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 $\tau = 3/2$ 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