11,861 research outputs found
Prediction of crushing stress in composite materials
A simple mathematical model for predicting the crushing stress of composite materials was derived and presented in this paper. The present knowledge of fracture mechanics and strength of materials are used as the basis for the model. The fracture mechanics part of the analysis was based on energy release rate approach; the energy release rate, G, of the proposed model was determined by this approach. This energy release rate was based on the Mode I (opening or tensile mode) failure. As for the strength of materials part analysis, buckling theory was used to determine the critical load of the fibre beams. These two engineering concepts were combined to form the equation for the proposed model. The derived equation is a function of the materials properties, geometric and physical parameters of the composite materials. The calculated stresses from the derived equation were compared with experimental data from technical and research papers. Good agreements shown in the results are encouraging and recommendations for future analysis with different modes of failure were also presented. This paper enables engineering designers to predict crushing stress in composite materials with confidence and makes their work more efficient and reliable
Learning activation functions from data using cubic spline interpolation
Neural networks require a careful design in order to perform properly on a
given task. In particular, selecting a good activation function (possibly in a
data-dependent fashion) is a crucial step, which remains an open problem in the
research community. Despite a large amount of investigations, most current
implementations simply select one fixed function from a small set of
candidates, which is not adapted during training, and is shared among all
neurons throughout the different layers. However, neither two of these
assumptions can be supposed optimal in practice. In this paper, we present a
principled way to have data-dependent adaptation of the activation functions,
which is performed independently for each neuron. This is achieved by
leveraging over past and present advances on cubic spline interpolation,
allowing for local adaptation of the functions around their regions of use. The
resulting algorithm is relatively cheap to implement, and overfitting is
counterbalanced by the inclusion of a novel damping criterion, which penalizes
unwanted oscillations from a predefined shape. Experimental results validate
the proposal over two well-known benchmarks.Comment: Submitted to the 27th Italian Workshop on Neural Networks (WIRN 2017
Sandpiles on multiplex networks
We introduce the sandpile model on multiplex networks with more than one type
of edge and investigate its scaling and dynamical behaviors. We find that the
introduction of multiplexity does not alter the scaling behavior of avalanche
dynamics; the system is critical with an asymptotic power-law avalanche size
distribution with an exponent on duplex random networks. The
detailed cascade dynamics, however, is affected by the multiplex coupling. For
example, higher-degree nodes such as hubs in scale-free networks fail more
often in the multiplex dynamics than in the simplex network counterpart in
which different types of edges are simply aggregated. Our results suggest that
multiplex modeling would be necessary in order to gain a better understanding
of cascading failure phenomena of real-world multiplex complex systems, such as
the global economic crisis.Comment: 7 pages, 7 figure
Banded Slug
NYS IPM Type: Fruits IPM Fact Sheet; NYS IPM Type: Vegetables IPM Fact Sheet; NYS IPM Type: Ornamentals Fact Sheet; NYS IPM Type: Field Crops Fact SheetThe banded slug was introduced from Europe during the 1800s. It has become a common pest of vegetables, field crops, and ornamentals throughout the United States and Canada. The banded slug attacks seedlings of a number of crops, particularly no-tillage corn and alfalfa, and strawberries. It is occasionally a pest in greenhouses
Chemical Pressure and Physical Pressure in BaFe_2(As_{1-x}P_{x})_2
Measurements of the superconducting transition temperature, T_c, under
hydrostatic pressure via bulk AC susceptibility were carried out on several
concentrations of phosphorous substitution in BaFe_2(As_{1-x}P_x)_2. The
pressure dependence of unsubstituted BaFe_2As_2, phosphorous concentration
dependence of BaFe_2(As_{1-x}P_x)_2, as well as the pressure dependence of
BaFe_2(As_{1-x}P_x)_2 all point towards an identical maximum T_c of 31 K. This
demonstrates that phosphorous substitution and physical pressure result in
similar superconducting phase diagrams, and that phosphorous substitution does
not induce substantial impurity scattering.Comment: 5 pages, 4 figures, to be published in Journal of the Physical
Society of Japa
Correlated multiplexity and connectivity of multiplex random networks
Nodes in a complex networked system often engage in more than one type of
interactions among them; they form a multiplex network with multiple types of
links. In real-world complex systems, a node's degree for one type of links and
that for the other are not randomly distributed but correlated, which we term
correlated multiplexity. In this paper we study a simple model of multiplex
random networks and demonstrate that the correlated multiplexity can
drastically affect the properties of giant component in the network.
Specifically, when the degrees of a node for different interactions in a duplex
Erdos-Renyi network are maximally correlated, the network contains the giant
component for any nonzero link densities. In contrast, when the degrees of a
node are maximally anti-correlated, the emergence of giant component is
significantly delayed, yet the entire network becomes connected into a single
component at a finite link density. We also discuss the mixing patterns and the
cases with imperfect correlated multiplexity.Comment: Revised version, 12 pages, 6 figure
Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.
Objective: To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions. Design: Systematic review. Setting/data source: CINAHL, Embase, MEDLINE from 2011 to 2015. Participants: All studies of 28-day and 30-day readmission predictive model. Outcome measures Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models. Results: Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21–0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables ‘comorbidities’, ‘length of stay’ and ‘previous admissions’ were frequently cited across 73 models. The variables ‘laboratory tests’ and ‘medication’ had more weight in the models for cardiovascular disease and medical condition-related readmissions.Conclusions: The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority
Bioaccessibility of Carotenoids and Tocopherols in Marine Microalgae, Nannochloropsis sp. and Chaetoceros sp.
Microalgae can produce various natural products such as pigments, enzymes, unique
fatty acids and vitamin that benefit humans. The objective of the study is to study the
bioaccessibility of carotenoids (β-carotene and lycopene) and vitamin E (α- and β-
tocopherol) of Nannochloropsis oculata and Chaetoceros calcitrans. Analyses were carried
out for both the powdered forms of N. oculata and C. calcitrans, and the dried extract
forms of N. oculata and C. calcitrans. In vitro digestion method together with RP-HPLC
was used to determine the bioaccessibility of carotenoids and vitamin E for both forms
of microalgae. Powdered form of N. oculata had the highest bioaccessibility of β-carotene
(28.0 ± 0.6 g kg-1), followed by dried extract N. oculata (21.5 ± 1.1 g kg-1), dried extract C.
calcitrans (16.9 ± 0.1 g kg-1), and powdered C. calcitrans (15.6 ± 0.1 g kg-1). For lycopene,
dried extract of N. oculata had the highest bioaccessibility of lycopene (42.6 ± 1.1 g kg-
1), followed by dried extract C. calcitrans (41.9 ± 0.6 g kg-1), powdered C. calcitrans (39.7
± 0.1 g kg-1) and powdered N. oculata (32.6 ± 0.7 g kg-1). Dried extract C. calcitrans had the
highest bioaccessibility of α-tocopherol (72.1 ± 1.2 g kg-1). However, β-tocopherol was
not detected in both dried extract and powdered form of C. calcitrans. In conclusion, all
samples in their dried extract forms were found to have significantly higher
bioaccessibilities than their powdered forms. This may be due to the disruption of the
food matrix contributing to a higher bioaccessibility of nutrients shown by the dried
extract form
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