Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

Sperry’s packing rule affects the spatial proximity but not clustering of xylem conduits: the case of Fagus sylvatica L.

Sperry’s packing rule predicts the optimum packing of xylem conduits in woody plants, where the frequency of xylem conduits varies approximately inversely with the square of the conduit radius. However, it is well established that such anatomical disposition does not remain fixed but is subject to a suite of adaptations induced by physiological constraints driven by both ontogenetic development and environmental characteristics. Here we challenge the hypothesis that increasing frequency of xylem conduits, concomitant with the decrease in their lumen area along the xylem pathway, would affect the spatial distribution of vessels inside tree-rings and their aggregation. To this end, we measured the vessels’ anatomical characteristics inside each tree-ring along with a complete radial series taken at different stem heights of Fagus sylvatica L. trees. Point pattern analysis indicated a significant effect of the distance from the tree base and a weak effect of cambial age on the nearest neighbour distance among xylem vessels, suggesting that vessels were closer to each other near the apex, and became progressively more distant toward the base. The spatial pattern of xylem vessels violated the assumption of complete spatial randomness, vessel spatial arrangement followed a uniform distribution at different distances from the tree base. Although there was an increase in the intensity and proximity among vessels, we demonstrated that no patterns of aggregation between vessels were found in sampled F. sylvatica trees. Rather, point pattern profiles clearly highlighted a lack of aggregation of vessels in the face of a regular spatial distribution in the annual growth rings along the stems.

Geospatial relationship of trauma and violent crime: An analysis of violent crime and trauma center utilization

Introduction Descriptive epidemiologic and geographic analysis utilizing geographic information science (GIS) has been used to determine the utilization of trauma systems and to spatially describe patterns of trauma and crime. We examined the relationship between spatial components of criminality and injuries in order to evaluate the optimal trauma center location and determine a correlation between reported violent crime and trauma center utilization. Methods All adult trauma and violent crime (VC) encounters in a defined area over a single year were included. Geospatial statistics pattern analysis tools of Median Center (MC) and the Average Nearest Neighbor analysis (ANNa) were used to determine if mapped points occurred in complete spatial randomness or were clustered in a significant pattern. Results ANNa of VC resulted in a z-score of –20.54 and a p-value of <0.001, indicating a <1% likelihood that violent crimes were distributed randomly. Further ANNa yielded a zscore of –5.67 and p-value of <0.001 for injuries. Our trauma center is 1.45 miles from the MC of VC and 2.28 miles from the MC for injuries. Spatial autocorrelation failed to demonstrate a direct relationship between criminality and trauma center utilization with a z-score of 0.030 and p-value of 0.98. Conclusion While not statistically significant, the spatial trends of violent crime and trauma center utilization demonstrated a clear pattern. GIS is a powerful tool for the trauma director, and examination of the local regional patterns of trauma should be undertaken by health systems to assist with optimizing outreach, expansion, and response times.

EVALUATION AND QUANTIFICATION OF HEAT HOTSPOTS IN THE CHAPADA DOS GUIMARÃES-MT NATIONAL PARK

The study aimed to analyze the spatial distribution of heat sources inside and outside the Chapada dos Guimarães National Park (PNCG) in the State of Mato Grosso. The analyzes were performed through the estimate of kernel density (KDE) and Ripley's K function from 2005 to 2014. The data related to the number of hot spots were obtained from the National Institute for Space Research (INPE) from 2005 to 2014, and the vector files were acquired from the cartographic base of the Brazilian Institute of Geography and Statistics (IBGE). In the 10 years of analysis, 579 hot spots were detected in the PNCG, where it was found that the months of August and September had the highest incidence of hot spots in the park. The kernel maps demonstrated that most hotspots were observed in the years 2007, 2010 and 2012. The years 2005 and 2013 presented average densities and the years 2006, 2008, 2009, 2011 and 2014 indicated low density of the hot spots. Ripley's K function, calculated to observe the spatial distribution of the hot spots, rejected the hypothesis of complete spatial randomness (CSR), indicating that they showed a tendency to cluster during the study time series at the PNCG.

Micro-CT for analysis of laser sintered micro-composites

Purpose X-Ray-computed micro-tomography (micro-CT) is relatively well established in additive manufacturing as a method to determine the porosity and geometry of printed parts and, in some cases, the presence of inclusions or contamination. This paper aims to demonstrate that micro-CT can also be used to quantitatively analyse the homogeneity of micro-composite parts, in this case created using laser sintering (LS). Design/methodology/approach LS specimens were manufactured in polyamide 12 with and without incorporation of a silver phosphate glass additive in different sizes. The specimens were scanned using micro-CT to characterise both their porosity and the homogeneity of dispersion of the additive throughout the volume. Findings This work showed that it was possible to use micro-CT to determine information related to both porosity and additive dispersion from the same scan. Analysis of the pores revealed the overall porosity of the printed parts, with linear elastic fracture mechanics used to identify any pores likely to lead to premature failure of the parts. Analysis of the additive was found to be possible above a certain size of particle, with the size distribution used to identify any agglomeration of the silver phosphate glass. The particle positions were also used to determine the complete spatial randomness of the additive as a quantitative measure of the dispersion. Practical implications This shows that micro-CT is an effective method of identifying both porosity and additive agglomeration within printed parts, meaning it can be used for quality control of micro-composites and to validate the homogeneity of the polymer/additive mixture prior to printing. Originality/value This is believed to be the first instance of micro-CT being used to identify and analyse the distribution of an additive within a laser sintered part.

Asymmetric Competition Can Shape the Size Distribution of Trees in a Natural Tropical Forest

The architecture (here, the size distribution combined with the spatial pattern of individuals) of natural forest at demographic equilibrium can be used to infer the demographic processes that drive the forest dynamics. In particular, a constant growth rate and a constant mortality rate for all trees would generate an exponential distribution of their size, whereas the metabolic scaling theory predicts a power distribution. In an undisturbed tropical rainforest in French Guiana, the diameter distribution was significantly steeper than the best-fit exponential distribution and significantly flatter than the best-fit power distribution. A simple individual-based model of forest dynamics with asymmetric competition between trees, where the strength of competition was regulated by a single parameter, was able to predict the observed distribution. Competition drove the forest into a self-organized state with stronger inequalities of size among trees, a lower mean competition index, and a spatial pattern of trees that deviated from complete spatial randomness.

Spatial statistics in star-forming regions: testing the limits of randomness

Observational studies of star formation reveal spatial distributions of young stellar objects (YSOs) that are ‘snapshots’ of an ongoing star formation process. Using methods from spatial statistics it is possible to test the likelihood that a given distribution process could produce the observed patterns of YSOs. The aim of this paper is to determine the usefulness of the spatial statistics tests Diggle’s G function (G), the ‘free-space’ function (F), Ripley’s K, and O-ring for application to astrophysical data. The spatial statistics tests were applied to simulated data containing 2D Gaussian clusters projected on random distributions of stars. The number of stars within the Gaussian cluster and number of background stars were varied to determine the tests’ ability to reject complete spatial randomness (CSR) with changing signal-to-noise. The best performing test was O-ring optimized with overlapping logarithmic bins, closely followed by Ripley’s K. The O-ring test is equivalent to the two-point correlation function. Both F and G (and the minimum spanning tree, of which G is a subset) performed significantly less well, requiring a cluster with a factor of two higher signal-to-noise in order to reject CSR consistently. We demonstrate the tests on example astrophysical datasets drawn from the Spitzer Gould's Belt Survey catalogue.