Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Fuzzy Logic Analysis of Alumina–Titania Deposition Yield During APS Process

International audienceThe behavior modeling of Atmospheric Plasma Spray (APS) process requires a global approach which considers interrelated non-linear relationships between coating characteristics / properties in-service and process parameters (power, feedstock injection, kinematics, etc.). Such an approach would permit to reduce the development costs. To reach this objective, the knowledge of the interactions between process parameters plays a relevant role in the optimization. This work intends to develop a behavior model based on fuzzy logic concepts. Here, the model considers the deposition yield as the result of the process and it establishes relationships with power process parameter (arc current intensity, plasma gas total flow rate, hydrogen content) on the basis of fuzzy rules. The model hence permits to discriminate the role and the effects of each power process parameters. The modeling results are compared to experimental data. The specific case of the deposition of alumina-titania (Al2O3-TiO2, 13% by weight

Artificial neural networks implementation in plasma spray process : prediction of power parameters and in flight particle characteristics vs. desired coating structural attributes

International audienceArtificial neural networks (ANN) were implemented to predict atmospheric plasma spraying (APS) process parameters to manufacture a coating with the desired structural characteristics. The specific case of predicting power parameters to manufacture grey alumina (Al2O3–TiO2, 13% by wt.) coatings was considered. Deposition yield and porosity were the coating structural characteristics. After having defined, trained and tested ANN, power parameters (arc current intensity, total plasma gas flow, hydrogen content) and resulting in-flight particle characteristics (average temperature and velocity) were computed considering several scenarios. The first one deals at the same time with the two structural characteristics as constraints. The others one deals with one structural characteristic as constraint while the other is fixed at a constant value

In-flight particle characteristics control by implementing a fuzzy logic controller

International audienceAn approach based on a fuzzy logic controller was implemented to control and regulate the atmospheric plasma spray processing parameters (arc current intensity, total plasma gas flow, hydrogen content) to the in-flight particle characteristics (average surface temperature and velocity). The specific case of predicting plasma power spray process parameters to manufacture grey alumina (Al2O3–TiO2, 13% by wt.) coatings was considered. This composition was selected due on the one hand to the large literature depicting coating characteristics and on the other one to pre-existing databases. The influence of the plasma spray process on the in-flight particle characteristics was investigated in order to build the experimental database

In-flight and upon impact particle characteristics modelling in plasma spray process

International audienceIn Atmospheric plasma spray process, the in-flight particle characteristics such as their particle size, velocity and surface temperature influence significantly their flight duration and consequently their melting degree. The knowledge of the correlations between process parameters and in-flight particle characteristics is very important for optimizing the coating qualities. Artificial neural networks was trained and optimized to establish the relationships linking in-flight particle average diameter and process parameters to in-flight particle average velocity and surface temperature. Then, the established ANN relationships permitted to determine the in-flight particle average velocity and surface temperature versus their diameter for given process parameters. These predicted average velocity and surface temperature data were then used to determine the time for complete melting of the particle and its dwell-time before impact by an analytical model for given operating conditions

Artificial Intelligence Computation to Establish Relationships Between APS Process Parameters and Alumina–Titania Coating Properties

Modeling the behavior of air plasma spray (APS) process, one of the challenges nowadays is to identify the parameter interdependencies, correlations and individual effects on coating properties, characteristics and influences on the in-service properties. APS modeling requires a global approach which considers the relationships between coating characteristics/ in-service properties and process parameters. Such an approach permits to reduce the development costs. This is why a robust methodology is needed to study these interrelated effects. Artificial intelligence based on fuzzy logic and artificial neural network concepts offers the possibility to develop a global approach to predict the coating characteristics so as to reach the required operating parameters. The model considered coating properties (porosity) and established the relationships with power process parameters (arc current intensity, total plasma gas flow rate, hydrogen content) on the basis of artificial intelligence rules. Consequently, the role and the effects of each power process parameter were discriminated. The specific case of the deposition of alumina–titania (Al2O3–TiO2, 13% by weight) by APS was considere

Characterization of the wire arc spray process via image analysis, in flight particle characteristics and coating properties

International audienceTo have a better understanding of the physical phenomena in wire arc spraying process, we investigated droplet formation by discriminating particles resulting from the anode and the cathode wire atomization. The investigation showed that by spraying simultaneously two different materials, steel and copper, particle trajectory and their diameter distribution, the results related to the processing parameters. By analyzing the characteristics of captured particles implementing image analyses, the crossover direction of in-flight particles was demonstrated. The droplets produced by the anode were bigger than those issued from the cathode and their fraction number, which was also more important. In addition, some important modifications in particle characteristics were observed by inverting a material by the other at the anode and at the cathode. The melting temperature of copper as anode was a critical parameter and was responsible for the copper vaporization. In-flight particle characteristics (temperature, velocity, and diameter) were also determined by using a Fast-Infrared Pyrometer (FIP DPV2000 type diagnostic system). Concerning particle diameter distribution, a good agreement was discovered for the two approaches (in-flight and a posteriori analyses). Major influences of the electrode nature and the radial location on particle velocity and temperature distribution, was pointed out. Finally, quantitative analyses of coating compositions corroborated previous results. Indeed, the coating thickness distribution was largely dependent on the anode nature. Next, the role of each electrode was related to the droplets formation and thus, optimized operating parameters of the arc spray process were deduced

Artificial Neural Networks vs. Fuzzy Logic: Simple Tools to Predict and Control Complex Processes—Application to Plasma Spray Processes

International audienceThe plasma-sprayed coating architecture and in-service properties are derived from an amalgamation of intrinsic and extrinsic spray parameters. These parameters are interrelated; following mostly non-linear relationships. For example, adjusting power parameters (to modify particle temperature and velocity upon impact) also implies an adjustment of the feedstock injection parameters in order to optimize geometric and kinematic parameters. Optimization of the operating parameters is a first step. Controlling these is a second step and consists of defining unique combinations of parameter sets and maintaining them as constant during the entire spray process. These unique combinations must be defined with regard to the in-service coating properties. Several groups of operating parameters control the plasma spray process; namely (i) extrinsic parameters that can be adjusted directly (e.g., the arc current intensity) and (ii) intrinsic parameters, such as the particle velocity or its temperature upon impact, that are indirectly adjusted. Artificial intelligence (AI) is a suitable approach to predict operating parameters to attain required coating characteristics. Artificial Neural Networks (ANN) and Fuzzy Logic (FL) were implemented to predict in-flight particles characteristics as a function of power process parameters. The so-predicted operating parameters resulting from both methods were compared. The spray parameters are also predicted as a function of achieving a specified hardness or a required porosity level. The predicted operating parameters were compared with the predicted in-flight particle characteristics. The specific case of the deposition of alumina-titania (Al2O3-TiO2, 13% by weight) by APS is considered.

Development of low pressure cold sprayed copper coatings on carbon fiber reinforced polymer (CFRP)

A new method which allows the development of Low Pressure Cold Sprayed copper coatings on PEEK (Poly-Ether-Ether-Ketone) based composites reinforced by carbon fibers is investigated. Due to the solid state and high velocities of impacting particles, cold spraying involves a high erosion on composite materials, leading to an absence of coatings and sometimes damaged carbon fibers. As a result, few dozen micrometers of pure PEEK matrix have been added on the surface of the composite to act as an interfacial layer between composite and coating. Optimization of the LPCS parameters has been carried out, using a careful choice of powder size distribution in order to avoid substrate damage, erosion and coating delamination. Dense copper coatings exceeding 100 μm thick have been obtained. SEM observations have been carried out to evaluate the microstructure of coatings, and the minimal required matrix thickness regarding the size distribution of the powder