Characterization of wood filament in additive deposition to study the mechanical behavior of reconstituted wood products

The use of materials derived from biomass is unavoidable to decrease environmental impact of products. The main advantage of the Additive Manufacturing (AM) concept is the ability to create complex geometries one layer at a time. The primary aim of this study was to create objects using reconstituted wood through manufacturing with low environmental impact. Wood can be converted into various derivatives allowing the introduction of sustainable material into the product lifecycle. This work uses an AM device adapted to a Computer Numerical Control (CNC) machine [1] to produce a reconstituted wood product by filament deposition. The first part assessed the deposit of wood pulp with a 3D printing head device, while the second part focuses on the characterization of microscopic structure of the material. Fiber morphology and mechanical properties of composite materials incorporating the filaments are characterized

Big IoT data mining for real-time energy disaggregation in buildings (extended abstract)

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question. In this paper, we propose a hybrid approach to address this problem. It combines sparse smart meters with deep learning methods, e.g. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs), to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset

A new approach to improving the efficiency of fel oscillator simulations

During the last year we have been benchmarking FEL oscillator simulation codes against the measured performance of the three Jefferson Lab oscillator FELs. While one might think that a full 4D simulation is de facto the best predictor of performance, the simulations are computationally intensive, even when analytical approximations to the electron bunch longitudinal distribution are used. In this presentation we compare the predictions of the 4D FEL interaction codes Genesis and Medusa, in combination with the optical code OPC, with those using a combination of the 2D & 3D versions of these codes, which can be run quickly on a single CPU core desktop computer

Characterization of wood filament in additive deposition to study the mechanical behavior of reconstituted wood products

The use of materials derived from biomass is unavoidable to decrease environmental impact of products. The main advantage of the Additive Manufacturing (AM) concept is the ability to create complex geometries one layer at a time. The primary aim of this study was to create objects using reconstituted wood through manufacturing with low environmental impact. Wood can be converted into various derivatives allowing the introduction of sustainable material into the product lifecycle. This work uses an AM device adapted to a Computer Numerical Control (CNC) machine [1] to produce a reconstituted wood product by filament deposition. The first part assessed the deposit of wood pulp with a 3D printing head device, while the second part focuses on the characterization of microscopic structure of the material. Fiber morphology and mechanical properties of composite materials incorporating the filaments are characterized

Vector lattice model for stresses in granular materials

A vector lattice model for stresses in granular materials is proposed. A two dimensional pile built by pouring from a point is constructed numerically according to this model. Remarkably, the pile violates the Mohr Coulomb stability criterion for granular matter, probably because of the inherent anisotropy of such poured piles. The numerical results are also compared to the earlier continuum FPA model and the (scalar) lattice $q$-model

Coexistence of double alternating antiferromagnetic chains in (VO)_2P_2O_7 : NMR study

Nuclear magnetic resonance (NMR) of 31P and 51V nuclei has been measured in a spin-1/2 alternating-chain compound (VO)_2P_2O_7. By analyzing the temperature variation of the 31P NMR spectra, we have found that (VO)_2P_2O_7 has two independent spin components with different spin-gap energies. The spin gaps are determined from the temperature dependence of the shifts at 31P and 51V sites to be 35 K and 68 K, which are in excellent agreement with those observed in the recent inelastic neutron scattering experiments [A.W. Garrett et al., Phys. Rev. Lett. 79, 745 (1997)]. This suggests that (VO)_2P_2O_7 is composed of two magnetic subsystems showing distinct magnetic excitations, which are associated with the two crystallographically-inequivalent V chains running along the b axis. The difference of the spin-gap energies between the chains is attributed to the small differences in the V-V distances, which may result in the different exchange alternation in each magnetic chain. The exchange interactions in each alternating chain are estimated and are discussed based on the empirical relation between the exchange interaction and the interatomic distance.Comment: 10 pages, 11 embedded eps figures, REVTeX, Submitted to Phys. Rev.

A uniqueness theorem for degenerate Kerr-Newman black holes

We show that the domains of dependence of stationary, $I^+$-regular, analytic, electrovacuum space-times with a connected, non-empty, rotating, degenerate event horizon arise from Kerr-Newman space-times

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős–Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible

Effective interaction between helical bio-molecules

The effective interaction between two parallel strands of helical bio-molecules, such as deoxyribose nucleic acids (DNA), is calculated using computer simulations of the "primitive" model of electrolytes. In particular we study a simple model for B-DNA incorporating explicitly its charge pattern as a double-helix structure. The effective force and the effective torque exerted onto the molecules depend on the central distance and on the relative orientation. The contributions of nonlinear screening by monovalent counterions to these forces and torques are analyzed and calculated for different salt concentrations. As a result, we find that the sign of the force depends sensitively on the relative orientation. For intermolecular distances smaller than $6\AA$ it can be both attractive and repulsive. Furthermore we report a nonmonotonic behaviour of the effective force for increasing salt concentration. Both features cannot be described within linear screening theories. For large distances, on the other hand, the results agree with linear screening theories provided the charge of the bio-molecules is suitably renormalized.Comment: 18 pages, 18 figures included in text, 100 bibliog