Buffer transference strategy for power control in B5G-ultra-dense wireless cellular networks

Wireless Networks - Beyond five generation (B5G) systems will demand strict and heterogeneous service requirements for the emerging applications. One solution to meet these demands is the dense...     

A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

Wireless Networks - Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood...     

Indoor wood dust analysis during machining with an original experimental device

Particles generated by wood machining have a proven impact on the health of users and woodworkers. The aim of this study was to quantify and describe wood particles in solid and gas phases to reliably and reproducibly characterise these emissions. First, we developed an experimental device that produced particles from solid wood and wood-based panels using portable machine tools. The objective was to study the particles emitted by wood machining while avoiding ambient pollution. Based on own technical specifications, the experimental system was defined and composed of various elements that integrated treatment of inlet air through wood machining to the analysis section that allows solid and gas phases. The first experiments were carried out in sanding and sawing modes on materials used in construction, including solid wood (spruce) and composite panels (particle board (PB), oriented strand board (OSB), and medium density fibreboard (MDF)). Wood-based panels showed more emissive behaviour than solid wood, both for the solid phase and the gas phase. These tests validate the feasibility of generating and measuring particles and emissions of volatile organic compounds (VOCs). Further modifications to the experimental device would enable us to integrate additional devices, such as toxicological ones, to better understand the impact of these wood particles on the health of woodworkers.

     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Experimental study and numerical modeling of mixing by air injection in yield stress fluids using the OpenFOAM software

Mixing by gas injection is an operation used in industrial processes such as wastewater treatment, metallurgy, or methanization in which pressurized gas is injected into a fluid in order to reduce concentrations and temperatures gradients. This study demonstrates how the CFD toolbox OpenFOAM can be used to simulate such flows. Experimental measurements and observations have been performed on a pilot-scale reactor where pressurized air is injected in a yield stress fluid. The volume of fluid method and an adaptive mesh with refinement at the interface have been used to track the gas inclusions. The numerical model accuracy has been assessed by comparing experimental and numerical results related to the bubble's frequency, dimensions, and rising velocities as well as the fluid recirculation, yielded, and unyielded regions in the tank. The influence of injection parameters such as the injection flow rate and the fluid rheological parameters has been quantified.     

Mathematical Modeling of Drying Processes of Selected Fruits and Vegetables

This study investigates the behavior of fruit and vegetable samples during drying. The experimental data are fitted to several different thin-layer drying models. Regression analysis is used to determine model parameters, while statistical indicators serve to evaluate the goodness of fit. The power function model gives the best fit for all examined samples. Based on this model, different drying and heat storage technologies can be combined to ensure that the required residual moisture content of an agricultural product is reached. It is demonstrated on the case of a specific Togolese processing plant that under favorable conditions, fossil fuel consumption can be decreased by 33 %.     

Effect of Nb content on the phase composition,densification, microstructure,and mechanical properties of high-entropy boride ceramics

Dense high-entropy (Hf,Zr,Ti,Ta,Nb)B₂ ceramics with Nb contents ranging from 0 to 20 at% were produced by a two-step spark plasma sintering process. X-ray diffraction indicated that a single-phase with hexagonal structure was detected in the composition without Nb. In contrast, two phases with the same hexagonal structure, but slightly different lattice parameters were present in compositions containing Nb. The addition of Nb resulted in the presence of a Nb-rich second phase and the amount of the second phase increased as the Nb content increased. The relative densities were all >99.5 %, but decreased from ～100 % to ～99.5 % as the Nb content increased from 0 to 20 at%. The average grain size decreased from 13.9 ± 5.5 μm for the composition without Nb additions to 5.2 ± 2.0 μm for the composition containing 20 at% Nb. The reduction of grain size with increasing Nb content was due to the suppression of grain growth by the Nb-rich second phase. The addition of Nb increased Young’s modulus and Vickers hardness, but decreased shear modulus. While some Nb dissolved into the main phase, a Nb-rich second phase was formed in all Nb-containing compositions.     

Revealing Nanomechanical Domains and Their Transient Behavior in Mixed-Halide Perovskite Films

Halide perovskites are a versatile class of semiconductors employed for high performance emerging optoelectronic devices, including flexoelectric systems, yet the influence of their ionic nature on their mechanical behavior is still to be understood. Here, a combination of atomic-force, optical, and compositional X-ray microscopy techniques is employed to shed light on the mechanical properties of halide perovskite films at the nanoscale. Mechanical domains within and between morphological grains, enclosed by mechanical boundaries of higher Young's Modulus (YM) than the bulk parent material, are revealed. These mechanical boundaries are associated with the presence of bromide-rich clusters as visualized by nano-X-ray fluorescence mapping. Stiffer regions are specifically selectively modified upon light soaking the sample, resulting in an overall homogenization of the mechanical properties toward the bulk YM. This behavior is attributed to light-induced ion migration processes that homogenize the local chemical distribution, which is accompanied by photobrightening of the photoluminescence within the same region. This work highlights critical links between mechanical, chemical, and optoelectronic characteristics in this family of perovskites, and demonstrates the potential of combinational imaging studies to understand and design halide perovskite films for emerging applications such as photoflexoelectricity.