Predicting the response of plates subjected to near-field explosions using an energy equivalent impulse

Recent experimental work by the current authors has provided highly spatially and temporally resolved measurements of the loading imparted to, and the subsequent dynamic response of, structures subjected to near-field explosive loading [1]. In this article we validate finite element models of plates subjected to near-field blast loads and perform a parametric study into the relationship between imparted load and peak and residual plate deformation. The energy equivalent impulse is derived, based on the theory of upper bound kinetic energy uptake introduced herein, which accounts for the additional energy imparted to a structure from a spatially non-uniform blast load. Whilst plate deflection is weakly correlated to total impulse, there is shown to be a strong positive correlation between deflection and energy equivalent impulse. The strength of this correlation is insensitive to loading distribution and mode of response. The method developed in this article has clear applications for the generation of fast-running engineering tools for the prediction of structural response to near-field explosions

Generating Sustainable Value from Open Data in a Sharing Society

Part 1: Creating ValueInternational audienceOur societies are in the midst of a paradigm shift that transforms hierarchal markets into an open and networked economy based on digital technology and information. In that context, open data is widely presumed to have a positive effect on social, environmental and economic value; however the evidence to that effect has remained scarce. Subsequently, we address the question how the use of open data can stimulate the generation of sustainable value. We argue that open data sharing and reuse can empower new ways of generating value in the sharing society. Moreover, we propose a model that describes how different mechanisms that take part within an open system generate sustainable value. These mechanisms are enabled by a number of contextual factors that provide individuals with the motivation, opportunity and ability to generate sustainable value

Evolutionary dynamics of group formation

This is an open access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Group formation is a quite ubiquitous phenomenon across different animal species, whose individuals cluster together forming communities of diverse size. Previous investigations suggest that, in general, this phenomenon might have similar underlying reasons across the interested species, despite genetic and behavioral differences. For instance improving the individual safety (e.g. from predators), and increasing the probability to get food resources. Remarkably, the group size might strongly vary from species to species, e.g. shoals of fishes and herds of lions, and sometimes even within the same species, e.g. tribes and families in human societies. Here we build on previous theories stating that the dynamics of group formation may have evolutionary roots, and we explore this fascinating hypothesis from a purely theoretical perspective, with a model using the framework of Evolutionary Game Theory. In our model we hypothesize that homogeneity constitutes a fundamental ingredient in these dynamics. Accordingly, we study a population that tries to form homogeneous groups, i.e. composed of similar agents. The formation of a group can be interpreted as a strategy. Notably, agents can form a group (receiving a ‘group payoff’), or can act individually (receiving an ‘individual payoff’). The phase diagram of the modeled population shows a sharp transition between the ‘group phase’ and the ‘individual phase’, characterized by a critical ‘individual payoff’. Our results then support the hypothesis that the phenomenon of group formation has evolutionary roots.Peer reviewedFinal Published versio

Optimization of the Strength-Fracture Toughness Relation in Particulate-Reinforced Aluminum Composites via Control of the Matrix Microstructure

The article of record as published may be found at http://dx.doi.org/10.1007/s11661-998-0119-9The evolution of the microstructure and mechanical properties of a 17.5 vol. pct SiC particulatereinforced aluminum alloy 6092-matrix composite has been studied as a function of postfabrication processing and heat treatment. It is demonstrated that, by the control of particulate distribution, matrix grain, and substructure and of the matrix precipitate state, the strength-toughness combination in the composite can be optimized over a wide range of properties, without resorting to unstable, underaged (UA) matrix microstructures, which are usually deemed necessary to produce a higher fracture toughness than that displayed in the peak-aged condition. Further, it is demonstrated that, following an appropriate combination of thermomechanical processing and unconventional heat treatment, the composite may possess better stiffness, strength, and fracture toughness than a similar unreinforced alloy. In the high- and low-strength matrix microstructural conditions, the matrix grain and substructure were found to play a substantial role in determining fracture properties. However, in the intermediate- strength regime, properties appeared to be optimizable by the utilization of heat treatments only. These observations are rationalized on the basis of current understanding of the grain size dependence of fracture toughness and the detailed microstructural features resulting from thermomechanical treatments.United States Army Research OfficeArmy Research LabratoryUnited States Air Force Office of Scientific ResearchWright Materials LabratoryDWA Composite

An updated radiocarbon-based ice margin chronology for the last deglaciation of the North American Ice Sheet Complex

The North American Ice Sheet Complex (NAISC; consisting of the Laurentide, Cordilleran and Innuitian ice sheets) was the largest ice mass to repeatedly grow and decay in the Northern Hemisphere during the Quaternary. Understanding its pattern of retreat following the Last Glacial Maximum is critical for studying many facets of the Late Quaternary, including ice sheet behaviour, the evolution of Holocene landscapes, sea level, atmospheric circulation, and the peopling of the Americas. Currently, the most up-to-date and authoritative margin chronology for the entire ice sheet complex is featured in two publications (Geological Survey of Canada Open File 1574 [Dyke et al., 2003]; ‘Quaternary Glaciations – Extent and Chronology, Part II’ [Dyke, 2004]). These often-cited datasets track ice margin recession in 36 time slices spanning 18 ka to 1 ka (all ages in uncalibrated radiocarbon years) using a combination of geomorphology, stratigraphy and radiocarbon dating. However, by virtue of being over 15 years old, the ice margin chronology requires updating to reflect new work and important revisions. This paper updates the aforementioned 36 ice margin maps to reflect new data from regional studies. We also update the original radiocarbon dataset from the 2003/2004 papers with 1541 new ages to reflect work up to and including 2018. A major revision is made to the 18 ka ice margin, where Banks and Eglinton islands (once considered to be glacial refugia) are now shown to be fully glaciated. Our updated 18 ka ice sheet increased in areal extent from 17.81 to 18.37 million km2, which is an increase of 3.1% in spatial coverage of the NAISC at that time. Elsewhere, we also summarize, region-by-region, significant changes to the deglaciation sequence. This paper integrates new information provided by regional experts and radiocarbon data into the deglaciation sequence while maintaining consistency with the original ice margin positions of Dyke et al. (2003) and Dyke (2004) where new information is lacking; this is a pragmatic solution to satisfy the needs of a Quaternary research community that requires up-to-date knowledge of the pattern of ice margin recession of what was once the world’s largest ice mass. The 36 updated isochrones are available in PDF and shapefile format, together with a spreadsheet of the expanded radiocarbon dataset (n = 5195 ages) and estimates of uncertainty for each interval

Soil biota in boreal urban greenspace : Responses to plant type and age

Plant functional type influences the abundance and distribution of soil biota. With time, as root systems develop, such effects become more apparent. The relationship of plant type and time with the structure and abundance of soil microbial and invertebrate communities has been widely investigated in a variety of systems. However, much less is known about long-term soil community dynamics within the context of urban environments. In this study, we investigated how soil microbes, nematodes and earthworms respond to different plant functional types (lawns only and lawns with deciduous or evergreen trees) and park age in 41 urban parks in southern Finland. As non-urban controls we included deciduous and evergreen trees in 5 forest sites. We expected that microbial biomass and the relative abundance of fungi over bacteria would increase with time. We also expected major differences in soil microbial and nematode communities depending on vegetation: we hypothesized that i) the presence of trees, and evergreens in particular, would support a greater abundance of fungi and fungal-feeding nematodes over bacteria and bacterial-feeding nematodes and ii) the fungi to bacteria ratio would be lowest in lawns, with deciduous trees showing intermediate values. In contrast to our predictions, we showed that old deciduous trees, rather than evergreens, supported the highest fungal abundances and fungal-feeding nematodes in the soil. Consistent with our predictions, microbial biomass in urban park soils tended to increase with time, whereas - in contrast to our hypotheses - fungal-feeding nematode abundance declined. Even in the oldest parks included in the current study, microbial biomass estimates never approximated those in the minimally managed natural forests, where biomass estimates were three times higher. Anecic earthworm abundance also increased with time in urban parks, whereas abundances of fungal-feeding, plant-feeding and omnivorous nematodes, as well as those of epigeic and endogeic earthworms remained constant with time and without any distinct differences between urban parks and the control forests. Our findings highlight that although urban park soils harbor diverse soil communities and considerable microbial biomass, they are distinct from adjacent natural sites in community composition and biomass.Peer reviewe

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Search for leptophobic Z ' bosons decaying into four-lepton final states in proton-proton collisions at root s=8 TeV

Peer reviewe