cells compared with fast ones. Larger cells moved less than smaller ones, and spindle-shaped cells had an average speed. Both fast cells and slow cells were characterized by a low differentiation potential, and average-moving cells were more effective in undergoing all three lineage differentiations. Furthermore, heterogeneity in single cell motility within a population correlated with the average-moving cells, and fast- and slow-moving cells tended toward homogeneity (i.e., a monotonous moving pattern). In conclusion, in vitro cell motility might be a useful tool to quickly characterize and distinguish the MSC population's differentiation potential before additional use.