Coupled temperature and moisture effects on the tensile behavior of strain hardening cementitious composites (SHCC) reinforced with PVA fibers

This paper reports the experimental findings on the tensile behavior of strain-hardening cement-based composites (SHCC). The composites were subjected to the combined effects of elevated temperatures and internal moisture condition. Uniaxial tensile tests on dumbbell-shaped SHCC specimens with in situ temperature control were performed at 22, 60 and 100 °C. In addition, the effect of the internal humidity of SHCC (95, 50, 20 and 0%) coupled to the elevated temperatures was investigated. It was shown that the tensile strength decreases and the strain capacity increases with an increase in temperature. The influence of the internal moisture conditions was more significant in high temperatures. The strain capacity reduced significantly with a decrease in the humidity level. The crack pattern of the SHCC specimens was determined. Furthermore, single fiber pullout tests were performed under the considered high temperatures condition. Finally, the results are discussed based on the thermogravimetry analysis of the PVA fiber, alterations on its microstructure and surface coating.     

Is floating photovoltaic better than conventional photovoltaic? Assessing environmental impacts

Photovoltaic (PV) solar energy installations are growing all over the world as a promising renewable alternative to generate electricity. However, many studies have highlighted some drawbacks associated with the installation and operation of conventional solar energy power plants. Thus, floating photovoltaic (FPV) systems have been emerging as a new concept in solar energy to lessen negative environmental impacts caused by allocation of conventional PV facilities. This paper is an overview of the potential negative and positive environmental impacts caused by photovoltaic systems with particular interest on large-scale conventional and floating photovoltaic. This study addresses and compares the impacts at all phases of project implementation, which covers planning, construction, and operation and decommissioning, focusing on ambient located in the tropics. The overall impacts associated with project allocation such as deforestation (for the project implementation and site accessing), bird mortality, erosion, runoff, and change in microclimate are expected to have higher magnitudes for the implementation of conventional PV facilities. The results highlight advantages of FPV over conventional PV during the operational and decommissioning phases as well. Though, further studies are required to assess both qualitative and quantitative aspects of installations in similar areas.     

Reengineering health care materials management

Health care executives across the country, faced with intense competition, are being forced to consider drastic cost cutting measures as a matter of survival. The entire health care industry is under siege from boards of directors, management and others who encourage health care systems to take actions ranging from strategic acquisitions and mergers to simple "downsizing" or "rightsizing," to improve their perceived competitive positions in terms of costs, revenues and market share. In some cases, management is poorly prepared to work within this new competitive paradigm and turns to consultants who promise that following their methodologies can result in competitive advantage. One favored methodology is reengineering. Frequently, cost cutting attention is focused on the materials management budget because it is relatively large and is viewed as being comprised mostly of controllable expenses. Also, materials management is seldom considered a core competency for the health care system and the organization performing these activities does not occupy a strongly defensible position. This paper focuses on the application of a reengineering methodology to healthcare materials management.     

Semantic fusion of laser and vision in pedestrian detection

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.     

Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach

Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km² for the floodplain examined (total area, 984 km²). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change.     

BirdVis: visualizing and understanding bird populations

Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.     

Segmentation of the carotid intima-media region in B-mode ultrasound images

This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty.     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

Performance analysis of available bandwidth estimation tools for grid networks

Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., ; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005), our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.     

Vehicle inductive signatures recognition using a Madaline neural network

In this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive signatures were pre-processed and both classes, named C2 and C3, were subdivided into four subclasses. Thus, the initial classification task was split into four smaller tasks (theoretically) easier to be performed. The heuristic adopted in the training attempts to minimize the effects of the input space non-linearity on the classifier performance by uncoupling the learning of the classes and, for this, we induce output Adalines to specialize in learning one of the classes. The percentages of correct classifications presented concern patterns which were not submitted to the neural network in the training process, and, therefore, they indicate the neural network generalization ability. The results are good and stimulate the maintenance of this research on the use of Madaline networks in vehicle classification tasks using not linearly separable inductive signatures.